Group
Control
Doodling
Names
Correct
4.00
1.41
3.48
1.73
False Alarms
0.64
0.91
0.64
0.76
3.36
1.89
2.84
2.10
Places
Correct
1.44
1.26
1.28
1.46
False Alarms
0.28
0.74
0.12
0.33
1.16
1.46
1.16
1.60
Submission of the correct-recall rates to a 2 (Group: doodling, control) x 2 (Stimulus-type: names, places) mixed-factors ANOVA, for example, revealed that there was no significant difference in the overall correct-recall performance of the doodlers compared to that of the control group (Group main-effect: F (1,48) = 1.47, p = .23, η p 2 = .03). Although significantly more party-goer names were recalled than places, overall, (Stimulus-type main effect: F (1,48) = 59.07, p <.001, η p 2 = .55), the magnitude of this effect did not significantly depend on whether participants were in the doodle group or the control group (Group-by-Stimulus-type interaction: F (1,48) = .34, p =.56, η p 2 = .007).
Submission of false-alarm numbers to a separate 2 (Group: doodling, control) x 2 (Stimulus-type: names, places) mixed-factors ANOVA, likewise revealed that there was no significant difference in the average numbers of false reports by the doodlers compared to that of the control group (Group main-effect: F (1,48) = .25, p = .62, η p 2 = .005). Although significantly more party-goer names were incorrectly reported than places, overall, (Stimulus-type main effect: F (1,48) = 12.23, p =.001, η p 2 = .20), the magnitude of this effect did not significantly depend on whether participants were in the doodle group or the control group (Group-by-Stimulus-type interaction: F (1,48) = .40, p =.53, η p 2 = .008).
After subtracting false alarms from correct-recalls for reported party-goer names and places, the resulting total memory scores were submitted to another 2 (Group: doodling, control) x 2 (Stimulus-type: names, places) mixed-factors ANOVA. This revealed that the average total-memory performance for the doodle group was not significantly different from that of the control group (Group main-effect: F (1,48) = .55, p = .46, η p 2 = .01). Although total-memory scores were again significantly higher for party-goer names than for places, overall, the magnitude of this effect did not significantly depend on whether participants were in the doodle group or the control group (Group-by-Stimulus-type interaction: F (1,48) = .52, p =.48, η p 2 = .01.).
The fidgeting-reduces-boredom-and-increases-attention hypothesis and the results reported by Andrade (2010) predicted that participants in the doodle condition of Experiment 1 should perform better on the subsequent memory test than those in the control condition. They did not. While the addition of doodling did not significantly impair performance on the subsequent memory test when compared to that of the control condition, the nominally-lower memory performance for those in the doodle condition than those in the control condition is more in line with the fidgeting-reflects-inattention hypothesis, which predicted that the addition of doodling would—if anything— reduce attention to the voicemail when compared to that of the control condition.
Previous studies have suggested that doodling may be a helpful intervention strategy in a boring situation by distracting the individual away from their boredom and thereby increasing the ability to recall information (Andrade, 2010). However, our results suggest that this may not be accurate, as there was no benefit of doodling (versus not doodling) for subsequent memory performance under conditions in which participants were shown to be experiencing elevated levels of boredom. Our failure to replicate Andrade’s (2010) finding that doodling aids recall ability for voicemail material may be due to the possibility that their participants were not experiencing boredom. To the extent that elevated boredom is associated with difficulties with task-focused attention, the potential for low levels of boredom in Andrade’s (2010) study could mean that their participants had available attentional resources to be better able to engage with the material while doodling than our participants, who may have had boredom-related difficulties with attentional engagement. If this is true, it may indicate that doodling is not effective at offsetting boredom or its negative consequences for learning.
However, while we took steps to ensure there were elevated levels of boredom for participants going into the voicemail task in Experiment 1, this study alone cannot explain exactly how boredom may have played a role in determining the observed results. In a typical learning environment, boredom tends to occur during the actual task not prior. Additionally, most educational settings require task-focused attention for much longer than two-and-a-half minutes. Thus, the length of the task we adapted from Andrade (2010) is far too short to be reflective of a real-life scenario, and may therefore not have been long enough to provide an opportunity for doodling to be fully effective in offsetting boredom-related disengagement with the material. It is also impossible to know whether doodling could have been functional for offsetting boredom or any of its negative correlates in Experiment 1— beyond subsequent memory performance— because there was no measure of task-focused attention or off-task mind-wandering. We address this in Experiment 2 by manipulating doodling behaviour during a longer lecture-listening task in which we measured in-task levels of attention, mind-wandering and boredom, as well as subsequent retention of the lecture material.
Experiment 2 builds on Experiment 1 by assessing the impact of doodling on boredom, mind-wandering, attention and retention in a more ecologically-valid lecture-listening task. The importance of including in-task measures of mind-wandering and attention is highlighted by the findings of previous research showing both links between mind-wandering and fidgeting behaviour (Carriere, Seli & Smilek, 2013), and how our body also becomes restless as our mind becomes restless (Seli et al., 2014). However, while these prior studies make clear a connection between fidgeting behaviours and difficulties maintaining task-focused attention, they did not include measures of boredom or associated memory performance, nor did they manipulate fidgeting behaviours to directly assess their impact on mind-wandering or attention. Experiment 2 therefore also extends this prior work by manipulating different types of fidgeting behaviours to directly assess their impact on boredom, mind-wandering and attention during a lecture-listening task, as well as the associated effects on retention of lecture material.
Our decision to include a manipulation of different types of fidgeting behaviours— the same ‘shade in shapes’ structured form of doodling used in Experiment 1, an ‘anything goes’ unstructured form of doodling, a note-taking condition, and a ‘listen-only’ control condition—was inspired by Boggs et al. (2017) who used these same conditions. Unstructured doodling more closely represents the type of doodling used in real-world settings, while note-taking represents another activity frequently used in real-world settings. Participants in Boggs et al.’s study were randomly assigned to one of these four conditions while they listened to a 5-minute fictional conversation between two friends, after which they completed a quiz that tested their ability to recall information from that conversation. Boggs et al. found that unstructured doodling while listening led to significantly worse recollection of the conversation content than the structured doodling or note-taking. They interpreted this learning impairment as being due to the additional attentional demands during unstructured doodling of having to decide what to doodle, which may have thereby reduced participants capacity to encode details of the conversation. However, the authors urged caution regarding this interpretation because they did not include a measure of attention in their study. Experiment 2 therefore also extends this prior work by using the same doodling conditions as Boggs et al. along with in-task measures that allow a direct assessment of their impact on attention, mind-wandering, and boredom during a lecture-listening task, as well as the associated effects on retention of lecture material.
The fidgeting-reduces-boredom-and-increases-attention hypothesis predicts that participants in both the structured-doodling or unstructured-doodling conditions should show lower levels of in-task boredom and mind-wandering, and higher levels of in-task attention and subsequent memory for lecture content, than those in the control condition. In contrast, the fidgeting-reflects-inattention hypothesis predicts that the addition of either doodling condition would reduce attention to the lecture, increase in-task boredom and mind-wandering and—if anything—impair performance on the subsequent memory test when compared to that of the control condition. Further, consider Boggs et al.’s (2017) speculation that unstructured doodling places additional demands on attention, due to the extra thought and effort required to generate doodle content, relative to structured doodling. If this is correct, then the unstructured-doodling condition in our experiment should also show reduced in-task attention, more mind-wandering, and worse memory for lecture content relative to the structured doodling condition. In terms of in-task boredom, however, it may be possible that unstructured doodling is relatively interesting and engaging when compared to the experience of structured doodling or passive listening. If so, it might produce lower levels of in-task boredom and mind-wandering than these other conditions, regardless of whether it results in less task-focused attention or relatively poor memory for lecture content.
