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Decades of research has demonstrated that people do not make economically rational decisions when risk is involved. While research has focused on how and why people’s decisions differ from economic rationality, an unanswered question is whether people can learn to improve their risky decision making in response to feedback on similar past decisions. In an experiment, we test whether people’s risk preferences change after they received either natural (a single outcome), or idealised (the average outcome) feedback about similar decisions in the past. Crucially, unlike studies of decisions from experience, they received feedback on several different decisions from description, rather than repeated feedback about a single choice. Compared to a control, who received no feedback, both forms of feedback led to an increase in choice of the higher Expected Value gamble. Modelling suggests, for natural feedback, this may be due to changes in how they treat probability information.

CogSci 2023

Effects of learning and feedback on risk preferences

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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).

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<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.

On-line decision problems – in which a decision is made based on a sequence of past events without knowledge of the future – have been extensively studied in theoretical computer science. A famous example is the Prediction from Expert Advice problem,
in which an agent has to make a decision informed by the predictions of a set of experts. An optimal solution to this problem is the Multiplicative Weights Update Method (MWUM).
In this paper, we investigate how humans behave in a Prediction from Expert Advice task. We compare MWUM and several other algorithms proposed in the computer science literature against human behavior. We find that MWUM provides the best fit to people’s choices.

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms

Developmental dyscalculia (DD) is a neurodevelopmental disorder characterized by persistent poor math performance despite normal intelligence and education opportunities. Existing behavioral and neuroimaging studies have demonstrated that quantity processing deficits in DD are accompanied by aberrant brain functions and neurobiological alterations. Although theories have argued that the behavioral impairments observed in DD result from neurobiological deficiency and imbalance of excitatory and inhibitory signals in the brain, these hypotheses are difficult to test in human subjects. Therefore, in the current study, we implemented convolutional neural network models and tested the causal influence of neural capacity (i.e., number of units) and system excitability (i.e., the slope of activation) during the learning of quantity information. For both symbolic and non-symbolic processing, we observed that reducing the number of units did not lead to changes in learning performance. In contrast, increased excitability largely impaired the accuracy of learning, especially for the non-symbolic representations. Therefore, our model simulations provided direct evidence that increased excitability in the brain could result in behavioral impairments in learning quantity information, potentially suggesting a neurobiological basis for DD.

Limited Neural Capacity and Hyper-Excitability Affect Quantity Processing: A Computational Account

Active learning techniques enhance understanding and engagement in the learner, but these techniques can be difficult to teach to instructors. The pedagogy of active learning typically either describes abstract concepts or it relies heavily on case studies of what other instructors have created. Because it can be difficult for interested instructors to put theory into practice without a concrete structure, active learning techniques can be hard to implement in the classroom. Behaviorism, cognitivism, constructivism, and connectivism can be used to describe ideas about how learning is occurring, but they do not accurately categorize commonly used teaching activities, which instead often contain elements of multiple learning theories. I propose a novel functional categorization of active learning techniques that is both brain-based and student-centered, placing emphasis on the knowledge journey. This work describes these seven evidence-based “knowledge catalysts” as a new user-friendly framework for both understanding, using, and teaching active learning techniques.

Knowledge Catalysts: A Structure to Promote Incorporation of Active Learning Techniques

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.

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

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.

Next from CogSci 2023

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms