false belief experiment

Theory of Mind in Psychology: People Thinking

Charlotte Ruhl

Research Assistant & Psychology Graduate

BA (Hons) Psychology, Harvard University

Charlotte Ruhl, a psychology graduate from Harvard College, boasts over six years of research experience in clinical and social psychology. During her tenure at Harvard, she contributed to the Decision Science Lab, administering numerous studies in behavioral economics and social psychology.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Theory of Mind refers to the ability to attribute mental states to oneself and others, understanding that others have beliefs, desires, intentions, and perspectives that are different from one’s own.

After its first identification in 1978, a large body of research in this field has accumulated, studying the developmental pathway, neural basis, and deficits of the theory of mind.

Key Takeaways

• Theory of mind (ToM) is the ability to attribute mental states to ourselves and others, serving as foundational elements for social interaction.
• Having a theory of mind is important as it provides the ability to predict and interpret the behavior of others.
• During infancy and early childhood, children learn the early skills that they’ll need to develop their theory of mind later on, such as paying attention to people and copying them.
• The traditional test for theory of mind is a false-belief task used to assess a child’s understanding that other people can have beliefs about the world that contrast with reality.
• Countless empirical studies reveal that this ability develops in toddlers as young as 15 months old and deteriorates with age. Research also demonstrates this ability in some of our closest relatives: apes.
• Some individuals with autism, Asperger’s, schizophrenia, depression, or social anxiety disorder exhibit a deficit in theory of mind and perform poorly on related tasks.

Theory of mind is the ability to attribute mental states — beliefs, intents, desires, emotions, and knowledge — to ourselves and others.

“As humans we assume that others want, think, believe and the like, and thereby infer states that are not directly observable, using these states anticipatorily, to predict the behavior of others as well as our own. These inferences, which amount to a theory of mind, are to our knowledge, universal in human adults” (Premack & Woodruff, 1978).

Having a theory of mind allows us to understand that others have unique beliefs and desires that are different from our own, enabling us to engage in daily social interaction as we interpret the mental states and infer the behaviors of those around us (Premack & Woodruff, 1978).

Here are some examples:

Perspective-taking : A child understands that just because they know a toy is hidden in a box doesn’t mean another person who wasn’t present during the hiding will know it’s there.

Understanding false beliefs : If a friend watches you place a candy in your pocket but you secretly move it to your bag when they’re not looking, a child with Theory of Mind understands the friend will mistakenly believe the candy is still in your pocket.

Empathy : If a sibling is crying because they dropped their ice cream, a child with Theory of Mind will understand the sibling is upset, even if the child still has their own ice cream.

Deception : A child decides to trick their friend by pointing in the wrong direction when asked about the location of a hidden object, understanding the friend does not know the actual location.

Understanding others’ ignorance : A child knows they learned something new at school and also understands that their parent doesn’t know this information yet because they weren’t at school with them.

Predicting behavior : A child expects a friend to look for a missing toy in the last place they left it, showing an understanding of the friend’s beliefs and intentions.

How Does ToM Develop?

We aren’t born immediately knowing that others have unique beliefs and desires that are unique from our own. It turns out that there are several developmental precursors (or skills) that infants need to develop their theory of mind later on Westby & Robinson, 2014).

These skills include the ability to comprehend the concept of attention, understand the intentions of others, and the ability to imitate others are all rungs on the ladder you must climb before reaching the platform of theory of mind.

Other developmental precursors necessary for theory of mind to develop include (i) pretending to be someone else (like a doctor or a cashier); (ii) understanding the causes and consequences of emotions; and (iii) understanding the people who have different likes/dislikes.

Paying Attention to Other People

According to psychologist Simon Baron-Cohen, attention is one of the first underlying precursors to the development of a fully-fledged theory of mind.

This involves recognizing that seeing is not merely looking; rather, we can selectively direct our attention to specific objects and people (Baron-Cohen, 1991). A key example of this attention is joint attention.

Joint attention occurs when two people direct their attention towards the same thing of interest – often done via pointing so as to direct another’s attention to the same source.

When infants understand this gesture, they are simultaneously processing another person’s mental state, recognizing that this object is something that another person thinks is of interest (Baron-Cohen, 1991), thus illustrating the beginning phases of the theory of mind.

Intentionality (knowing that people act according to the things they want)

A second core component that contributes to the development of the theory of mind is that of intentionality, or the understanding that others’ actions are goal-directed and arise out of unique beliefs and desires, as defined by philosopher Daniel Dennett (1983).

Toddlers as young as 2 years old exhibit an understanding of intentionality (Luchkina et al., 2018) as do chimpanzees and orangutans (Call & Tomasello, 1998).

To understand that people act in a way that is motivated by their desires (for example, I am hungry so I will reach for that apple) is to understand that other people have their own desires (she must be hungry), thus demonstrating a theory of mind, or attributing mental states to others.

Imitation (Copying Other People)

Imitating others is a third building block of theory of mind. The ability to imitate others is to recognize recognizing that others have their own beliefs and desires.

For example, bridging attention and intentionality, imitation can result when a child realizes that others direct their attention (to an object, etc.) and do so intentionally (motivated by goal-directed behavior).

Internalizing these two concepts, the child then engages in imitation and may direct his or her eyes toward that specific object or scene.

However, there is some pushback that imitation is not as much of a crucial precursor for theory of mind. A 2000 longitudinal study found that the infants’ imitation scores were not associated with later theory of mind ability (Charman, 2000).

Stages of Theory of Mind

Between ages 4-5, children really start to think about others’ thoughts and feelings, and this is when true theory of mind emerges. The actual development of the theory of mind generally follows an agreed-upon sequence of steps (Wellman, 2004; Wellman & Peterson, 2012):

Tasks Listed From Easiest to Most Difficult

• Understanding “wanting” : The first step is the realization that others have diverse desires, and to get what they want, people act in different ways.
• Understanding “thinking” : The second step is the understanding that others also have diverse beliefs about the same thing and that people’s actions are based on what they think is going to happen.
• Understanding that “seeing leads to knowing” : The third stage is recognizing that others have different knowledge access, and if someone hasn’t seen something, they will need extra information to understand.
• Understanding “false beliefs” : The fourth stage is being aware of the fact that others may have false beliefs that differ from reality.
• Understanding “hidden feelings” : The final stage is being aware that other people can hide their emotions and can feel a different emotion from the one they display.

Cultural Differences

While these developmental stages seem universal across demographic groups in laying the groundwork for the formation of theory of mind, different cultures place varying levels of emphasis on each of the five skills, causing some to be developed later than others.

In other words, cultural importance plays a role in determining the specific order in which these five milestones are cemented into the mind of a toddler.

Those that are more valued tend to be developed before those that are less so (and this makes sense from an evolutionary perspective, too).

For example, in individualistic cultures, such as the U.S., a greater emphasis is placed on the ability to recognize that others have different opinions and beliefs. However, in more collectivistic cultures such as China, this skill is not as valued and, as a result, might not develop until later (Shahaeian, 2011).

A study conducted by developmental psychologist Ameneh Shahaeian and colleagues found that knowledge access was understood earlier than diverse beliefs for Iranian children, aligning with this collectivist culture’s emphasis on filial respect and knowledge acquisition (Shahaeian, 2011).

Whereas with Australian participants from a more individualist culture, knowledge access was understood after comprehending that others have diverse beliefs.

Notably, the researchers found that there was no significant cross-cultural difference in overall rates of theory of mind mastery (Shahaeian, 2011), indicating that individuals of all cultures are able to master this skill (Callaghan et al., 2005) despite following different developmental pathways to do so.

False-Belief Tasks

Most theory of mind studies are conducted with toddlers and infants. Because this is a developmental concept, researchers are concerned with the age at which individuals adopt a theory of mind.

Most studies that measure theory of mind rely on a false-belief task.

The traditional test for theory of mind is a false-belief task. A false-belief task is commonly used in child development research to assess a child’s understanding that other people can have beliefs about the world which are not true.

The false-belief task allows researchers to distinguish unambiguously between the child’s (true) belief and the child’s awareness of someone else’s different (false) belief (Dennett, 1978).

First-order false-belief tasks assess the realization that it is possible to hold false-beliefs about real events in the world. An example of a commonly used first-order false-belief task is the “Unexpected contents”, or “Smarties” task.

Experimenters ask children to predict another child’s perception of the contents of a box that looks as though it holds a candy called “Smarties” (that actually includes a pencil) (Gopnik & Astington, 1988).

First-order false-belief tasks involve attribution about others’ false-belief with regard to real events.

In second-order false-belief tasks, the child is required to determine what one character in a pictured scenario thinks regarding another character’s beliefs (Baron-Cohen, 1995).

Thus, can a child understand that another person’s belief about a situation can be different from their own and also from reality?

For example, a character leaves an object in one location and while he or she is outside the room, the object is transferred to a new location.

Passing this task demonstrates the realization that it is possible to hold a false-belief about someone else’s belief.

A commonly used second-order false-belief task is the Sally-Anne task, in which a character leaves an object in one location, and while he or she is outside the room, the object is transferred to a new location.

The Sally-Anne Task

Simon Baron-Cohen (1985) used the Sally–Anne task to investigate whether autistic children could understand false-belief.

The child who is being tested sits at a table on which two dolls (Anne and Sally) are positioned facing lidded containers (a box and a basket). The experimenter enacts a scenario with the dolls.

In this task, Sally first places a marble into her basket and then leaves the scene. Anne then enters, takes the marble out of the basket, and places it into a closed box. The experimenter then asks the participant where Sally will look for the marble.

Three groups of children were tested (one at a time) – 20 children with autism (experimental group), 14 children with Down’s syndrome (control group 1), and 27 typically developing children (control group 2).

If the child passes, he or she will point to the basket, understanding that, although this is no longer reality (as the marble is now in the basket), Sally possesses a false belief that the marble is in the basket because she did not watch Anne move it (Baron-Cohen et al., 1985).

To point to the basket is to understand that Sally has her own set of beliefs about the world that differ from the child’s (he or she knows where the marble actually is).

• 85% of the typically developing children and 86% of the children with Down’s syndrome answered the false-belief question correctly.
• 80% of the autistic children fail the false-belief question.

Several studies indicate that children around four or five years of age are able to pass this false-belief task (Baron-Cohen et al., 1985; Gopnik & Astington, 1988; Nelson et al., 2008; Sung & Hsu, 2014).

However, other studies indicate otherwise – that toddlers as young as 15 months old have some understanding of a theory of mind. A nonverbal version of the false belief task is employed for babies of this age, with their looking time serving as the dependent variable.

In other words, following the traditional false belief task in which a toy or object is hidden, instead of verbally asking the participant where Sally would look, she would come back and either looks in the basket or box, and experimenters would measure the duration that participants looked at Sally performing this action.

If the toddlers looked longer when Sally reached for the box, this would indicate that they expected Sally to look in the basket.

And the results demonstrated this, revealing that, even from a very young age, children do have some understanding of the mental states of others (Onishi & Baillargeon, 2005; replicated by Träuble et al., 2010).

Problems With ToM

Theory of mind is an important underlying mechanism that allows human social interaction. Without it, we would greatly struggle to communicate with each other, understand each other’s behavior, and we wouldn’t be known as the unique social beings that make us so special.

Theory of mind problems can have a range of serious complications.

Although research demonstrates that humans have the capacity to understand theory of mind, some have a better ability to do so than others.

Children who are diagnosed with autism, a spectrum disorder marked by challenges with social skills, repetitive behaviors, and nonverbal communication (Speaks, 2011), exhibit a deficit in theory of mind capabilities.

Eighty percent of participants with autism failed a false belief task in an initial study conducted by Simon Baron-Cohen (1985).

And while more recent studies support this claim, they also reveal that children with autism can pass false belief tasks when explicitly asked to do so, as opposed to five-year-old children who can do so automatically.

The difference, however, is that outside of the lab setting, individuals with autism do not show spontaneous false belief attribution (Senju, 2012). On the neurological side, children and adults with autism also show less activation in brain regions, such as the mPFC and TPJ, that are associated with theory of mind (Castelli et al., 2002).

For individuals with Asperger’s, a disorder marked by similar, though less severe symptoms than in ASD, also exhibit a lessened ability to express theory of mind, illustrated by their impaired performance on various theory of mind-related tasks (Happe et al., 1996; Spek et al., 2010)

Schizophrenia

Some people with schizophrenia, a mental disorder characterized by a loss of touch with reality, also struggle with theory of mind.

A 2007 meta-analysis (an analysis that combines the results of multiple empirical studies) reveals a stable deficit of theory of mind in people with schizophrenia, as evidenced by their consistent, poor performance on false belief tasks (Sprong et al., 2007).

And similar to individuals with autism and Asperger’s, schizophrenic people have reduced recruitment of the mPFC during false belief tasks (Dodell-Feder, 2014).

Depression and Anxiety

Likewise, individuals with depression struggle with theory of mind and experience deficits in integrating contextual informational about other people (Wolkenstein et al., 2011) as well as deficits in theory of mind decoding (Lee et al., 2005).

A 2008 study revealed that both nonpsychotic and psychotic depressed individuals were significantly impaired in tasks involving theory of mind’s social-perceptual and social-cognitive components (Wang et al., 2008).

Similarly, people with social anxiety disorder, which is marked by interpersonal impairment, are also significantly less accurate at decoding mental states than control groups (Washburn et al., 2016)

Together, these examples illustrate that while humans do have a unique ability to detect mental states in others, for some, this ability is reduced or not present at all, thus making social interaction challenging and all the more stressful.

ToM in The Brain

Like all psychological concepts, our brain is activated when we rely on theory of mind. Countless neuroimaging studies have helped pinpoint the specific regions that are activated when we engage in theory of mind tasks, identifying a few key areas of our brain.

Administering false belief tasks while simultaneously scanning the brain and pinpointing which regions are active has led researchers to identify the medial prefrontal cortex (mPFC) and temporo-parietal junction (TPJ), among a few other regions, as the main structures that are responsible for theory of mind.

To determine this, researchers have conducted various experimental designs.

A common paradigm relies on a false belief story and false photograph story. As discussed, a false belief test would involve a story similar to that of Sally and Anne, followed by asking the participant a question such as “Does Sally expect to find her doll in the basket or box?”

An example of the control condition, referred to as the false photograph story, is “A photograph was taken of an apple hanging on a tree branch. The film took half an hour to develop.

In the meantime, a strong wind blew the apple to the ground,” followed by asking the participant, “Does this developed photograph show an apple on the ground or branch” (Callejas et al., 2011).

Here, there is no inference about another’s mental state but rather about the state of the apple in the photograph.

Studies that utilize this method illustrate that the temporo-parietal junction (TPJ) is active during the false belief story but not in the brains of participants who are part of the control group (Saxe & Kanwisher, 2003; Saxe & Powell, 2006; Saxe, Schultz, & Jiang, 2006).

Additionally, when participants are asked to read stories that describe the thoughts and beliefs of a protagonist as opposed to a story that merely describes the protagonist’s physical characteristics, the TPJ activates in the former condition (Saxe & Powell, 2006).

These findings have allowed researchers to conclude that the TPJ is located where the temporal and parietal lobes meet.

Research studies also examine the role other brain regions play in theory of mind. The medial prefrontal cortex (mPFC), the area that covers part of the frontal lobe, is responsible for predicting behavioral and emotional consequences of mental states (Aichhorn et al., 2006).

And other studies reveal the role of the precuneus and amygdala (Gallagher & Frith, 2003; Stone, 2000), namely in patients with left amygdala damage (Fine et al., 2001). 

Learning Check

Which of the following best characterizes theory of mind?

• The child understands that others have different perspectives and beliefs. (Correct)
• The child begins to use deception or tell lies. (Correct)
• The child starts predicting others’ behavior based on their supposed thoughts and feelings. (Correct)
• The child believes everyone knows what they know, regardless of the situation. (Incorrect)
• The child shows empathy by consoling peers who are upset. (Correct)
• The child assumes that others share their food preferences. (Incorrect)
• The child understands that their own knowledge can be different from others. (Correct)
• The child can identify false beliefs in others. (Correct)
• The child shows surprise when their predictions about others’ behavior are wrong. (Correct)
• The child insists that their favorite color must be everyone’s favorite color. (Incorrect)
• The child believes the world is as they see it and cannot be any different. (Incorrect)

In what ways does one’s theory of mind relate to their moral stance on societal issues?

Theory of Mind (ToM) refers to the ability to understand others’ perspectives, thoughts, and feelings. It plays a crucial role in shaping one’s moral stance on societal issues.

ToM fosters empathy, enabling understanding of others’ experiences and viewpoints, which can influence our judgments on fairness, rights, and justice.

It also helps in appreciating the diversity of perspectives in societal matters, fostering tolerance, and shaping nuanced moral and ethical positions.

When does theory of mind develop?

Theory of Mind starts developing around age 2, with simple understanding of others’ perspectives.

More complex aspects, such as understanding false beliefs, typically develop around age 4 to 5. However, it continues to mature and refine throughout adolescence and into adulthood.

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Keep Learning

• How and where: Theory of mind in the brain
• Age and gender dependent development of Theory of Mind in 6-to 8-years old children
• Deconstructing and reconstructing theory of mind
• Sally-Anne Task Materials (Zip File)

Theory Of Mind: Test, Example & Experiments

The emergence of theory of mind in children is a vital developmental milestone; some think a failure to develop it is central to autism.

One superpower all psychologists would kill for is the ability to read minds.

Not only would it make psychology research a lot easier, we would be able to experience what it is like to be someone else – a fascinating prospect.

Although telepathy is still science fiction most of us can do something clever that, while only a pale imitation, does allow us to step inside other people’s minds in a limited way.

We can do this because our brains are fantastic simulators – we can, for example, predict the paths objects will take through space and the decisions we should make now to cause a future event.

Similarly, we can put ourselves in other people’s shoes to try and imagine their thoughts, intentions and possible actions.

In fact, without the ability to simulate what other people are thinking we would be lost in the social world.

Theory of mind experiment

Psychologists call this ability to simulate or work out what others are thinking ‘theory of mind’.

The emergence of theory of mind in children is a vital developmental milestone; some psychologists think that a failure to develop a theory of mind is a central component of autism.

The first experiment to provide evidence about when theory of mind emerges using a test of false beliefs was carried out by Heinz Wimmer and Josef Perner from the University of Salzburg ( Wimmer & Perner, 1983 ).

To test the emergence of ‘theory of mind’ the researchers wanted to find out whether children could pass a false belief test.

To pass the test, children have to understand that it’s possible for other people to hold beliefs that are different to their own.

This is a surprisingly tricky task when your brain is so new it’s still under warrantee.

The Maxi task

Wimmer and Perner tested children between 3 and 9-years-old by telling them a story about a boy called Maxi whose mother had brought home some chocolate to make a cake.

When she gets home Maxi sees her put the chocolate into a blue cupboard.

Then Maxi goes out to play.

Meanwhile, his mother uses the chocolate for the cake but happens to put it back in the green cupboard.

When Maxi comes back in he feels hungry and wants some chocolate.

The children in the experiment are then asked, not where the chocolate is, but which cupboard Maxi will look in.

In the experiment the story is also acted out using dolls and matchboxes to make the story explicit for the children.

Test results

The results showed that 3 to 4-year-olds tended to fail the test by pointing to the actual position of the chocolate rather than where Maxi thought it was.

They seemed unable to understand that although they knew where it was, Maxi didn’t.

Wimmer and Perner argued that this was because they could not construct a separate mental model of the world that represented Maxi’s experience – they weren’t capable of a theory of mind.

From about 4 to 5-years-old the situation changed dramatically.

Suddenly, the children tended to point to the cupboard where Maxi thought the chocolate was, rather than where they knew it was.

However in some variations of the experiment children up to 5-years-old still had problems understanding someone else’s false belief.

Finally, at 6-years-old, the children did consistently understand that another person can hold a false belief about the world.

End of innocence

This experiment suggested that at about 4 to 6-years old a range of remarkable skills start to emerge in young children that are vital for their successful functioning in society.

They begin to understand that others can hold false beliefs, they themselves can lie, and that others can lie to them.

From one perspective it is a sad end to innocence, but from another it is a necessary base for a skill required for social success.

At around 4-years-old children are starting to understand that we don’t live out there in the world, we actually create a model of the world in our heads, a model that can easily be wrong.

Criticisms and alternative explanations

Like many child psychology studies, this experiment has sparked much debate about what its results mean.

Here are some of the alternative explanations addressed by the experimenters:

• Were the kids concentrating? Yes, they correctly answered questions that showed they were concentrating.
• Had the younger children forgotten the story? No, they were given a memory test which they passed.
• Were the younger children just pointing at where the chocolate was without thinking about the question? In another experiment children were specifically told to stop and think – this didn’t help the younger children.

While this experiment has been criticised, and other methods have been developed for examining theory of mind in children, tasks like this one are still in use around the world to this day, helping to uncover how and when we first develop the ability to understand other people’s thoughts.

→ This article is part of a series on 10 crucial developmental psychology studies:

• When infant memory develops
• The mirror test reveals when self-concept emerges in infants
• How children learn new concepts
• The importance of attachment styles
• When infants learn to imitate others
• Theory of mind reveals the social world
• Understanding object permanence
• How infants learn their first word
• The six types of play
• Piaget’s stages of development theory

Author: Dr Jeremy Dean

Psychologist, Jeremy Dean, PhD is the founder and author of PsyBlog. He holds a doctorate in psychology from University College London and two other advanced degrees in psychology. He has been writing about scientific research on PsyBlog since 2004. View all posts by Dr Jeremy Dean

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Smarties Task, The

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• Simge Topaloglu 3  

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Appearance-reality task ; Unexpected contents task

The Smarties Task is a subtype of false-belief tasks that is used to test for theory of mind.

Introduction

The Smarties Task constitutes a by now classical paradigm often used in theory of mind experiments. In this procedure, children are shown a tube of “Smarties” (the brand name of a kind of chocolate candy) and asked to guess its contents. As expected, they typically reply by saying that the tube contains Smarties. Then the experimenter opens the tube, and it turns out that it actually contained an unlikely object (e.g., pencils). At this point, children may be confronted with two different situations: (1) a situation where they should state what another child (who has been waiting outside the room and did not witness the disclosure of the contents of the Smarties tube) would think the tube contained and (2) a situation where the child is asked what he or she initially thought the tube contained (Perner et al. 1987 ...

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Carlson, S. M., Moses, L. J., & Breton, J. (2002). How specific is the relation between executive function and theory of mind? Contributions of inhibitory control and working memory. Infant and Child Development, 11 (2), 73–92. https://doi.org/10.1002/icd.298 .

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Simge Topaloglu

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Topaloglu, S. (2019). Smarties Task, The. In: Shackelford, T., Weekes-Shackelford, V. (eds) Encyclopedia of Evolutionary Psychological Science. Springer, Cham. https://doi.org/10.1007/978-3-319-16999-6_3121-1

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Children do not understand concept of others having false beliefs until age 6 or 7

New study upends research that previously suggested theory of mind in children as young as 4

In a theory-of-mind experiment, a child decides whether a character named Maxi will look for the chocolate bar in the blue, red or green box after he has left the room and the chocolate was moved. When there are two possible locations of the chocolate bar, children can answer correctly without understanding how other people think. When there are more than two locations, children must understand how Maxi can have a false belief to answer correctly. Research shows that children do not reliably understand others’ false beliefs until they are 6 or 7 years old. Photo by Robert Ewing/ASU

New developmental psychology work has upended decades of research suggesting that children as young as 4 years old possess theory of mind.

Having theory of mind means understanding how others think, including the ability of someone else to have a false belief.

In a famous theory-of-mind experiment that includes false beliefs, children watch scenes involving a character named Maxi, his mother and a chocolate bar. Maxi places the chocolate bar into a blue box and then leaves. Unbeknownst to Maxi, his mother shows up and moves the chocolate from the blue box into a green box. After Maxi’s mother leaves, Maxi returns and then the child is asked where Maxi will look for the chocolate. 

By 4 years old, children can answer correctly: Maxi will look in the blue box. 

But do young children really understand that because Maxi did not see his mother move the chocolate, he falsely believes it is still in the blue box?

The answer is no, according to William Fabricius , associate professor of psychology at Arizona State University. For more than a decade, Fabricius and his collaborators have carried out new experiments and have also analyzed previous experiments that collectively show children do not actually understand false beliefs until they are 6 or 7 years old.

This  work  was published in Monographs of the Society for Research in Child Development on Sept. 28. 

“When we overestimate what young children understand about the mind, and thus how others think, we can expect too much from them in terms of social behavior or performance in school,” said Fabricius, who is the lead author of the paper.

Three locations to hide the chocolate bar

One of the first ways the research team tested what children actually understand about Maxi’s false belief was to add a third possible location of the chocolate bar. 

In these experiments, there is a blue box, a green box and a red box. Maxi again places his chocolate bar in the blue box. His mother again moves the chocolate bar into the green box.  

When young children are asked where Maxi will look for the chocolate, they answer the blue box 50% of the time and the red box 50% of the time.

“When there are only two locations, 4- and 5-year-old children can answer correctly without truly understanding that Maxi has a false belief about the location of the chocolate bar,” Fabricius said. “Adding a third location results in them guessing at chance between the two empty locations. Because young children can pass the two-option false-belief task without understanding Maxi’s thought processes, this experiment does not test theory of mind.” 

The random choices children make when there are three possible locations of the chocolate bar suggest they rely on their rudimentary understanding of seeing and knowing. This research team has named this process “perceptual access reasoning.” 

Children use perceptual access reasoning in the following way:

• Seeing leads to knowing.
• People who cannot see something do not know about it.
• People who do not know will always do the wrong thing.

Based on these rules, 4- and 5-year-old children reason that when Maxi returns, he cannot see that the chocolate is in the green box, so he does not know that the chocolate is in the green box. Children reason that Maxi will make the wrong choice and will look in an empty location.

When there is only one empty location (the blue box), children answer correctly by default. When there are two empty locations (blue and red boxes), they guess.

What happens when Maxi has a true belief, and his mother leaves the chocolate bar alone 

Another way the research team tested what young children understand about others’ thoughts was to have the chocolate bar remain where Maxi put it. When Maxi returns, he has a true belief about where the chocolate is.  

In this experiment, Maxi again puts the chocolate bar in the blue box and leaves. This time when Maxi’s mother comes in, she leaves the chocolate bar where it is.