As for the note-taking condition, writing down information as you hear it may aid the encoding of that information. This could explain both Boggs et al.’s (2017) finding that note-taking leads to better memory performance than passive listening or unstructured doodling, and Meade, Wammes & Fernandez’s (2019) finding that writing down words as they were heard also resulted in better memory for those words than for words that were heard during unstructured doodling. However, the extent to which any difference in memory performance for note-taking compared to the other conditions is associated with differences in boredom, mind-wandering or attention remains unclear as these prior studies did not include measures of these factors.
Participants in Experiment 2 completed several questionnaires to allow us to examine whether there is a relation between an individual’s tendency to doodle (DSAQ: Doodle Spontaneous Activity Questionnaire; developed for this study based on Carriere et al., 2013), their tendency to fidget (SAQ: Spontaneous Activity Questionnaire; Carriere et al., 2013), and the extent to which they routinely experience boredom (BPS: Boredom Proneness Scale; Farmer & Sundberg, 1986) or attention-related difficulties in terms of mind-wandering (MWQ: Mind-Wandering Questionnaire; Mrazek, Phillips, Franklin & Broadway, 2013), attention-related cognitive errors (ARCES: Attention-Related Cognitive Errors; Carriere, Cheyne & Smilek, 2008), or lapses in mindful attention (MAAS-LO: Mindful Attention Awareness Scale-Lapses Only; Carriere et al., 2008, cf. Brown & Ryan, 2003). Of the potential relations between individual-difference measures, we were particularly interested in the extent to which those who self-report being high in fidgeting behaviour (SAQ), also report being high in doodling behaviour (DSAQ). If doodling is just a specific form of the more general category of fidgeting, then DSAQ scores and SAQ scores should be positively correlated.
Obtaining a measure of trait mind-wandering (MWQ) for each participant, along with a measure of self-reported doodling frequency (DSAQ) was intended to help test our competing hypotheses and thereby address the discrepancies between prior work implying that doodlers mind-wander less (Andrade, 2010) and subsequent results suggesting doodlers mind-wander more (Boggs et al., 2017). Specifically, the fidgeting-reduces-boredom-and-increases-attention hypothesis predicts negative correlations between the self-reported tendency to doodle (DSAQ) and measures of boredom proneness (BPS), the tendency to experience attentional failures (ARCES) and lapses in attention (MAAS-LO). In contrast, the fidgeting-reflects-inattention hypothesis predicts positive correlations between DSAQ scores and both ARCES scores and MAAS-LO scores. The questionnaires we included to look at individual differences in self-reported doodling, fidgeting, attentional lapses/cognitive errors, boredom and mind-wandering also allowed us to assess whether there are any particular subsets of individuals for whom fidgeting/doodling may be especially effective, as has previously been suggested (e.g., those who routinely experience attention-related difficulties; Kercood & Banda, 2012; Rotz & Wright, 2005; Zentall, 1975). Including these measures also allows an assessment of the speculation by Boggs et al. (2017) that doodling might have differing cognitive impacts based on whether a given individual already has a tendency to doodle.
Participants
Participants consisted of 172 (43 per group, age: M = 19.08, SD = 2.46; self-reported gender: female = 145, male = 27) undergraduate students at the University of Guelph. They were recruited using the Department of Psychology participant pool and received course credit for their participation. The University of Guelph Research Ethics Board approved this study (REB protocol #16-12-398).
Materials: Mass testing
Spontaneous Activity Questionnaire. (SAQ; Carriere et al., 2013). The SAQ was completed by participants during mass testing to get a measure of an individual overall tendency to fidget. The questionnaire has a total of 8 questions with excellent internal consistency (α = 0.94). Examples of the SAQ questions are “I fidget while I am in deep thought” and “I fidget when I am worried about something”. Answers for the SAQ range from 1 (never) to 7 (always), with higher scores (max score is 56) indicating an individual have a greater tendency to fidget.
Boredom Proneness Scale. (BPS; Farmer & Sundberg, 1986). The BPS was completed during mass testing to measure an individual’s propensity to experience boredom. Unlike the other boredom questionnaires that look at boredom at that specific time, the BPS looks at how often the individual experiences boredom in his/her daily life. The BPS includes 28 items with questions such as “I find it easy to entertain myself” and “It takes me more stimulation to get me going than most people”. The BPS has acceptable reliability (α = .79). Participants may answer True (1 point), False (0 points) or not answer at all (Note: items 1, 7, 8, 11, 13, 15, 18, 22, 23 and 24 are reversed scored). The higher the sum of the responses (max score is 28) is indicative that the individual has a higher trait tendency to experience boredom.
Mind-wandering Questionnaire. ( MWQ; Mrazek, Phillips, Franklin & Broadway, 2013). The MWQ is completed during mass testing to measure an individual’s overall tendency to mind-wander. The questionnaire consists of 5 items with good internal consistency (α = .85). Responses range from 1 (almost never) to 6 (almost always). Examples of the questions included in the MWQ are “I do things without paying full attention” and “I mind-wander during lectures or presentations. Higher scores (max score of 30) indicate a higher propensity to mind-wander. Previous research has shown that there is a relation the tendency to experience boredom measured by the BPS and instances of mind-wandering, suggesting that higher boredom proneness is related to more off-task thought (Isacescu, Struk & Danckert, 2017).
Materials: In-session
Recorded lecture. Participants listened to the first 45-minutes of the recorded lecture titled “The Dark Ages” from an introductory Ancient Greek History course (same as Experiment 1).
Thought probes. To obtain in-task measures of state boredom, mind-wandering and task-focused attention, we presented thought probes at four different points throughout the recorded lecture (9, 18, 27, and 36 minutes). For each probe, the lecture audio would stop playing and participants would be asked to use a Likert-scale to rate their level of boredom (“How bored were you prior to the probe?”: 1 = “Not at all bored” to 7 = “Extremely bored”), mind-wandering (“Where was your attention focused just before the probe?”: 1 = “Not at all on task” to 7 = “Completely on task”), and attention (How much attention were you paying prior to the probe?”: 1 = “Not paying attention at all” to 7 = “Full attention”).
Multidimensional State Boredom Scale-8 (MSBS-8 ). Participants indicated the level of boredom they were experiencing both before and after listening to the recorded lecture by completing the MSBS-8 (Hunter, Dryer, Cribbie & Eastwood, 2015) using Qualtrics online-survey software (same as experiment 1).
