United States

This study investigates how individuals collaboratively constructed shared knowledge during a group activity. The dataset was collected from group activities for pre-service teachers in professional development. Participants designed body poses and action sequences that could help their students’ mathematical conceptualization. Using k-means clustering and principal component analysis, patterns of individuals’ contributions based on their verbal and gestural behavior identified two groups of individuals: (1) Those who contributed to the discussion by speaking and gesturing frequently (~ 25% of the participants), and (2) those who mostly listened and focused on design ideas presented by others. Furthermore, epistemic network analysis corroborated significant differences in discourse patterns between the clusters, the results of which has significant implications for collaborative embodied learning and application for teacher education and professional development.

CogSci 2023

Multimodal Behavior Analysis: Two Patterns of Collaborative Construction of Embodied Knowledge

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The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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#### **CogSci 2023 Program Book**
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Emotion words help children to learn about emotions, yet the mechanism behind this remains unclear. These words may be helpful to children because they are novel, but this emotion learning may also be driven by familiarity. To address this, we conducted a pre-test post-test examination of emotion learning (matching faces to scenarios) with 3-year-olds (N=72). Between pre- and post-test, children either heard explicit emotion labels (“she feels annoyed”), novel labels (“she feels wuggy”), or irrelevant information (“she sits down”). Results revealed that children’s change score (post-test – pre-test) significantly differed by condition (F(2,69)=4.51, p=.014). The most positive change occurred in the explicit label condition (M=0.96, SD=1.57) followed by novel labels (M=0.08, SD=1.69) and irrelevant information (M=-0.38, SD=1.41). Children learned when provided with explicit emotion labels, but not novel labels. This suggests that familiarity (e.g., from previous experience), and not novelty, may best explain how emotion words influence children’s learning about emotions.

How Do Emotion Labels Help Emotion Learning? The Role of Novelty Versus Familiarity

A lack of consensus exists on the cognitive process of food craving. Thus, the current study aimed to develop a model for food craving by examining the effects of intrusive thought and sensory imagery on craving intensity. Participants (297 adults, aged 18–49 years) completed the Japanese version of the Craving Experience Questionnaire about strength after food craving induction through a guided imagery of eating. Using structural equation modeling, the study evaluated the validity of the cognitive model of craving, in which only intrusive thought influences craving intensity. Furthermore, it examined two models derived from the elaborated intrusion theory, in which sensory image entirely or partially mediated the relationship between intrusive thought and craving intensity. Analysis revealed the best fit of the partial mediation model of the elaborated intrusion theory, which suggests that intrusive thought directly influences craving intensity and indirectly via sensory imagery.

Development of the cognitive model of food craving: Examining the relationship among craving intensity, intrusive thought, and sensory imagery by structural equation modeling

The crowd wisdom effect is a well-established phenomenon used for predicting and estimating unknown variables. Previous research focused on enhancing crowd wisdom by improving individual estimates, while maintaining some of the initial opinion diversity. However, it is theoretically possible to increase collective accuracy by increasing diversity in the crowd. In this study, we propose a method that leverages the anchoring effect to extremize individual judgments and increase the diversity of opinions. This is achieved by dividing the crowd into two groups, anchoring them to either a low or high value, and aggregating all estimates. We develop a mathematical model to determine when this strategy is expected to outperform the crowd wisdom effect. Results from three experiments provide converging evidence that this approach outperforms traditional methods in estimating and forecasting unknown quantities. This research presents a novel approach to reduce collective error by maximizing diversity, at the cost of extremizing judgements.

Extremizing Judgements Produces More Inaccurate Individuals but Wiser Crowds

We investigated the effect of implicit theories (belief about malleability of ability) on help-seeking behavior in peer learning situation. We predicted that entity theorists (those who believe that ability is fixed) are less likely to seek help because they anticipate that peer members would lower their evaluation of competence. We conducted a scenario experiment and required participants to indicate to what extent they would ask questions to the peer member when they encounter an incomprehensive term which was already explained, and others seemed to understand. The results did not support our hypotheses. The results revealed that the interaction between one’s own and perceived others implicit theories predicted intention to ask questions. Specifically, while perceived others’ implicit theory did not affect incremental theorists’ (those who believe that ability is malleable) intention to ask a question, entity theorists were more likely to ask a question when they perceive others as entity theorists.

The effect of implicit theories on help-seeking behavior: Focusing on anticipated evaluation and perceived implicit theories of the peer member.

