Igniting interest in insight

doi:10.21203/rs.3.rs-3896330/v1

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Igniting interest in insight

https://doi.org/10.21203/rs.3.rs-3896330/v1

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In generating novel ideas during the creative process of insight, what ignites positive emotions like “Aha” experiences of interest? This study explored this by validating the biological plausibility of a mathematical model predicting emotions when shifting perspectives to recognize alternate information regarding an event. Using functional magnetic resonance imaging, we assessed brain activity as participants watched card magic videos with experimentally varied ease of information recognition. Results indicated that when shifting from certain to uncertain belief-based recognition, subjective interest arises if the new recognition is distant from the prior certain belief but close to the subsequent uncertain belief, accompanied by brain activations related to positive emotions. These suggest that interest emerges when deviating from conventional ideas towards unexpected yet easily comprehensible new ones, providing strategic insights for ideation.

Biological sciences/Neuroscience/Cognitive neuroscience

Biological sciences/Neuroscience/Computational neuroscience

Biological sciences/Neuroscience/Emotion

Biological sciences/Neuroscience/Motivation

Biological sciences/Psychology/Human behaviour

Creative processes (creativity) exist in all aspects of our lives, including science, technology, education, and culture, and serve to solve problems and enrich our lives¹. When faced with a problem, we attempt to solve it by accessing our memories and information from the outside world². We deductively formulate hypotheses based on our experiences and inductively test them to reach a solution³. However, complex or new problems that have not been experienced previously may not be solved easily. In such cases, we actively access our memories and information from the outside world, motivated by problem solving. By shifting perspectives, we recognize strange or surprising events and generate hypotheses through abduction⁴. In this regard, the process of “insight,” in which sudden inspiration is obtained, is necessary. When an idea obtained through insight is starkly different from what was previously thought, positive emotions such as interest and surprise are generated⁵. Such an experience is called the “Aha” experience or the Eureka effect⁶. In the scientific field, the positive emotion of an “Aha” experienced with a new discovery, which overturns conventional wisdom, increases the spirit of inquiry for further discovery^7,8. In the field of technology, the “Aha” experience with a major shift in perspective upon developing a new technology triggers the next technological innovation. Regarding the process of insight, the fluency theory⁹ explains that the reason for positive emotions during this process is that cognitive processing of information becomes smoother, leading to a heightened sense of confidence when new information is recognized, namely imbuing a sensation of being certainly convinced. The pleasure interest model of aesthetic liking (PIA) model¹⁰ divides human cognitive processing into unconscious automatic and conscious controlled processing. When people can recognize information smoothly during automatic processing, they have high fluency and experience pleasant feelings of pleasure; however, when they cannot do so, they have low fluency and experience unpleasant feelings of displeasure. The emotional response is caused by the disfluency reduction to the information, i.e., the resolution of the uncertainty of recognition, which is a process of control to understand the information further. When disfluency reduction occurs for an information, positive feelings of interest are also generated for other information. Contrarily, when disfluency reduction does not occur, the information cannot be recognized, and a negative emotion called confusion is generated. In the insight process, the positive emotion of interest is considered the positive emotion of the “Aha” experience when other information is recognized by changing the perspective.

Prediction about the emergence of interest from the free energy model

Our group proposed the general framework of a mathematical free-energy model of emotional valence to explain emotions such as pleasure with recognition of information and interest with shifting perspectives and recognition of other information¹¹. Based on the minimized free-energy model of emotion¹² and the PIA model¹⁰, we formulated the change in the fluency of information processing in unconsciously automatic and consciously controlled processes when recognizing information from one perspective and from another perspective as an increase or decrease in free energy.

Consider an instance of observing the hidden Dalmatian dog illusion. At first glance, a cluster of black dots is perceived. This initial perception is an unconscious, automatic process that results in pleasure due to the recognition of a familiar pattern. This process is characterized by high information processing fluency and decrease in free energy (ΔFi in Fig. 1). If this image is attempted to be viewed from another perspective, the process shifts to conscious and controlled, leading to reduced fluency in information processing and increased free energy. If one cannot make sense of another perspective, one might feel negative emotions, such as confusion. However, upon deducing that the image represents a Dalmatian dog, the inefficiency in processing the new pattern is resolved. This resolution, known as the “disfluency reduction,” decreases the free energy again and leads to an “Aha” experience, which is accompanied by a positive emotional response. We interpret this emotion as that of interest.