Mindful Attention Awareness Scale – Lapses Only (MAAS-LO). To assess individual differences in participants propensity to experience attentional lapses, participants were asked to complete the 12-question MAAS-LO (Carriere et al., 2008), which is a version of the Mindful Attention Awareness Scale originally developed by Brown & Ryan (2003) that was modified by Carriere et al. to focus exclusively on attentional lapses. The MAAS-LO has good reliability (α = 0.83) as shown in a variety of studies (e.g., Carriere, Seli & Smilek, 2013; Cheyne, Carriere, Smilek, 2006; Carriere, Cheyne & Smilek, 2008). Examples of the MAAS-LO questions include “I find myself doing things without paying attention” and “I rush through activities without really being attentive to them”. Responses are made using a Likert-scale ranging from 1 (almost never) to 6 (always). The sum of the responses provides a score that indexes attentional lapses, with higher scores indicating greater tendencies to experience attentional lapses (max score of 72). Previous research has shown that the tendency to fidget measured by the SAQ is positively correlated with the attentional lapses measured by the MAAS-LO (Carriere et al., 2013). Moreover, the tendency to routinely experience boredom measure by the BPS has been shown to positively correlate with the attentional lapses measured by the MAAS-LO (Carriere, Cheyne & Smilek, 2008), such that higher instances of boredom are related to increased attentional lapses. Participants completed the MAAS-LO via Qualtrics online survey software immediately after the lecture-listening task.
Attention-Related Cognitive Errors Scale. (ARCES; Cheyne, Carriere & Smilek, 2006). To assess individual differences in participants propensity to experience cognitive failures due to attention lapses, participants were asked to complete the 12-question ARCES. Examples of the ARCES questions are “I have gone to the fridge to get one thing (e.g., milk) and taken something else (e.g., juice)” and “I have lost track of a conversation because I zoned out when someone else was talking”. Responses are made using a Likert-scale ranging from 1 (never) to 5 (very often). The sum of the responses provides a score that indexes the cognitive consequences of attentional lapses, with higher scores indicating greater tendencies to experience attention-related cognitive errors (max score of 60). ARCES scores have been shown to be positively correlated with MAAS-LO scores, suggesting that there is a relation between the increased lapses in attention measured by the MAAS-LO and the increased cognitive consequences of such lapses measured by the ARCES (Carriere, Seli & Smilek, 2013; Cheyne, Carriere, Smilek, 2006; Carriere, Cheyne & Smilek, 2008). Previous research has shown that the tendency to fidget measured by the SAQ is positively correlated with the cognitive consequences of attentional lapses measured by the ARCES (Carriere et al., 2013). Moreover, the tendency to routinely experience boredom measure by the BPS has been shown to positively correlate with the cognitive consequences of attentional lapses measured by the ARCES (Carriere, Cheyne & Smilek, 2008), such that higher instances of boredom are related to increased attention-related cognitive errors. Participants completed the ARCES via Qualtrics online survey software immediately after the lecture-listening task.
Doodle Spontaneous Activity Questionnaire. (DSAQ) . To assess individual differences in participants propensity to doodle, we modified the SAQ (Carriere et al., 2013) to focus specifically on doodling behaviour. We simply changed each instance of “I fidget” in the questions to “I doodle”. Examples of the modified DSAQ questions are “I doodle while I am in deep thought” and “I doodle when I am worried about something”. Responses are made using a Likert-scale ranging from 1 (never) to 7 (always). The sum of the responses provides a score that indexes the propensity to engage in doodling, with higher scores indicating greater tendencies to doodle (max score is 49). If doodling is just a specific form of the more general category of fidgeting, then DSAQ scores and SAQ scores should be positively correlated.
Retention questions. Participants were asked to complete a set of 28 multiple-choice questions to measure their retention of lecture material. The questions asked had to do with specific lecture content (i.e., “Where do we see civilization for the first time in the Aegean Sea area?”.
Participants were required to complete an online mass-testing series of questionnaires to be eligible for participation. Once in the lab, informed consent was obtained in writing. The participant was then asked to complete a demographic survey and the MSBS-8 using Qualtrics online survey software. Participants were told they would be listening to the audio of a university lecture through headphones and that the headphones were not to be removed. They were also told that questions would occasionally appear on the screen during the lecture audio that would require them to read the question and make an honest response using the numbers on the keyboard. Participants were informed that there would be a memory test at the end of the lecture, so they should pay close attention to the material. At this point, they were given instructions based on their randomly assigned condition and then the audio was started.
The control condition was not given any secondary task to do while listening. The note-takers were instructed to take notes while listening to the lecture anytime they were not answering questions on the screen (thought probes). Participants were given five sheets of blank white paper with two pencils and a pen to use. The structured doodlers were instructed to shade in shapes while listening to the lecture anytime they were not answering questions on the screen (thought probes). They were not to do anything else or write notes. Participants were given five sheets of paper with alternating shapes on them, with two pencils and a pen to use. Lastly, the unstructured doodlers were instructed to doodle while listening to the lecture anytime they were not answering questions on the screen (thought probes). Participants were given five sheets of blank white paper with two pencils and a pen and told they could doodle anything, except lecture material or write notes. to use. Upon completion, the participants completed the MSBS-8, DSAQ, MAAS-LO, ARCES and multiple-choice retention test via Qualtrics. Participants were then debriefed on the purpose of the study and thanked for their participation.
Pre-experiment levels of boredom
To confirm that there were no pre-existing differences in the level of boredom being experienced by our different experimental groups, we submitted the summed pre-task MSBS-8 scores to a one-way ANOVA with Group (Listen-only, Note-taking, Structured-doodle, and Unstructured-doodle) as the between-subjects factor. The analysis confirmed that there were no significant group differences in levels of state boredom prior to the experiment, F (3,168) = 0.27, p = 0.85, η 2 = 0.0048.
In-task measures of boredom, mind-wandering, and attention
To assess the effects of the different types of fidgeting behaviours on in-task levels of boredom, mind-wandering, and attention throughout the lecture-listening task, we conducted a separate 4 (Group: Listen-only, Note-taking, Structured-doodle, and Unstructured-doodle) x 4 (Time: 9, 18, 27, and 36 minutes) mixed-factors ANOVA for each of these measures. As detailed below, there was no benefit on any of these measures for either type of doodling, relative to the listen-only control. There was, however, a distinct advantage for the note-takers relative to all other groups that was specific to levels of mind-wandering and task-focused attention.
Boredom. As shown in Figure 1, self-reported state boredom increased over time for all groups. This main effect of Time was significant, F (3, 504) = 22.07, p < .001, η p 2 = .12. Figure 1 shows that the overall levels of boredom over time were similar for the different groups, which was reflected in the lack of a significant main effect of Group, F (3,168) = .94, p = .42, η p 2 = .02, and the lack of a significant Time-by-Group interaction, F (9, 504) = 1.24, p = .27, η p 2 = .02. As a further test of the possibility that different fidgeting behaviours might differentially influence the experience of boredom, we submitted the post-task MSBS-8 scores to a one-way ANOVA with Group (Listen-only, Note-taking, Structured-doodle, and Unstructured-doodle) as the between-subjects factor. This confirmed that there were also no significant group differences in levels of state boredom after the listening task, F (3,168) = 1.03, p = 0.38, η 2 = 0.018.
Mind-wandering. As shown in Figure 1, self-reported levels of mind-wandering increased over time for all groups. This main effect of Time was significant, F (3,504) = 36.94, p <.001, η p 2 = .18. However, Figure 1 also shows that the overall levels of mind-wandering over time were noticeably lower for the Note-taking group than for the other groups, which was reflected in a significant main effect of Group, F (3,97) = 11.49, p < .001, η p 2 = .17 . Post hoc tests using the Bonferroni correction revealed that the Note-taker group mind-wandered significantly less than the Listen-only control group, ( t = -4.96, p < .001, d = -0.83), Structured-doodle group ( t = -4.60, p < .001, d = -0.77), and Unstructured-doodle group ( t = -4.79, p < .001, d = -0.81), none of which significantly differed from each other (for each comparison, t < 0.37, p > .99, d < 0.06). Moreover, the extent to which note-taking reduced levels of mind-wandering relative to the other conditions did not change over time, as reflected by the lack of a significant Time-by-Group interaction, F (9,504) = .55, p = .84, η p 2 = .02.