Even with just two options – the blue and green boxes – young children fail the true-belief task. They incorrectly answer that Maxi will make the wrong choice and look in the green box. 

“Perceptual access reasoning users have an immature concept of knowing as tied to the present situation, and do not yet understand that people have memories that persist across situations. They do not understand that Maxi might remember putting the chocolate bar into the blue box,” Fabricius said. “The evidence from this series of experiments is consistent that children do not understand mental representation until they are 6 or 7 years old. 

What perceptual access reasoning means for preschoolers

The finding that young children do not understand true or false beliefs and instead rely on perceptual access reasoning is relevant for how they are taught.

“There are strong correlations between theory of mind and a child’s ability to share, be socially appropriate and be able to problem solve and plan,” said Anne Kupfer , director of ASU’s Child Study Lab  and co-author of the Monograph paper. 

The lab partners with developmental psychology faculty to put research findings into practice and has implemented the findings from the Monograph paper into its preschool curriculum.

“It is important for educators to know at what age a child can finally realize that how they feel, how they think or what they want are not necessarily what everyone else feels, thinks or wants,” Kupfer said.

Sharing a toy is a common situation that requires lab staff to leverage how young children use perceptual access reasoning. Kupfer described a scenario in which a child wants a toy, but another classmate is playing with it. The child takes the toy and because they are happy holding the toy, they think everyone is happy. But the child who just lost the toy starts to cry, and the child who took the toy is puzzled. 

“That’s where we come in. In this situation we narrate what is happening and role model responses that are based on what the kids understand from perceptual access reasoning,” Kupfer said. “We say to the child who is crying, ‘I can see you are upset and saw that Johnny took the toy away from you. Is that why you are upset?’ We then role model and ask the crying child to tell Johnny why they are upset, because he took their toy. Then we direct Johnny to look at the sad child’s face and say, ‘She just told you she is upset. Why is she upset?’ Johnny can then answer, ‘Because I took her toy.’”

This example demonstrates how educators can help children learn about others’ mental representations. The child who took the toy begins to understand why they feel happy but the other child does not — a precursor to having theory of mind. 

In addition to Fabricius and Kupfer, the research team consisted of Christopher Gonzales, who graduated with his doctorate in psychology from ASU and is now at the University of California, Davis; Annelise Pesch of Temple University; Amy Weimer of Texas State University; John Pugliese of California State University, Sacramento; Kathleen Carroll of STARS, Student Therapy Inc.; Rebecca Bolnick of Kyrene School District; Nancy Eisenberg of the ASU Department of Psychology; and Tracy Spinrad of the T. Denny Sanford School of Social and Family Dynamics.

Video courtesy of  Society for Research in Child Development

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Brain activation for spontaneous and explicit false belief tasks overlaps: new fMRI evidence on belief processing and violation of expectation

1 Department of Experimental Psychology

Charlotte Desmet

Annabel nijhof.

2 Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium

Jan R. Wiersema

Marcel brass.

There is extensive discussion on whether spontaneous and explicit forms of ToM are based on the same cognitive/neural mechanisms or rather reflect qualitatively different processes. For the first time, we analyzed the BOLD signal for false belief processing by directly comparing spontaneous and explicit ToM task versions. In both versions, participants watched videos of a scene including an agent who acquires a true or false belief about the location of an object (belief formation phase). At the end of the movies (outcome phase), participants had to react to the presence of the object. During the belief formation phase, greater activity was found for false vs true belief trials in the right posterior parietal cortex. The ROI analysis of the right temporo-parietal junction (TPJ), confirmed this observation. Moreover, the anterior medial prefrontal cortex (aMPFC) was active during the outcome phase, being sensitive to violation of both the participant’s and agent’s expectations about the location of the object. Activity in the TPJ and aMPFC was not modulated by the spontaneous/explicit task. Overall, these data show that neural mechanisms for spontaneous and explicit ToM overlap. Interestingly, a dissociation between TPJ and aMPFC for belief tracking and outcome evaluation, respectively, was also found.

Introduction

Our capacity to represent others’ mental states, goals, beliefs and intentions is of paramount importance for successful social interaction. In traditional theory of mind (ToM; Premack and Woodruff, 1978 ) tasks participants are required to explicitly reason about the other’s mental states. In the ‘Sally–Anne’ false-belief task, Sally observes an object being placed in a box and then leaves the room. Following this, Anne moves the object to a different box. When Sally reenters the room, participants must indicate the location, in which they think Sally will look for the object, that is thus they must represent Sally’s false belief ( Wimmer and Perner, 1983 ). Based on this research, ToM has been characterized as a hallmark of human cognitive development: the attribution of mental states to others requires executive resources (i.e. the ability to inhibit one’s own perspective) and language resources and emerges relatively late in the development. Only by the age of 4 years, children are able to pass false belief tasks ( Wimmer and Perner, 1983 ; Baron-Cohen et al. , 1985 ; McKinnon and Moscovitch, 2007 ; Wellman et al. , 2001 ; Gweon et al. , 2012 ).

Recently a mounting body of evidence has challenged this traditional view. Behavioral tasks, measuring reaction times or spontaneous looking patterns in adults and infants, suggest that the ability to track beliefs and even false beliefs of others may be engaged spontaneously in adults ( Senju et al. , 2009 ; Kovács et al. , 2010 ; Schneider et al. , 2011 ) and present already in infants, before children are able to pass standard false belief tasks ( Clements and Perner, 1994 ; Onishi and Baillargeon, 2005 ; Southgate et al. , 2007 ; Surian et al. , 2007 ; Kovács et al. , 2010 ; Senju et al. , 2011 ). This research shows that we represent other’s beliefs even when we are not required to do so and even in situations where other’s mental states are completely irrelevant for our current goals.

In the study by Kovács et al. (2010) , 7-month-old infants are presented with a video representing an agent who obtains certain knowledge about the location of an object. After that, infants display surprise, as indexed by increased looking time, when they are presented with a picture of the object that is contrary to the agent’s belief ( Kovács et al. , 2010 ). In this study, the same paradigm was also applied to adults, who showed a similar effect; in their case, it was indicated by their reaction times to the (expected or unexpected) presence of the object. After the presentation of a scene, reaction times to the appearance of an object were short not only when the participant expected the object to be present but also when the agent only (false belief condition) believed the object would be present. Critically, participants were never asked to consider the agent’s belief. Schneider et al. (2011) provided evidence for such spontaneous belief processing using a false-belief anticipatory looking paradigm. Here, participants observed some movie clips depicting an agent having true or false beliefs about the location of an object. At the end of the movies, participants had eye movement patterns consistent with belief tracking even though they reported not to have been consciously engaged in mentalizing. This suggests that people spontaneously engage in belief tracking as if they were explicitly asked to do it. Stronger support for the overlap between spontaneous and explicit mentalizing comes from a recent study. In their elegantly designed work, Schneider et al. (2014a ) investigated the extent to which the operation of spontaneous ToM is modulated by task instructions. One group of participants was given no task instructions, another was instructed to track the position of the ball in the scene, and a third was asked to do a tracking of the agent’s belief. Despite different task goals, all groups’ eye-movement patterns were consistent with belief analysis.

These findings represent evidence that humans spontaneously track the belief states of others in an unintentional manner.

However, the question remains whether spontaneous and explicit forms of ToM are based on the same cognitive/neural mechanisms or rather reflect qualitatively different processes. While some authors have generally questioned that spontaneous ToM tasks reflects mentalizing of any kind ( Heyes 2014 ; Phillips et al. , 2015 ), others ( Apperly and Butterfill, 2009 ) have proposed two distinct ToM systems. They suggest the spontaneous ToM system is present early in life, is fast and efficient and operates spontaneously/unconsciously whereas the explicit form would develop later and would be slower, more deliberate and flexible, but therefore also more cognitively demanding. Finally, Carruthers (2016) postulates just a single mindreading system, which sometimes operates fully automatically, sometimes in conjunction with the standing goal of anticipating people’s behavior, and sometimes in a more controlled way (by involving executive function and working memory).

Despite this lively debate, studies investigating similarities and differences between spontaneous and explicit ToM are still scarce ( Schneider et al. , 2014b ; Van der Wel et al. , 2014 ; Rosenblau et al. , 2015 ). To date, neuroimaging studies have mostly focused on the neural correlates of explicit belief processing (e.g. Fletcher et al. , 1995 ; Gallagher et al. , 2000 ; Ruby and Decety, 2003 ; Saxe and Kanwisher, 2003 ). These studies have revealed a quite consistent pattern of brain regions involved when participants are asked to reason about somebody else’s false belief. What is referred to as the ‘ToM network’ includes the temporo-parietal junction (TPJ), medial prefrontal cortex (MPFC), superior temporal sulcus (STS) and precuneus (PC). A number of recent meta-analysis studies confirm that ToM, across different tasks, consistently activates TPJ and MPFC (e.g. Decety and Lamm, 2007 ; Van Overwalle, 2009 ; Schurz et al. , 2014 ).

To the best of our knowledge, only one study investigated the neural bases of spontaneous ToM alone ( Kovács et al. , 2014 ) and only two studies have compared brain activation for spontaneous and explicit ToM in the same participant with conflicting results ( Schneider et al. , 2014b ; Hyde et al. , 2015 ). In the study of Schneider et al. (2014b ), brain activity was measured both during the spontaneous ToM task of Schneider et al. (2011) described above and a classical explicit task based on the presentation of a text describing short stories. The authors first identified a set of ROIs through an explicit localizer task where participants had to read short stories and answer questions about a person’s belief. Following this, they observed that only a subset of the regions showed significant activation for false beliefs in the spontaneous task. In particular, the left STS and posterior cingulate (PC), showed the predicted pattern (false belief > true belief) during the spontaneous ToM video clips, while TPJ did not. This outcome contrasts with the results of Hyde et al. (2015) who, using Near-Infrared-Spectroscopy, found significant activation in the TPJ ROI during spontaneous false belief task.

However, it is important to note that both studies used spontaneous and explicit tasks that involved different stimulus materials and different procedures, making it difficult to interpret possible similarities/differences in brain activation.

The aim of the current study was therefore to compare brain activity related to spontaneous and explicit mentalizing directly, using a within-subjects design and identical stimuli and procedure. To this end, participants, during fMRI, were presented with a new developed task. In this task, participants watch short movies depicting an object moving in the scene and an agent forming a true or false belief about the location of the object in the outcome (belief formation phase; Kovács et al. , 2010 ; Deschrijver et al. , 2015 ). Participants are instructed to respond to the presence of the object in the outcome phase at the end of the movie (object detection). The new procedure integrates catch questions presented at the end of the movies in a small percentage of the trials so that the agent’s belief either remained irrelevant for the task (spontaneous version; the questions concerned the color of the agent’s cap) or was relevant (explicit version; participants were interrogated about the agent’s belief). After the spontaneous task, that was always presented first, a debriefing session was included to ensure that participants were unaware of the belief manipulation.

We compared brain activity during the belief-tracking phase for false and true belief conditions in the spontaneous and explicit ToM tasks. Furthermore, we also looked at violation of expectation in the outcome phase. If the other’s beliefs are spontaneously represented on-line, the evaluation of an outcome will be affected by both the belief of the participant and the belief of the agent.

Participants

Twenty-three healthy students (5 males; age: mean= 22, ranging from 19 to 25) participated on the basis of written informed consent. One participant was excluded from the final analyses due to an error in data saving. The study was conducted according to the Declaration of Helsinki, with approval of the local ethics committee of the University Hospital Gent. All subjects had normal or corrected-to-normal vision. No subject had a history of neurological, major medical or psychiatric disorder. All participants were right-handed as assessed by the Edinburgh handedness questionnaire ( Oldfield, 1971 ).

Procedure and design

The experiment comprised two main parts presented in a fixed order: an spontaneous ToM task and an explicit version of the same task. The two versions of the ToM task were identical except for the presentation of catch questions at the end of some trials. The catch questions were included to distract participants from the belief manipulation and to induce active belief processing in the spontaneous and explicit task, respectively (see below). The entire testing session was limited to 1 hour. Participants were lying in the MRI scanner while watching short videos via a mirror. Our stimuli were created based on the study of Kovács et al. (2010) .

All movies consisted of two phases: the belief formation phase and the outcome phase. The movies in the belief formation phase differed along two aspects of the belief attributable to the agent (Buzz Lyghtyear from the cartoon Toy Story): the agent’s belief could be true or false (true: matching reality and participant knowledge; false: not matching reality and participant’s knowledge) and belief content (positive content: the agent believes the ball is present; negative content: the agent believes the ball is absent). The presence or the absence of the ball in the outcome phase was completely independent of the belief formation phase, because the ball was randomly present in 50% of the trials in all the conditions (see below). Combined with the two versions of the outcome phase (ball does or does not appear from behind the occluder), there were 8 different conditions (8 movies) and movies were repeated 10 times in a random order for each task version resulting in a total of 160 experimental trials. Responses were given through a response box.

Participants kept their right and left index and middle fingers on different buttons. The experiment consisted of two sessions in which the spontaneous and the explicit ToM versions were presented. Each version lasted about 25 min and consisted of 2 separate blocks (fMRI runs) with a short break in between. After completion of the spontaneous version of the ToM task, participants filled in a debriefing form based on the one used by Schneider et al. (2013) , which was adapted to the current task and translated to Dutch. It consisted of five questions (see Appendix A for an English translation). By the use of this form we checked whether participants were aware of our belief manipulation.

Stimuli and task

All movies comprised a belief formation phase and an output phase. Each movie lasted 13.8 s.

Belief formation phase . As shown in Figure 1 , all movies started with an agent placing a ball on a table in front of an occluder. Then the ball rolled behind the occluder. Following this, the movies could continue in four ways depending on the experimental conditions: (i) In the True Belief-Positive Content condition (P+ A+), the ball rolled out of the scene from behind the occluder, and then rolled back behind the occluder (ball last seen by the participant at 10 s; time information is given relative to the beginning of the movie) in the agent’s presence. The agent left the scene at 11 s. Thus, the agent could rightly believe the ball to be behind the occluder. (ii) In the True Belief-Negative Content condition (P− A−), the ball emerged from behind the occluder without leaving the scene, then rolled back behind the occluder, and finally left the scene (ball last seen at 10 s), all in the agent’s presence. The agent left the scene at 11 s. Thus, the agent could rightly believe the ball not to be behind the occluder. (iii) In the False Belief-Positive Content condition (P− A+), the order of when the ball and the agent left the scene was reversed relative to the True Belief-Negative Content condition. Thus, the agent left the scene at 6 s. Then, the ball emerged from behind the occluder without leaving the scene, rolled back behind the occluder, and finally left the scene (ball last seen at 11 s), all in the agent’s absence. Thus, the agent could wrongly believe the ball to be behind the occluder. (iv) In the False Belief-Negative Content condition (P+  A − ), the ball rolled out of the scene from behind the occluder in the agent’s presence. Then, the agent left the scene at 9 s. In his absence, the ball rolled back behind the occluder at 11 s. Thus, the agent could wrongly believe the ball not to be behind the occluder. As in the original task, in order to keep participants’ attention during the presentation of the movies, they were instructed to press a key with the index finger of their left hand when the agent left the scene.

Frames of two of the movies presented during the tasks. Example of a false belief condition (P–A−) and a true belief condition (P–A+). There were eight conditions in total, resulting from the combination of belief formation phase and outcome phase. In the first part of the movie, the ball rolls behind the screen. In the second part (belief formation phase), in the presence of the agent, the ball can change location or stay behind the occluder. Afterwards the agent leaves the scene and the ball can change its location or not. In the outcome phase, the agent comes back to the scene and the occluder is lowered. The ball is present or not (50% of the cases). Please note that, in the ‘no change’ video fragment, the ball was moving anyway. For example, it would roll out from the occluder and then roll back behind the occluder. In all movies, the ball was visible to the participant for the same amount of time.

Outcome phase . At the end of each movie, the agent re-entered the scene and the occluder fell down. The four conditions were paired with two equally probable outcomes, in which the ball was either present or absent behind the occluder. Participants were instructed to press a key as fast as possible with the index finger of their right hand when they detected the ball. The presence or the absence of the ball was completely independent of the belief formation phase, because the ball was randomly present in 50% of the trials in all the conditions. As a result of the combination of belief formation phase (P− A − , P+ A+, P+  A − , P− A+) and output phase (ball present B+, ball absent B−) there were eight different movies. Each movie was repeated 10 times. Therefore, the entire experiment comprised 80 trials for the spontaneous version and 80 for the explicit version.

Spontaneous/explicit manipulation . The movies were identical for the spontaneous and explicit versions. During both the spontaneous and the explicit task, a question appeared randomly in 18 trials after the end of the movie. Questions were presented in black text on a light grey background for 1000 ms. In the spontaneous version, the question was: ‘Did Buzz have a blue cap?’ The cap could be either blue (50% of the movies) or red (50%). In the explicit version, the question was: ‘Did Buzz think the ball was behind the screen?’ This was also true in 50% of the movies. Questions were presented with a variable jitter interval. A pseudo-logarithmic jitter was applied. Half of the inter-trial intervals were short (range between 200 and 2000 ms steps of 600 ms), one-third was intermediate (range between 2600 and 4400 ms) and one-sixth was long (range between 5000 and 6800 ms) with a mean inter-trial interval of 2700 ms.

The words ‘Yes’ and ‘No’ were presented on the left or right of the screen in both task versions. A 50% of catch questions had ‘Yes’ printed left and ‘No’ right, 50% vice versa . In this way, responses could not be planned in advance. Participants had to respond to the answer on the left with their left middle finger and to the answer on the right with their left index finger.

fMRI data acquisition

Images were collected with a 3 T Magnetom Trio MRI scanner system (Siemens Medical Systems, Erlangen, Germany) using a 32-channel radiofrequency head coil. Before the experiment started, 176 high-resolution anatomical images were acquired using a T1-weighted 3D MPRAGE sequence [repetition time (TR) = 2530 ms, echo time (TE) = 2.58 ms, image matrix = 256 × 256, field of view (FOV) = 220 mm, flip angle = 78°, slice thickness = 0.90 mm, voxel size = 0.9 × 0.86 × 0.86 mm (resized to 1 × 1 × 1 mm)]. Next, the experiment was performed during which whole brain functional images were obtained. The functional images were acquired using a T2*-weighted EPI sequence sensitive to BOLD contrast (TR = 2000 ms, TE = 28 ms, image matrix = 64 × 64, FOV= 224 mm, flip angle = 80°, slice thickness = 3.0 mm, distance factor = 17%, voxel size = 3.5 × 3.5 × 3.0, 34 axial slices). Volumes were aligned along the AC–PC axis.

fMRI data preprocessing

The fMRI data were analyzed with SPM8 software (Wellcome Department of Cognitive Neurology, London, UK). The first four volumes of all EPI series were used to allow the magnetization to approach a dynamic equilibrium and were excluded from the analysis. Data preprocessing started with spatially realigning the functional images using a rigid body transformation. Then the realigned images were slice time corrected with respect to the first slice. The high-resolution structural image of each subject was co-registered with the mean image of the EPI series. During segmentation, the structural scans were brought in line with the tissue probability maps available in SPM. The parameters estimated during the segmentation step were then used to normalize the functional images to standard MNI space. Finally, the functional images were resampled into 3 × 3 mm voxels and spatially smoothed with a Gaussian kernel of 8 mm (full-width at half maximum).

Behavioral data analysis

In both versions, spontaneous and explicit, we recorded RTs for the detection of the ball at the end of each movie. Behavioral performance therefore reflects a spontaneous measure of ToM in both cases. Behavioral data were analyzed with IBM SPSS Statistics 20 (SPSS, Inc., Chicago, IL, USA). For one participant, detection responses were not correctly recorded due to technical problems (therefore, his/her data were excluded from this analysis). However, the participant performed correctly on the catch questions, showing that he/she was engaged in the task. We performed a repeated-measures ANOVA on reaction times, with task (spontaneous, explicit), belief (true belief, false belief) and belief content (positive content, negative content) as within-subject factors.

fMRI data analysis

The subject-level statistical analyses were performed using the general linear model. The model contained separate regressors for all possible combinations of Belief (true belief, false belief) and Belief Content (positive content, negative content) for the belief formation phase (duration of 9 s from the moment in which the agent places the ball on the table to the moment in which the agent comes back to the scene). For the outcome phase, there were separate regressors for all possible combinations of Belief, Belief Content and Outcome (ball present, ball absent) (duration of 0 s). In total, the model included 12 regressors of interest for the spontaneous task and 12 regressors of interest for the explicit task. Six subject-specific regressors that were obtained during the realignment step were added to account for head motion. All resulting vectors were convolved with the canonical hemodynamic response function to form the main regressors in the design matrix (the regression model). The statistical parameter estimates were computed separately for each voxel for all columns in the design matrix. Contrast images of interest were created at the first level and were then entered into a second level analysis with subject as a random variable. Contrasts at this group level were made using one-sample t -tests.

Contrasts were run separately for the belief formation phase and the outcome phase. For the belief formation phase, in order to identify regions involved in false belief tracking (belief formation phase), our main contrast of interest was computed as follow: false belief P− A+ and  P+  A −  (participant’s and agent’s belief do not match) > true belief P− A −  and  P+A+ (participant’s and agent’s beliefs match). The interaction between belief and task (explicit > spontaneous task) was also calculated as follow: (P− A+ and P+  A −  explicit > P− A −  and P+ A+ explicit) > (P− A −  and P+ A+ spontaneous > P− A+ and P+  A −  spontaneous). In addition, we calculated a contrast based on the content of the agent’s belief as follows: A+ >  A − .

For the outcome phase, the agent’s and the participant’s belief about the presence of the ball were considered with respect to the actual presence of the ball in the outcome phase (B −  ball absent, B+ ball present). Therefore, we analyzed separately conditions B −  and B+ conditions. Violation of expectation was calculated as a mismatch of the belief content and the actual presence of the ball in the output phase. For example, if the ball was absent at the end of the movie (B − ), the agent’s expectation (only) would be violated in the P− A+ condition. To identify regions involved in the violation of expectation, we computed the main contrasts of interest as follows: (i) violation of expectation based on the agent’s belief was calculated as follow: P− A+ > P− A −  for B −  trials and P+  A −  > P+ A+ for B+ trials. (ii) Violation of expectation based on participant’s belief: P+  A −  > P− A −  for outcomes with no ball (B − ) and P− A+ > P+ A+ for outcomes with ball (B+). We also computed the interaction between agent’s violation of expectation and task: spontaneous > explicit and explicit > spontaneous and between participant’s violation of expectation and task (spontaneous > explicit and explicit > spontaneous). To correct for multiple comparisons a cluster-extent based thresholding approach was used ( Friston et al. , 1996 ). First a primary uncorrected threshold of P  < 0.001 at voxel level was used to identify groups of suprathreshold voxels. Second, a cluster-level extent threshold, represented in units of contiguous voxels ( k ), was determined by SPM 8 ( P  < 0.05 FWE cluster corrected threshold). Only clusters that have a k value that is equal or larger than this threshold are reported. The coordinates reported correspond to the MNI coordinate system.

In addition to the main analyses, we carried out a signal-change analysis in the a priori defined region of interest (ROI) based on a meta-analysis of peaks reported in 26 studies on mentalizing (see Kovács et al. , 2014 ). The ROI was a sphere with a radius of 10 mm centered on the coordinates 56 −47 33. Mean β 's for the events of interest were extracted using the MARSBAR toolbox for SPM ( Brett et al. , 2002 ). The β values obtained were then subjected to a repeated-measures ANOVA containing the factors task (spontaneous task, explicit task), belief (false belief, true belief) and belief content (positive content, negative content).

Behavioral results

Performance on the catch question was well above chance both for the spontaneous and the explicit version, with an accuracy level of 82% (range of correct responses: 7–18) and 74% (range of correct responses: 4–18), respectively. For the ball detection task, a significant main effect of belief was found [ F (1, 20) = 14.9, P  < 0.05, η 2  = 0.43), with RTs in false belief conditions being faster than RTs in true belief conditions. More importantly, we found a significant interaction between belief and belief content [ F (1, 20) = 7.94, P  < 0.05, η 2  = 0.28). Pairwise comparisons of conditions revealed that participants were significantly slower in P− A −  trials than in all other trials ( P s < 0.05). This pattern of results overlaps completely with data from the original paper of Kovács et al. (2010) . The fact that participants are faster both when they expected the ball to be behind the occluder and when only the agent expected the ball to be behind the occlude (P− A+ condition) confirms that participants spontaneously represent the other’s belief during the detection task. Interestingly, although spontaneous and explicit versions exhibited a very similar pattern of RTs, the impact of the agent’s belief on performance, was even slightly stronger in the spontaneous version as attested by the interaction between task and belief content [ F (1, 20) = 4.4, P = 0.049, η 2  = 0.18). In the spontaneous task version, the difference in RTs between positive content trials (A+) and negative content trials (A − ) was larger than in the explicit version. This reveals that the effect of the agent belief’s content was even stronger in the spontaneous than in the explicit version. No interaction between belief, content and task was found. Overall, this outcome supports the idea that, in spontaneous version, beliefs are spontaneously processed. Therefore, task instructions requiring participants to explicitly attend the other’s perspective do not induce qualitative changes in behavioral performance ( Figure 2 ). This pattern of data completely overlaps with the results obtained in a recent behavioral study from our group using the same stimulus material (Nijhof et al. , submitted for publication) and with what has been previously shown with a different task ( Schneider et al. , 2014a ).