In our model, we quantified the amount of reduction in free energy (ΔFj in Fig. 1) upon the recognition of something from another perspective. We calculated this reduction based on the following factors: how certain we were about what we first recognized (si in Fig. 2, i.e., prior belief uncertainty of the first recognized information: the variance of the prior distribution [pi(x) in Fig. 2, i.e., a prior belief of first recognized information]), how certain we are about another recognition (sj in Fig. 2, i.e., prior belief uncertainty of another recognized information: the variance of the prior distribution [pj(x) in Fig. 2, i.e., the prior belief of another recognized information]), how different the prior belief of another recognition is from the first one and recognized information later i.e., the recognition distance.

The recognition distance is the ratio of two distances, where the first distance is between the recognized information later (p (y|x) in Fig. 2) and the prior belief of another recognized information (δj in Fig. 2). The second distance is between the prior distribution (µij in Fig. 2)

We used numerical simulations to understand how the recognition distance affected the amount of free energy reduction. In other words, we studied how expanding our understanding of an information (disfluency reduction) depended on how much we ought to change our perspective.

We predicted that disfluency reduction would be greater for larger recognition distances when the belief uncertainty for the first recognized piece of information (si) was larger than that for another recognized piece of information (sj). Therefore, in the hidden Dalmatian dog illusion, when our prior beliefs are highly uncertain, such as the set of black dots that we initially recognized (i.e., the arrangement of the dots is random), and when our prior beliefs are highly uncertain, such as another recognized Dalmatian dog (i.e., we are familiar with the features of the Dalmatian dog), a larger recognition distance (i.e., we recognize the Dalmatian dog only from the observed picture) causes more surprise and interest once the Dalmatian dog is recognized.

However, this mathematical model predicts that the amount of disfluency reduction is larger when the uncertainty of belief in another recognized information (sj) is larger than the uncertainty of belief in the first recognized information (si), when the recognition distance is smaller. This means that when recognition switches from information with certain beliefs to information with uncertain beliefs, the change in recognition is easier and more interesting. The predictions of this mathematical model explain the shift from existing solid knowledge (i.e., common sense) to unprecedented and uncertain knowledge (i.e., ideas that nobody has thought of before) in the creative process of science and technology. The model predicts that, in this case, the easier it is to change recognitions (i.e., the less surprising another idea is), the more positive feelings of interest arise. The fact that less unexpectedness is more interesting differs from our intuition and is based on our experience. However, if we look at this situation from a different perspective, the recognition distance is proportional to the distance between the observed information and the prior distribution of the newly recognized information. Furthermore, a small recognition distance indicates that another idea is far from the existing solid knowledge. In other words, an idea may be interesting if nobody has ever thought of it before and if it is easy to recognize and accept as another idea. Therefore, the predictions of our model are important for understanding processes that are essential for creativity, such as abduction.

This study aims to demonstrate the biological validity of the predictions of a mathematical model that explains the emotions involved in shifting perspectives and recognizing another information. Specifically, by measuring human brain functions, we tested the prediction that a smaller recognition distance (i.e., easier recognition) would produce more positive emotions such as interest, when shifting from recognition based on certain beliefs to recognition based on uncertain beliefs.

Neuroscientific research related to interest

A functional brain measurement study investigating the transition of emotion and brain processing in the automatic and deliberate process showed that during preference judgments for human faces, rapid automatic processing occurs in the nucleus accumbens (NAC), followed by slower control processing in the orbitofrontal cortex (OFC) and insula¹³. Additionally, neuroimaging studies of positive emotion^14–17 have shown that the anterior cingulate cortex^18–20, amygdala^21–25, hippocampus, striatum, caudate, and putamen are involved in positive emotions. Furthermore, hippocampal and parahippocampal activity during the presentation of an interest question²⁶ are activated during positive emotions, which facilitate subsequent memory recall²⁷, suggesting that memory retrieval and consolidation are facilitated during positive emotions such as interest.

The purpose of this study is to analyze the brain activity related to subjective interest and positive emotions associated with changes in the recognition of information, as well as to verify the validity of model predictions based on the free-energy model of emotion. We experimentally created a situation wherein participants shifted from certain recognition to uncertain recognition by watching card magic, namely a trick where the objective is to determine the position of the target card, and experimentally manipulated the size of the recognition distance by the difficulty of recognizing the card magic technique (the trick). Brain activity during the task was measured using functional magnetic resonance imaging (fMRI), and the aforementioned brain regions were analyzed as the regions of interest (ROI).