Attention. Whereas boredom and mind-wandering increased over time, Figure 1 shows that the self-perceived amount of attention paid to the lecture-listening task decreased over time. This main effect of Time was significant, F (3, 504) = 37.81, p <.001, η p 2 = .18. Moreover, as with levels of mind-wandering, Figure 1 also shows that the levels of attention paid over time were noticeably different for the Note-taking group than for the other groups, which was reflected in a significant main effect of Group, F (3,168) = 6.09 p = .001, η p 2 = .10. Post hoc tests using the Bonferroni correction revealed that the Note-taker group reported paying significantly more attention than the Listen-only control group, ( t = 3.20, p < .001, d = 0.55), Structured-doodle group ( t = 2.95, p = .022, d = 0.51), and Unstructured-doodle group ( t = 3.98, p < .001, d = 0.68), none of which significantly differed from each other (for each comparison, t < 1.04, p > .99, d < 0.18). Moreover, the extent to which note-taking increased levels of attention relative to the other conditions did not change over time, as reflected by the lack of a significant Time-by-Group interaction, F (9,504) = 1.24, p = .27, η p 2 = .02.
The notion that fidget behaviours might reduce boredom and mind-wandering, and thereby boost task-focused attention and aid learning (or, conversely that fidget behaviours are an index of a lack of engagement and may thereby be linked to less task-focused attention and impaired learning), is based on the possibility that higher levels of boredom and mind-wandering and lower levels of attention all lead to impairments in the ability to encode and retain information encountered during a learning experience. To test this, we examined the extent to which levels of state boredom, mind-wandering, and task-focused attention were correlated, overall, with subsequent performance on the multiple-choice test of memory. This showed that the number of correct memory-test answers was indeed negatively correlated with overall levels of in-task boredom (average of the four ratings), r = -0.21, p = .006, post-task boredom (MSBS-8 score obtained after the lecture), r = -0.28, p < .001, and overall levels of mind-wandering (average of the four in-task ratings, r = -0.36, p < .001), while memory-test performance was positively correlated with levels of self-perceived attention paid to the lecture-listening task (average of the four in-task ratings, r = 0.34, p < .001). This is consistent with prior evidence that boredom and its corresponding difficulties with attentional engagement have clear negative consequences for academic outcomes (Pekrun, Hall, Goetz & Perry, 2014; Fitea & Fritea, 2013; Tze, Daniels & Klassen, 2016). It also underscores the potential value for learning contexts of any fidgeting-based intervention that may be effective in reducing boredom and mind-wandering, and increasing task-focused attention.
Table 2. Average retention score (number correct answers on the 28-question multiple-choice test of memory for lecture content) for the Listen-only, Structured-doodle, Unstructured-doodle, and Note-taking groups ( n = 43 per group).
Retention Score | ||
|
|
|
Note Taking | 19.09 | 2.79 |
Unstructured Doodle | 15.69 | 3.45 |
Structured Doodle | 15.95 | 3.40 |
Listen-only | 16.63 | 3.47 |
As shown in Table 2, while there was little difference among the average numbers of correct multiple-choice answers for the Structured-doodle, Unstructured-doodle and Listen-only control groups, the memory performance of the Note-taking group was notably higher. A one-way ANOVA with Group (Listen-only, Structured-doodle, Unstructured-doodle, and Note-taking) as the between-subjects factor confirmed that this main effect of Group was significant, F (3,168) = 9.55, p < .001, η p 2 = .15. Post hoc tests using the Bonferroni correction revealed that the Note-taker group remembered significantly more of the lecture content than the Listen-only control group, ( t = 3.47, p < .001, d = 0.75), the Structured-doodle group ( t = 4.42, p < .001, d = 0.95), and the Unstructured-doodle group ( t = 4.79, p < .001, d = 1.03). There were no significant differences among the Structured-doodle, Unstructured-doodle and Listen-only control groups (for each comparison, t < 1.32, p > .56, d < 0.29). In other words, neither type of doodling led to any better retention of the lecture content than passively listening.
Individual differences
Descriptive statistics for all of our individual difference measures are shown in Table 3. We analyzed our individual-difference measures to assess whether there are any particular subsets of individuals for whom fidgeting/doodling may be especially effective and to test our competing hypotheses regarding the cognitive-affective correlates of self-reported tendencies to doodle and fidget.
Table 3. Descriptive statistics (SD = Standard deviation) for all individual-difference measures, including trait mind-wandering (MWQ), tendency to experience attentional lapses (MAAS-LO) and attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviour (DSAQ), and fidgeting behaviour (SAQ).
|
| Median | Mean |
|
MWQ | 161 | 20 | 20.0 | 4.7 |
MAAS-LO | 172 | 52 | 52.8 | 8.5 |
ARCES | 172 | 38 | 37.8 | 6.3 |
BPS | 161 | 11 | 10.8 | 4.6 |
DSAQ | 172 | 15 | 17.7 | 9.4 |
SAQ | 162 | 34 | 33.5 | 12.0 |
Participant subsets . Perhaps our failure to observe an overall benefit of doodling for mid-task levels of boredom, mind-wandering, and task-focused attention, or for subsequent memory performance is due to the possibility that doodling and other forms of fidgeting only have cognitive-affective benefits for certain types of people. Fidgeting and doodling have been purported, for example, to be particularly helpful for individuals with attention-related difficulties (e.g., Kercood and Banda, 2012; Rotz & Wright, 2005). The possibility that fidgeting/doodling may help to reduce boredom suggests that such behaviours might also be of particular value to individuals who are prone to experience boredom, or to those who, through experience, have come to routinely engage in fidgeting or doodling. We therefore identified the subsets of participants who had scores higher than the median of all participants on the individual-difference measures of mind-wandering (MWQ), attentional lapses (MAAS-LO), attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviours (DSAQ), and fidgeting behaviours (SAQ). For each of these participant-subsets, we then conducted separate 3 (Group: Listen-only, Structured-doodle, and Unstructured-doodle) x 4 (Time: 9, 18, 27, and 36 minutes) mixed-factors ANOVAs to assess the effect of the different types of doodling behaviours on in-task levels of boredom, mind-wandering, and attention. Note that, to focus solely on the potential benefits of doodling, per se , we omitted all note-taking participants from these additional analyses. The effect of doodling condition on subsequent retention of lecture content was also assessed for each subset of participants using a one-way ANOVA with Group (Listen-only, Structured-doodle, and Unstructured-doodle) as the between-subjects factor. The results for the main effect of Group from these ANOVAs are shown in Table 4.