Reaction times for ball detection (spontaneous measure) are displayed for the four conditions of the task. (A) Behavioral performance under spontaneous ToM task instructions. (B) Behavioral performance under explicit task instructions.

fMRI results

First, we aimed to identify regions that were involved in false belief processing (false belief). Concerning the belief formation phase, higher activity during false belief than true belief occurred in angular gyrus (AG) (peak coordinates: 42 −67 43) and in fusiform gyrus/collateral sulcus (peak coordinates: 33 −52 1). Importantly, the computation of the interaction between belief and task did not lead to any significant cluster of activation. Also, no clusters emerged for the contrast positive > negative belief content. Concerning the outcome phase, for outcomes where the ball was absent (B − ), violation of expectation based on participant’s belief (P +  A −  > P− A − , B − ) revealed activation in the anterior MPFC (peak coordinates: 9 38 7). Violation of expectation based on the agent’s belief (P− A+ > P− A − , B − ) also activates the aMPFC (peak coordinates: 6 38 −8). The AG and the aMPFC clusters are shown in Figure 3 . Moreover, the left anterior insula (peak of activation: −33 32 −11), the right occipital cortex, the left fusiform gyrus and the right sensorimotor cortex were activated ( Table 1 for a complete list of the areas and coordinates). The anterior insula has been associated with the evaluation of the affective consequences of our action ( Brass and Haggard, 2010 ; Koban and Pourtois, 2014 ) and it has been considered crucial for the social interaction (e.g. Cracco et al. , 2016 ). For outcomes where the ball was present (B+), violation of expectation based on participant’s belief (P− A+ > P + A+, B+) revealed activation in the supplementary motor area (SMA), right sensorimotor cortex and the occipital cortex. Violation of expectation based on the agent’s belief (P +  A −  > P + A+, B+) did not lead to any cluster of activation. Importantly, neither agent’s violation of expectation nor for the participant did the interaction between expectation and task reveal any result. Motor activation emerging in different contrasts probably reflects motor preparation of the left-side response that was needed to respond to the catch questions. Although the catch questions were only presented in a small percentage of trials (about 20%), participants could not predict in which trials they would be present and therefore always prepared the response. Results are summarized in Table 1 .

Left panel . Cluster of activation in the PPC for the contrast false belief  > true belief (irrespective of the task) during the belief formation phase. Right panel . Clusters of activation in the MPFC for the participant’s violation of expectation (participant positive content prediction > negative outcome in green) and the agent’s violation of expectation (agent positive content prediction > negative outcome in blue) (irrespective of the task) in the outcome phase.

Peaks of activation from different contrasts in the belief formation phase and outcome phase of the videos

ROI analysis. With this analysis we wanted to further explore potential differences in belief processing between spontaneous and explicit ToM versions of the task. More specifically, in the study of Kovács et al. (2014) , an asymmetric effect has been found in activation of the right TPJ during the spontaneous ToM version. Results showed that TPJ is only active when a false belief attributed to the agent has a positive content (the agent thinks that the ball is behind the occluder). Such an asymmetry may be due to task instructions, which required participants to respond only to the presence of the target, but not to its absence. An alternative explanation is that this asymmetry is a functional characteristic of the spontaneous belief tracking system, which leads to preferential encoding of certain types of belief contents, while ignoring others in specific situations. If the asymmetry noticed by Kovacs et al. (2014) only occurs in the spontaneous task then this would suggest that only spontaneous processing has this specialization.

We found a main effect of task [ F (1, 21) = 4.39, P  < 0.05, η 2  = 0.17) with higher activation in the spontaneous when compared with the explicit version. This effect is attributable to a general decrease of the BOLD signal across the task blocks and should not be interpreted as a specific effect of task manipulation on TPJ activity.

More important, there was a main effect of belief with higher activation values for false than for true belief conditions [ F (1, 21) = 4.81, P  < 0.05, η 2  = 0.17). Importantly, no interaction between belief and task emerged, supporting the results on our whole brain analysis. Furthermore, there was a main effect of content [ F (1, 21) = 4.62, P  < 0.05, η 2  = 0.18] with higher activation when the agent belief has a positive content (Ball+). Importantly, a significant interaction effect of belief and content was found [ F (1, 21) = 7.98, P  < 0.05, η 2  = 0.27]. Post hoc comparisons revealed a significantly higher activation for the false belief, positive content condition as compared to the false belief, negative content ( P < 0.05). However, no interaction with the task emerges. Our data therefore support the idea that our task is sensitive to the content of false belief. However there is no evidence for a dissociation between the spontaneous and explicit version. Percentages signal change has been depicted in Figure 4 for all task conditions.

Percentages signal change (Beta signal change) in the TPJ ROI for all task conditions are depicted.

To the best of our knowledge, this is the first study that compares spontaneous and explicit false beliefs processing by adopting the same task procedure and stimuli in both task versions. Our data strongly support the idea that during spontaneous and explicit ToM, we track others’ belief by using the same neural mechanisms that have been shown to be involved in explicit mentalizing. Importantly, the two task versions only differed for the spontaneous/explicit processing of the agent’s belief. A debriefing procedure, allowed us to verify that participants were unaware of the belief manipulation during the spontaneous version of the ToM task. On the other hand, in the explicit version, we have ensured that participants would explicitly track the agent’s belief. When we analyzed behavioral results from the ball detection task, we have shown the pattern of reaction times is not affected by task instructions (spontaneous vs explicit). This outcome support previous observations derived from the same task (Nijhof et al. , in press) or a different one using eye gaze as dependent variable ( Schneider et al. , 2014a ). In the study of Schneider et al. (2014a ), the authors gave participants either no instruction, the instruction to track the position of the ball in the scene, or the instruction to track the agent’s belief. Here, eye-movement patterns were consistent with belief analysis, irrespective of the task.

A possible limitation of our design is the fixed order of the tasks. The spontaneous ToM task had to come always first as participants would otherwise become aware of the belief manipulation. Although a general decrease of participants' attention to the stimuli is expected in the second part of the experiment, it is important to note that responses to catch questions in the explicit task were still well above chance level (74%), showing that participants were engaged in the task.

Our results show that spontaneous ToM processing activates the same neural network that has been previously pointed out as being critically involved in explicit ToM, specifically the right TPJ and anterior MPFC (e.g. Fletcher et al. , 1995 ; Gallagher et al. , 2000 ; Ruby and Decety, 2003 ; Saxe and Kanwisher, 2003 ). A number of recent meta-analysis studies indicate that explicit ToM, across different tasks, consistently activates TPJ and MPFC (e.g. Decety and Lamm, 2007 ; Van Overwalle, 2009 ; Schurz et al. , 2014 ). During the belief formation phase, greater activity for false vs true belief conditions was found in the right TPJ. Importantly, no interaction was found with the spontaneous/explicit task. Overlapping results were obtained for the whole-brain analysis (AG activation) and the ROI analysis when an a priori TPJ ROI was defined based on a meta-analysis on explicit ToM. Moreover, the MPFC was active during the outcome phase. The MPFC showed greater activation when the outcome did not match expectations based on the preceding events contained in the movie, as compared to when the outcome did match expectations. Interesting, two partially overlapping clusters were found for participant and agent’s violation of expectation. Again, no interaction was found with the task version.

Our outcome on the TPJ is in line with previous data from ROIs analyses on a spontaneous ToM task ( Kovács et al. , 2014 ; Hyde et al. , 2015 ). Moreover, activation of the TPJ in false belief trials seems to be higher when the belief of the agent has a positive content (i.e. the agent falsely believed the ball was behind the occluder). This pattern is in line with what was found in a previous study adopting the spontaneous version of the same task ( Kovács et al. , 2010 ). In the same vein, violation of expectation based on the agent’s belief, led to stronger activation in the MPFC in trials with ball absent outcomes (that is, when the agent expected the ball to be behind the occluder, positive content belief). This bias for agent’s beliefs with positive content can reflect the specific instructions of our detection task. In fact, participants were asked to respond only when the ball was present at the end of the movie (ball present outcomes). This can explain why the belief of the agent is more salient when it has a positive content, which is when the agent expected the ball to be behind the occluder. On the other hand, this effect might reflect a content-dependent representational constraint, or limit, on spontaneous ToM, which restricts the system to tracking false beliefs that may favor potential behaviorally relevant beliefs. This may reflect a functional difference between spontaneous and explicit ToM ( Kovács et al. , 2014 ). A possible representational limit of the spontaneous ToM system has been previously identified for object identity ( Low and Watts, 2013 ). However, since responses in our ball detection task reflect a spontaneous measure of ToM in both our task, future studies are needed to give a more definitive answer to this question.

It has been proposed that behavioral effects in spontaneous ToM tasks do not reflect spontaneous ToM but could rather be explained by domain-general cognitive mechanisms, such as response selection, attentional orienting or spatial coding of response locations, which simulate the effects of mentalizing ( Heyes, 2014 ; Philips et al. , 2015). On the contrary, if a spontaneous ToM task reflects mentalizing, one would expect task manipulation to induce activation in brain areas that are commonly associated with ToM (i.e. TPJ and aMPFC). Our fMRI data, together with previous observations ( Kovács et al. , 2014 ; Hyde et al. , 2015 ) support this idea. Moreover, Deschrijver et al. (2015) recently carried out the same paradigm (spontaneous version only) in a group of adults with autism spectrum disorder (A). Here a ‘ToM index’ has been calculated as the difference between the P− A −  and the P− A+ conditions, representing the degree to which the agent's belief about the presence of the ball influences RTs. The size of individuals’ ‘ToM index’ was found to correlate with A symptom severity in the A group.

Our results are in contrast with the previous neuroimaging study of Schneider et al. (2014b ). There, having identified ROIs based on an explicit ToM task, during the spontaneous ToM task, the authors found a significant difference in the BOLD signal between false and true belief conditions only in the left STS and precuneus (PC) but not in the TPJ, although the comparison was in the right direction (higher activation for false vs true belief). One possible explanation for this discrepancy lies in differences in the tasks and stimuli used. In effect, the explicit task adopted in the study of Schneider et al. was a common task used in false belief research involving reading stories describing someone’s knowledge and beliefs (linguistic material) and then answering a question. Moreover, in that study, the analysis of the BOLD signal has not been performed on the belief formation phase (referred to as belief set-up sequence) but only on the output phase (belief test phase). In our experiment, TPJ activation only emerges in the belief formation, and not in the outcome phase. Therefore, methodological and analysis differences between the two studies can account for the discrepancy. Finally, there is another, perhaps theoretically more interesting, reason for why our results (and the results of Hyde et al. , 2015 ) differ from those obtained by Schneider et al. (2014b ). This difference concerns the true belief condition used to compute the main contrast of interest (FB > TB). In the true belief condition of our task, since both the agent and the participant observe the ball reaching its last position before the agent leaves the scene (no changes occur after that), the agent has all the information the participant has. In other words, in the participant's perspective, the agent has knowledge about the position of the ball. In the true belief condition of Schneider et al. (2014b ), the agent does not have all relevant knowledge, but instead holds a belief that accidentally becomes true at the end. In effect, the ball still moves from its location when the agent is not in the scene and reaches its final position only at the end of the movie. Although it is a matter of debate whether true beliefs are processed differently from the state of reality, or knowledge ( Sommer et al. , 2007 ; Aichhorn et al. , 2009 ; Back and Apperly, 2010 ), both behavioral and neuroimaging evidence suggests that true belief reasoning is different from reasoning about the state of reality. For example, in a study by Döhnel et al. (2012) , brain activity for false and true belief reasoning has been compared with state of reality-control conditions. When compared with this control condition, right TPJ activity was observed both for true and false belief reasoning. We can argue that the probability of representing true beliefs is higher if true beliefs do not match the state of reality; and therefore, do not completely overlap with the participant's knowledge. In this sense, contrasting a false with a true belief condition (equal to knowledge) as in the present study (and in the study of Hyde et al. , 2015 ) would be more sensitive in capturing belief processing than contrasting a false belief with a true belief (different from knowledge) condition as in Schneider et al. (2014b ). In any case, further studies are needed to understand whether and how true belief tracking occurs in spontaneous ToM tasks.

Although we are aware that the time resolution of fMRI does not allow us to argue about a strong dissociation between the belief formation phase and the outcome phase in our task, separate regressors for the two phases have been included in the model. Our results suggest that TPJ is more involved in the belief formation phase while the aMPFC seems to be implicated to a greater extent in the outcome phase. Despite extensive research on the role of TPJ and aMPFC in ToM and social cognition in general, the differential role of these two areas is still poorly understood. However, from a functional–anatomical point of view, it is unlikely that both areas serve the same function. For example, Saxe and Powell (2006) suggest that the MPFC recruitment is not restricted to reasoning about another person's thoughts (the later-developing component of ToM) or even subjective, internal states in general, but may be involved more broadly in representing socially or emotionally relevant information about another person. Van Overwalle (2009) hypothesized that the mPFC is engaged in making inferences about permanent social and psychological properties of others, such as personality traits. Moreover, outside the domain of social cognition, the aMPFC has long been associated with the encoding of the outcome ( O'Doherty et al. , 2002 ; Kennerley and Wallis, 2009 ; Rushworth et al. , 2011 ).

In the current work, we have shown neural activation patterns for spontaneous and explicit ToM overlap. However, this does not provide definitive evidence that spontaneous ToM is based on the same mechanisms involved in explicit ToM. We cannot rule out the possibility that activation in the spontaneous condition was driven by attentional processes in the TPJ, causing an overlap with activity for explicit ToM. This interpretational challenge is a limitation for the present and numerous fMRI-based studies in the social neuroscience domain, as interpretations rely on reverse inference ( Poldrack, 2006 ).

The TPJ has been related to key computations in the social domain, such as ToM, self-other distinction in the control of imitation, agency processing and perspective taking (e.g. Ruby and Decety, 2001 ; Blanke et al. , 2002 ; Farrer and Frith, 2002 ; Farrer et al. , 2003 ; Saxe and Wexler, 2005 ; Legrand and Ruby, 2009 ; Brass et al. , 2009 ) but also in other non-social processes, such as spatial attention ( Corbetta et al. , 2002 ; Mitchell, 2008 ). Although it is out of the scope of this work to enter the discussion about domain-specific or domain-general neural computation of the TPJ, recent models try to reconcile observations from the social and other cognitive neuroscience fields suggesting that TPJ is involved in reorienting of attention toward unexpected relevant events ( Corbetta et al. , 2008 ) or ‘contextual updating, updating of internal models based on incoming incongruent information’ ( Geng and Vossel, 2013 ). Accordingly, TPJ would help updating mental representations based on changes (we did not necessary attend to) occurring in the environment. In line with this idea, we have found preferential activation of TPJ during the tracking period of other’s beliefs (belief formation phase). Moreover, following the same idea, we should not expect differences between spontaneous and explicit ToM. Moreover, this outcome is in line with the self-other distinction model were TPJ detects an incongruence between internally generated representation and externally triggered representation (e.g. Brass et al. , 2009 ).

Recently, the temporal profile of processing another person's visual perspective has been investigated using event-related potentials (ERPs; McCleery et al. , 2011 ). In this study, participants were asked to actively take the perspective of an agent or simply their own regarding the number of displayed disks on a wall. McCleery et al. concluded that early in visual perspective processing the temporal and parietal cortices distinguish between self and other perspectives and then, later, the frontal cortex resolves conflicts between these representations during response selection. Importantly, however, McCleery et al. 's study design was focused on visual perspective taking and did not examine brain regions involved in a classic false-belief processing task. Moreover, source analysis of the EEG signal referred to the later prefrontal cortex. Although behavioral studies have suggested a possible dissociation between belief calculation/tracking and response selection in ToM ( Leslie and Thaiss, 1992 ; Leslie et al. , 2005 ; Qureshi et al. , 2010 ), it remains unclear what the specific role of TPJ and MPFC is and how these regions may interact with each other.

In summary, our findings suggest that mechanisms underlying the spontaneous tracking of others’ beliefs may exploit similar representational systems as explicit ToM judgments do. In fact, neural mechanisms for spontaneous and explicit ToM overlap when tasks are equal in terms of stimulus materials and procedure. Interestingly, the analyses on the belief formation phase and outcome phase suggest dissociation between TPJ and aMPFC, with TPJ being preferentially activated for belief tracking and the aMPFC for outcome evaluation.

Acknowledgment

We would like to deeply thank the reviewers for their fruitful comments on the manuscript.

This study has been funded by Research Foundation – Flanders (FWO) Pegasus Fellowship to Lara Bardi and grant 331323-Mirroring and ToM, Marie Curie Fellowship (Marie Curie Intra-European fellowship for career development) to L.B.

Conflict of interest . None declared.

Appendix. Debriefing form (translated from Dutch)

• Do you have an idea what the goal of this experiment was?
• Did you notice anything unusual about the movies?
• Did you notice any particular pattern or theme to the movies?
• Did you have any particular goal or strategy?
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ORIGINAL RESEARCH article

Pragmatics in the false-belief task: let the robot ask the question.

• 1 Laboratoire Cognition Humaine et Artificielle, Université Paris 8, Paris, France
• 2 Probability, Assessment, Reasoning and Inferences Studies (P-A-R-I-S) Association, Paris, France
• 3 Facultés Libres de Philosophie et de Psychologie (IPC), Paris, France
• 4 CY Cergy-Paris Université, ESPE de Versailles, Paris, France

The poor performances of typically developing children younger than 4 in the first-order false-belief task “Maxi and the chocolate” is analyzed from the perspective of conversational pragmatics. An ambiguous question asked by an adult experimenter (perceived as a teacher) can receive different interpretations based on a search for relevance, by which children according to their age attribute different intentions to the questioner, within the limits of their own meta-cognitive knowledge. The adult experimenter tells the child the following story of object-transfer: “Maxi puts his chocolate into the green cupboard before going out to play. In his absence, his mother moves the chocolate from the green cupboard to the blue one.” The child must then predict where Maxi will pick up the chocolate when he returns. To the child, the question from an adult (a knowledgeable person) may seem surprising and can be understood as a question of his own knowledge of the world, rather than on Maxi's mental representations. In our study, without any modification of the initial task, we disambiguate the context of the question by (1) replacing the adult experimenter with a humanoid robot presented as “ignorant” and “slow” but trying to learn and (2) placing the child in the role of a “mentor” (the knowledgeable person). Sixty-two typical children of 3 years-old completed the first-order false belief task “Maxi and the chocolate,” either with a human or with a robot. Results revealed a significantly higher success rate in the robot condition than in the human condition. Thus, young children seem to fail because of the pragmatic difficulty of the first-order task, which causes a difference of interpretation between the young child and the experimenter.

1. Introduction

For almost 40 years, the explicit question in false belief tasks (FBT) of Wimmer and Perner (1983) , in which the child must express the false belief of a character on the state of the world, has been the commonly accepted task to study the Theory of Mind (ToM). Understanding the false beliefs of others is of considerable importance for the cognitive and social development of children. It is required to grasp that others have mental states, subjective representations conditioned to specific knowledge and experiences, distinct from ours. Thus, understanding that beliefs can be different from one person to another ( Perner, 1991 ). Sabbagh and Bowman (2018) highlight that explicit FBT are a simple test paradigm perfectly representative of this understanding. In these tasks, children must recognize that someone else will behave in a way that does not correspond to how they understand the state of the world.

Explicit FBT require a direct verbal answer to an explicit question of the experimenter. The expected answer seems to be very intuitive and is traditionally considered to be a reliable indicator of the understanding of false beliefs. The explicit FBT of Wimmer and Perner (1983) is the following task: The experimenter tells the child participant a story of object transfer through the use of clips 1 : Before going out to play, the child Maxi puts his chocolate into the green cupboard. While he is outside, his mother moves the chocolate and puts it into the blue cupboard. Maxi then comes back to get his chocolate (see Figure 1 ) 2 .

Figure 1 . The story “Maxi and Chocolate” of Wimmer and Perner (1983) in clips (taken from Duval et al., 2011 , p. 45). Left clip : Maxi comes home from shopping with his mother, and puts the chocolate into the green cupboard before going outside to play. Middle clip: While Maxi is gone, Maxi's mother takes the chocolate from the green cupboard to make a cake and puts it back into the blue cupboard. Right clip: Maxi comes home for a snack. He still remembers where he put the chocolate.

The child must predict which cupboard Maxi will open to try to get his chocolate. To get the child's answer, the experimenter asks the following test question (ToM question): “Where will Maxi look for the chocolate?” In this question, children are invited to indicate that Maxi will look for the chocolate where he believes it is (i.e., where he left it) instead of where the children know it really is. To answer correctly (green cupboard), the child must activate in their mind the false belief of the character Maxi, who doesn't know the chocolate has been moved, while inhibiting their own knowledge of the world (the chocolate is in the blue cupboard). A control question is then asked by the experimenter following the ToM question to make sure the child understood the story. If the child answers correctly to the ToM question, a “reality question” is then asked regarding the true location of the chocolate at the end of the story: “Where is the chocolate really?” If the child instead fails to answer the ToM question, the next question is then a “memory question” to see if they remember where the chocolate was at the beginning of the story: “Do you remember where Maxi put the chocolate in the beginning?” Results of numerous studies done with neuro-typical children of various cultures ( Callaghan et al., 2005 ) indicate that the majority of 4 years-old children answer the blue cupboard to the ToM question (where the chocolate actually is). It is necessary to wait 4–5 years to see children answering correctly that Maxi will look into the green cupboard ( Wimmer and Perner, 1983 ; Baron-Cohen et al., 1985 ; Wellman et al., 2001 ; Sabbagh and Bowman, 2018 ). The explanation being that children between 3 and 5 learn conceptual knowledge necessary to make explicit decisions about the representative mental state of others 3 .

Yet, these results seem to be in contradiction with behavior observed in 3 years-old children requiring first order abilities, such as the game of Hide and Seek. In this game, the child must go somewhere they will not be seen by others. To succeed the child must understand the difference between their knowledge and what others will perceive. Children younger than 4 are able to evaluate what can be perceived by others, and thus to adopt a point of view different from their own ( Shatz et al., 1983 ; Reddy, 1991 , 2007 , 2008 ; Bartsch and Wellman, 1995 ). The first order ToM then seems to be an ability acquired before the age of 4 ( Baillargeon et al., 2010 ; Westra and Carruthers, 2017 ). Hala et al. (1991) show that children who failed an explicit FBT, in an ecological situation, can understand and use the false beliefs to explain the mental state of the protagonist of the story. The reasons of the systematic failure of 3 years-old children would be, for these authors, due to the specificity of the explicit FBT. The child must give conscious and declarative answers to the questions of the experimenter. Explicit tasks with verbal answers would require important cognitive resources. These tasks would greatly involve executive functions; such as the ability of the child to inhibit their own point of view to consider that of others. These executive functions would still be immature at the age of 4 ( Leslie, 2005 ; Baillargeon et al., 2010 ; Westra and Carruthers, 2017 ; Oktay-Gür et al., 2018 , for a discussion). In implicit FBT, in which the answers of children are deduced from actions or gazes and not from explicit pointing or linguistic replies, a much more precocious success (starting from 15 months old) is observed ( Onishi and Baillargeon, 2005 ; Southgate et al., 2007 ; Surian et al., 2007 ; Baillargeon et al., 2010 ; Scott et al., 2010 ; Heyes, 2014 ).

In consequence, there is a “developmental paradox of the understanding of false beliefs” ( De Bruin and Newen, 2012 ; Newen and Wolf, in press ): toddlers succeed in implicit FBT using behavioral responses but kids below the age of 4 generally fail the explicit FBT in which they must explicitly answer the experimenter's question. Some following a “nativist” approach argue in favor of an early ability to detect false beliefs (based on an innate module) allowing toddlers to succeed in implicit FBT ( Leslie et al., 2004 ). Others following a more “empiricist” approach argue that the ability to understand false beliefs is due to the development of cognitive abilities. It is that development which is responsible for the change of performance in explicit FBT at the age of 4 (see Newen and Wolf, in press , for a recent review). Newen and Wolf (in press) point out a distinction dividing both nativists and empiricists into those who give a cognitive explanation and those who give a situational explanation to the failure of children. For the former, explicit FBT would be difficult because the correct answer would require cognitive resources not yet developed for children between 3 and 4. For the latter, the failure in explicit FBT would actually be the result of the procedure itself; which would be a source of the misunderstanding of the question for these children. Our study focuses on this situational explanation (and in particular the pragmatic explanation) of the failure of toddlers in explicit FBT. We suggest a new procedure able to cancel out situational factors without modifying the structure of explicit FBT themselves. Still, we believe that the situational explanation is profoundly cognitive as well as the Relevance Theory ( Sperber and Wilson, 1986 ) we use to explain the influence of the situation is a fundamentally cognitive theory.

Helming et al. (2014 , 2016) , Westra (2017) , and Westra and Carruthers (2017) all consider the failure of children younger than 4 to be caused by a defective understanding of the expectations of the experimenter in the question. The correct interpretation of the ToM question would require a cognitive effort too great for children of that age. Furthermore, since discussions on beliefs are not common, children would systematically interpret the expectations of the experimenter to be about testing the child's knowledge about the state of the world (i.e., indicating where the chocolate really is) compared to the beliefs of a fictive character. This incorrect interpretation of the ToM question would be caused by the conversational context: the attribution of the status of teacher to the experimenter, and their own status of pupil. We suggest transforming this context (1) By switching the roles and specific statuses of the experimenter and of the child participant and (2) By replacing the experimenter with an “ignorant and slow entity.” This context, we call “mentor-child,” disambiguates the ToM question asked by the ignorant entity by making it clear that it expects to understand the false belief of Maxi. To do this, we replace the experimenter with a humanoid robot NAO. This work will be organized in the following way: After recalling the obligation to consider the pragmatic implicatures in all acts of communication, we will expose those driving the child to produce an incorrect answer in the explicit FBT. We will then explain a new procedure to diminish the ambiguity of the questions. After describing the results, we will discuss them and conclude with the suggestion of future areas of research.

2. The Ambiguity of the ToM Question

Sperber and Wilson (1986 , 2002) have shown that all communication is inevitably of a pragmatic nature. A communicator performs in a way, such as producing a speech act or a gesture, and the receiving audience must understand the intent hidden beneath the surface. It is especially important to understand that most of the experimental paradigms in cognition, social cognition and developmental cognition correspond to an act of communication between an experimenter and participants. There are many examples in the psychological literature that answers given, considered to be incorrect by the experimenter, by adult participants are actually the result of the participants' misunderstanding of the intentions of the experimenter. The utterances used and the context of the experimental task trigger implicatures in the participants that can induce answers that are different from those expected by the experimenter (see Dulany and Hilton, 1991 ; Sperber et al., 1995 ; Baratgin and Noveck, 2000 ; Macchi, 2000 ; Politzer and Macchi, 2000 ; Baratgin, 2002 , 2009 ; Bagassi and Macchi, 2006 ; Baratgin and Politzer, 2006 , 2007 , 2010 ; Macchi and Bagassi, 2012 ; Macchi et al., 2019 , 2020 , for examples). Many developmental studies also give pieces of evidence for the ability of children, given their age, to recognize the intentions of the communicator (see Braine and Shanks, 1965a , b ; McGarrigle and Donaldson, 1974 ; Rose and Blank, 1974 ; Markman and Wachtel, 1988 ; Politzer, 1993 , 2004 , 2016 ; Gelman and Bloom, 2000 ; Diesendruck and Markson, 2001 ; Bagassi et al., 2020 , for examples).