Peer-assisted learning (PAL) can have both benefits and costs when compared to individual learning. An empirical study aimed at testing whether PAL yields better learning outcomes than individual learning in a set of insight problems is presented. Participants in the PAL condition learned from interacting with their peers and outperformed participants in the individual learning condition. This learning transferred to a final test that was completed individually. The benefit of peer-assisted learning persisted in spite of significantly greater error exposure in the PAL condition.

Interaction Among Peers Increases Performance in the Remote Associates Test

How do essentialist beliefs about categories arise? We hypothesize that such beliefs are transmitted via language. We subject large language models (LLMs) to vignettes from the literature on essentialist categorization and find that they align well with people when the studies manipulated teleological information – information about what something is for. We examine whether in a classic test of essentialist categorization – the transformation task – LLMs prioritize teleological properties over information about what something looks like, or is made of. Experiments 1 and 2 find that telos and what something is made of matter more than appearance. Experiment 3, manipulates all three factors and finds that what something is for matters more than what it’s made of. Overall, these studies suggest that language alone may be sufficient to give rise to essentialist beliefs, and that information about what something is for matters more.

You are what you’re for: Essentialist categorization in large language models

Interpersonal physiological connection is thought to play a crucial role in group activities. A previous study with participants who had little musical experience has shown that the groups with higher heart rate synchronization were better able to perform simple drumming tasks in a coordinated manner. In the present study, we investigated whether heart-rate synchronization is associated with improvement in a complex harmonic performance of an orchestra. The subject of this study was an orchestra composed of several semi-professional and amateur musicians rehearsing a new original piece. The heart rates of sixteen musicians were simultaneously measured during a joint rehearsal. The results indicate that while the average heart rate of the musicians decreased, heart-rate synchronization among the musicians increased over the course of repeated group rehearsals. For a harmonic performance, the musicians may need to have a physiological connection to each other without their physiological arousal being too high.

Interpersonal physiological connection is associated with improved orchestral performance

For effective collaboration between humans and intelligent agents that employ machine learning for decision-making, humans must understand what agents can and cannot do to avoid over/under-reliance. A solution to this problem is adjusting human reliance through communication using reliance calibration cues (RCCs) to help humans assess agents’ capabilities. Previous studies typically attempted to calibrate reliance by continuously presenting RCCs, and when an agent should provide RCCs remains an open question. To answer this, we propose Pred-RC, a method for selectively providing RCCs. Pred-RC uses a cognitive reliance model to predict whether a human will assign a task to an agent. By comparing the prediction results for both cases with and without an RCC, Pred-RC evaluates the influence of the RCC on human reliance. We tested Pred-RC in a human-AI collaboration task and found that it can successfully calibrate human reliance with a reduced number of RCCs.

Selectively Providing Reliance Calibration Cues With Reliance Prediction

Video games represent a complex, engaging task domain that has shown great promise as an experimental paradigm for the study of cognition and skill acquisition. The methods that have thus far been used to study video games, however, have been sub-optimal, and will likely become more outdated as the video gaming landscape continues to change. In this work, I first give a brief overview of the previous literature surrounding video games and cognition. I next address the critical methodological problems such as measuring expertise based solely through time-on-task as a heuristic of expertise rather than true metrics of skilled performance itself. I then suggest how the burgeoning sub-domain of competitive video gaming, or "eSports," provides an elegant solution to these problems. Finally, I discuss the current and future directions of eSports cognition research.

Competitive eSports as a New Paradigm for Cognitive Science: Current State and Future Directions

Deep reinforcement learning (DRL) is capable of training agents that exceed human-levels of performance in multi-agent tasks. However, the behaviors exhibited by these agents are not guaranteed to be human-like or human-compatible. This poses a problem if the goal is to design agents capable of collaborating with humans or augmenting human actions in cooperative or team-based tasks. Indeed, recommender systems designed to augment human decision making need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. The current study simultaneously explored skill learning performance of human learners when working alongside different artificial agents (AAs) during a collaborative problem-solving task, as well as evaluated the effectiveness of the same AAs as action decision recommender systems to aid learning. The action decisions of the AAs were either modelled by a heuristic model based on human performance or by a deep neural network trained by reinforcement learning in self-play. In addition to evaluating skill learning performance, the current study also tested the congruence between the decisions of the AAs with actual decisions made by humans. Results demonstrate that the performance of humans was significantly worse when working alongside the DRL AA compared to the heuristic AA. Additionally, the action decisions participants made also showed less allignment with the recommendations made by the DRL AA.

Next from CogSci 2023

How Do Emotion Labels Help Emotion Learning? The Role of Novelty Versus Familiarity