Table 4. Average mid-task ratings of Boredom, Attention, Mind-wandering, and subsequent Retention scores (# correct out of 28) for the subsets of participants with scores that were higher than median of all participants in their tendencies to experience Mind-wandering, Attentional lapses, Attention-related cognitive errors, Boredom, Doodling, and Fidgeting. F -values and η 2 p -values are reported for the main effect of Group (Listen-only control, Structured-doodle, Unstructured-doodle) for each measure obtained from each participant subset. ** p < .01
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High | Boredom | 4.6 (3.9 - 5.3) | 4.2 (3.5 - 4.8) | 4.5 (3.7 - 5.3) | 0.41 | 0.02 | |
Mind-wandering | Attention | 4.0 (3.4 - 4.7) | 3.9 (3.4 - 4.5) | 3.6 (2.9 - 4.2) | 0.65 | 0.02 | |
(MWQ > 20) | Mind-wandering | 4.2 (3.6 - 4.8) | 4.3 (3.8 - 4.9) | 4.5 (3.8 - 5.2) | 0.19 | 0.01 | |
Retention | 16.3 (14.9-17.7) | 15.3 (14.0-16.6) | 15.5 (14.0-17.1) | 0.53 | 0.02 | ||
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High | Boredom | 4.4 (3.8 - 5.0) | 3.9 (3.3 - 4.5) | 4.7 (4.1 - 5.3) | 1.89 | 0.06 | |
Attentional lapses | Attention | 4.1 (3.5 - 4.7) | 4.2 (3.7 - 4.8) | 3.7 (3.1 - 4.3) | 0.91 | 0.03 | |
(MAAS-LO > 52) | Mind-wandering | 4.4 (3.9 - 4.9) | 3.8 (3.3 - 4.4) | 4.5 (4.0 - 5.1) | 2.01 | 0.06 | |
Retention | 16.6 (15.0-18.1) | 16.4 (14.8-18.0) | 15.7 (14.0-17.4) | 0.32 | 0.01 | ||
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High | Boredom | 4.8 (4.3 - 5.4) | 3.8 (3.3 - 4.4) | 4.9 (4.4 - 5.5) |
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Cognitive errors | Attention | 4.0 (3.5 - 4.5) | 4.1 (3.6 - 4.6) | 3.5 (3.0 – 4.0) | 1.81 | 0.06 | |
(ARCES > 38) | Mind-wandering | 4.4 (3.9 - 4.9) | 4.1 (3.6 - 4.6) | 4.7 (4.2 - 5.2) | 1.53 | 0.05 | |
Retention | 16.3 (14.9-17.7) | 15.3 (14.0-16.6) | 15.5 (14.0-17.1) | 0.07 | 0.00 | ||
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High | Boredom | 4.8 (4.1 – 5.5) | 4.2 (3.6 – 4.9) | 4.6 (3.9 – 5.4) | 0.75 | 0.03 | |
Trait boredom | Attention | 4.1 (3.5 – 4.7) | 3.7 (3.2 – 4.3) | 3.7 (3.0 – 4.3) | 0.51 | 0.02 | |
(BPS > 11) | Mind-wandering | 4.4 (3.8 – 5.0) | 4.5 (3.9 – 5.0) | 4.4 (3.8 – 5.0) | 0.03 | 0.00 | |
Retention | 15.1 (13.3-17.0) | 15.9 (14.4-17.3) | 14.6 (12.7-16.5) | 0.64 | 0.03 | ||
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High | Boredom | 4.4 (3.7 – 5.0) | 4.2 (3.6 – 4.9) | 4.5 (4.0 – 5.1) | 0.22 | 0.01 | |
Doodling | Attention | 4.0 (3.3 – 4.2) | 3.8 (3.3 – 4.2) | 4.0 (3.5 – 4.5) | 0.42 | 0.01 | |
(DSAQ > 15) | Mind-wandering | 4.4 (3.9 – 4.9) | 4.4 (3.9 – 4.9) | 4.2 (3.7 – 4.7) | 0.13 | 0.00 | |
Retention | 17.1 (15.4-18.9) | 15.7 (13.8-17.5) | 15.5 (13.8-17.2) | 1.07 | 0.03 | ||
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High | Boredom | 4.5 (4.0 - 5.0) | 4.0 (3.4 - 4.5) | 4.6 (4.0 - 5.1) | 1.30 | 0.04 | |
Fidgeting | Attention | 4.0 (3.5 - 4.5) | 4.0 (3.5 - 4.0) | 3.5 (3.1 - 4.0) | 1.31 | 0.04 | |
(SAQ > 34) | Mind-wandering | 4.2 (3.7 - 4.7) | 4.0 (3.5 - 4.5) | 4.5 (4.0 - 5.0) | 0.88 | 0.03 | |
| Retention | 17.3 (15.8-18.9) | 15.8 (14.2-17.4) | 16.1 (14.5-17.8) | 0.99 | 0.03 |
As can be seen in Table 4, there was a significant main effect of doodling condition on the mid-task level of boredom experienced by the subset of participant who self-reported high levels of attention-related cognitive errors (ARCES). Inspection of the corresponding means and confidence intervals shows that boredom was lower for these high-ARCES participants that engaged in structured doodling, compared to those who engaged in unstructured doodling or just passively listened. The high-ARCES participants that engaged in structured doodling also reported nominally lower levels of mind-wandering and nominally higher levels of self-perceived attention, compared to those who engaged in unstructured doodling or just passively listened, although they also had nominally lower retention scores than either of these other conditions. None of the other measures for any of the other subsets of participants showed a significant effect of doodling condition. Thus, the lower level of mid-task boredom for high-ARCES participants is the only indication from our additional individual-difference analyses that any type of doodling could have any type of beneficial effect. To the extent that structured doodling was effective in reducing mid-task boredom for this specific subset of participants, it is noteworthy that this was not accompanied by a corresponding benefit for learning/remembering lecture content.
Individual differences: Cognitive-affective correlates of fidgeting/doodling
To test our competing hypotheses regarding the cognitive-affective correlates of self-reported tendencies to doodle and fidget, we calculated the correlation between each of our individual-difference measures, including trait mind-wandering (MWQ), attentional lapses (MAAS-LO), attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviour (DSAQ), and fidgeting behaviour (SAQ), as well as between our self-reported measures of cognitive-affective states, including boredom before the listening tasks (pre-task MSBS1), during the task (average of Boredom ratings obtained at each of the four timepoints) and after the task (post-task MSBS1), self-perceived levels of attention (average of Attention ratings obtained at each of the four timepoints), mind-wandering (average of Mind-wandering ratings obtained at each of the four timepoints), plus the score on the subsequent lecture-retention exam. These are reported in Table 5.
As shown in Table 5, our measures of individual difference in the tendency to doodle (DSAQ) and the tendency to fidget (SAQ) were positively correlated, which is consistent with the view that doodling is a specific form of the more general category of fidgeting. However, the fact that these measures were only moderately correlated makes clear that these measures do not merely tap into the exact same behavioural traits. Indeed, whereas the tendency to fidget was significantly positively correlated with the tendency to mind-wander, the tendency to doodle was not.
Table 5. Pearson Product-Moment Correlations for all individual-difference measures, including trait mind-wandering (MWQ), attentional lapses (MAAS-LO), attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviour (DSAQ), and fidgeting behaviour (SAQ), as well as self-reported cognitive-affective states, including boredom before the listening tasks (pre-task MSBS1), during the task (average of Boredom ratings obtained at each of the four timepoints) and after the task (post-task MSBS1), self-perceived levels of attention (average of Attention ratings obtained at each of the four timepoints), mind-wandering (average of Mind-wandering ratings obtained at each of the four timepoints), plus the score on the subsequent lecture-retention exam.