Sperber (1994) suggests that the child uses the simplest procedure of interpretation which consists in inferring from the communicative stimulus the most relevant intention in relation to their own point of view. However, what is relevant for the child may be different from what the experimenter actually intends to communicate. Thus, by analyzing the experimental task of Piaget and Szeminska (1941) on the class inclusion question, Politzer (1993 , 2004 , 2016) has shown that the performances of children in relation to their age could be explained by the differences of their interpretation of the question. The experimenter showed five asters and three tulips. The child was then asked whether “there are more asters or more flowers.” The typical answer of children under 8 is “There are more asters.” Politzer demonstrates that the question can be characterized by an ambiguity at the root of the response of the youngest children. While the question of class inclusion is enunciated, according to the relevance principle ( Sperber and Wilson, 1986 ), children will try to infer the expectations of the experimenter and to adapt their answer so that it feels relevant to them. Questions are relevant when they make the person to whom they are asked answer in a relevant way (i.e., questions that require the least cognitive cost for the most contextual effect). These assumptions depend on the representational attributions of the child for the experimenter which are a function of their development ( Hayes, 1972 ). According to Politzer, young children do not make mistakes of class inclusion. They simply have a different representation of the question, making them give an incorrect answer.

“This is a fundamental insight. Once this view is adopted, the disambiguation of the question must be envisaged in relation to the child's development. From the notion that the children attempt to render the question optimally relevant it follows that the way they do so will vary with their cognitive development. In other words, the interpretation chosen by the children is constrained by their level of development. Therefore, the interpretation can be predicted based on what is likely to be the children's estimation of the relevance of the question” ( Politzer, 2016 , p. 3).

Politzer observed that when he disambiguated the question of class inclusion the success of participants was significantly improved and came earlier: between 5 and 6 years-old (see also Jamet et al., 2018 ).

It is then legitimate to wonder if, like with the question of class inclusion, the incorrect reply given by young children in explicit FBT could also be the result of a different interpretation of the ToM question which would be caused by an incorrect inference of the experimenter's expectations. With the years, and with the acquisition of the pragmatic skills of the child, the ambiguity of the question would later decrease. This pragmatic hypothesis could explain the early success in implicit FBT, which are simplifications of explicit FBT in which the ToM question is not explicitly asked. To succeed in these tests, the child does not need to correctly interpret the question or to correctly infer the intention of the experimenter. They only need to understand the false beliefs. For Siegal and Beattie (1991) ; Westra (2017) ; Westra and Carruthers (2017) , since the beginning of their development, young children can create representations of others' beliefs and understand the false beliefs. However, 3 years-old children do not expect beliefs to be a likely topic of conversation ( Westra, 2017 ). It is difficult for them to induce that facts relative to someone's beliefs can be a relevant topic in the conversation with the experimenter and that this is what the question is about. Despite the fact that young children constantly attribute propositional attitudes to other agents, understanding when these pre-linguistic concepts play a part in the conversation is not only a question of acquisition of the adequate vocabulary but would also be a question of the development of pragmatic skills ( Westra and Carruthers, 2017 ). The child must be exposed to conversations for these social stimuli to play a crucial part in the strengthening of their linguistic and pragmatic skills ( Astington and Olson, 1995 ; Carpendale and Lewis, 2004 ; Antonietti et al., 2006 ; Westra, 2017 ) 4 .

This lack of pragmatic skill is even more salient in explicit FBT as the conversational interaction happens between the child and a stranger (the experimenter). At the age of 3, even if the young child has had numerous interactions with their parents and family, interactions with adults are generally limited, except for the teacher which is for most still a recent interaction (3 is usually the age at which children start school). The teacher is certainly an important reference for the young child during the experiment. After 2 months of class, preschool children have integrated the didactic contract wanted by the teacher. The teacher explicitly invites the pupils to work well, to show everything they know. Each time the child returns to class, after completing an activity, the teacher will ask them if they worked well. As Westra and Carruthers (2017) explained, children are readily able to consider that the interaction with the experimenter has an educational intention. Indeed, educational clues are almost always present in an explicit FBT. The experimenter, for the child, is in a social position much superior to theirs and, just like their teacher, has the encyclopedic knowledge. The experimenter-child relationship reinforces this impression of superiority since the experimenter is introduced to the child as an authority figure to whom they must obey. This attribution of teacher is facilitated even more by the fact that the experiments most often happen at school, during school time. This supposition of an educational intention in the task implies, for the child, that an educational behavior is expected of them, as it is usually the case in this context. Therefore, they are in a position of pupil during the experiment. How uncommon the situation is, an adult replacing the teacher for an educational exercise, can strengthen the idea that this exercise is really important and that this new teacher may be special and knows more than the usual teacher. This attribution is all the easier since the experimenter is often presented as a researcher, a specialist. Preschool children indeed seem to be already sensitive to the knowledge of the informant in educational activities ( Jeong and Frye, 2018b ).

When teaching a new concept in an example or a story, the teacher later checks the child understood correctly through simple and direct questions linked to what was just told. These questions are very rarely ambiguous. The correct answer expected by the teacher is usually meant to prove that they understood the story correctly. Thus, to the child, the same can be expected of the questions asked by the experimenter. The main difficulty in explicit FBT is the fact that they involve four different elements of knowledge: (1) Where Maxi initially put the chocolate, (2) The change of location done by Maxi's mother, (3) The fact that this change of location happened in Maxi's absence, and (4) The fact that Maxi is looking for his chocolate, probably in the wrong place. For the child, there are multiple possible interpretations of the experimenter's expectations when they ask the ToM question. They can be: trying to assess whether the child understood the change of location of the chocolate (steps 1 and 2), or assess whether the child understood the fact that Maxi was not there during the change of location, and that in consequence he will look for the chocolate in the wrong place: the initial location (steps 3 and 4). Along these interpretations, the one which concerns the attribution of beliefs to someone else has a greater cognitive cost for young children. They are generally not experienced enough in interacting with adults to grasp the relevance of this expectation. Children of 3 years-old will instead use the more familiar interpretation: they will think that the experimenter expects the reply to be about the child's understanding of the change of location ( Siegal and Beattie, 1991 ; Hansen, 2010 ; Lewis et al., 2012 ; Westra, 2017 ).

Helming et al. (2014 , 2016) offer a more elaborate pragmatic explanation of young children's answer. For them, explicit FBT force the children to adopt two points of views at the same time. One is more detached: “spectating” in the third person the action of the main character of the story, in particular focusing on the character's beliefs; and the other is more communicative: interacting with the experimenter in the second person. This first point of view being disrupted by the second. The ToM question then generates two biases: one “referential” and one “cooperative.” Children have the possibility of mentally representing the real location of the chocolate or where Maxi wrongly believes it is. Using the word chocolate in the question can bias children toward answering with the real location (referential bias). The interaction with the experimenter would bring the child to focus on the knowledge they share (i.e., the real location of the item). This would then disrupt the ability of the child to track the false belief of the main character from the third person point of view. In essence: when the experimenter refers to the target item, they direct the attention toward the real location. The cooperative bias is the result of the tendency of toddlers to want to make themselves useful by spontaneously helping others (even adult strangers) to reach their goals, even if it requires a greater effort and if they are busy with a task of their own (see Warneken and Tomasello, 2007 , 2009 , 2013 ; Liszkowski et al., 2008 ; Buttelmann et al., 2009 , 2014 ; Warneken, 2015 ). This helpfulness seems to be mainly motivated by an intrinsic care for the other and not for any personal reward ( Hepach et al., 2012 , 2016 ). This tendency to help others made it possible to create implicit FBT. The task given to toddlers consisted in helping an adult reach their goal. Yet to infer this goal, the toddlers needed to consider what the adult believed. This tendency would drive children to adopt a second person point of view toward the main character of the story, rather than a spectating point of view in the third person, this in turn driving them to incorrectly interpret the expectations of the experimenter. Children understand that the main character needs help, because he has false beliefs, to avoid picking the wrong location. They spontaneously want to help him by telling him the correct location and can readily expect to be invited to do so. This, for the child, would strengthen the interpretation of the ToM question “Where will Maxi look for his chocolate?” as an invitation to help the main character find the item. This means interpreting the question as a normative question “Where should he look for his chocolate?” or even “Can you tell Maxi where to find his chocolate?” As Newen and Wolf (in press) point out, this pragmatic explanation is not in contradiction with the cognitive explanation (in terms of “mental files” by Recanati, 2012 ) suggested by Perner et al. (2015) , Perner and Leahy (2016) , and Huemer et al. (2018) . These mental files, or mental representations, include the “information management tools about an object in the world” and the links between the different files which make it possible to share information between them. In “Maxi and the chocolate,” the child has two mental files of the situation: one “regular” file with the information that the chocolate is in the blue cupboard, and one “indirect by proxy” file indexed on Maxi with the information that the chocolate is in the green cupboard. According to Perner and Leahy (2016) , when children below the age of 4 are faced with the ToM question, they are not yet able to switch between the indirect mental file and the regular mental file in a controlled and systematic way. It is only once the mental files are linked that the child can access the information about Maxi's beliefs. The pragmatic explanation, through the Relevance Theory, allows us to understand which mental file will be activated. In a traditional context, the mental file which has the least cognitive cost and the greatest contextual effect is the regular mental file which answers what the child believes to be the experimenter's expectation.

Thus, as Westra and Carruthers (2017) pointed out, there are two interpretations at stake in addition to the correct interpretation of the ToM question for a total of three possible interpretations: (1) The “helpfulness-interpretation” where the question corresponds to an invitation to help the character, (2) The “knowledge-exhibiting-interpretation” where the question corresponds to an invitation to show one's knowledge of the events in the story (steps 1 and 2 as described in the previous paragraphs), and (3) The “psychological knowledge-exhibiting-interpretation” where the question corresponds to an invitation to report the character's false beliefs about the location of the object (steps 3 and 4) 5 . The child's task is to determine which of these three competing interpretations is most likely to meet the experimenter's. Interpretation (3) is the one expected by the experimenter. Each of the other two leads to the incorrect answer of indicating the actual location of the chocolate. As indicated above toddlers do not yet have the pragmatic experience required to understand that people's beliefs are a valid topic of conversation. In consequence, they are more inclined to interpret the ToM question as a kind of indirect language act to verify their knowledge of the real location of the chocolate (interpretation 1). This will also help the character find the chocolate (interpretation 2). As children gain experience in discourse about the beliefs of others, they begin to be able to recognize the true purpose of the question and their true expectation (interpretation 3): reporting explicitly the false belief of the character called Maxi ( Westra and Carruthers, 2017 ; Frank, 2018 ). They then understand that the question “Where will Maxi look for the chocolate?” implicitly means “What does Maxi falsely think about the location of the chocolate?”

A number of authors have tried to directly disambiguate the ToM question. Siegal and Beattie (1991) give the following question [reformulated to fit Maxi and the Chocolate]: “Where will Maxi look for the chocolate first?” which directly explains the experimenter's expectation. The authors observe a significant increase in correct answers ( Yazdi et al., 2006 ; Białecka-Pikul et al., 2019 ). Hansen (2010) also observes much better results when the experimenter directly specifies in their question that they are not interested in the child's knowledge of the state of the world [reformulated to fit Maxi and the Chocolate]: “You and I know where Maxi's chocolate is, but where does he think it is?”

Another solution is to explicitly and conceptually explain the important clues in the story to make the correct interpretation (3) of the ToM question more conceptually relevant ( Newen and Wolf, in press ), for example by making the false belief of the main character more salient. Mitchell and Lacohée (1991) noticed that children participating in explicit FBT who kept an explicit aide-memoire of their prior belief (the cupboard where the chocolate was [reformulated to fit “Maxi and the Chocolate”]) was much more successful at avoiding a later deformation of this belief. Lewis et al. (2012) showed that the explanation of the false beliefs of another person is improved if we add another character to the story who is also observing object's change of location. The presence of the other person conceptually highlights the possible point of views in the story. In this situation the ToM question, being explicitly directed at the character who did not see the change of location, increases the relevance of interpretation (3) on the false beliefs of the character. Rubio-Fernández and Geurts (2013 , 2016 ) demonstrated that toddlers can also succeed in explicit FBT if the task is modified in such a way that, first, the point of view of the other person is frequently repeated to the child during the experiment and, second, the ToM question asked to the child is transformed into “What happens next?” Here the disruption induced by the experimenter focusing on the item is no longer possible. It is also possible to make interpretation (2), of exposing the child's knowledge about the real location of the item, less contextually relevant. Wellman and Bartsch (1988) , Mascaro and Morin (2015) , and Mascaro et al. (2017) indeed notice better performances when the children themselves do not know where the actual location of the item is or if the item is removed from the scene.

Finally, it is possible to change the experimental procedure to make the spontaneous tendency of children to be useful, which usually drives children toward the “helpfulness-interpretation” (1), to become an indicator of the effective false belief of the character. Matsui and Miura (2008) showed that toddlers succeeded more easily when the task was changed to have them choose a character whom they had to help find the item (pro-social context).

To sum up: whether children can disambiguate the ToM question depends on their meta-cognitive development. Toddlers make the question more relevant by interpreting it as a question about their knowledge of the story or, with the same result, a question about their knowledge of the story to help the main character. Older children interpret it correctly to be a request for them to report their knowledge of the false belief of the character in the story.

3. Changing the Context to Disambiguate the ToM Question

In all these experiments the original task is modified. The ToM question is sometimes modified, the participant is sometimes asked to keep in memory the initial belief, a character is sometimes added or some information is sometimes removed. Our objective is to decrease the salience of incorrect interpretations without changing neither the story nor the question asked: by playing with the global context of the experiment itself. A good example is the length and number conservation task ( Piaget and Szeminska, 1941 ). Assessing the conservation of number is done by presenting two lines of tokens, equal in number and arranged in a one-to-one correspondence, in front of a child who judges them to be the same. When the experimenter rearranges one of the rows the non-conserving child changes their judgment in favor of the longer row. McGarrigle and Donaldson (1974) showed that when the transformation of the row of tokens is the indirect result of an action with a different goal, such as a transformation effected by a “naughty teddy bear” who wants to “spoil the game,” children are more conservant. In this “accidental transformation,” there are no structural modification of the task.

As explained above, the way the child interprets the questions of the experimenter in explicit FBT depends, in part, on their understanding of the nature of the communicative exchange (i.e., its topic and goal). For toddlers, the context of the task, as shown above, strongly expresses that of a school activity with the status of the experimenter-teacher, able to judge, and the location. Thus, the child infers effortlessly their role in this task will be the one they already know and are used to during classes: that of a pupil with the goal of learning and show their knowledge. These assumptions made by the child for the experimenter to be testing their knowledge are the origin of interpretations (2) and (3). The “helpfulness-interpretation” (1) can be considered to be the desired expectation in order to help the character in the story (numerous studies cited above indicate how spontaneously, and without ulterior motives, the toddler displays an altruistic behavior). Thus, if we had an experimental context in which exposing the knowledge of the false beliefs of the character (interpretation 3) could also satisfy a “helpfulness-interpretation” (interpretation 1), then interpretation (2), about the actual state of the world described in the story, could be inhibited.

3.1. A Mentor-Child and an Ignorant, Naive, and Slow Pupil

To do this we must consider a situation which would change the assumptions of the child about the person asking the ToM question; a situation in which the child could spontaneously infer that an answer indicating the false belief of the character would help the person asking this question 6 . A first modification of the context would then have the person asking the ToM question display an explicit need to know the false belief of the character. The person would have trouble understanding the story, as for them the answer to the ToM question is far from obvious, even if they asked it. This person must have less knowledge than the child and must consider the child to be someone who knows more. Thus, we must consider a context in which the status of the child and of the experimenter are switched compared to the original context.

We can imagine a “mentor-child” context in which the young child must answer the questions of an ignorant entity introduced by an authority figure: “You are the teacher and this is your pupil. It doesn't know much. It needs you 7 .” In the conversational act, the expectation of the child regarding the questions of the entity is to be able to help it learn new things. Let us imagine that this ignorant being tells the child: “I was told a story that I didn't understand very well. I'll tell it to you and then please explain it to me.” After telling the story, the ignorant entity asks the ToM question in a naive tone. The child answering correctly shows their knowledge while helping the entity. The question here is disambiguated and reliably drives the child toward interpretation (3). The question asked in this context becomes natural, for the child knows the entity to be ignorant and that it can ask trivial questions. This is not the case in the traditional context where it can seem surprising that a “knowing” adult could ask such a question.

Another important aspect is to highlight the “naive,” “unsure of itself,” and “slow” traits of the ignorant entity. This aspect helps the child consider themselves knowledgeable compared to it. It also helps the child feel useful when helping it. More importantly, the “slow” aspect of the entity can favor the interpretation of the control questions asked depending on the success or failure in the ToM question (respectively the reality question and the memory question). To our knowledge, the pragmatic analysis of these questions has never been explicitly done in the literature. This can certainly be explained by the fact that in Wimmer and Perner (1983) , all children succeeding in the ToM question also correctly answered the reality question 8 . In the standard context, the interpretation of the reality question is indeed completely obvious for the child. It corresponds to the question that is the most expected; which has the strongest contextual effect and requires the least cognitive cost to answer it. After indicating the false belief of the character in the ToM question, the reality question makes it even more explicit by indicating where the item actually is. This second question does not seem to be incongruous in the standard context in which the child assigns the status of teacher to the experimenter. A teacher often asks multiple questions to test the knowledge of the child. In our “mentor-child” context, the reality question asked by the ignorant entity can seem to be a bit odd to the child in their role of teacher. Indeed, asking this second question requires understanding that the chocolate is in a different place than where the character believes it is 9 . Thus, the ignorant entity, if it did understand correctly the answer given by the “mentor-child” to the ToM question, should have also understood that Maxi has a false belief and will look into the green cupboard which is now empty. Frequently, when a pupil asks a second question to the teacher just after receiving an answer to another, it is often because they need more precision or because they did not understand the answer. In this case, there are two possible answers for the “mentor-child”: (1) Thinking that they were not clear enough with their first answer and be inclined to repeat the same answer as in the ToM question, or (2) Accurately answer the reality question to give some new information to help the entity understand their first answer to the ToM question. In order to increase the chances of this second option, the entity must not simply be perceived by the “mentor-child” as “ignorant” but also “a bit slow.”

In a similar fashion, the memory question, asked after an incorrect reply to the ToM question, can also be interpreted as a request for confirmation of the understanding of the first answer. Yet, in the standard context, the memory question can seem to be disrupting for children younger than 4 incorrectly answering the ToM question as the final location of the item is given at the end of the story. Indeed, this answer implies having followed the change of location of the chocolate during the story and to remember the initial location of the chocolate. The weak performances observed (37.8% of success in the memory question) in Wimmer and Perner (1983) for children between 3 and 4 may not be the result of the difficulty of the task but instead be the result of the ambiguity of the question for their age. Older children, because of their conversational experience, may more readily reinterpret the memory question to be controlling their initial answer to the ToM question 10 .

3.2. The Robot-Pupil Solution

There is an important literature showing the advantages of using a humanoid NAO robot in social interactions with young children, especially in situations of learning by teaching (see Jamet et al., 2018 ). Studies have shown that in conversational interaction with an artificial agent, even completely virtual ones, humans automatically detect pragmatic violations of their speaker ( Jacquet et al., 2018 ; Jacquet et al., 2019a , b , c ; Lockshin and Williams, 2020 ). It was shown that children as young as two can be susceptible to the conversational violations of a robot ( Ferrier et al., 2000 ). Recent studies ( Yasumatsu et al., 2017 ; Martin et al., 2020a , b ) also showed that the natural and spontaneous propensity of young children to try being useful extends to humanoid robots seeming to be in difficulty. It seems that 3 years-old children assign mental states to a robot ( Di Dio et al., 2018 , 2020a ; Marchetti et al., 2018 ). Di Dio et al. (2020a) observed in 3 years-old children who had already developed a first-order ToM skill a tendency to represent the emotional state of a robot in terms of mental states. For these authors, there could be an attempt to anthropomorphize the robot on the emotional dimension which, at the age of 3, could be particularly salient. This suggests that young children are eager to think about the robot mind in the same way they do about the human mind ( Di Dio et al., 2018 ). The NAO robot was also used to study the endowment effect in adults ( Masson et al., 2015 , 2016 ; Masson et al., 2017a , b ).

The effectiveness of our “mentor-child” context 11 was tested with children between 5 and 6 in the class inclusion task and successfully made the question of class inclusion more relevant for the child when it was asked ( Masson et al., 2017a ; Jamet et al., 2018 ). We hypothesize that the “mentor-child” context should similarly decrease the ambiguity of the ToM question to make it clearer that it is a request about the mental states of the character Maxi. The performance of preschool children should then be significantly improved without changing the original explicit FBT. Should this be observed, we would conclude that the understanding of false beliefs develops before the age of 4 and that the abilities of young children are underestimated due to pragmatic factors.

We also believe that the “mentor-child” context can keep the control questions unambiguous. Therefore, we expect to have a rate of correct responses to the control questions that should be roughly equivalent to that of older children in the standard context.

4. Experiment: Explicit False Belief Task in the Mentor-Child Context

4.1. materials and methods, 4.1.1. participants.

We recruited 62 native French children in preschool at “Les Petits Princes” in Versailles, France. The sample chosen in the classes was composed of 34 girls and 28 boys, from 38 months-old (3 years and 2 months) to 49 months-old (4 years and 1 month) 12 . The mean age of children was 44 months-old ( N = 62, M = 44 months-old, SD = 2.82 months-old) 13 . The children were randomly assigned a condition depending on their age and gender. These conditions were “human experimenter” (“human” condition) and “robot experimenter” (“robot” condition). Each condition contained 31 children between 38 months-old and 49 months-old ( N = 31, M = 44 months-old, SD = 3.47 months-old for the “human” condition and N = 31, M = 44 months-old, SD = 3.09 months-old for the “robot” condition).

4.1.2. Materials

The story “Maxi and the chocolate” was shown to the child with the clips displayed in Figure 1 . Each clip was 6.4 × 5.8 cm (2.5 × 2.3 in). The clips were shown in a black and opaque folder containing a cardboard spacer in A4 format (21 × 29.7 cm or 8.3 × 11.7 in). All three clips of the task were attached to the cardboard spacer in advance. The robot used in this experiment was a 58 cm tall (23 in) NAO robot created by Aldebaran Robotics (Aldebaran version 4—“Evolution”). It has a moving head, arms and hands, each with three fingers, allowing it to point at the clips of the story to punctuate its discourse with gestures. NAO is also equipped with a microphone and speakers to communicate with humans. The robot was remotely controlled by the experimenter using a computer, but its gestures and speech were recorded in advance. They could see the child thanks to a camera in the eyes of the robot. The movements and the speech sections triggered in real time avoided having too much variability between the different participants, while still making it possible to make the answers of the robot fit those of the child. We chose to remotely control the robot for logistical reasons: even though NAO does have the ability to recognize speech making it possible for it to autonomously react, the behaviors of children can sometimes be unpredictable. Some flexibility was needed to reproduce with fluidity a natural conversation with a human. Moreover, children could sometimes speak too low to be understood by the robot, which would have made the interaction impossible. Finally, the robot also allowed a better standardization of the enunciation context thanks to its intonations and utterances being strictly identical across all participants. To make NAO more childish and less intimidating, its voice was manipulated so that it had a higher pitch and spoke more slowly. NAO was programmed to blink randomly during the experiment to strengthen its humanness.

4.1.3. Procedure

Before the beginning of the experiment one member of the research team, that we call the companion, was welcomed into the class and gave their name. Children were sitting in a circle in front of the teacher. She explained that this new person was there to make all the children of the class work on a task, a bit like teacher. The procedure in both conditions was subdivided into two sequential steps: the priming step and the explicit FBT.

4.1.3.1. Human Condition

In this condition the companion told the children they would be participating in an activity if they agreed. After this introduction each child was guided to the location of the experiment, in a quiet multi-purpose room of the school. During the walk, the companion told the child the didactic contract: “You're about to listen to a story, like in class, and my colleague [name of the experimenter] will ask you some questions. You will need to answer them.” The companion then asked for the agreement of the child. If the child agreed, the child then entered the room without the companion and stayed with the experimenter.

The experimenter then introduced themselves to the child who was seated on a chair in front them. The child's ability to correctly name the two colors (blue and green) was checked before the main task 14 . The false belief story was then verbally told and illustrated with clips, which allowed non-verbal answers for children which preferred to point at their answer instead of saying them.

If the child's answer to the ToM question was the green cupboard, the experimenter pointed at it on the clip and said “ah it is there.” If the child did not change their initial answer, the answer was considered to be correct. When the child instead gave the incorrect answer, no confirmation was required, and the answer was immediately considered to be false.

The reality question and the memory question were then asked (respectively following the success and failure to the ToM question).

Finally, the experimenter thanked the child, and the companion guided them back to the classroom while congratulating them.

4.1.3.2. Robot Condition

In this condition, the companion explained that they came with a NAO robot. They told the class NAO needed the children's help because it knew nothing while they all knew a lot. If one child doubted of their knowledge, the companion told them that they were learning many things in class but also that they already knew a lot. More importantly: they knew more than the robot. The companion then asked if the children agreed to teach things to NAO 15 .

Like in the “Human” condition, the companion guided each child individually from the class to the location of the experiment and told them the didactic contract: “Your job is to teach lots of new things to NAO. NAO is a little robot who knows nothing. NAO needs you to learn new things. NAO doesn't know anything. You will be his teacher. Do you agree to be his teacher?” To make the child understand NAO's ignorance the companion pointed at the child's clothes, or various items in the location of the experiment. They asked the child to name them, which was done without any difficulty, and then they told the child:

“You see, NAO doesn't know all that. If NAO asks you weird, strange questions, you must answer him. Remember that he knows nothing. If NAO tells you strange things, or if he makes mistakes, you correct him 16 . You are his teacher. Do you agree to be NAO's teacher [name of the child]?”