Subjective evaluation

Figure 3 shows a scatter plot of individual subjective scores for comprehending the card magic technique, with the high-difficulty condition on the horizontal axis and low-difficulty condition on the vertical axis. The subjective scores for the low-difficulty condition were higher than those for the high-difficulty condition, indicating that the technique was more easily recognized in the low-difficulty condition.

Further scatter plots of individual subjective scores for the degree of admiration and interest of the technique are shown in Figs. 4 and 5, respectively (data input errors in the degree of admiration for Participants 1 and 2 were excluded from the figures). Moreover, Fig. 5 shows that the five participants’ subjective interest in the technique was higher in the high-difficulty condition; however, as a whole, subjective interest was higher in the low-difficulty condition.

The mean subjective scores for all the participants are shown in Fig. 6. For each subjective score, a two-tailed paired t-test was conducted with the condition as the independent variable and score as the dependent variable. We found significant differences in comprehension scores (t(19) = 5.90, p = 0.00, Cohen's d = 1.05, 95% CI = 0.83–1.74), degree of admiration (t(17) = 3.63, p = 0.00, Cohen's d = 0.72, 95% CI = 0.36–1.23), and interest (t(19) = 2.27, p = 0.03, Cohen's d = 0.49, 95% CI = 0.05–1.15). Furthermore, both subjective scores were significantly higher under the low-difficulty condition than under the high-difficulty condition.

Brain activity linked to interest

Supplementary Video 3 presents the magician's point of view in both the conditions, and it provides a clue about the technique of the card magic 9 seconds after the video begins. Therefore, we expected that the participants understood the technique and started to change their perception after 9 seconds of watching the video, and that between 9 and 15 seconds of watching the video, the low-fluency condition would be resolved, and a positive emotion, interest, would occur. We compared the MRI signals of the ROI during the period between 9 and 15 seconds from the start of the video in the low- and high-difficulty conditions. The results showed that activity in the anterior cingulate gyrus and hippocampus was greater in the low-difficulty condition than that in the high-difficulty condition (Fig. 7). To examine how each brain region in the ROI worked together between 9 and 15 seconds in Supplementary Video 3, functional connectivity analysis was performed for each condition (Fig. 8). In the high-difficulty condition, strong connectivity was observed in the caudate, putamen, insula, and orbitofrontal cortex. For the low-difficulty condition, besides the connectivity between the aforementioned regions, there was strong connectivity between the left amygdala and hippocampus as well as between the right amygdala and hippocampus.

In this study, we developed a mathematical model to explain the emotions involved in shifting perspectives and recognizing another information⁹. The validity of this prediction was demonstrated by measuring fMRI while the participants watched a card magic video wherein the ease of recognition was experimentally manipulated.

The results showed that the degree of subjective admiration and interest were higher in the low-difficulty condition, wherein the true position of the target card was easier to estimate, as compared to that in the high-difficulty condition, wherein the position of the target card was harder to estimate. This was tested when the participants first watched a card magic video (Supplementary Video 1) that they had a certain belief about, and they subsequently watched Supplementary Video 3, for which they had an uncertain belief. This was consistent with the model prediction that upon shifting from recognition based on certain beliefs to recognition based on uncertain beliefs, more positive emotions such as interest are generated with smaller recognition distance (i.e., easier recognition).

In addition, the low-difficulty condition showed greater activity in the anterior cingulate cortex and hippocampus, and greater connectivity between the amygdala and hippocampus, as compared to the high-difficulty condition while watching Supplementary Video 3, wherein the information was recognized from different perspectives. These results suggest that when shifting from recognition based on certain beliefs to recognition based on uncertain beliefs, the recognition distance is smaller and the recognition is easier; hence, the activity of the cingulate gyrus^18–20 and hippocampus²⁶ that are involved in positive emotion is higher. Additionally, the amygdala^21–25 and hippocampus, which showed increased connectivity in the high-difficulty condition, are related to positive emotion, and these results are consistent with the model predictions.

The psychological phenomena investigated in our model predictions and human brain function measurement experiments—that is, the emotional reactions accompanying changes in the recognition of information—are thought to strongly influence creative behavior.