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1. MWQ | — | ||||||||||
2. MAAS-LO | .50 | — | |||||||||
3. ARCES | .38 | .60 | — | ||||||||
4. BPS | .35 | .37 | .22 | — | |||||||
5. DSAQ | .07 | .23 | .25 | -.01 | — | ||||||
6. SAQ | .46 | .38 | .30 | .15 | .25 | — | |||||
7. pre-task MSBS1 | .30 | .41 | .29 | .41 | .24 | .26 | — | ||||
8. Mean Boredom | .11 | .07 | .07 | .17 | -.06 | .07 | .15 | — | |||
9. post-task MSBS2 | .38 | .45 | .32 | .32 | .19 | .18 | .45 | .44 | — | ||
10. Mean Attention | -.21 | -.25 | -.26 | -.26 | -.13 | -.17 | -.13 | -.28 | -.41 | — | |
11. Mean Mind-wandering | .17 | .20 | .22 | .20 | .07 | .14 | .13 | .34 | .42 | -.86 | — |
12. Retention | -.06 | -.09 | -.15 | -.22 | -.04 | .08 | .00 | -.21 | -.28 | .34 | -.36 |
* p < .05, ** p < .01, *** p < .001
The fidgeting-reduces-boredom-and-increases-attention hypothesis predicts negative correlations between the self-reported tendency to doodle (DSAQ) and measures of boredom proneness (BPS), the tendency to experience lapses in attention (MAAS-LO) and the cognitive consequences of attentional errors (ARCES). In contrast, we found that DSAQ was not correlated with BPS, but was moderately-positively correlated with both MAAS-LO and ARCES. Inspection of Table 5 also shows that DSAQ scores were positively correlated with the levels of state boredom reported both before (pre-task MSBS1) and after (post-task MSBS2) the lecture-listening task. These significant positive correlations between the tendency to doodle, levels of state boredom, and tendencies to experience attentional lapses/errors is therefore more consistent with the fidgeting-reflects-inattention hypothesis. Likewise, we observed that the tendency to fidget (SAQ) was also positively correlated with both MAAS-LO and ARCES (as well as MWQ and the levels of state boredom reported both before and after the lecture-listening task) and negatively correlated with the average level self-perceived attention paid to the listening task, providing added converging support for the fidgeting-reflects-inattention hypothesis. These results are not consistent with the fidgeting-reduces-boredom-and-increases-attention hypothesis.
It is worth noting that we observed a positive correlation between ARCES and MAAS-LO, thus providing a replication of their relationship (e.g., Carriere, Seli & Smilek, 2013; Cheyne, Carriere, Smilek, 2006; Carriere, Cheyne & Smilek, 2008). Additionally, the results show a positive relationship between the BPS and the ACRES and MAAS-LO measures of attentional difficulties as found in previous research (e.g., Carriere, Cheyne & Smilek, 2008).
Experiment 2 aimed to further examine the competing fidgeting-related hypotheses. Recall that the fidgeting-reduces-boredom-and-increases-attention hypothesis predicts that participants in both the structured-doodling or unstructured-doodling conditions should show lower levels of in-task boredom and mind-wandering, and higher levels of in-task attention and subsequent memory for lecture content, than those in the control condition. However, the results failed to support this hypothesis. In terms of boredom, group allocation made no difference to boredom scores at any time point. In fact, boredom increased significantly over time for all groups, importantly including those who doodled. Similar results were seen for mind-wandering and attention self-reports over time, such that all groups reported increased mind-wandering and decreased attention as the lecture progressed. On top of that, the memory performance of doodlers was nominally worse than those who did nothing and significantly worse than those who took notes. In terms of individual differences, this hypothesis would predict negative correlations between the self-reported tendency to doodle (DSAQ) and measures of boredom proneness (BPS), the tendency to experience attentional failures (ARCES) and lapses in attention (MAAS-LO). Yet, upon examination, this was not the case. The tendency to doodle was not associated with boredom proneness and was positively associated with attentional failures/lapses.
The counter hypothesis suggesting that fidgeting-reflects-inattention predicts that either doodling condition would reduce attention to the lecture, increase in-task boredom and mind-wandering and impair performance on the subsequent memory test when compared to that of the control condition. Specifically, those who are in the unstructured doodle should show reduced in-task attention, more mind-wandering, and worse memory for lecture content relative to the structured doodling condition. The results did not support this hypothesis either. Although attention did decrease, and boredom and mind-wandering increased for those in the doodle conditions, these results did not significantly differ from those who were in the control condition. Beyond that, the memory test findings did not support this hypothesis as scores were slightly worse than the control, but not nearly large enough to be a significant difference. For individual differences, the hypothesis would suggest positive correlations between DSAQ scores and both ARCES scores and MAAS-LO scores. As stated previously, DSAQ was in fact positively correlated with attentional lapses and failures. The individual difference measures suggest that a higher tendency to doodle is associated with a higher tendency to experience lapses and/or failures in attention. As well as a higher tendency to doodle is related to higher state boredom. Thus, by examining individuals’ own tendency to doodle outside of a forced condition, we are better able to see how it relates to attentional engagement and state boredom.
While traditionally viewed in educational contexts as markers of inattention and poor classroom behaviour doodling and fidgeting have more recently been considered as possible routes to improve performance. Across two experiments, we directly tested the competing hypotheses about the extent to which different methods of doodling may indeed be helpful for cognitive functioning: the ‘fidgeting reduces boredom and increases attention’ hypothesis (e.g., Andrade, 2010 ), which posits that doodling is a beneficial form of fidgeting that can reduce boredom and increase attention to promote better learning, and the ‘fidgeting reflects inattention’ hypothesis, which maintains that doodling is merely an indication of the mind taking a mental break, thereby reflecting the absence of task-focused attention (mind-wandering; Boggs et al., 2017 ) and is therefore linked to relatively poor learning. Across our studies, doodling neither reduced boredom or mind-wandering nor increased attention or retention of information when compared to conditions without doodling.
In Experiment 1 we sought to replicate and extend Andrade’s ( 2010 ) observation that doodling can reduce feelings of boredom and levels of mind-wandering while improving task-focused attention and memory for associated information. Instead, we found no evidence that doodling was any better than solely listening when it came to remembering task-relevant information. Indeed, participants who doodled did nominally worse on the memory assessment. The main difference between our study and Andrade’s study is that we used a boredom-induction procedure to ensure our participants were experience significantly elevated levels of state boredom prior to the listening task that contained the doodling manipulation. Thus, a potential reason why our results differ from those of her study could be that Andrade’s participants were not experiencing the same levels of boredom prior to the task and thus they did not have the same difficulty staying engaged in the task in the first place. Boredom is thought to be a pervasive affective state that arises when we want to, but are unable to, engage attention in a satisfying activity (Eastwood et al., 2012 ). A study conducted by Sinclair et al. ( 2018 ) looked at positive and negative emotions across time during a computer-based learning program. The results from their study suggest that of all the emotions measured (e.g., boredom, frustration, pride, etc.) boredom was the only state that caused concern for students because of the relatively small chance of being able to escape from that emotion. Thus, it could also be that a more effective way to alleviate and prevent boredom would be to change the task altogether, rather than to add doodling.