If the child agreed, the companion let the child enter the experiment room and left the child “alone” with the robot (see Figure 2 ). This is an especially important detail with the robot. Indeed, should the companion remain in the room, the child may be tempted to answer the robot in the same way they would with a human experimenter because of the presence of an adult in the room. Pragmatic interpretations would then be modified. The actual experimenter was hidden behind a screen, without the child knowing about it, and remotely controlled NAO using a laptop.

Figure 2 . Robot experimenter and materials for the “Robot” condition. The NAO robot is seated on a table in front of the child.

NAO introduced itself to the child. It asked if the child agreed to be its teacher because he was there to “learn many things.” Once again, if the child did not agree, the experiment stopped. The robot then asked the child if they can help it learn colors. NAO then pointed the colored cardboard sheets and made mistakes (for example: NAO said “That's yellow?” while designating the blue cardboard, making it more believable the fact that NAO did not know much and thus strengthening the role of the child as a teacher). To further strengthen the naive aspect of the robot, NAO insisted on its ignorance all along the experiment (e.g., “Alright, I had not understood that. I am really stupid.” It is important to note that great care was given to not overdoing the “stupidity” of the robot. Indeed, if its mistakes became too predictable, there was a great risk of losing the child's interest in teaching it anything. A child could quickly have inferred that “NAO will make mistakes no matter what I tell him.” which could have biased the child's experience in the task if it had not been controlled.

NAO initiated the story “Maxi and the Chocolate” by telling the child “A man told me a story that I did not understand. Do you want to help me?” Identically to the “Human” condition, NAO told the story and then asked the ToM question and the control question.

The answer to the ToM question was considered to be correct if the robot was corrected by the child when it made a mistake trying to repeat the answer. For example, if the child answered that Maxi will look for the chocolate in the green cupboard (initial position), NAO said: “Ah thanks, so if I understood well the chocolate is in the blue cupboard.” If the child corrected NAO and said: “No, the chocolate is there.” (indicating the green cupboard) or “No, it is in the green one.” the answer was considered to be correct. Note that, just like in the human condition, the child could also point at the clips directly instead of answering verbally.

Once the task was over, NAO thanked the child for being its teacher “Thank you, you've been an awesome teacher. I've learned many things thanks to you!” and told them goodbye. The companion then came to bring the child back to the classroom. Sometimes the teacher asked how the task went. The companion congratulated the child for the quality of their teaching. They told the rest of the class that NAO still needed to work to learn things. This way, the fact that NAO needed help was progressively very well-communicated to the whole class while they did their usual class activities.

4.2. Results

In this experiment, the dependent variable was a dichotomous variable which modalities were interpreted in terms of success or failure . According to the procedure used by Wellman and Liu (2004) , the child's response was a success when they produced correct answers to both the ToM question and the reality question. This variable will be noted below: TR (ToM and Reality). We also analyzed the data from a less conservative perspective ( Wimmer and Perner, 1983 ) by interpreting the success as being simply a correct answer to the ToM question. This second version of the dependent variable will be symbolized by the letter T . Finally, we also analyzed the answers to the memory question for children who had failed to answer the ToM question correctly. This variable will be designated by ¬ TM (Not ToM and Memory) 17 .

The independent variable (noted C ) had two modalities: “Human” vs. “Robot.” We also tested the influence of two other variables: the sex of the child (noted S , with two modalities: Girls vs. Boys), and the age of the child in months (noted A , numerical variable ranging from 38 to 49 months-old). In the first step of the analysis, we adjusted a linear model on our data with a link logit function and a binomial distribution of the errors. We applied this treatment to all three versions of the dependent variable TR , T and ¬ TM . For all of them we included, in the linear predictor of the models, the main effects of each of the three factors C , S , and A , as well as all the possible interactions which includes the triple interaction. We refer to these saturated models by using the following expressions: firstly TR ↫ C * S * A , secondly T ↫ C * S * A and thirdly ¬ TM ↫ C * S * A . The “↫” symbol refers to the influence, supposed or real, of the independent variables on the dependent variable while the “*” symbol indicates that all the possible interactions between the independent variables are taken into account. We then used a procedure of automatic backward simplification on all the saturated models to lead to the corresponding final models.

The principal characteristics of these four models are shown in Table 1 . Results show, regardless of the version of the dependent variable ( TR , T , and ¬ TM ), that the simplification model systematically terminated on a final model containing only the C factor. This means that the success rate for ToM, defined either as the conservative model ( TR ) or as a more permissive model ( T ), remained completely explainable by the condition (i.e., “Human” vs. “Robot”). The same was also observed for the final model of the memory question (¬ TM ). Therefore, in our study neither the sex ( S ) nor the age ( A ) of the children can significantly improve the prediction of the success we observed. Thus, in the rest of the paper these two variables will be omitted.

Table 1 . Main characteristics of the data-adjusted models.

A summary of the data we collected is shown in Table 2 .

Table 2 . Distribution of the children's answers depending on the experimental condition ( N = 62).

Table 3 shows the coefficients associated with each condition for the three resulting models required to estimate the effect size of the condition ( C ).

Table 3 . Estimated β coefficients associated with belonging to the conditions for the three models.

The model TR ↫ C has a significant coefficient (β = 1.23, p < 0.05). This coefficient is also significant for the model T ↫ C (β = 1.38, p < 0.05). We show in Table 3 the odds ratio (OR) corresponding to the β coefficients. We obtained OR = 3.43 for the model TR ↫ C , which means that the chances of success for a child in the “Robot” condition are almost 3.5 times greater than that of children in the “Human” condition. Regarding the model T ↫ C , we obtained OR = 3.98 indicating that, when simplifying the success criterion, a child was four times more likely to succeed in the “Robot” condition than one in the “Human” condition. We also observed a tendency for children who failed the ToM question to answer the memory question with more success when it was asked by the robot (β = 1.62, p = 0.06). While not significant we can still point out that children were five times more likely to correctly answer with the robot than they were with the human ( OR = 5.04).

Table 2 shows the distribution of the participants depending on the condition and on whether they succeeded in the task (depending on the criterion used to define success). An unilateral proportion test with no continuity correction reveals that the success rate for TR is significantly different from chance (χ 2 = 2.77, df = 1, p < 0.05). When only looking at the ToM question ( T ) the test does not show a significant difference (χ 2 = 0.4, df = 1, p > 0.05). However, as explained above, an answer scored as “correct” for the ToM question needed to be confirmed. Thus, we can probably think that the 58% of children in the “Robot” condition did not simply give the correct answer at random. Only 2 children changed their choices for ToM question in the “Robot” condition (and were counted as a wrong answer for ToM) and none in the “Human” condition.

5. Discussion

The goal of this study was to propose a new methodology of the explicit FBT. With it, we hoped to inhibit the erroneous interpretations made by 3 years-old children regarding the ToM question. Our “mentor-child” context seems to have changed the prevailing interpretation of the ToM question in the way we hoped: a request to report the false belief of the character. The young children who participated in our study did better in the “Robot” condition than those in the “Human” condition. This result has three important consequences:

1. It provides new experimental arguments for a pragmatic explanation of the failure of young children in explicit FBT ( Cummings, 2013 ; Helming et al., 2014 , 2016 ; Westra, 2017 ; Westra and Carruthers, 2017 ; Frank, 2018 ).

2. It indicates that a significant proportion of pre-school children can correctly answer the original ToM question.

3. This result, following those of Jamet et al. (2018) on Piaget's class inclusion task 18 , supports the relevance of our methodology to disambiguate the experimenter's expectations through their question in developmental tasks.

In our study, the performance of children in the conjunction of the ToM and reality questions was significantly improved, with children in the “Robot” condition being about 3.5 times more likely to succeed than those in the “Human” condition. Moreover, in the “Human” condition the performances were comparable to those observed in the literature ( Wimmer and Perner, 1983 ; Hogrefe et al., 1986 ; Perner et al., 1987 ). The previous result is also amplified if, as Wimmer and Perner (1983) , one adopts a laxer interpretation of the success. Indeed, looking only at the recorded responses to the ToM question, our results show that children belonging to the “Robot” condition are 4 times more likely to succeed. Furthermore, although we focused on the performance of pre-school children with participants between 3 and 4 years-old, it is interesting to note that the success rate in our “Robot” condition (58%) is similar to what Wimmer and Perner (1983) considers to be a successful completion of the task for children between 4 and 6 years-old (57%). However, this proportion remains lower than the one recorded by the same authors for children between 6 and 9 years-old (89%). We can point out that this success rate is not as sensational as those observed in some studies (such as the 90–100% observed in Rubio-Fernández and Geurts, 2013 , 2016 ). To our knowledge, we are the first to find such a performance without any modification being made to the initial paradigm with the same scenario, the same questions and the same procedure for analyzing the answers given by the child. Besides, the experimental protocol also considers several methodological criticisms made in the studies cited above ( Wellman et al., 2001 ; Wellman and Liu, 2004 ; Kammermeier and Paulus, 2018 ; Priewasser et al., 2020 ). Indeed, we considered a correct answer to be when the child answered both questions (ToM and reality) correctly. Our participants were also randomly assigned to the “Robot” and “Human” conditions in a homogeneous way.

Replicating our procedure with children between 4 and 9 would be important and interesting in order to see if our methodology produces a similar improvement for the 4–6 years-old age group or if this level of performance corresponds to a plateau for children below the age of 6. In the first case, the traditional results found in the literature of explicit FBT, showing a progression with age, would not qualitatively change but simply be shifted toward younger age groups. It would then be essential to replicate our procedure with children between 2 and 3 to decide at what age the explicit FBT can start to become successful. In the second case, with a limited success rate before the age of 6, the 6–7 age group would be the pivotal age for reaching almost a 90% success rate with explicit FBT. This would imply that important pragmatic and/or cognitive capacities would still be lacking at the age of 5, preventing a total success at this age. This would not necessarily contradict our pragmatic approach. Indeed, numerous studies report that 6 is the pivotal age to be able to correctly generate relevance implicatures ( Bosco and Gabbatore, 2017 ; Grigoroglou and Papafragou, 2017 ). As explained above, explicit FBT are complex as they require a “triple attribution of mental states” ( Helming et al., 2014 , 2016 ; Westra and Carruthers, 2017 ). They imply not only that the child must take into account the perspective of the character of the story but also that of their interlocutor, who is an adult experimenter in the standard test, since the child infers expectations from them and finally their own perspective. Consequently, this task would not be a first order task, but rather a second order task, thus explaining the threshold of a 60% success rate.

It is also interesting to look specifically at how the children responded to the control questions in our “mentor-child” context. As was shown by Perner and Wimmer (1985) the two types of success coding (with or without the reality question) slightly modify the results downwards without changing the interpretation: the chances of success in the “Robot” condition relative to that in the “Human” condition went from 4 times higher to about 3.5 times higher. The success rate decreased when the answer to the reality question was considered. In terms of proportions, both conditions had a similar success rate in the reality question (75% in “Human” and 77.7% in “Robot” conditions). This may confirm that the emphasis on the “slow” trait of the robot allows us to disambiguate a large part of the reality question. For the memory question, as predicted, we found a higher success rate (85%) in the “Robot” condition which is similar to the 83.7% observed in Wimmer and Perner (1983) with children between 4 and 5 years-old. This result seems to confirm our hypothesis that this question is noticeably ambiguous in the standard context for preschool children.

The fact that this “mentor-child” context works with 3 years-old children also provides new arguments in favor of the use of a humanoid robot as a tool in experimental research on children and adults. Our study did not have as its main objective to measure the importance of the robot tool itself but rather the influence of the context it allowed to produce. However, it would be important in a future study to see if there is a specific robot effect in our results that can stand out on its own. We can run the experiment with puppets or other objects representing an “ignorant,” “naive,” and “slow” entity (in an unpublished exploratory study on the class inclusion task Jamet, Saïbou-Dumont and Baratgin (2018) obtain, from children of French Guyana, similar performances to those obtained in Jamet et al. (2018) with the use of a puppet or a man disguised as a robot instead of the NAO robot 19 ). A second possibility would be to run the study with a knowledgeable and intelligent “NAO teacher” in addition to the human experimenter and to the slow robot. Many studies have shown that children as young as 3 years-old accepted the NAO robot as a possible teacher ( Rosanda and Istenic Starcic, 2020 ). Oranç and Küntay (2020) observed in children from 3 to 6 years-old a clear preference to ask the robot questions about machines, and less about biology and psychology. Thus, one could expect that in this situation children would be even less inclined to interpret the ToM question correctly as being a question about Maxi's beliefs. All this seems to indicate that our results are largely the consequence of the “mentor-child” context.

Our study also brings two important new elements on child-robot interaction. Firstly, our study seems to confirm that preschool children attribute beliefs to the robot as was also indicated in recent studies ( Di Dio et al., 2018 , 2020a ; Marchetti et al., 2018 ). Secondly, in our study the child can behave like a mentor, with the motivation to help a robot understand a story. This helping behavior still happened even though physical interactions are quite limited. Indeed, the robot did not have a great autonomy of movement when seated in front of the child and it displayed few expressions (the NAO robot cannot smile and its facial expressions are very limited: only its eyes can change colors to signify an emotion). This is coherent with results from Martin et al. (2020a , b ) which indicate that the helping behavior of children does not seem to be conditioned to the level of animated autonomy nor to the friendly expressions of the robot's voice.

While our methodology seems to work for an interaction with children older than 3 years and 2 months, children between 5 and 6 years-old, and also with adults ( Masson et al., 2015 , 2016 ; Masson et al., 2017a , b ), children under the age of 3 did not agree to stay “alone” with NAO. It is possible that the choice of a humanoid robot may trouble young children. Di Dio et al. (2020b) shows that 3 years-old children tend to trust humans more than robots, as opposed to 7 years-old children. Manzi et al. (2020) showed that children of 5, 7, and 9 years-old differently assign mental states to two humanoid robots, NAO and Robovie, differing on their level of anthropomorphism. It is possible that, for very young children under 3 years-old, the NAO robot may not be the most adequate tool (see Damiano et al., 2015 , for a review of the different types of robots). This would explain the low number of studies with children of this age. Recent reviews on the interactions between neuro-typical children and a robot ( Jamet et al., 2018 ; Neumann, 2020 ; van Straten et al., 2020 ) indicate that only one study was conducted using NAO and a group of children from 2 to 8 years-old ( Yasumatsu et al., 2017 ). The few other studies conducted on 2 years-old either used the tiny humanoid robot QRIO that is smaller than a 2 years-old child ( Tanaka et al., 2007 ), the iRobiQ robot that looks more like a toy ( Hsiao et al., 2015 ), or robots specifically designed to be enjoyed by young children like the stuffed dragon robot Dragonbot ( Kory Westlund et al., 2017 ) and the RUBI-4 ( Movellan et al., 2009 ). Thus, should we decide to do a longitudinal study from 2 to 9 years-old using our contextual procedure we would need to study which robot is the most relevant to play the role of a rather slow and ignorant being for all ages.

6. Conclusion

The essential proposition that has been developed and tested in our study is that the answer to the ToM question crucially depends on the “conversational logic” at play in the contextualized interactions between the experimenter and the child. This interaction shapes the child's interpretation of the question. Our contextual modification pragmatic filters the ToM question, removing irrelevant interpretations. The standard paradigm forces the child to perform a relevance search to interpret an ambiguous question asked by an expert (with a status like that of a teacher) within the limits of the child's own meta-cognitive knowledge. In our “mentor-child” context the child answers an unequivocal question about the beliefs of the protagonist of the story asked by a somewhat slow entity who needs their help. Here, the 3 years-old child can answer correctly even if their meta-cognitive knowledge is poorly developed. This procedure helps us become more “competent” speaker-experimenters ( Sperber, 1994 ) as it offers a tool to place ourselves at the level of the young child's interpretation strategy. This allows them to realize what is relevant to answer the question correctly. For similar reasons we believe that this procedure may also help with the understanding of the second-order ToM ( Perner and Wimmer, 1985 ). It could reduce the ambiguity of the question of the experimenter which exists in many experimental paradigms. Results of Lombardi et al. (2018) indeed indicate, using a dialogical perspective, that a considerable part of the supposed failures observed with children in the second order task are in fact the result of an adverse pragmatic context. In addition to the Piagetian tasks of length and number conservation ( McGarrigle and Donaldson, 1974 ), volume conservation ( Jamet et al., 2014 ), or class inclusion ( Politzer, 2016 ), there are a variety of experimental paradigms that lend themselves well to our disambiguation methodology. The “mentor-child” context could also facilitate some studies with atypically developing participants, such as individuals with an Autism Spectrum Disorder who show both deficient performance on the false belief task ( Baron-Cohen, 1997 ) and in language pragmatics ( Angeleri et al., 2016 ). Finally, our methodology also offers new clues on the relevance of human-robot interaction, and in particular on child-robot interaction. More studies should most certainly focus on the interaction between children and robots, taking in consideration the beliefs they associate to these tools, and their effect on well-known psychological results.

Data Availability Statement

The datasets analyzed for this study can be found in the Open Science Framework repository at the following address https://osf.io/ey4n5/?view_only=d8d2e16f39ea4186b994e2468a7408cd .

Ethics Statement

The studies involving human participants were reviewed and approved by M. Charles El-nouty, Professeur des Universités en Mathématiques, LAGA UMR7539, Université Paris 13: President of the Committee. M. Jean-Yves Henry, Chirurgien-Dentiste diplômé de l'Université Paris 7; M. Michel Dionnet, Chef de cuisine, Membre titulaire de l'Académie Culinaire de France; M. Fabrice Gutnick, MCF associé en Sciences de l'Éducation, Université Jules Vernes Amiens, Psychologue du travail; Mme Dominique Klarsy Médecin du travail. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

JB and FJ: conceptual elaboration. JB, FJ, and MD-S: design of the study. MD-S and FJ: data collection. J-LS: data analysis. JB and MD-S: draft of the manuscript. JB, BJ, and FJ: critical revision of the manuscript. All authors contributed to the article and approved the submitted version.

We thank the P-A-R-I-S Association for the technical and financial help we received as well as the CHArt laboratory which participated in financing the publication of the article in open access. P-A-R-I-S Funding number: 2020-0301728-5 CHArt-Paris8 Funding number: 2020-0331087-0.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to express our gratitude to the National Education Inspector Eugénie Montes of Versailles and to the pedagogical team of Les Petits Princes de Versailles school for welcoming us, for their involvement and for their interest in this research project. We would like to thank in particular the headmaster Mrs. Wiklacz, as well as the teachers of the school: Mrs. Moussette, Combe, Dupuy, and Delehaye. We also thank Natalia Obrochta, Olivier Masson, and Youri Minne for their help during the pre-tests of this experiment. We also thank Research Director (DR) Béatrice Desgranges and Anne Chevalier, copyright manager of Revue de Neuropsychologie , for their authorization to republish Figure 1 , originally from Duval et al. (2011) . We would finally like to thank Andrew Hromek for proof-reading the paper and Guy Politzer for his careful review of the first draft of this document.

1. ^ The situation is more or less complex depending on the level of false belief evaluated. Three levels of representation (three gradual orders of difficulty) obtained at different ages can be distinguished ( Duval et al., 2011 ). Order zero is automatically acquired. It corresponds to what we are currently thinking about. First order corresponds to the inference of the mental state of someone else and would only be acquired at the age of 4. The second order refers to the inference of the mental state of another person about another person and should be acquired between 6 and 7. This paper is mainly interested in the age at which children acquire the first order ToM. For the sake of simplicity, we will omit to specify “first order” when we refer to “explicit FBT” in the rest of this paper.

2. ^ In Wimmer and Perner (1983) Maxi would put the chocolate in the “blue” cupboard and his mother would move it to the “green” cupboard. We've interchanged the colors in this article to match the clips used in our experiment that were taken from Duval et al. (2011 , p. 45).

3. ^ See for a recent argument in favor of this hypothesis ( Doherty and Perner, 2020 ).

4. ^ Clues exist seeming to indicate that the late success in explicit FBT may indeed be the result of learning from repeated social experiences ( Wang and Su, 2009 ). Studies show that a correlation exists between the number of brothers and sisters of a similar age and the comprehension of false beliefs ( Perner et al., 1994 ; Ruffman et al., 2012 ; Jenkins and Astington, 2014 ). From the age of 3 the child can use language from a meta-cognitive point of view to lie ( Lewis and Saarni, 1993 ) and start to be able to use contextual information ( Salomo et al., 2013 ). It is necessary to wait the age of 4 to see children able to adapt their discourse by taking into account the age of the listener, their status and their gender; and can adapt their discourse to younger people. They can also ask a conversation partner to reformulate an utterance if they did not understand it ( Clark and Amaral, 2010 ).

5. ^ These two additional interpretations were already evoked in ( Perner et al., 1987 , p. 126): “They may have misinterpreted the test question: ‘Where will the protagonist look for the chocolate?' as meaning, ‘Where should he look?' or ‘Help him to find it!”' These authors changed the question to “Where does he think the chocolate is?” However, as pointed out in Westra and Carruthers (2017) , the term “think” requires more cognitive resources than the term “to look.” Also, this version complicates rather than simplifies the issue, which explains why it does not improve the performance of young children.

6. ^ Several pieces of experimental evidence indicate that 3 years-old children easily distinguish between what another person knows and does not ( Hogrefe et al., 1986 ; Perner and Leekam, 1986 ).

7. ^ Young children seem to better understand their role in educational activities when they are explicitly formulated ( Jeong and Frye, 2018a ).

8. ^ It was only children 4 years-old (no younger children had answered the ToM question correctly).

9. ^ The reality question, in this context, looks like a violation of the principle of informativeness ( Grice's Maxim of Quantity, 1975 ; Ducrot's law of exhaustiveness, 1980/2008 ) which requires that each participant in a conversation answer their partner's utterance with an appropriate quantity of information (neither too little nor too much). If multiple experimental clues question the complete acquisition of this principle at the age of 3 ( Conti and Camras, 1984 ; Noveck, 2001 ; Eskritt et al., 2008 ), other studies indicate that some children of that age show skills like adapting their communicative behavior to the state of knowledge of their partners ( O'Neill, 1996 ; Dunham et al., 2000 ; Ferrier et al., 2000 ). Perner and Leekam (1986) show that from the age of 3, children prefer mentioning first the most informative element and avoid mentioning elements already known by their listener.

10. ^ It can be noted that this interpretation of the memory question (an expectation of the experimenter to be controlling the answer to the ToM question) requires skills of second order ToM.

11. ^ Since the “ignorant,” “naive,” and “slow” robot was only there to strengthen the child's impression to be the one with the knowledge, its presence will be implied each time we refer to the “mentor-child” context.

12. ^ Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. All data was collected anonymously. The experiment was reviewed and approved by the Ethics Committee of the CHArt Laboratory. The Ethics statement can be obtained here: https://osf.io/wk4af?view_only=d8d2e16f39ea4186b994e2468a7408cd .

13. ^ The initial sample contained five classes of preschool children for a total of 70 children from 34 to 49 months-old. We had at least one 34 months-old child, one 35 months-old child, and so on, in each of the two conditions. During the experiment, we noticed that 3 children in the “robot” condition (the youngest: 34, 35, and 36 months-old) became really scared when the NAO robot started moving, lifting its head and looking at the child. The movement of the robot is not as fluid as that of a human, and the noise of the motors is quite noticeable. In consequence these three children had to be removed from the condition. In order to keep the conditions homogeneous in terms of age, we removed one child under 3 years-old in the “robot” condition who had succeeded in the task, and all 4 youngest children in the “human” condition, who had all failed in the task.

14. ^ In both conditions all children correctly named the two colors.

15. ^ A script of the interaction between a “mentor-child” and the NAO robot, written as a clinical and critical Piagetian interview ( Ducret, 2016 ) can be found at https://osf.io/z5s7k/?view_only=d8d2e16f39ea4186b994e2468a7408cd .

16. ^ The companion insisted on this specific point.

17. ^ The complete R script of the analyses is available at https://osf.io/34hzn/?view_only=d8d2e16f39ea4186b994e2468a7408cd and the data itself is available at https://osf.io/wzx7g/?view_only=d8d2e16f39ea4186b994 e2468a7408cd .

18. ^ Jamet et al. (2018) randomly assigned 40 children (between 5 and 6 years-old) to two conditions similar to those we created for the present study (“Human Experimenter” vs. “Ignorant NAO Robot”). The authors observed a clear improvement in performance in the “Ignorant NAO Robot” condition: one child out of five answered correctly in the “Human Experimenter” condition, and more than six children out of ten in the “Ignorant NAO Robot” condition.

19. ^ Experiments were carried out during the MIN formation of teachers requested by the rector of the academy of French Guyana.

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Keywords: theory of mind, preschool children, pragmatics, humanoid robot, mentor-child context, ignorant robot, human robot interaction, first-order false belief task

Citation: Baratgin J, Dubois-Sage M, Jacquet B, Stilgenbauer J-L and Jamet F (2020) Pragmatics in the False-Belief Task: Let the Robot Ask the Question! Front. Psychol. 11:593807. doi: 10.3389/fpsyg.2020.593807

Received: 11 August 2020; Accepted: 28 October 2020; Published: 23 November 2020.

Reviewed by:

Copyright © 2020 Baratgin, Dubois-Sage, Jacquet, Stilgenbauer and Jamet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jean Baratgin, jean.baratgin@univ-paris8.fr

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• Published: 01 August 2024

An Integrated theory of false insights and beliefs under psychedelics

• H. T. McGovern   ORCID: orcid.org/0000-0002-4050-6300 1 , 2 ,
• H. J. Grimmer 1 ,
• M. K. Doss 3 ,
• B. T. Hutchinson 4 ,
• C. Timmermann 5 ,
• A. Lyon 6 ,
• P. R. Corlett   ORCID: orcid.org/0000-0002-5368-1992 7 , 8 &
• R. E. Laukkonen 9  

Communications Psychology volume  2 , Article number:  69 ( 2024 ) Cite this article

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Psychedelics are recognised for their potential to re-orient beliefs. We propose a model of how psychedelics can, in some cases, lead to false insights and thus false beliefs. We first review experimental work on laboratory-based false insights and false memories. We then connect this to insights and belief formation under psychedelics using the active inference framework. We propose that subjective and brain-based alterations caused by psychedelics increases the quantity and subjective intensity of insights and thence beliefs, including false ones. We offer directions for future research in minimising the risk of false and potentially harmful beliefs arising from psychedelics. Ultimately, knowing how psychedelics may facilitate false insights and beliefs is crucial if we are to optimally leverage their therapeutic potential.