Among the rewards that determine behavior, internally generated desires and emotions may themselves be rewards, and the state in which we are motivated by these rewards is called intrinsic motivation²⁸. Compared to extrinsic motivation, which is motivated by externally given rewards such as money or status, intrinsic motivation is more likely to be motivated by satisfaction with work²⁹, motivation to learn³⁰, and well-being³¹. The emotion of interest is one of the determinants of intrinsic motivation³². We are intrinsically motivated by events and actions that we are interested in, while intrinsic motivation is suppressed for events and actions that we find boring²⁸. In other words, the generation of positive emotions, such as interest, is considered to be an important condition for promoting actions to acquire information²⁹. For example, the “Aha” experience during insight, accompanied by surprise, facilitates information processing and generates positive emotions, thereby promoting the next exploratory behavior³⁰. In addition, when interest is aroused by the acquisition of another information, the hippocampus²⁶, which processes memories, is activated, and memory consolidation and recall are promoted, which may promote the next action. This finding could have a significant impact in two areas, i.e., creativity and education. Interest in uncertain perspectives and ideas is part of creative thinking: exploring new possibilities and combining seemingly unrelated information to generate new ideas. Abduction, i.e., hypothesis generation and inference, is an important part of a deeper understanding of this knowledge. Increasing interest in uncertain perspectives and ideas is deeply related to the abductive thinking process of generating new hypotheses and speculations. Abduction is inferring plausible explanations or solutions for unknown phenomena or problems. It is often used to find patterns in observational data and generate new theories or hypotheses to explain those patterns. Based on the findings of this study, when presented with an uncertain perspective or idea that deviates from common sense ideas and is unexpected, people attempt to generate a new explanation (hypothesis) to make sense of it and connect it to their existing perceptions. Furthermore, when this is supported by evidence close to cognitive distance, indicating that the conceived idea is not far-fetched, but rather feasible, interest in the viewpoint or idea is likely to increase. On the other hand, in education, this knowledge may improve the approach that learners use to acquire new knowledge and skills. Specifically, understanding new perspectives and knowledge that are distant from the learners’ current beliefs and knowledge by using evidence that is close to cognitive distance (concrete examples and experiences from daily life) may attract the learners’ interest and deepen their understanding.

Based on the findings of this study, in the context of learning in education, it is necessary to discuss the effects of emotional reactions when shifting perspectives and recognizing another information This includes the promotion of memory retention with the “Aha” experience by shifting perspectives and the promotion of intrinsic motivation (motivation to learn) for the next learning behavior. Additionally, measures to increase job satisfaction and well-being are required and can be explored through these findings.

Participants

In total, 20 healthy participants (10 males, 10 females, aged 24.3 ± 3.3 years, all right-handed) who had not mastered card magic skills were included in this study. This study was approved by the Research Ethics Committee of the Graduate School of Engineering at the University of Tokyo (approval number: KE22-3). Written informed consent was obtained from all the participants. All procedures conducted in this study complied with the guidelines and regulations established by the Research Ethics Committee of The Graduate School of Engineering at the University of Tokyo.

Stimuli, task, and design

In this experiment, the participants were requested to estimate the position of the target card by watching a video of card magic. Twelve card-magic techniques were used in this study. The participants watched three videos of each technique and estimated the position of the target card based on the information provided by the video content. In the first video (Supplementary Video 1), a specific card was indicated by an arrow as the target card. The second (Supplementary Video 1 and 2) and third (Supplementary Video 3) videos were designed to change participants’ perceptions of the target card’s position.

We manipulated the uncertainty of beliefs regarding the recognized information in our mathematical model by the uncertainty of the position of the target card in the card magic trick. We also manipulated the recognition distance by changing the ease of estimating the true position of the target card. Furthermore, we designed the videos in such a way that the ease of estimating the card’s true location varied depending on the second video (Supplementary Video 2. The condition in which it was easy to estimate the true position of the card was defined as the low-difficulty condition (Supplementary Video 2), while the condition in which it was difficult to estimate the true position of the card was defined as the high-difficulty condition (rewatch Supplementary Video 1).

The length of all videos was set to 14 seconds, and they were edited so that the participants’ recognition would change 9 seconds after the beginning of Supplementary Video 3.

All participants encountered high- and low-difficulty conditions (within-participant factorial design). The order of the high- and low-difficulty conditions was counterbalanced across participants (refer to Stimulus.xlsx in the Supplementary Materials).