In Experiment 2, we sought to further contrast the fidgeting-reduces-boredom-and-increases-attention hypothesis against the fidgeting-reflects-inattention hypothesis using a more ecologically valid task in which we manipulated different types of fidgeting behaviours to directly assess their impact on boredom, mind-wandering and attention during a lecture-listening task, as well as the associated effects on retention of lecture material. We found that doodling neither reduced boredom or mind-wandering nor increased attention or retention of information compared to other conditions. In contrast, attention and test performance were highest (and boredom and mind-wandering lowest) for those focused solely on note-taking. Moreover, our inclusion of self-report measures of the tendency to doodle, fidget, experience boredom and attentional lapses and failures revealed that higher levels of self-reported doodling behaviours were associated with higher levels of attentional lapses, attentional failures, and state boredom, which resonates more with the fidgeting-reflects-inattention hypothesis than the fidgeting-reduces-boredom-and-increases-attention hypothesis.
We formally considered the possibility that our failure to observe an overall benefit of doodling for mid-task levels of boredom, mind-wandering, and task-focused attention, or for subsequent memory performance in Experiment 2 was due to doodling and other forms of fidgeting only having cognitive-affective benefits for certain types of people, such as those with attention-related difficulties, those prone to experience boredom, or those who routinely engage in fidgeting or doodling. We found no evidence that doodling reduced boredom or mind-wandering, or increased task-focused attention or subsequent memory performance for individuals who are relatively high in routinely experiencing attention-related difficulties, including mind-wandering or attentional lapses, or who are relatively high in boredom proneness. Doodling also provided no observable benefits for those who are relatively high in the extent to which they routinely engage in doodling or fidgeting behaviours. Indeed, the only group for whom we found any benefit of doodling was for participants with relatively high scores on the attention-related cognitive errors scale: participants in this subset that engaged in structured doodling experienced less boredom than those that engaged in unstructured doodling or who solely listened to the lecture. The doodling-related benefit for this participants subset, however, did not lead to better retention of the lecture material.
Experiment 2 provided additional evidence confirming the benefits of taking notes for retaining crucial information. These findings replicate Boggs et al. ( 2017 ) and Meade, Wammes & Fernandez’s (2019) findings showing individuals are better able to retain attended content if they take notes rather than simply listening or doodling while listening. Boggs et al. hypothesized that this finding was due to the fact that taking notes would enhance the encoding process. This seems very plausible as previous research has shown that note-taking has an instant positive effect on memory providing individuals with a deeper level of processing (i.e., the encoding effect; Di Vesta & Gray, 1972 ; Peper & Mayer, 1978 ). Boggs et al. speculated that this finding was also due to note-taking maintaining arousal levels and thus reducing boredom, which is consistent with the result of our study. Note-takers throughout the lecture reported paying more attention and experiencing mind-wandering less than the other groups. Although taking notes could not eliminate boredom altogether, it did appear to mitigate the negative consequences associated with boredom (e.g., reduced performance, reduced attention, increases in mind-wandering). Therefore, promoting note-taking in situations in which it is important for students to be able to later recall associated information appears be a more effective strategy than promoting doodling.
It is important to note that doodling in the lab may not produce the same effects as when doodling in real-world situations. Although we tried to emulate what it would be like in a regular classroom, there are a lot of variables that we cannot account for. In the lab, we take away the naturalism of doodling by placing participants in conditions and asking them to do a specific doodling-type task. It may be the case that students stagger their doodling by using it for short periods, or during moments when they know that the information taught is not as useful. As a result, we may not be able to replicate the specific type of doodling behaviour in the lab that is effective for a given participant in real-world settings. In most learning environments, students have the choice on whether and how they want to doodle, and maybe our providing strict guidelines on whether and how they doodle undermined the potential benefits. Beyond that, motivation could play a factor in the results. In the lab the consequences of not paying attention to the lecture material do not exist, the student completes the study and is able to leave while receiving the course credit. Whereas in a real-life lecture, the consequences of not keeping your attention engaged could be missing information about an upcoming assignment or content for an exam. Thus, students may lack the naturalistic motivation that happens when in a classroom. Future research would benefit from examining doodling in the classroom, rather than a lab, while probing students’ motivation to learn the material being presented.
Contrasting the fidgeting-reduces-boredom-and-increases-attention hypothesis and the fidgeting-reflects-inattention hypothesis is important for understanding how the mechanisms subserving attentional, behavioural, and affective engagement operate together and for resolving the conflict in prior evidence about the extent to which doodling is a viable method for educators and students to implement in the context of learning. With the overall consistently of our findings with the fidgeting-reflects-inattention hypothesis, our results suggest that the adoption of doodling exercises or other fidgeting-based interventions within classroom settings may not be an effective strategy for increasing classroom engagement or promoting learning.
Acknowledgments
This work was supported by the Natural Science and Engineering Research Council of Canada (Discovery Grant #401526).
Ethical Approval
All methods and procedures were approved by Research Ethics Board at the University of Guelph.
Consent to participate
Informed consent was obtained from all individual participants included in the studies.
Consent to publish
Consent to publish was obtained from all individual participants included in the studies.
Competing interests
The authors have no competing interests to declare that are relevant to the content of this article.
Authors' contributions
E.K.S-M designed the study, developed study materials, coordinated participant recruitment and data collection, cleaned and analyzed the data, interpreted results, and wrote the manuscript. M.J.F. secured funding, designed the study, interpreted results, and edited the manuscript.
This work was supported by the Natural Science and Engineering Research Council of Canada (Discovery Grant #401526).
Availability of data and materials
The datasets in the presented studies are available from the corresponding author upon reasonable request.
No competing interests reported.
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September 24, 2024
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by King's College London
Three new papers refute claims for the assembly theory of molecular complexity being claimed as a new "theory of everything."
First publicly posited in 2017, assembly theory is a hypothesis concerning the measurability of molecular complexity that claims to characterize life, explain natural selection and evolution, and even to redefine our understanding of time, matter, life and the universe.
However, researchers led by Dr. Hector Zenil from the School of Biomedical Engineering & Imaging Sciences (BMEIS), in collaboration with colleagues from King Abdullah University for Science and Technology (KAUST) and the Karolinska Institute in Sweden, have successfully demonstrated in a paper published in npj Systems Biology , that the same results can be achieved by using traditional statistical algorithms and compression algorithms.
In a second paper just published by PLoS Complex Systems , they have also mathematically proven that assembly theory is an equivalent to Shannon Entropy and therefore not a novel approach to any of those applications and is an implementation of a well-known and popular compression algorithm used behind ZIP compression and image encoding formats such as PNG.
The third paper, "Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms," is available on the arXiv preprint server.
"Our research demonstrated that the Assembly Index, the core component of assembly theory which determines the 'aliveness' of an object by the number of exact copies it possesses, as an original method, is not, and its conclusions are flawed," says Dr. Hector Zenil.
"When we applied traditional compression algorithms to molecular or chemical data, the same verified results were obtained as under assembly theory. This means that, rather than being a new framework, assembly theory is indistinguishable from other pre-existing measures of complexity. Yet, the original authors did not test for any other algorithms."
"Despite some vegetables and plants such as onions and ferns having up to 50 times longer genomes with their many numerous gene copies, it is difficult to argue that onions or ferns are more complex or alive than humans, like assembly theory would suggest based on such unidimensional index," says Prof Jesper Tegner.
"What truly defines life is not merely genetic length or number of components but the intricate relationship with their environment, the agency life exhibits, and its resilience in preserving its essential properties."