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Introduction.

When an idea or problem solution appears, it can produce a distinct and powerful phenomenology—a feeling of profound understanding and truth known as an insight moment 1 , 2 , 3 . This largely ineffable experience appears to play a pivotal role in the development and adjustment of beliefs and although often associated with verifiably correct discoveries and adaptive personal growth 4 , 5 , insight phenomenology can be triggered by unrelated or misleading information 6 , 7 and for objectively incorrect problem solutions 8 . Insight moments are also a defining feature of psychedelic experiences 9 , 10 , and could underlie the profound belief changes seen during and after psychedelic drug use 11 , 12 . Thus, psychedelics are increasingly recognised for their potential to restructure maladaptive beliefs underlying mental illness 13 , 14 , 15 . However, given the fallibility of insight moments, how can we ensure that psychedelic insights align with desired outcomes, or simply with reality itself? Here, we discuss the different theoretical frameworks of insight, belief change, and the neuropharmacology of psychedelics and present an integrated model for how psychedelics can engender false or maladaptive insights, which have not yet been addressed in the literature — which we term False Insights and Beliefs Under Psychedelics (FIBUS).

Insight and the psychedelic experience

Insight, defined as the sudden appearance of a problem solution in consciousness, has a long history in psychological research 1 , 16 , 17 , 18 , 19 , 20 , 21 . Insight moments—also known as “Aha!” moments—are a special type of problem-solving process where a problem-solver achieves a sudden and complete mental restructuring of a problem 1 , 2 , 3 accompanied by a distinct rush of satisfaction, surprise, and confidence 22 , 23 , 24 , 25 , 26 . A substantial literature suggests that insights are often associated with correct solutions to problems, at least when using constrained and artificial stimuli 27 , 28 , 29 , 30 . Insight experiences have been observed in several recent studies on recreational psychedelic use and psychedelic assisted therapy 31 , 32 , 33 , 34 , suggesting that subjective experiences of insight play a key role psychedelic assisted therapy 9 , 35 , 36 . These findings have garnered excitement in the field of psychedelic research, as insight moments have a long tradition of research and have generally been found to predict accuracy in problem-solving experiments 22 , 29 . Indeed, psychedelic assisted therapy trials have reported many instances of insight moments during psychedelic experiences precipitating positive changes to mind and behaviour such as smoking cessation 9 , 31 , 37 , 38 , 39 , 40 , potentially making them a crucial lever for clinical improvement 32 , 41 , 42

The eureka heuristic

This varied body of evidence, therefore, generally supports a link between insight moments and what we deem to be “true”, either in the narrow sense of objectively correct problem solutions or broader, idiosyncratic changes in perspective and beliefs that are “verified” by their connection to positive outcomes, relative to one’s goals or values. However, recent research has shown that this link may be explained as a metacognitive process wherein insight phenomenology guides the selection of ideas—the “truth” of which depends more critically on one’s prior information. Laukkonen and colleagues 5 (2023) propose The Eureka Heuristic—the theory that feelings of insight play a heuristic role in guiding epistemic decision-making about which ideas to trust by imbuing them with a sense of obviousness. Usually, this sense aligns with reality, as other heuristics are often grounded in statistical norms, making them a generally sensible shortcut. However, as with other heuristics, The Eureka Heuristic can fail under conditions that violate these statistical norms.

False insights and metacognitive illusions

Indeed, while often correct, a proportion of insight moments are false 22 , 43 and false insights can be experimentally induced 8 . For example, Grimmer et al 8 . had participants read a series of words with high semantic similarity (e.g., Remember, Significant, Honour, Tribute, Memorial), before solving anagrams that were visually similar to another word sharing the same semantic space to the studied list, (e.g., MEMUNOMT tends to be incorrectly solved as MONUMENT instead of MOMENTUM). Participants had more false insights for the anagrams that were visually similar (to a primed associate) compared to a range of controls. The accuracy of insight moments can thus partly depend on the relevance of one’s prior information, the ease (or fluency) with which it is processed, and whether the available information to the problem-solver encourages accurate or inaccurate associations.

Another downfall of using feelings of insight as a guide for truth can be seen when insight phenomenology can be misattributed, making irrelevant facts and worldviews feel true 6 , 7 . Across several studies, Laukkonen and colleagues 6 , 7 , presented participants with propositions (e.g., “there is no such thing as free will”) or factual statements (e.g., “lobsters can be trained to understand verbal commands”) containing anagrams (e.g. the word “lobsters” was scrambled). When participants solved the anagram via an insight moment, the irrelevant insight was temporally associated with a worldview, and participants’ belief in the worldviews and statements were stronger than when no insight moment was reported. Insight phenomenology thus appears to contaminate participants’ judgements during claim evaluation.

In the episodic memory literature, there is a similar concept of misattribution that can produce memory distortions 44 . Like insight feelings, fluency driven by semantic activation or repetition enhances feelings of familiarity that can be misattributed to novel stimuli, resulting in false memories 45 , 46 , 47 , 48 . A parallel between the episodic memory literature and psychedelic literature is that the feeling of knowing from familiarity 49 has been referred to as “noetic consciousness,” and the undeniable sense of knowledge produced by psychedelics has been referred to as the “noetic quality” 36 . The noetic quality has also been linked to the experiences of acquiring knowledge in a seemingly unmediated fashion during spiritual or religious experiences 50 , 51 . Lastly, the noetic quality is closely linked to feelings of ‘truthiness’ as described above, or ‘obviousness’ as it is often called in insight research.

However, the impact of insights goes beyond the moment of their occurrence —they may also recursively reinforce certain beliefs. For example, an individual may ‘do their own research’ about what caused the twin-towers to collapse. An insight moment at an early stage of research that makes an unfounded claim appear true could lead an individual down unproductive research pathways, and recursively induce further misleading insights. Along similar lines, false insights can also become entrenched via unfounded plausibility 8 , 52 , 53 . Again, parallels can be found in the episodic memory literature. Emotionally positive memories tend to engage more associative and semantic processes 54 , 55 that can result in memory distortions 56 , 57 , and negative valence can attenuate fluency-driven false memories 46 . Intriguingly, the noetic quality tends to occur with positive affect during psychedelic-induced mystical-like states 58 .

Although the interaction between positive valence and false beliefs is thought to be evolutionarily adaptive 59 , 60 , Laukkonen et al 5 . proposed these related processes may mutually reinforce each other. When a false insight occurs, the positive affect accompanying the insight affirms the other beliefs the individual has—a process through which an agent can form a model increasingly out of touch with reality 61 . The strong link between insight phenomenology, belief, and accuracy, may hold particular importance in the context of psychedelic use, as many users cite a desire for knowledge and understanding in their motivation for taking psychedelics. As interest in psychedelics has grown, the mechanisms behind these phenomena have been mapped within the now dominant computational paradigm known as active inference or predictive processing, a framework that unpacks potential mechanisms of the power of insights in determining belief formation over time. Below, we discuss the broad principles of predictive processing and outline a related theory of how psychedelics change beliefs via this process, before synthesising these perspectives into a novel model of how psychedelics can induce (false) insights and (false) beliefs – which we have termed FIBUS.

Active inference: a neurocomputational understanding of insight, ideas and beliefs

An increasingly popular view conceives of the brain as an inference machine 62 , 63 , 64 that infers the most likely cause of sensory data so that it can optimally infer their hidden causes (i.e., what “out there” is causing these electrical signals?) These predictions (i.e., guesses about the cause of sense data) are compared to likelihoods (i.e., clarity or precision of sense data and its weighting) to arrive at posteriors (i.e., the updated prediction about the cause of sense data following the comparison between priors and likelihoods). If sense data refute priors, this solicits a prediction error – a signal informing the brain it must update its prior with respect to the stimulus at hand, or to look elsewhere for confirmatory data (i.e., active inference 63 ). In this way, the brain seeks to continually minimise prediction error 64 , 65 (see refs. 64 , 65 for conceptual overview).

For example, if agent A thinks that the only red fruit is an apple, they will expect that the red fruit in their hand is an apple. In other words, their prior (i.e., existing expectation) is that the red fruit-like objects are most likely to be apples. If they were given another red fruit to hold (e.g., a tomato),’ A’ may notice textural differences and be told that this is another type of red fruit. This departure between A’s prior that the only red fruits are apple, and the sensory data at hand (i.e., likelihood) that tells them they are holding another type of red fruit, would solicit this prediction error signal. This prediction error signal would thus inform ‘A’s internal model of the world that the initial prior was incorrect, allowing A to change the model such that ‘red fruit’ can include apples and tomatoes. Via this process, beliefs evolve in a continuous trade-off between priors, likelihoods, and posteriors (which then inform priors). Beliefs can thus be conceived as the priors carried into each sensory encounter and are equivalent to probability distributions of possible sensory encounters, evolving alongside the agent’s interactions with the world. This is the definition of belief we adopt hereafter.

Predictive processing assumes the process of prediction error minimization occurs along a hierarchical neural architecture 66 . Hierarchically higher cortical regions encode complex concepts pertaining to longer timescales and higher abstraction 66 , 67 , 68 , 69 , 70 . In contrast, lower-level sensory regions (e.g., visual cortex; see refs. 67 , 71 , 72 , 73 ) typically encode more domain-specific, concrete information pertaining to shorter timescales 66 , 74 . Predictions are carried down the hierarchy from higher-level cortices (e.g., associative areas such as frontal cortex) to lower-level cortices (i.e., early columns of the visual system such as V1) to narrow the range of explanations for sense data 67 . In contrast, prediction errors propagate up the hierarchy such that if a prediction error cannot be explained by the next level, where more complex abstractions are encoded, it is carried to the next level 64 , 67 . This process repeats until the prediction error reaches a level where it can be sufficiently explained, and the brain updates its predictions to maintain an accurate (generative) world model. This scheme allows the brain to continually refine its model of self and world for adaptive actions that aid survival 66 , 70 , 75 .

Underwriting much of the trade-off between priors, likelihoods and posteriors is precision, an index of how narrow each probability distribution is 76 . Higher precision distributions are narrower, representing increased confidence or clarity. The precision of priors and likelihoods is traded off to arrive at a posterior distribution that appropriately weighs them 77 , 78 . To illustrate, imagine Agent X carries a strong but false prediction (i.e., high precision) that the red ball they are holding in their hand is a tomato. In this scenario, it is a cold night and X is wearing gloves. Therefore, the grainy, low-precision sensory data is incapable of updating the strong expectation that “this is a tomato,” leaving the prior intact that the red ball in X’s hand is a tomato. In another scenario, X carries a lower-precision, weaker expectation about the identity of the red ball that is met with strong sensory evidence because now it is daytime, and the gloves are off. This high precision evidence (or likelihood) suggests the red ball is an apple – making it more likely X will revise their weak prior and arrive at the posterior that the red ball is instead an apple. Here, we are referring to the fact that this process allows the agent to arrive a precise posterior distribution over models, allowing the agent to optimally navigate its sensory landscape and thus their environment.

With respect to the brain, more generalized and coarse-grained representations (based on many observations) are thought to be encoded in domain-general cortex 70 . During perception, predictions cascade down to sensory regions of cortex which encode more specified and faster-updating predictions. Prediction errors ascend this cortico-perceptual hierarchy until the error can be explained by the level above. In the event the error cannot be explained at the next level, it ascends all the way up to the most coarse-grained level such that the agent’s model is updated to account for this new contingency 67 , 79 . In the example above, if we assume the agent has observed many times that red circular shapes are tomatoes, they will have a relatively stronger prediction that the red ball is a tomato and be less susceptible to updating this belief when the sensory data was unclear (assuming they do not actually find out that the red ball is not a tomato).

Greater (environmental) statistical regularities often accompany increased prior precision and should therefore be accompanied by strong contradictory empirical data to be refuted and updated. For example, a black sky almost always means it is night, and only strong evidence, such as knowledge of a solar eclipse occurring, enables one to suspend the belief that the black sky they are observing is not evidence of it being night time. The difference in outcomes to which these scenarios underscore is a process called precision weighting , the relative weighting of priors and likelihoods during the perceptual process, given the context and prior learning. The clarity of sensory data and the relative confidence in expectations thus play a crucial role in how beliefs evolve. Reliance on sense data or likelihoods, based on their precision, is crucial for determining which beliefs are ultimately reached, with the higher-precision distribution typically being more influential.

Insight, beliefs, and active inference

Friston et al. 63 posited an account of insight experiences nested in the active inference and predictive processing frameworks. Under this view, refinement or modification of one’s generative model (i.e., world model) need not rely on new information—a process deemed fact-free learning . Fact-free learning occurs via a process of Bayesian model reduction, wherein the brain arrives at models providing more parsimony of sense data already accrued, rather than continued sampling. Fact-free learning is said to be metaphorically similar to the way that a “sculpture is revealed by the artful removal of stone” 63 .

Such learning is proposed to occur implicitly via simplification of one’s model, in states where people are not actively taking in sensory information, such as in sleep 80 , or states of interoceptive reflection 81 . The key point is that via Bayesian model reduction, no further sensory sampling is necessary for refining and updating beliefs about the matter at hand.

An extension to Friston et al‘s 63 model of insight has been proposed that considers the experiential quality of insight and its effects higher in the cortical hierarchy 5 . According to the ‘Eureka Heuristic’, we experience feelings of insight because they help ‘highlight’ which ideas we should trust in light of past learning. In other words, insight moments play a role in heuristically selecting ideas from the stream of consciousness by capturing attention, inducing confidence, and boosting drive to act on them. In an uncertain world where time is limited, ideas cannot always be evaluated analytically. The feeling of insight plays a key role in permitting quick and efficient action on novel ideas (e.g., when running from a lion on the savannah).

This view is consistent with work suggesting that insight moments increase confidence 25 , 26 , can be misattributed 6 , 7 , lead to beliefs that are difficult to forget 82 , and are resistant to revision 28 . In some ways, insights can be considered a fast-track to semantic memory, bypassing the slow training process between the hippocampus and cortex that typically give rise to semantic memories imbued with noetic consciousness. The Eureka Heuristic also includes computational mechanisms that extend the Bayesian reduction account above, and helps us understand the recursive, reinforcing role that insights may play during a psychedelic experience. We summarise the model below.

The Eureka Heuristic proposes that when the implicit process of Bayesian reduction results in a novel (i.e., updated) model, it necessarily solicits a prediction error at a higher-order conscious level of abstraction, under the hypothesis that precisely held beliefs enter consciousness. This prediction error in turn can change one’s model at a conscious level, making it possible to have a reportable insight (and meta-awareness of an insight having occurred). In other words, while Bayesian reduction inherently reduces global prediction errors across the system, a certain amount of time is required to share this information via ascending prediction errors.

Crucially, since many ideas can appear in the mind, it is only the ideas that have high expected precision (i.e., subjective confidence in the ideas)­—thus feeling insightful—which are selected and meaningfully impact beliefs. This is analogous to the way that an organism must infer both the action policy and (dopaminergic) confidence in it 63 , 83 . Similarly, organisms infer both the content of ideas as well as their (dopaminergic) insightfulness.

Prediction errors ascending the cortical hierarchy can be ascribed higher or lower precision weighting, as can predictions that descend the cortical hierarchy (although it is worth noting that this may not always be the case, as lower level prediction errors may not invariably propagate to higher levels if the loss of accuracy is accompanied by complexity reduction). Notably, insight experiences have all the neural characteristics of a higher-order prediction error (e.g., restructuring and insight is associated with the event related potential component N320 84 , 85 , 86 . More precise prediction errors (i.e., very sharp departures from predictions with strong evidence) are thought to enact a larger influence in (Bayesian) belief updating, such that they bear increased weighting compared to the agent’s priors. Increasing the precision weighting of prediction errors is thought to be instantiated by higher synaptic gain (i.e., the inhibitory or excitatory strength of connections between neurons) 5 , 87 , 88 . One way synaptic gain is instantiated is the up-regulation of dopamine, through which belief updates and thus confidence in belief updates are thought to occur 87 , 88 , 89 , 90 .

Like the construct of precision, the feeling of insight is thought to be implemented through dopamine and has been linked to the reward system 91 , 92 . Just like precision, insight experiences are associated with attentional capture 30 , 93 , higher confidence and (phenomenological) pleasure 22 , 25 , and seem to map onto the dopaminergic reward system 91 , 92 . Moreover, in contrast to norepinephrine, which is thought to retain the dependency of episodic memories on the hippocampus, dopamine is thought to facilitate the integration of episodic memories into cortical semantic networks 94 . Precision also drives model selection, just like insight drives the selection of new ideas 5 , 12 . Thus, what we call ‘insight experiences’ map extraordinarily well to the computational construct of a precise prediction error at an abstract level. Put simply, insights are a surprising inner event (prediction error) imbued with noeticism given what one knows (high expected precision), thus permitting idea selection and action.

We note that dual process theories provide a framework for understanding cognition as a binary of ‘conscious, deliberate, effortful’ and ‘unconscious, rapid, and largely involuntary’ thought 95 . However, more recent work has expanded upon this concept and empirical findings (such as the phenomenology of insight occurring in traditionally analytic problems 96 ) and identified the need for a more comprehensive view of cognition as occurring as a hierarchy, with “system 1” and “system 2” effectively existing on opposite ends of a continuum encompassing all of conscious thought, unconscious judgement and decision-making, as well as even ‘lower-order processes’ such as perception and emotion” 4 . Indeed, insight can occur across both types of thinking 25 , 26 , 81 , 97 , 98 . One of the key deviations that PP takes from these earlier theories is it emphasises these lower-level processes as occurring mostly prior to (outside of) conscious awareness. This is also a key component of our argument, as predictive processing can be applied to phenomena that have until now been thought of as completely “conscious” such as belief, insight, and even cognitive dissonance theory.

Psychedelics and belief change: two possible pathways

We have thus far covered the notion of insight and the key role that it can play in updating beliefs via predictive processing. Similarly, predictive processing has been suggested to explain belief change under psychedelics in an influential theory known as Relaxed Beliefs Under psychedelics or REBUS 99 . We discuss how REBUS effects from psychedelics can result in belief changes. Following the description of REBUS and how it can drive belief change, we then describe an alternative pathway to psychedelic-facilitated belief change that does not rely on the assumptions of REBUS. After introducing these relevant theories, we will then present our integrative account for how belief change under psychedelics can engender false beliefs drawing upon components of each.

REBUS and belief change

Psychedelic substances, such as LSD, psilocybin, and DMT, primarily act as agonists at the brain’s 5-HT 2A serotonin receptors, which are widely distributed throughout the brain, particularly in regions associated with high-level cognition, such as the prefrontal cortex, and sensory processing, like the visual cortex 100 . When psychedelics bind to 5-HT 2A receptors, they cause increased excitation of neurons, leading to altered patterns of neural activity and communication. This heightened excitation is thought to contribute to the profound perceptual, cognitive, and emotional effects of psychedelics 101 . In addition to their actions on 5-HT 2A receptors, psychedelics can also influence other neurotransmitter systems, such as dopamine and glutamate, which further modulate neural activity and contribute to their complex effects 102 . Neuroimaging studies have shown that psychedelics induce changes in brain connectivity, reducing the connectivity of the default mode network (DMN), a group of brain regions involved in self-referential processing and inner thought 103 , 104 . This disruption of the DMN is hypothesized to underlie the “ego dissolution” and sense of unity often reported during psychedelic experiences 103 , 105 . Simultaneously, psychedelics enhance connectivity between other brain networks, potentially facilitating novel associations, insights, and perspectives. The combination of receptor-level effects, neurotransmitter modulation, and large-scale network changes induced by psychedelics is thought to create a unique brain state that supports profound alterations in consciousness, perception, and cognition 99 , 101 . Below, Fig.  1 provides an overview of the neuropharmacology of hallucinogenic substances mode of action.

Cortical regions that comprise the DMN (medial prefrontal cortex, posterior cingulate cortex, angular gyrus, and precuneus) are shaded in purple. These DMN regions include the densest expression of 5-HT2A receptors, which psychedelic drugs bind to, resulting in disrupted functioning of the DMN. We note this is a simplified portrait (see de Vos et al.105 for a detailed overview) (created with BioRender.com).

REBUS proposes that psychedelics facilitate belief change via a two-step process 99 (Carhart-Harris & Friston, 2019). First, psychedelics disproportionately diminish the precision weighting of high-level priors (e.g., reducing confidence in ‘beliefs’ in the colloquial sense, formalised as higher variance probability distributions) that otherwise constrain lower levels (e.g., perception). This assumption of disproportionate higher-level effects is due to the densest distribution of 5-HT 2A receptors found in certain association cortices, including parts of the default mode network (DMN), proposed to be the top of the brain’s hierarchy (note, however, that 5-HT 2A receptors are also densely distributed in visual and auditory cortices 106 ). By relaxing the DMN’s constraints on the rest of the brain, the brain’s hierarchy of information processing is thought to be “flattened” or less controlled and constrained by higher-order abstraction and ‘freer’ to change according to new input (note that by higher order, and in a more technical sense, we are referring to beliefs about plausibility’s of a set of models that are updated). A secondary consequence of the system being unable to rely on prior assumptions is the relatively increased precision weighting of sensory data, resulting in novel input becoming more likely to impinge on high-level beliefs. An agent under psychedelics may therefore consider any number of alternative hypotheses about the causes of sensory data, perhaps rapidly, and revise higher-order beliefs that were held in a sober state. Particularly under high doses, psychedelics can produce a collapse of complex assumptions such as one’s sense of self, one’s membership to a group, and typical knowledge about the world, coinciding with the DMN’s role in self-referential processing 107 , social processing 108 , and semantic memory 109 .

The REBUS hypothesis is supported by neuronal, behavioural, and clinical data. Evidence suggests that psychedelics reduce top-down connectivity and dampen the power of backward travelling waves (i.e., signature of neural activity traveling across cortex, suggesting a decreased activity between higher and lower levels in the brain) 110 , 111 , both suggested mechanisms for the influence of priors on brain activity 112 . After the acute psychedelic experience, there are documented changes to metaphysical beliefs, particularly away from a physicalist worldview (we note this does not provide evidence exclusively for REBUS- but just that psychedelics can seemingly change beliefs) 113 , 114 , 115 . Finally, qualitative studies from clinical trials suggest that revision of self-related beliefs (arising from REBUS processes) may underpin positive psychological changes 10 , 40 , 116 . Whilst these effects may be beneficial as a metaphorical ‘reset’ if one holds an array of maladaptive beliefs, there is no guarantee that relaxing one’s hard-earned abstract understanding results in positive change.

Alternate pathways to belief revision under psychedelics

Outside of the active inference or computational frameworks, psychedelics may impact beliefs via effects on fluency and relative weighting of hippocampal and cortically dependent memories. This pathway to belief revision, which we will term a ‘memory systems account’ does not preclude REBUS effects, but we highlight this account since there may be differing predictions on the specific brain-based substrates to belief changes.

Feelings of insight and familiarity can come from fluency manipulations such as semantic priming (e.g., seeing the word whisker could activate the category of cat) 8 , 45 , which can be enhanced by psilocybin 117 . Moreover, although psychedelics impair the formation of hippocampally-dependent recollection memories (e.g., remembering/recollecting where or when an event took place), they spare or even enhance formation of cortically dependent memories that solicit feelings of familiarity (e.g., knowing a face, without who the individual was or where they met them 118 ).

Typically, hippocampal recollection may constrain the interpretation of noetic feelings driven by fluency/familiarity. If one can explicitly recall semantically relevant words or the multiple repetitions of a stimulus’ presentation, they may be better able to understand the source of their noeticism and not misattribute it to irrelevant stimuli. For example, a person might continually observe they are in a friend’s house one evening and can thus attribute this fact to explaining why they keep remembering the presence of their friend, instead of mistakenly attributing this feeling to the fact they saw their friendship bracelet that reminded them of their friend. In contrast, non-drug studies have found that when recollection fails and familiarity is high, presque vu (illusory feelings of insight) can emerge 119 , as well as other phenomena sometimes reported under psychedelics such as déjà vu 120 and premonition 121 .

In models of memory systems, the hippocampus is thought to “train” the cortex over time such that greater statistical regularities between episodic memories are what eventually become semi-permanent semantic memories (e.g., one may no longer have memory for every instance they had pizza or even the first time they had pizza, but they have learned what a pizza is) 122 . The hippocampus may even constrain what the cortex can learn by providing contextual information that biases cortical processing 123 . Some work suggests that conditions in which hippocampal activity is relatively disconnected from the cortex such as during rapid eye movement sleep is important to the instantiation of new cortical information 124 . High-level beliefs can be thought of as semantic memories not necessarily shared by others (e.g., “I am a bad person”), as they are typically slowly learned over time, difficult to revise (it would be hard to forget what a pizza is) and represented by association cortices including the DMN but especially the anterior temporal lobe 125 . In fact, the anterior temporal lobe is an important site for insight learning 93 , 126 , 127 , familiarity 128 , 129 , 130 , semantic priming 131 , the illusory truth effect 132 , and the formation of beliefs such as prejudice 133 .

By reducing the constraints of recent hippocampal memory (i.e., impairments of forming recollections) via inhibitory 5-HT 2A receptors in entorhinal cortex (i.e., the input to the hippocampus) and the hippocampus itself 134 , 135 , 136 , 137 and facilitating cortical processing (i.e., fluency) via excitatory 5-HT 2A receptors in the cortex, psychedelics may be able to revise semantic stores supporting high-level beliefs. Less constraints may provide greater exploration of a conceptual search space allowing one to reach veridical insights. However, noetic feelings arising from aberrant semantic activation could also be misattributed to unrelated or bizarre ideas produced by psychedelics, resulting in false insights.

REBUS proposes that the hippocampus is one of the regions that becomes less constrained by the cortex under psychedelics and thus should increase its influence on the cortex, especially the DMN 99 . In contrast, this memory systems account predicts that typically the cortex is constrained by the hippocampus and that under psychedelics, the cortex becomes free of such constraints. It has been found that psilocybin attenuates hippocampal-DMN coupling 138 and hippocampal glutamate, which is predictive of feelings of insight, but not necessarily veridical insights 139 . Nonetheless, all accounts converge on the general notion that psychedelics change beliefs, even if mechanisms are debated. We now turn our attention back to insight. Crucially, we suggest that it may play a key role in entrenchment of new beliefs following psychedelics.