The following is a description of the trial flow using one technique of playing card magic as an example:

From the spectator’s point of view (Supplementary Video 1, Supplementary Fig. 1). First, the magician held a deck of cards facing down in his right hand. Subsequently, the cards were individually dropped onto the left hand. This technique is known as dribbling. The dribble stopped midway, and a card was selected and shown to the camera. The card indicated by an arrow was the target card (five of spades). The target card was then returned to its original position, and the dribbling continued. The magic trick ended when the top card dropped onto the left hand. This video was designed to make the participants guess that the final position of the target card was in the middle of the deck of cards. In other words, the final position of the target card in Supplementary Video 1 was easy to understand, with low uncertainty.

For the next step, in the high-difficulty condition, participants rewatched Supplementary Video 1. However, in the low-difficulty condition, participants watched the same technique as in Supplementary Video 1, but from a different audience perspective. Supplementary Video 2 for the low-difficulty condition was created so that the final position of the target card could be easily confirmed (Supplementary Fig. 2). Specifically, the target card was selected from the middle of a deck of face-up cards and displayed to the camera. Next, the target card was turned over and returned to its original position. Only the target card was turned face down. Subsequently, as shown in Supplementary Video 1, the cards held in the right hand were dropped individually into the left hand using a dribble. At the end of the video, a face-down card was placed on top of the deck. This was the target card, which was supposed to be in the middle of the deck; however, it was moved to the topmost position of the deck.

Supplementary Video 3 was filmed from the magician’s point of view (Supplementary Fig. 3): In Supplementary Video 3, a card held in the right hand was dropped individually into the left hand using a card dribble. Next, the magician stopped midway and selected a card (the target card) to be shown to the camera. The player then returned the target card to its original position and continued the dribbling. The technique was the same as that in Supplementary Video 1, except from a different perspective. In Supplementary Video 3, a clue to the technique of the card magic began 9 seconds after the start of the video. In the example shown in Supplementary Fig. 3, in Supplementary Video 3, just before continuing to dribble, the little finger pulled the target card to a vertical position. Supplementary Video 3 ended at this stage, so that the final position of the target card was unknown (forming an uncertain belief).

Functional magnetic resonance imaging scanning protocol

For functional magnetic resonance imaging (fMRI), we used the 3T Magneton Prisma (Siemens Medical Systems, Erlangen, Germany) with gradient echo and echo planar imaging (EPI) sequences. The interval between two successive acquisitions of the same image (TR) was 3,000 ms and the echo time (TE) was 30 ms. Furthermore, volumes were acquired with 96 interleaved slices of 2.0 × 2.0 × 1.5 mm voxel size.

In contrast, for anatomical MRI, we used a Tl-weighted gradient-echo pulse sequence (TR = 2,300 ms, TE = 2.26 ms; FOV = 256 mm; voxel dimensions = 1 × 1 × 1 mm) that facilitated localization.

fMRI data preprocessing and analysis

Brain activity in the regions of interest (ROI) was analyzed using the Statistical Parametric Mapping (SPM 12) software (Wellcome Department of Cognitive Neurology, London, UK). Additionally, functional connectivity between the ROI was analyzed using the SPM toolbox CONN. These analyses were performed on MRI signals from 9 to 15 seconds after the start of Supplementary Video 3. All the EPI images were spatially normalized using the Montreal Neurological Institute (MNI) T1 template for group analysis. Moreover, imaging data were corrected for motion and smoothed using an 8-mm full-width half-maximum Gaussian filter. Using group analysis based on a random-effects model, we identified the regions that showed significant responses during the task.

The ROIs included the anterior cingulate cortex, amygdala, insula, orbitofrontal cortex, parahippocampus, hippocampus, caudate, and putamen.

Competing interests:

Authors declare that they have no competing interests.

Author Contribution

Conceptualization: KU, XW, HY; Methodology: KU, XW, HY; Investigation: KU, XW; Visualization: KU, XW, HY; Funding acquisition: HY; Project administration: HY; Supervision: HY; Writing – original draft: KU, XW, HY; Writing – review & editing: KU, HY

Acknowledgments:

This study was supported by KAKEN grant number 21H03528 from the Japan Society for the Promotion of Science.

Data availability:

All data are available in the main text or the supplementary

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Igniting interest in insight

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Abstract

Figures

Introduction

Prediction about the emergence of interest from the free energy model

Neuroscientific research related to interest

Results

Subjective evaluation

Brain activity linked to interest

Discussion

Methods

Participants

Stimuli, task, and design

Functional magnetic resonance imaging scanning protocol

fMRI data preprocessing and analysis

Declarations

Competing interests:

Author Contribution

Acknowledgments:

Data availability:

References

Additional Declarations

Supplementary Files

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