"Our analysis sheds light on the limitations of assembly theory's numerical indices, attempting to define 'aliveness' and life characteristics. What truly surprises me is the neglect of the crucial role of dynamic interactions in understanding life complexity. Even more alarming is the decision to propose a fixed life-detection threshold with no basis," says Dr. Narsis A. Kiani.
"The real breakthrough lies in building upon established knowledge, integrating seemingly diverse theories to unravel the complex multidimensional dynamics that shapes life rather than rehashing what we already knew with tools we had already developed."
While characterizing life is hard and still an open problem, it has been studied from many angles, from modular units by Gregor Mendel to thermodynamics by Erwin Schrödinger to Statistical Entropy by Claude Shannon to Algorithmic Information by Gregory Chaitin.
Equipped with all this knowledge and much more from complexity sciences and systems' biology, it is known today that one key aspect of life is that of open-endedness, the fact that life's agency seems not bounded to regular behavior or repetition in its adaptation and relationship to its environment.
Areas such as Algorithmic Information Dynamics (AID) led by Dr. Hector Zenil and his collaborators, are shedding light on how to find causal models for natural phenomena and mechanistic explanations for processes of living systems.
AID is fully based on the current combined knowledge of information theory and causal inference to this date and builds upon and bridges these fundamental areas used today to understand the world.
The methods behind AID already count for exact copies of modules but that is the most obvious first step and something Dr. Zenil reported before assembly theory as capable of separating organic compounds from non-organic as a function of molecular length.
Felipe S. Abrahão et al, Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution, PLOS Complex Systems (2024). DOI: 10.1371/journal.pcsy.0000014
Luan Ozelim et al, Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms, arXiv (2024). DOI: 10.48550/arxiv.2408.15108
Journal information: arXiv
Provided by King's College London
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COMMENTS
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Annotate articles, websites, videos, documents, apps, and more - without clicking away or posting elsewhere. Hypothesis is easy to use and based on open web standards, so it works across the entire internet. 01. Comment & Highlight. 02.
There are so many ways to use doodle note templates! Click the image above for 15 different ideas. Click the image above for a set of templates with a creative, artistic theme. These graphic organizers are like mini doodle notes for review! Each card in the deck of 100 templates is a manageable study guide.
Pick one or two topics in your first unit to change up and see how it goes. Also recognize that new things take time to master. Your first time using a new strategy is going to require some grace. Pre-made doodle notes make the process much easier for both the teacher and the students. Our NGSS-aligned notes are set up to maximize student ...
This dynamic collection is designed to make your middle school science lessons more engaging and effective. Each set of doodle notes transforms complex topics into fun, visually appealing act. 76. Products. $259.99 $366.81 Save $106.82. View Bundle. Middle School General Science Activities Growing Bundle. If you are looking for a one-stop shop ...
I love to use these doodle note sheets to engage my students as I explain how to create a testable hypothesis before completing experiments. My classes love the visual, creative, instructive notes. I even included some great practice labs to use on the optional last page.
These scaffolded Experimental Design and Scientific Method Cornell Doodle Notes can be used to introduce students to the concepts of writing testable questions, defining manipulated (independent), responding (dependent), and standardizing (constant) variables, as well as controls. The notes stress the importance of making precise and accurate ...
Scientific Method Doodle Notes. Subject: Biology. Age range: 11-14. Resource type: Assessment and revision. File previews. pdf, 1.88 MB. Help your students learn the scientific method. Teach them the steps of the scientific method, vocabulary related to designing experiments, how to write a hypothesis and much more!
The doodle note strategy integrates both hemispheres of the student's brain and helps maximize focus, learning, and retention of the lesson material. The brain is divided into two hemispheres. Between the left and the right sides of the brain lies a bundle of neural fibers called the corpus callosum. The goal is to integrate the left brain and ...
Students are proud of their creative work on their page and suddenly begin pulling out their notes sheets consistently to review, show them off, and reference them as a study guide. Added bonuses include relaxation, coordination, and a boost in problem solving skills. Click Here to Scroll Through Doodle Notes Ideas & Examples.
Adding just a little bit of shape will help students remember key terms better. Option 3: Tell Stories to Connect Ideas, Teach Concepts, and Promote Dialogue. This approach is perfect for when you're talking about a subject where you want to animate meaning or share and connect ideas.
Creating a vocabulary doodle notebook will help your students practice science vocabulary specifically aligned to the Next Generation Science Standards. Vocabulary science doodle notes make great bellringer or independent practice activities! Read on to learn how to create a custom doodle notebook that matches the standards covered in YOUR course!
Description. This resource is a two-page doodle notes sheet regarding Experimental Design. This doodle note best aligns with the NGSS Science and Engineering Practice of Planning and Carrying Out Investigations. The vocabulary includes observation, asking scientific questions, hypothesis, science variables, independent variable, dependent ...
STEM: Doodle-Engineering-Challenge (Scientific Method) In this lesson, pairs of students will use the 3Doodler in an attempt to build the tallest structure in the class. In addition to the 3Doodler, students will be given either toothpicks or straws as construction materials. The 3Doodler will be used to adhere the building materials together.
Interactive Lecture Notes for Introductory Statistics 11: Hypothesis Testing 11.5: The Summary of Hypothesis Testing for One Parameter ... reject or not reject the null hypothesis. Note that the double negative in this case is never interpreted as a positive that is, we never-never accept the null hypothesis! Therefore, the interpretation is ...
Specific doodles. There is a common debate regarding which types of doodles should be used while studying. The types of doodling that students should focus on are repetitive designs that are meaningless and completed at the student's own pace (Perles). In contrast, specific doodles should be avoided while trying to focus while studying.
The 'fidgeting reduces boredom and increases attention' hypothesis posits that doodling is a beneficial form of fidgeting that can reduce boredom and increase attention to promote better learning (Andrade, 2010). ... (Listen-only, Structured-doodle, Unstructured-doodle, and Note-taking) as the between-subjects factor confirmed that this ...
This resource is a two-page doodle notes sheet regarding Experimental Design.This doodle note best aligns with the NGSS Science and Engineering Practice of Planning and Carrying Out Investigations. The vocabulary includes observation, asking scientific questions, hypothesis, science variables, independent variable, dependent variable, controlled variable, experimental and control groups, data ...
response sheet (range 3-110). One participant did not doodle and was replaced. Participants in the control condition did not doodle. Three doodlers and four controls suspected a memory test. None said they actively tried to remember information. Control participants correctly wrote down a mean of 7.1 (SD¼1.1) of the eight names of
Description. INCLUDED in this download: Science Doodle Sheet. Two versions of doodle sheet included: . Interactive Notebook Size and large 8.5 x 11 size. PowerPoint - to show students the KEY. These flat doodle sheets are different than my foldables. It is the same content but in a flat sheet format. They are a great ADD ON to what I have ...
The "fidgeting reduces boredom and increases attention" hypothesis posits that doodling is a beneficial form of fidgeting that can reduce boredom and increase attention to ... doodle if you are in the doodle condition and take notes if you are in the note-taking condition), they were not monitored for other forms of fidgeting behaviour (e.g ...
First publicly posited in 2017, assembly theory is a hypothesis concerning the measurability of molecular complexity that claims to characterize life, explain natural selection and evolution, and ...
Also included is an EDITABLE outline style skeleton note sheet. I love to use doodle note sheets to engage my students as I explain how to create a testable hypothesis before completing experiments. My classes love the visual, creative, instructive notes. I even included some great practice labs to use on the optional last page.