Psychedelic-induced insights: a possible pathway to false beliefs

Considering the theories of insight and belief change under psychedelics discussed thus far, we now turn to the pressing issue identified at the outset—the possibility that psychedelics could engender false beliefs. Although psychedelics show promise as tools for engendering insight and therapeutic belief change, the neurocomputational perspective on insight and belief change in general suggests that psychedelics could also elicit false beliefs under some circumstances. For instance, psychedelics could merely increase the frequency of belief changes, orthogonal to accuracy, with the utility of these belief changes depending on the accuracy of one’s prior information at the time of restructuring. Give the evidence that insight moments can often be wrong or misleading due to cognitive or environmental factors 4 , 8 , 52 , 53 , 140 , a higher frequency of insights (both true and false) could also increase the probability of psychedelic-induced maladaptive, or potentially false, insights 11 . We now sketch a candidate framework for how psychedelics engender belief changes via soliciting insight moments.

Our proposal is as follows: Psychedelics imbue a decreased ability to make sense of sensory data, leading to an increased number of insight moments and noetic feelings. Following the experience, the person may be left with a lack of detailed memory, but an increased noetic confidence in the insight moments encountered during the experience. Crucially, the increased quantity of insights and acute malleability may leave one vulnerable to empirically false, misleading, or maladaptive insights, alongside the prospect of obtaining valuable new perspectives.

Note that our focus on false beliefs is not because we believe that psychedelics only solicit incorrect ideas, but because the potential for false beliefs under psychedelics have been somewhat overlooked 11 . Secondly, if psychedelics do hold potential to change deeply held beliefs—as they are believed to 99 —and some proportion of these are likely to be false but feel profoundly true and motivating, there are major consequences to consider. Given the renaissance that is currently underway, mass adoption of psychedelic use both clinically and beyond have an important epistemic task to address: how do we improve the likelihood that the insights and subsequent belief changes engendered by psychedelics result in beliefs that move one closer to reality? Below, Box  1 provides a summary of the similarities and differences between these models, including our proposed model.

Box 1 Outline of the overlap and departure between REBUS model and the model of psychedelic belief change forwarded here: FIBUS. Overlap between REBUS and FIBUS is bolded, and differences in the FIBUS model are non-bolded

False insights and beliefs under psychedelics (FIBUS): towards a theoretical account

Based on our prior discussion of precision, model reduction, and fact-free learning, we propose a process for how insight-derived belief changes under psychedelics may reorganise belief structures – which we have coined FIBUS. We do not just account for the mechanism of insight and belief change under psychedelics, but also articulate how insights can be true or false, with clear implications for future studies (see below) involving psychedelics. We propose this process approximates four steps as follows, drawing on all the research thus-far reviewed.

Figure  2 illustrates the process by which psychedelic induced insights may engender false beliefs. First, increased agonism of serotonin 5-HT 2A receptors results in decreased precision weighting of priors, such that priors now no longer characteristically constrain perception and cognition (See 141 , 142 , 143 for in-depth discussion on serotonin, dopamine, and precision). Second, this collapse in the perceptual-belief landscape results in novel thoughts and perceptions that then increase the incidence of prediction errors (e.g., through new sensory input or via ‘fact-free learning’). These predictions errors facilitate new ways of interpreting sensory data and generate new ideas and perspectives. Third, the decreased precision weighting of prior beliefs affords increased precision to the novel input passed to higher levels (i.e., everything feels more insightful because it is not constrained by prior belief). This increased precision weighting is thought to be implemented via dopaminergic release (note this is a secondary release not directly facilitated by drug effects; see 5 , 87 , 88 , 91 , 92 , 144 ), affording higher precision to the insights encountered in step two. Fourth, this increased precision weighting given to the insights makes them more likely to feature in model selection (i.e., the feeling of insight has an unusually strong effect on belief updating).

The red distributions represent prediction errors, and green distributions represent predictions. Higher levels encode more domain general, complex abstractions (i.e., high-level beliefs). Lower levels, such as the sensory cortex, encode simpler and domain-specific concepts (i.e., low levels, such as edge detectors 172 ). High levels send descending predictions, while prediction errors ascend the hierarchy until they are explained away by higher levels. Insights (i.e., prediction errors) arrived at during the trip are afforded higher precision due to decreased precision weighting of priors, rendering insights and consequent beliefs higher precision thereafter — a process that we have termed FIBUS.

This process, we suggest, can describe how beliefs arising from psychedelic insights can become entrenched in working models thereafter. Such entrenchment may be especially true for psychedelics that also activate dopamine receptors such as LSD, which tends to have more lingering effects on perception 145 . Critically, we suggest that while this process can impart adaptive, meaningful, and lasting belief changes, it can also facilitate false insights (and hence false beliefs). Below, we discuss implications for this model, with a particular eye toward how practitioners and researchers alike may consider, test, and optimally refine these dynamics to ensure optimal treatment protocol.

A model of psychedelic insight and belief change, and its implications

We suggest that psychedelics can provide a genesis for false beliefs as follows. First, REBUS effects (or other mechanisms of decreased precision weighting) may induce belief relaxation, including those that are true, and increase precision weighting of novel dopaminergically modulated insights. The insights afforded by increased precision subsequently bear disproportionate weighting on model building. In some instances, ‘fact-free’ learning may therefore be occurring primarily with respect to erroneous or embellished sensory data. Moreover, the insights may then play a recursive role of preferencing and entrenching ideas consistent with the new beliefs. Concurrently, the impairment of hippocampally modulated recollection may lead to a decreased ability to remember veridical details of the experience, while cortically facilitated memory encoding leads to increased semantic aberrance and noeticism. In the experience, one encounters 1) an impaired apparatus to make sense of incoming sensory data, 2) increased insight moments, and 3) increased feelings of familiarity irrespective of accuracy. After the experience, one is left with a lack of detail of memory, but an enhanced noetic sense about the insight moments of the experience. Psychedelics may thus facilitate insights and increase the perceived novelty in new ideas and original thoughts 11 , 139 . However, nowhere does this experience preference accuracy, or a necessary nudge toward more adaptive beliefs characteristic of improved mental health, leading us to describe this process as one of False Insights and Beliefs Under psychedelics (FIBUS). Below, we outline considerations that accompany this empirically derived model and proposal. We divide our considerations between the acute and post-acute phases.

Acute effects

A potential downside of (higher order) belief relaxation is that some adaptive priors that typically constrain perceptual inferences may also be relaxed in the process leading to false insights and hence false beliefs. This amplifies the oft-cited importance of set and setting, which are crucial predictors of the psychedelic experience 146 . With respect to setting, misleading contextual information could serve to increase the possibility of false insights. Psychedelics may serve as amplifiers of environmental influence rather than pushing someone toward one set of views over another 147 . A recent study found that changes in metaphysical beliefs following a psychedelic experience were mediated by a range of factors (i.e., age, personality traits, suggestibility) reinforcing the notion that non-pharmacological factors play an important role in adopting novel beliefs 11 , 115 .

Aspects typically emphasised in the clinical setting, such as safety and control, may additionally provide patients overly precise priors of felt safety or control in a non-clinical setting, where psychedelic consumption may not be safe nor well controlled. If psychedelics do acutely enhance fluency (ease of retrieval in memory 148 ), this may result in an exaggerated mere exposure effect in which patients become more attached to those they are interacting with whilst under psychedelics. There might also be aspects of the clinical setting conferring discomfort or distrust of clinicians, making future care more difficult. Of course, this could simply mean that any aspect of the experience that is out of step with day-to-day life could be overweighted, and thus garner outsized influence following the acute phase – consistent with the neutral amplifier of set and setting discussed above. Out of the psychedelic context, insights and beliefs arrived at may no longer carry their adaptive zeal. This amplifies the importance of an epistemically congruent (i.e., with the goals of the patients who ingests the psychedelic) set and setting – given that beliefs become more malleable during the acute phase, contextual factors in the post-acute phases (in which ‘integration’ therapy occurs) need to optimally encourage and support positive and adaptive insights.

We also note there may exist theoretical shortcomings to the REBUS model, which can paint an incomplete picture of belief change, and thus constrain our FIBUS model given we derive our predictions partly from the REBUS model. Some psychedelic studies find larger effect sizes outside of associative cortex, including in sensory areas with lower 5-HT 2A distribution 102 , 104 , 149 , 150 . Another outstanding question on REBUS effects is the presence of hallucinations during the acute phase. People often report reliving scenes from one’s past and immersive hallucinations of beings or ‘entities’, often considered high level-hallucinations. If the sense of self collapses under psychedelics, then it is unclear how one could have hallucinations that assume a sense of self (e.g., I am having a memory of something I have experienced before). Given our FIBUS model partially derives from and assumes REBUS effects, these shortcomings should be noted.

Another consideration with respect to our FIBUS model is the suggestion that hallucinations are due to overly strong priors - such as in acute episodes of psychosis 151 . If complex hallucinations are occasioned by strong priors, then we might expect less complex hallucinations at higher doses. Indeed, complex hallucinations are typically only occasioned with high doses and sensory deprivation (for example, in the case of Charles Bonnet syndrome 151 , 152 ). One explanatory model for hallucinations from dissociative hallucinogens (i.e., NMDA antagonists such as ketamine), which share some subjective, clinical, and neural effects as psychedelics, proposes the opposite of REBUS 153 . That is, hallucinations come from an overweighting of priors (e.g., “I am seeing my mother”) and an underweighting of sensory information (e.g., external input that would otherwise lead one to reject the idea that their mother is present). Indeed, the intensity and complexity of the hallucination (e.g., whether it is just geometric shapes, or reliving complex past experiences) could be highly dependent on dose, as well as effects on other cortical regions.

Recent work suggests that the degree of belief changes rests on how strong the prior initially was 154 , 155 . As such, weaker priors may be further weakened (e.g., psychedelics shifting a slightly differing opinion closer to the social norm 156 ), and stronger priors may become stronger due to the sociocultural and local environment tipping cognitive systems toward one belief (e.g., political liberalism) or another (e.g., political conservatism) during and after the acute phase 157 . Indeed, the current evidence seems to suggest that higher-level beliefs may be susceptible to change, although environmental noise (e.g., local, and broader sociocultural setting) may act as mediators of belief change as well 115 , 155 , 157 . Whilst these concerns do not preclude the FIBUS proposal here, future work should aim to further investigate the relationship between hallucinations, mechanisms pertaining to REBUS, and belief alterations.

Post-acute effects

The dopaminergic surge accompanying insights, combined with the memory alterations, may result in an undue sense of confidence for insights accrued. Insights gleaned during psychedelic experiences may therefore bear increased weighting in model selection (i.e., the set of beliefs that make up the world model following the acute phase). For example, mystical experiences encountered during the acute phase, and insights they incur may be primary mediators of beneficial belief updates 4 , 9 , 32 , 158 , 159 , 160 . However, it must also be noted that the subjective feeling of insight is not the same thing as a genuine breakthrough, as even mundane ideas engendered by the experience can seem more meaningful than what they really are 99 , 139 , 161 , 162 . For example, Mason et al 139 . found that higher decreases in functional connectivity within the default mode network predicted increased feelings of insightfulness, but decreases in objective originality. The key point is that consistent with our FIBUS proposal, insights seem to be exaggerated both in quality (subjectively defined) and in quantity during psychedelics, and they can deeply impact belief updating (perhaps even personality change 163 ), and thus subsequent model selection.

A second-order consequence is that these insights and subsequent beliefs may be more difficult to revise, particularly when ascribed the (memory systems modulated) noetic feelings accompanying them. A potential by-product of the precision (and associated noetic feelings modulated by memory systems 148 ) afforded to insights is the resulting belief updates may be less amenable to change 5 , 28 . With psychedelic occasioned insights, participants report a non-specific feeling of truth associated with insights 164 . If it is not clear why an insight feels true, it can be difficult to revise since it is not clear what information could contradict it. For example, if I have the insight that I am possessed by a negative entity, the prospect of which is central to some shamanic traditions, it may be extremely difficult to revise because the very foundation for the idea is simply the feeling. These insights seem to have lasting behavioural effects, as discussed, such as reductions in depression symptoms 10 , 41 , cessation of smoking and substance use 165 , 166 . This is important in a clinical context if the goal is to increase psychological flexibility such that more adaptive beliefs might be considered and adopted.

A recently developed framework aimed at addressing issues of psychedelic-induced false insights proposed the fostering of a ‘gentle touch’ for revelations occurring during psychedelic therapy sessions. In this framework (deemed ‘psychedelic apprenticeship’), the relational processes (e.g., therapeutic interventions performed by a therapist) occurring before, during, and after the psychedelic session could serve as a scaffold for users’ to hold novel insights lightly 11 . These relational processes could be seen as a form of ‘thinking through other minds’ or ‘cultural affordances’ 167 , whereby an experienced facilitator or therapist can aid in the modulation of the users’ precision weighting of newly acquired insights or beliefs during psychedelic therapy. With respect to our FIBUS model, the gentle touch framework, alongside a rubric for making sense of the adaptiveness, veracity, and falsifiability of psychedelically derived insights (Fig.  3 ), could offer clinicians a framework for integrating psychedelic insights in a clinically useful way. Below, we divide discussion of our FIBUS model into research and clinical implications, offering novel hypotheses and considerations for clinicians involved in integrating psychedelic insights.

The colored circles are illustrative examples only, and all these quantities and qualities may vary from person to person. Each example insight in the box has a number which corresponds to a particular insight under the ‘Insights’ title on the right-hand side. The green axis refers to the direction which may be the overall ideal therapeutic direction (i.e., veridical, adaptive, and, where possible, falsifiable beliefs) although there are likely exceptions. Each of these dimensions is explained in the box below with reference to the examples shown. Notably, some insights may confer a sense of wellbeing (i.e., adaptiveness), but are inherently difficult to verify. As such, the challenge for a clinician may be to unpack each insight along the falsifiability-veridicality-adaptiveness axis, making sure that insights are optimally leveraged to facilitate clinical improvements.

Future directions, insights, and epistemic hygiene

Our FIBUS model lends itself to novel empirical predictions. First, FIBUS predicts that subjective veracity and perceived number of insights under psychedelics should linearly increase alongside dopaminergic release during the acute psychedelic phase. This could be tested by leveraging tools such as Positron Emission Tomography (PET) 168 or blood sampling in the acute psychedelic phase. In the post-acute phase, researchers could ask how frequently and strongly participants experienced these insight moments during the acute phase. A second prediction here is that psychedelics should induce more false insights than during ordinary cognition, as psychedelics can increase the perceived (but not necessarily the objective) novelty or accuracy of new ideas 4 , 139 , 169 . To test this, future researchers could have participants perform tasks whilst under varied doses of psychedelics and see whether the number insights preceding a solution increase in number, and whether they are less accurate than during ordinary cognition. To test for strength of belief accuracy, researchers could administer, for example, a Brown Assessment of Beliefs Scale 170 post acutely. Of course, testing for the number and subjective veracity in these insight moments should themselves predict the strength of beliefs, without having to test for dopamine. The implication is that these beliefs should be stronger but less accurate when there was higher dopamine, and more false insights (both true and false), arising during the psychedelic experience.

A clinically relevant issue requiring elaboration is the relationship between truth and adaptiveness. As we have alluded to throughout this paper, determining what counts as a “true” belief or insight is challenging in complex domains beyond problem solving. We are also not making the claim that true beliefs and adaptive beliefs are synonymous in all circumstances. A related notion here is that of falsifiability. Indeed, many metaphysical beliefs that one may adopt following psychedelics do not easily lend themselves to testing, at least not at the individual level. For example, if one adopts panpsychist views (i.e., “everything in the universe is conscious”) following the acute phase, this is not a belief system that carries a clear criterion on which it may be empirically proven or disproven. As such, the challenge for the clinician may be to determine whether the newly found panpsychist views are supportive or refutative of the persons psychopathologies. Put differently, if one does adopt views that are not easy to amend or dispute, the clinical challenge becomes whether the belief betters or worsens the patients’ clinical symptoms. It could well be that when objectivity and falsifiability are not able to be established, adaptiveness could become a more important locus for care. As such, the relative weighing of these factors may depend on clinical judgement and the specific clinical goals of the patient.

Optimal integration of psychedelic insights in the clinical setting will be an important aspect of psychedelic-assisted interventions. However, this is no easy task given that any one insight can vary along several dimensions including subjective intensity, emotional valence, as well as veridicality. Moreover, as touched upon above, the contents of insights may vary along dimensions including veridicality (i.e., how likely or unlikely an insight or belief is to be objectively true), adaptiveness (i.e., whether an insight conducive to better or worse clinical outcomes), and falsifiability (i.e., whether the insights and consequent beliefs easily are updatable based on sampling more sensory data). In Fig.  3 , we provide a tool for thinking about this potential space of psychedelic insights, which may assist in identifying relatively more and relatively less desirable insights. This relates to the notion of ‘epistemic hygiene’ 171 , in essence a directive of ensuring healthy, appropriate, and (where possible) rigorous evaluation of claims arising from psychedelic insights. To establish epistemic hygiene, then, is to imbue thoughtful methods or frameworks for 1) demarcating insights and beliefs according to the norms and values of a specific social or cultural context and 2) developing techniques for promoting desired insights. We therefore offer a candidate tool or rubric for thinking through the issue of epistemic hygiene during psychedelic therapy, which may also be highly relevant for any practices or interventions that increase the incidence of insights. Of course, it is worth noting that where each insight ultimately falls is debatable, but the point here is that this veridicality-adaptiveness-falsifiability axis could offer a framework from which psychedelic induced insights can be optimally integrated in clinical settings. We invite future empirical work to shed light on these questions, particularly given forthcoming legalisation (for clinical purposes) of psychedelics in several jurisdictions.

Summary and conclusion

Psychedelics are increasingly considered a viable and effective treatment option for several psychiatric ailments. As such, understanding the mechanisms by which psychedelics confer insight and new beliefs is essential as they become increasingly integrated into clinical settings. However, extant research and theorising has not sufficiently considered the fact that psychedelics, and the feeling of insight they engender, do not necessarily prefer accuracy, and are not necessarily adaptive from a clinical perspective. This leaves open the possibility that rather than purely offering amelioration, psychedelics may also act as an amplifier for beliefs that enhance existing pathologies or even create new ones. To this end, we have offered the first cohesive account of how psychedelics may confer false beliefs through insight from a neurocomputational perspective – a process we have coined as FIBUS. While we remain optimistic about the future of psychedelic-assisted-therapy, in the interest of averting unfortunate surprises as psychedelic use increases it is important not to overlook the potential for epistemic harm. We also hope that our paper encourages future research on the effects of set, setting, and therapeutic interventions on facilitating valuable insights and adaptive beliefs.

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IELTS Reading Test Academic 236 – Implication of False Belief Experiments

You should spend about 20 minutes on Questions 1-13 which are based on IELTS  Reading Test Academic 236 – Implication of False Belief Experiments  Reading Passage Below:-

Implication of False Belief Experiments 2

{A} A considerable amount of research since the mid-1980s has been concerned with what has been termed children’s theory of mind. This involves children’s ability to understand that people can have different beliefs and representations of the world-a capacity that is shown by four years of age. Furthermore, this ability appears to be absent in children with autism. The ability to work out what another person is thinking is clearly an important aspect of both cognitive and social development. Furthermore, one important explanation for autism is that children suffering from this condition do not have a theory of mind (TOM). Consequently, the development of children’s TOM has attracted considerable attention.

{B} Wimmer and Perner devised a ‘false belief task’ to address this question. They used some toys to act out the following story. Maxi left some chocolate in a blue cupboard before he went out. When he was away his mother moved the chocolate to a green cupboard. Children were asked to predict where Maxi will look for his chocolate when he returns. Most children under four years gave the incorrect answer, that Maxi will look in the green cupboard. Those over four years tended to give the correct answer, that Maxi will look in the blue cupboard. The incorrect answers indicated that the younger children did not understand that Maxi’s beliefs and representations no longer matched the actual state of the world, and they failed to appreciate that Maxi will act on the basis of his beliefs rather than the way that the world is actually organized. 

{C} A simpler version of the Maxi task was devised by Baron-Cohen to take account of criticisms that younger children may have been affected by the complexity and too much information of the story in the task described above. For example, the child is shown two dolls, Sally and Anne, who have a basket and a box, respectively. Sally also has a marble, which she places in her basket, and then leaves to take a walk. While she is out of the room, Anne takes the marble from the basket, eventually putting it in the box. Sally returns, and the child is then asked where Sally will look for the marble. The child passes the task if she answers that Sally will look in the basket, where she put the marble; the child fails the task if she answers that Sally will look in the box, where the child knows the marble is hidden, even though Sally cannot know, since she did not see it hidden there. In order to pass the task, the child must be able to understand that another’s a mental representation of the situation is different from their own, and the child must be able to predict behavior based on that understanding. The results of research using false-belief tasks have been fairly consistent: most normally-developing children are unable to pass the tasks until around age four.

{D} Leslie argues that, before 18 months, children treat the world in a literal way and rarely demonstrate pretence. He also argues that it is necessary for the cognitive system to distinguish between what is pretend and what is real. If children were not able to do this, they would not be able to distinguish between imagination and reality. Leslie suggested that this pretend play becomes possible because of the presence of a de-coupler that copies primary representations to secondary representations. For example, children, when pretending a banana is a telephone, would make a secondary representation of a banana. They would manipulate this representation and they would use their stored knowledge of ‘the telephone to build on this pretence.

{E} There is also evidence that social processes play a part in the development of TOM. Meins and her colleagues have found that what they term mindmindedness in maternal speech to six-month old infants is related to both security of attachment and to TOM abilities. Mindmindedness involves speech that discusses infants’ feelings and explains their behaviour in terms of mental states (e.g. ‘you’re feeling hungry’). 

{F} Lewis investigated older children living in extended families in Crete and Cyprus. They found that children who socially interact with more adults, who have more friends, and who have more older siblings tend to pass TOM tasks at a slightly earlier age than other children. Furthermore, because young children are more likely to talk about their thoughts and feelings with peers than with their mothers, peer interaction may provide a special impetus to the development of a TOM. A similar point has been made by Dunn, who argues that peer interaction is more likely to contain pretend play and that it is likely to be more challenging because other children, unlike adults, do not make large adaptations to the communicative needs of other children.

{G} In addition, there has been concern that some aspects of the TOM approach underestimate children’s understanding of other people. After all, infants will point to objects apparently in an effort to change a person’s direction of gaze and interest; they can interact quite effectively with other people; they will express their ideas in opposition to the wishes of others; and they will show empathy for the feelings of others. All this suggests that they have some level of understanding that their own thoughts are different to those in another person’s mind. Evidence to support this position comes from a variety of sources. When a card with a different picture on each side is shown to a child and an adult sitting opposite her, then three year olds understand that they see a different picture to that seen by the adult

{H} Schatz studied the spontaneous speech of three-year-olds and found that these children used mental terms, and used them in circumstances where there was a contrast between, for example, not being sure where an object was located and finding it, or between pretending and reality. Thus the social abilities of children indicate that they are aware of the difference between mental states and external reality at ages younger than four.

{I} . A different explanation has been put forward by Harris. He proposed that children use ‘simulation’. This involves putting yourself in the other person’s position and then trying to predict what the other person would do. Thus success on false belief tasks can be explained by children trying to imagine what they would do if they were a character in the stories, rather than children being able to appreciate the beliefs of other people. Such thinking about situations that do not exist involves what is termed counterfactual reasoning. 

Questions 1-7

Use the information in the passage to match the people (listed A-G) with opinions or deeds below. Write the appropriate letters A-G in boxes 1-7 on your answer sheet.

Question 1:- Giving an alternative explanation that children may not be understanding others’ beliefs.

Question 2:- found that children under a certain age can tell difference between reality and mentality

Question 3:- conducted a well-known experiment and drew the conclusion that young children were unable to comprehend the real state of the world

Question 4:- found that children who get along with adults often comparatively got through tests more easily

Question 5:- revised an easier experiment to rule out the possibility that children might be influenced by sophisticated reasoning.

Question 6:- Related social factors such as mother-child communication to capability act in TOM

Question 7:- explained children are less likely to tell something interactive to their mother than to their friends

Questions 8-14

Complete the following summary of the paragraphs of Reading Passage, using no more than three words from the Reading Passage for each answer. Write your answers in boxes 8-14 on your answer sheet. 

Answers IELTS Reading Test Academic 236 – Implication of False Belief Experiments

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October 6, 2016

Chimps May Be Capable of Comprehending the Minds of Others

A gorilla-suit experiment reveals our closest animal relatives may possess “theory of mind”

By Catherine Caruso

A chimpanzee at the Kumamoto Sanctuary prepares to watch experimental scenarios that will test her understanding of false belief, a hallmark of theory of mind.

KUMAMOTO SANCTUARY, KYOTO UNIVERSITY, JAPAN

A chimpanzee, a scientist with a stick and a researcher in a King Kong suit may sound like the setup for a bad joke, but it is in fact the basis of a recent study that provides the first evidence that great apes—that is, bonobos, chimpanzees and orangutans—possess an understanding of false belief, a hallmark of “theory of mind.” This ability to understand that others have mental states and perspectives different than our own has long been considered unique to humans.

In the study, published Thursday in Science, a team of scientists recorded the eye movements of three great ape species while the animals watched videos of a man searching for a hidden object that had been moved without his knowledge, and found that they looked more frequently at the location where the man expected the object to be (a belief the apes knew was false), even though the object was no longer there. The findings suggest the apes were able to intuit what the human was thinking.

Theory of mind is central to our social functioning as humans, but scientists have long wondered whether it is, in fact, a uniquely human trait. There is evidence that apes can understand other’s mental states when they match up with reality, but apes have consistently failed tests of false belief—the idea that someone else may act according to a belief that is untrue. Fumihiro Kano of Kyoto University’s Kumamoto Sanctuary and a co-leader of the study calls this ability a “litmus test” for theory of mind. Traditional false-belief tests for apes have involved complicated tasks such as moving around cups to reveal hidden food. This is why Kano and study co-leader Christopher Krupenye of the Max Planck Institute for Evolutionary Anthropology adapted a simpler false-belief test designed for human infants that utilizes an eye-tracking method called anticipatory looking, or gazing at where you expect a person to look for an object.

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During the study, bonobos, chimpanzees and orangutans were “invited” one at a time to sit in a room and drink juice while watching a sequence of scenarios on a video monitor. An infrared camera below the monitor recorded where on the screen the animals were looking as they watched the scenes unfold. To capture the apes’ attention, the researchers made each experimental scenario into a high stakes television drama starring a mysterious apelike character (a researcher in a gorilla suit), whom they dubbed King Kong.

Like humans, great apes are “pretty obsessed with social information—when there's a conflict within their group everybody stops and pays attention,” Krupenye says. “This is just their version of the Jersey Shore that we made so they would be really engaged and curious about what was going to happen.”

In one scenario the King Kong figure pretended to attack a researcher, then hid in one of two hay bales, moving to the other bale while the researcher watched. Then the researcher left for awhile before returning with a stick to look for King Kong, who had left the scene while the researcher was away. In another scenario the costumed figure moved to the other hay bale after the researcher left and then departed entirely. The researchers also set up the same two scenarios in a slightly different setting—instead of hiding himself, King Kong hid a stolen rock under one of two boxes before removing it completely.

Apes from all three species consistently passed the test; even though the animals knew King Kong or the rock was gone, when the researcher returned to search for it, they consistently looked at the hay bale or box where the person had last seen the object and presumably still thought it was hidden. These results are particularly surprising because they challenge the large body of previous work that suggests great apes are not capable of comprehending beliefs that are untrue. “People have thought for awhile that false-belief understanding is unique to humans,” Krupenye says, “and so this suggests that apes do have at least a basic, implicit understanding of false belief, which has been seen as a signature of theory of mind.”

Their findings drew both praise and debate in the field. In an article about the study also published today in Science , Frans de Waal, a primatologist who studies social intelligence at Emory University and was not involved with the work, wrote the study design “is a genuine breakthrough, not only because it avoids an undue reliance on language skills required to understand narrative and questions in theory of mind testing in children, but also because it highlights the mental continuity between great apes and humans.”

Tecumseh Fitch, an evolutionary biologist and cognitive scientist at the University of Vienna, also not part of the research, sees this as the “final nail in the coffin of the long-standing idea that humans are the only species with ‘theory of mind.’”

Others are skeptical of that interpretation, however. Carla Krachun at the University of Saskatchewan and Robert Lurz at Brooklyn College, who both study theory of mind in primates, are excited that the researchers were able to indirectly measure apes’ mental processes using eye tracking, which “opens up all sorts of possibilities for studying theory of mind in apes,” they wrote in an e-mail. Krachun and Lurz do not think the study definitively demonstrates false-belief understanding, however. “The issue is that subjects could use a simple behavior rule—‘agents search for things where they last saw them’—to pass the tests without understanding anything about the agent’s false beliefs,” they explained.

Kato and Krupenye acknowledge the difficulty of interpreting their findings but still see them as an important step forward in our understanding of great ape cognition. “There are other kinds of false beliefs that I think we need to test in order to be sure that apes are relying on this more sophisticated skill,” Krupenye says. “But the big thing here is that the apes clearly have a more sophisticated understanding of others than we previously thought, and that means they can predict others' behavior even in contexts when the actor is misguided, and that's something that humans do all the time.”

Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation

• Danry, Valdemar
• Pataranutaporn, Pat
• Groh, Matthew
• Epstein, Ziv
• Maes, Pattie

Advanced Artificial Intelligence (AI) systems, specifically large language models (LLMs), have the capability to generate not just misinformation, but also deceptive explanations that can justify and propagate false information and erode trust in the truth. We examined the impact of deceptive AI generated explanations on individuals' beliefs in a pre-registered online experiment with 23,840 observations from 1,192 participants. We found that in addition to being more persuasive than accurate and honest explanations, AI-generated deceptive explanations can significantly amplify belief in false news headlines and undermine true ones as compared to AI systems that simply classify the headline incorrectly as being true/false. Moreover, our results show that personal factors such as cognitive reflection and trust in AI do not necessarily protect individuals from these effects caused by deceptive AI generated explanations. Instead, our results show that the logical validity of AI generated deceptive explanations, that is whether the explanation has a causal effect on the truthfulness of the AI's classification, plays a critical role in countering their persuasiveness - with logically invalid explanations being deemed less credible. This underscores the importance of teaching logical reasoning and critical thinking skills to identify logically invalid arguments, fostering greater resilience against advanced AI-driven misinformation.

• Computer Science - Artificial Intelligence;
• Computer Science - Computers and Society

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‘Metaphysical Experiments’ Probe Our Hidden Assumptions About Reality

July 30, 2024

Nico Roper/ Quanta Magazine

Introduction

Metaphysics is the branch of philosophy that deals in the deep scaffolding of the world: the nature of space, time, causation and existence, the foundations of reality itself. It’s generally considered untestable, since metaphysical assumptions underlie all our efforts to conduct tests and interpret results. Those assumptions usually go unspoken.

Most of the time, that’s fine. Intuitions we have about the way the world works rarely conflict with our everyday experience. At speeds far slower than the speed of light or at scales far larger than the quantum one, we can, for instance, assume that objects have definite features independent of our measurements, that we all share a universal space and time, that a fact for one of us is a fact for all. As long as our philosophy works, it lurks undetected in the background, leading us to mistakenly believe that science is something separable from metaphysics.

But at the uncharted edges of experience — at high speeds and tiny scales — those intuitions cease to serve us, making it impossible for us to do science without confronting our philosophical assumptions head-on. Suddenly we find ourselves in a place where science and philosophy can no longer be neatly distinguished. A place, according to the physicist Eric Cavalcanti , called “experimental metaphysics.”

Eric Cavalcanti of Griffith University in Brisbane, Australia, calls himself an “experimental metaphysicist.”

Luke Marsden for  Quanta Magazine

Cavalcanti is carrying the torch of a tradition that stretches back through a long line of rebellious thinkers who have resisted the usual dividing lines between physics and philosophy. In experimental metaphysics, the tools of science can be used to test our philosophical worldviews, which in turn can be used to better understand science. Cavalcanti, a 46-year-old native of Brazil who is a professor at Griffith University in Brisbane, Australia, and his colleagues have published the strongest result attained in experimental metaphysics yet, a theorem that places strict and surprising constraints on the nature of reality. They’re now designing clever, if controversial, experiments to test our assumptions not only about physics, but about the mind.

While we might expect the injection of philosophy into science to result in something less scientific, in fact, says Cavalcanti, the opposite is true. “In some sense, the knowledge that we obtain through experimental metaphysics is more secure and more scientific,” he said, because it vets not only our scientific hypotheses but the premises that usually lie hidden beneath.

THE DIVIDING LINE between science and philosophy has never been clear. Often, it’s drawn along testability. Any science that deserves its name is said to be vulnerable to tests that can falsify it, while philosophy aims for pristine truths that hover somewhere beyond the grubby reach of experiment. So long as that distinction is in play, physicists believe they can get on with the messy business of “real science” and leave the philosophers in their armchairs, stroking their chins.

As it turns out, though, the testability distinction doesn’t hold. Philosophers have long known that it’s impossible to prove a hypothesis. (No matter how many white swans you see, the next one could be black.) That’s why Karl Popper famously said that a statement is only scientific if it’s falsifiable — if we can’t prove it, we can at least try to disprove it. In 1906, though, the French physicist Pierre Duhem showed that falsifying a single hypothesis is impossible. Every piece of science is bound up in a tangled mesh of assumptions, he argued. These assumptions are about everything from underlying physical laws to the workings of specific measurement devices. If the result of your experiment appears to disprove your hypothesis, you can always account for the data by tweaking one of your assumptions while leaving your hypothesis intact.

Take, for instance, the geometry of space-time. Immanuel Kant, the 18th-century philosopher, declared that the properties of space and time are not empirical questions. He thought not only that the geometry of space was necessarily Euclidean, meaning that a triangle’s interior angles add up to 180 degrees, but that this fact had to be “the basis of any future metaphysics.” It wasn’t empirically testable, according to Kant, because it provided the very framework within which we understand how our tests work in the first place.

And yet in 1919, when astronomers measured the path of distant starlight skirting the gravitational influence of the sun, they found that the geometry of space wasn’t Euclidean after all — it was warped by gravity, as Albert Einstein had recently predicted.

Or did they? Henri Poincaré, the French polymath, offered up an intriguing thought experiment. Imagine that the universe is a giant disk that conforms to Euclidean geometry, but whose physical laws include the following: The disk is hottest in the middle and coldest at the edge, with the temperature falling in proportion to the square of the distance from the center. Moreover, this universe features a refractive index — a measurement of how light rays bend — that is inversely proportional to the temperature. In such a universe, rulers and yardsticks would never be straight (solid objects would expand and shrink with the temperature gradient) while the refractive index would make light rays appear to travel in curves rather than lines. As a result, any attempt to measure the geometry of the space — say, by adding up the angles of a triangle — would lead one to believe that the space was non-Euclidean.

Any test of geometry requires you to assume certain laws of physics, while any test of those laws of physics requires you to assume the geometry. Sure, the disk world’s physical laws seem ad hoc, but so are Euclid’s axioms. “Poincaré, in my opinion, is right,” Einstein said in a 1921 lecture. He added, “Only the sum of geometry and physical laws is subject to experimental verification.” As the American logician Willard V. O. Quine put it, “The unit of empirical significance” — the thing that’s actually testable — “is the whole of science.” The simplest observation (that the sky is blue, say, or the particle is there) forces us to question everything we know about the workings of the universe.

But actually, it’s worse than that. The unit of empirical significance is a combination of science and philosophy. The thinker who saw this most clearly was the 20th-century Swiss mathematician Ferdinand Gonseth. For Gonseth, science and metaphysics are always in conversation with one another, with metaphysics providing the foundations on which science operates, science providing evidence that forces metaphysics to revise those foundations, and the two together adapting and changing like a living, breathing organism. As he said in a symposium he attended in Einstein’s honor, “Science and philosophy form a single whole.”

With the two tied together in a Gordian knot, we might be tempted to throw up our hands, since we can’t put scientific statements to the test without dragging metaphysical statements along with them. But there’s a flipside to the story: It means that metaphysics is testable. That’s why Cavalcanti, who works at the very edges of quantum knowledge, doesn’t refer to himself as a physicist, or as a philosopher, but as an “experimental metaphysicist.”

I MET WITH CAVALCANTI on a video call. With his dark hair pulled back into a bun, he had a brooding look about him, his careful, serious demeanor offset only by a 15-week-old puppy squirming in his lap. He told me how, as an undergraduate in Brazil in the late 1990s, he worked on experimental biophysics — “very wet stuff,” as he describes it, “getting hearts out of rabbits and putting them under [superconducting] magnetometers,” that sort of thing. Though he soon moved on to drier territory (“working in particle accelerators, studying atomic collisions”), the work was still far from the metaphysical questions already lingering in his mind. “I had been told that the interesting questions in foundations of quantum mechanics had all been resolved by [Niels] Bohr in his debates with Einstein,” he said. So he measured another cross section, churned out another paper, and did it all again the next day.

Cavalcanti clears his mind in a forest near campus.

Luke Marsden

He ended up working for Brazil’s National Nuclear Energy Commission, and it was there that he read books by the physicists Roger Penrose and David Deutsch, each offering up a radically different metaphysical story to account for the facts of quantum mechanics. Should we give up the philosophical assumption that there’s only one universe, as Deutsch suggested? Or, as Penrose preferred, perhaps quantum theory ceases to apply at large scales, when gravity gets in on the action. “Here were these brilliant physicists who not only are directly discussing questions about foundations but profoundly disagreeing with each other,” Cavalcanti said. Penrose, he added, “even went beyond physics into what’s traditionally metaphysics, asking questions about consciousness.”

Inspired, Cavalcanti decided to pursue a doctorate in quantum foundations and found a place for himself at the University of Queensland in Australia. His dissertation began, “To understand the source of the conflicts of quantum foundations, it is essential to know where and how our classical models and intuitions start to fail to describe a quantum world. This is the subject of experimental metaphysics.” A professor put the thesis down and declared, “This isn’t physics.”

But Cavalcanti was prepared to make the case that the line between physics and philosophy had already been blurred beyond repair. In the 1960s, the Northern Irish physicist John Stewart Bell had also encountered a culture of physics that had no patience for philosophy. The days of Einstein and Bohr arguing over the nature of reality — and engaging deeply with philosophy in the process — were long over. Postwar practicality reigned, and physicists were eager to get on with the business of physics, as if the Gordian knot had been cut, as if it were possible to ignore metaphysics and still manage to do science at all. But Bell, doing his heretical work in his spare time, discovered a new possibility: While it’s true that you can’t test a single hypothesis in isolation, you can take multiple metaphysical assumptions and see if they stand or fall together.

For Bell, those assumptions are typically understood to be locality (the belief that things can’t influence each other instantaneously across space) and realism (that there’s some way things simply are, independent of their being measured). His theorem, published in 1964, proved what’s known as Bell’s inequality : For any theory operating under the assumptions of locality and realism, there’s an upper limit on how correlated certain events can be. Quantum mechanics, however, predicted correlations that busted through that upper limit.

As written, Bell’s theorem wasn’t testable, but in 1969 the physicist and philosopher Abner Shimony saw that it could be rewritten in a form suitable for the lab. Along with John Clauser, Michael Horne and Richard Holt, Shimony transformed Bell’s inequality into the CHSH inequality (named for its authors’ initials), and in 1972, in a basement in Berkeley, California, Clauser and his collaborator Stuart Freedman put it to the test by measuring correlations between pairs of photons.

The results showed that the world bore out the predictions of quantum mechanics, showing correlations that remained far stronger than Bell’s inequality allowed. This meant that locality and realism can’t both be features of reality — though which of the two we ought to abandon, the experiments couldn’t say. “To my mind, the most fascinating thing about theorems of Bell’s type is that they provide a rare opportunity for an enterprise which can properly be called ‘experimental metaphysics,’” Shimony wrote in 1980 in the statement that’s widely believed to have coined the term.

As it happens, though, the term goes back further, to a most unlikely character. Michele Besso, Einstein’s best friend and sounding board, was the only person Einstein credited with helping him come up with the theory of relativity. But Besso helped less with the physics than with the philosophy. Einstein had always been a realist, believing in a reality behind the scenes, independent of our observations, but Besso introduced him to the philosophical writings of Ernst Mach, who argued that a theory should only refer to measurable quantities. Mach, by way of Besso, encouraged Einstein to give up his metaphysical notions of absolute space, time and motion. The result was the special theory of relativity.

Upon its publication in 1905, physicists weren’t sure whether the theory was physics or philosophy. All of its equations had already been written down by others; it was only the metaphysics behind them that was new. But that metaphysics was enough to lead to new science, as special relativity gave way to general relativity, a new theory of gravity, complete with new, testable predictions. Besso later befriended Gonseth; in Switzerland, the two took long walks together, where Gonseth argued that physics could never be placed on firm foundations, since experiments can always overturn the most bedrock assumptions on which it is built. In a letter, which Gonseth published in a 1948 issue of the journal Dialectica , Besso suggested that Gonseth refer to his work as “experimental metaphysics.”

Experimental metaphysics gained something of an official headquarters in the 1970s with the founding of the Association Ferdinand Gonseth in Bienne, Switzerland. “Science and philosophy form one body,” it stated in its founding values, “and all that happens in science, whether in its methods or in its results, may resound on philosophy even in its most fundamental principles.” This was a radical statement — equally shocking to both science and philosophy. The association published an underground newsletter called Epistemological Letters , a kind of physics “zine,” with typed, mimeographed pages speckled with hand-drawn equations that was mailed out to 100 or so physicists and philosophers who comprised a new counterculture — the daring few who wanted to discuss experimental metaphysics. Shimony served as editor.

Bell’s theorem was always at the center of those discussions, because where previous work in physics let its metaphysics go unacknowledged, in Bell’s work the two were truly and explicitly inseparable. The theorem was not about any particular theory of physics. It was what physicists call a “no-go” theorem, a general proof showing that any theory operating under the metaphysical assumptions of locality and realism can’t describe the world we live in. You want a world that just is some particular way even when it’s not being measured? And you want locality? No go. Or, as Shimony put it in Epistemological Letters , in a play on Bell’s name, those who want to hold such a worldview “should remember the sermon of Donne: ‘And therefore never send to know for whom the bell tolls; it tolls for thee.’”

“Bell was both a philosopher of physics and a physicist,” said Wayne Myrvold , a philosopher of physics at Western University in Canada. “And in some of his best papers, he’s basically combining the two.” That rattled the editors of traditional physics journals and other gatekeepers of science. “This kind of work was definitely not seen as respectable,” Cavalcanti said.

The physicist John Clauser attends to the experiment he and Stuart Freedman built to test Bell’s theorem in the 1970s.

Courtesy of Lawrence Berkeley National Laboratory

That’s why, when the French physicist Alain Aspect went to Bell suggesting a new experiment that could test Bell’s inequality while ruling out any residual influence propagating between the measurement devices used to detect the photons’ polarizations, Bell asked him whether he had a permanent faculty position. “The worry was that doing that experiment would be a career killer for a young physicist,” Myrvold said.

Fast-forward to 2022, and there’s Aspect, along with Clauser and Anton Zeilinger, headed to Stockholm to receive a Nobel Prize. Those Bell’s inequality-violating correlations have, as it turns out, led to revolutionary technologies including quantum cryptography, quantum computing and quantum teleportation. But “despite the technological payoff,” Myrvold said, “the work was motivated by philosophical questions.” According to the Nobel citation, the three physicists won for “pioneering quantum information science.” According to Cavalcanti, they won for experimental metaphysics.

BELL’S THEOREM WAS only the beginning.

In the wake of experiments violating Bell-type inequalities, several views of reality remained on the table. You could keep realism and give up locality, accepting that what happens in one corner of the universe instantaneously affects what happens in another and therefore that relativity must be modified. Or you could keep locality and give up realism, accepting that things in the universe don’t have definite features prior to being measured — that nature is, in some profound sense, making things up on the fly.

But even if you gave up on a pre-measurement reality, you could still hang on to a post-measurement reality. That is, you could imagine taking all those measurement outcomes and piecing them together into a single, shared reality. That’s typically what we mean by “reality.” It’s the very notion of an objective world.

A thought experiment posed in 1961 casts doubt on that possibility. Eugene Wigner, the Nobel Prize-winning physicist, proposed a scenario in which an observer, call him “Wigner’s friend,” goes into a lab where there’s a quantum system — say, an electron in a quantum combination, or superposition, of two states called “spin up” and “spin down.” The friend measures the electron’s spin and finds that it’s up. But Wigner, standing outside, can use quantum mechanics to describe the entire state of the lab, where, from his perspective, no measurement has taken place. The state of the friend and the state of the electron are merely correlated — entangled — while the electron remains in a superposition of states. In principle, Wigner can even perform a measurement that will show physical effects of the superposition. From the friend’s perspective, the electron has some post-measurement state, but this doesn’t seem to be part of Wigner’s reality.

In 2018, that nagging doubt about a shared reality became a full-blown dilemma. Časlav Brukner , a physicist at the University of Vienna, realized that he could combine Wigner’s friend with a Bell-type experiment to prove a new no-go theorem. The idea was to have two friends and two Wigners; the friends each measure half of an entangled system, and then each of the Wigners makes one of two possible measurements on his friend’s lab. The Wigners’ measurement outcomes will be correlated, just like the photons’ polarizations in the original Bell-type experiments, with certain metaphysical assumptions imposing upper bounds on the strength of those correlations.

Eric Cavalcanti and Nora Tischler, colleagues at Griffith University, plan experiments that use optical devices and lasers to test inequalities in experimental metaphysics.

As it turned out, Brukner’s proof relied on an extra assumption that weakened the strength of the resulting theorem, but it inspired Cavalcanti and colleagues to make their own version. In 2020, in the journal Nature Physics , they published “A Strong No-Go Theorem on the Wigner’s Friend Paradox,” which proved two things. First, that experimental metaphysics, previously relegated to underground zines, is now worthy of prestigious scientific journals, and second, that reality is even stranger than Bell’s theorem ever suggested.

Their no-go theorem showed that, if the predictions of quantum mechanics are correct, the following three assumptions cannot all be true: locality (no spooky action at a distance), freedom of choice (no cosmic conspiracy tricking you into setting your detectors so that the outcomes seem to violate Bell’s inequality even though they don’t), and absoluteness of observed events (an electron with spin up for Wigner’s friend is an electron with spin up for everyone). If you want local interactions and a conspiracy-free cosmos, then you have to give up on the notion that a measurement outcome for one observer is a measurement outcome for all.

Significantly, their no-go theorem “constrains the space of possible metaphysical theories more tightly than Bell’s theorem does,” Cavalcanti said.

“It’s an important improvement,” Brukner said. “It’s the most precise, strongest no-go theorem.” Which is to say, it’s the most powerful piece of experimental metaphysics yet. “The strength of these no-go theorems is exactly that they do not test any particular theory, but a worldview. By testing them and showing violations of certain inequalities, we don’t reject one theory, but a whole class of theories. That’s a very powerful thing. It allows us to understand what is possible.”

Brukner laments that the implications of experimental metaphysics haven’t yet been fully incorporated into the rest of physics at large — especially, in his view, to the detriment of research on the quantum nature of gravity. “This is really a pity, because we end up with wrong pictures of, say, how the vacuum looks, or what goes on in a black hole, where they are described without any reference to modes of observation,” he said. “I don’t think that we will make significant progress in these fields until we really do much work on the theory of measurement.”

Whether experimental metaphysics can ever lead us to the correct theory of quantum gravity is unclear, but it could at least narrow the playing field. “There’s a story, I don’t know if it’s apocryphal, but it’s a nice one,” Cavalcanti wrote in a 2021 paper , “according to which Michelangelo, when asked about how he sculpted David, said: ‘I just removed anything that was not David.’ I like to think of the metaphysical landscape as the raw block of marble — with different points in the block corresponding to different physical theories — and of experimental metaphysics as a chisel to carve the marble, eliminating corners that do not describe the world of our experience. It may turn out that we are unable to reduce the block to a single point, corresponding to the one true ‘theory of everything.’ But we may hope that after we carve out all the bits that experiment allows us to, what remains forms a beautiful whole.”

AS I SPOKE with Cavalcanti, I tried to get a read on which interpretation of quantum mechanics he subscribed to by feeling out which metaphysical assumptions he hoped to hang on to and which he was ready to toss. Did he agree with the Bohmian interpretation of quantum mechanics, which trades locality for realism? Was he a “ QBist ,” with no need for the absoluteness of observed events? Did he believe in the cosmic conspiracies of the superdeterminists , who attribute all correlated measurements in the present-day universe to a master plan set out at the beginning of time? How about measurements spawning parallel realities, as in the many-worlds hypothesis ? Cavalcanti kept a true philosopher’s poker face; he wouldn’t say. (The puppy, meanwhile, was waging an all-out tug-of-war against the carpet.) I did, however, catch one hint. Whatever interpretation he eventually chooses, he wants it to touch on the mystery of the mind — what consciousness is, or what counts as a conscious observer. “I still think that that is the deepest mystery,” he said. “I don’t think that any of the available interpretations actually quite get to the right story.”

In their 2020 Nature Physics paper, Cavalcanti and colleagues reported the results of what they called a “proof-of-principle version” of their Bell-cum-Wigner’s-friend experiment, which showed a clear violation of inequalities derived from the joint assumptions of locality, freedom of choice, and the absoluteness of observed events. But the experiment is inherently tricky to carry out, because something — or someone — has to play the role of an observer. In the proof-of-principle version, Wigner’s “friends” were played by photon paths, while photon detectors played the part of the Wigners. Whether something as simple as a photon path counts as an observer is notoriously hard to say.

“If you think that any physical system can be considered an observer, then the experiment has already been done,” Cavalcanti said. “But most physicists will think, no, I don’t buy that. So what are the next steps? How far can we go?” Is a molecule an observer? An amoeba? Could Wigner be friends with a fig? Or a ficus?

If the friend has to be human, it’s hard to overstate just how difficult it would be to measure one in a superposition, which is exactly what the Wigners of the experiment are supposed to do. It’s hard enough to keep a single atom in a superposition. Sustaining an atom’s superposed states means isolating it from virtually all interactions — including interactions with air — which means storing it just a hair’s breadth above absolute zero. The average adult human being, besides needing air, is made of some 30 trillion cells, each containing some 100 trillion atoms. The technology, fine motor skills and questionable ethics a Wigner would need to perform his measurement would stretch the imagination of any physicist or institutional review board. “It’s not always emphasized that this [proposed] experiment is a violent act,” Myrvold said. “It basically involves destroying the person and then reviving them.” Good luck getting the grant money for that.

Brukner, for one, wonders whether the measurement is not merely difficult, but impossible. “I suspect if we put it all down on paper, we will see that the resources required for Wigner to make this measurement go far beyond what is available in the universe,” he said. “Maybe in some more fundamental theory, these limitations will be part of the theory, and it will turn out that there is no meaning to this question.” That would be quite the twist for experimental metaphysics. Maybe our deepest insights into the nature of reality will come when we realize what’s not testable.

Cavalcanti, however, is holding out hope. We may never be able to run the experiment on a human, he says, but why not an artificial intelligence algorithm? In his newest work , along with the physicist Howard Wiseman and the mathematician Eleanor Rieffel , he argues that the friend could be an AI algorithm running on a large quantum computer, performing a simulated experiment in a simulated lab. “At some point,” Cavalcanti contends, “we’ll have artificial intelligence that will be essentially indistinguishable from humans as far as cognitive abilities are concerned,” and we’ll be able to test his inequality once and for all.

But that’s not an uncontroversial assumption. Some philosophers of mind believe in the possibility of strong AI, but certainly not all. Thinkers in what’s known as embodied cognition, for instance, argue against the notion of a disembodied mind, while the enactive approach to cognition grants minds only to living creatures.

All of which leaves physics in an awkward position. We can’t know whether nature violates Cavalcanti’s inequality — we can’t know, that is, whether objectivity itself is on the metaphysical chopping block — until we can define what counts as an observer, and figuring that out involves physics, cognitive science and philosophy. The radical space of experimental metaphysics expands to entwine all three of them. To paraphrase Gonseth, perhaps they form a single whole.

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