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People are known for their ability to learn probabilistic rewarding rules of the environment. However, the rules tested in most studies consisted of one feature dimension or a linear combination of multiple features. It is unclear how well people can learn non-linear combinatory rules where each single dimension is noninformative. In an adapted matching-pennies game, we asked participants to predict whether their opponent would choose head or tail next trial. Though the feature space for possible rules was small, including only two binary dimensions (i.e., the two players’ current choices), participants had striking failures in learning the probabilistic XOR rules (e.g., the opponent would have a probability of 0.8 to choose head if the two players had matching pennies and tail for unmatching pennies). The failure persisted in a series of experiments, even when the feature space was made explicit, after 320 trials of learning, and for different cover stories.

CogSci 2023

People fail to learn probabilistic XOR rules even in a small feature space

poster

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)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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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.

Despite the importance of risk attitude on decision making, its role on belief updating has been overlooked. In this paper, we aim to answer this fundamental question. We show, using economic theory, that stronger risk aversion drives more conservative actions, decreases the instrumental value of information relative to the importance of belief-based utility. Thus, with utility for clarity, stronger risk attitude yields to more belief change; whereas with updating cost, stronger risk attitude leads to less belief change. We confirm the result experimentally in two settings where subjects update their belief about their IQ and a randomly drawn number, respectively.

Belief Attitude and Belief Updating

Nudges have been shown to be successful behavioral interventions. However, nudges have also been criticized to be paternalistic and infringe upon decision makers’ (DMs) autonomy. One solution that has been proposed is self-nudges—let DMs nudge themselves. Using default nudges, we tested whether self-nudges are as effective as traditional nudges. Participants were presented with a choice between two videos. We either nudged the participants by preselecting one video as default on their behalf, or asked participants to self-nudge by preselecting a video for themselves prior to their choice. We found that a much larger percentage of participants stayed with the default option and watched the preselected video when they preselected the video themselves (82.5% on average between the two videos) than when we preselected the video for them (59.5% on average between the two videos). Our findings provide preliminary evidence for the efficacy of self-nudges.

Are self-nudges effective?

Serial dependence is a phenomenon in which the perception of current stimuli is induced by the perception of past stimuli. We examined the serial dependence when participants estimated the total number of coins, or the monetary value displayed and observed attractive effects in both tasks. We also analyzed the effect of response history, and found that the immediately prior response had an attractive effect on current responses, while the immediately prior stimuli exerted a repulsive effect. These results suggest that serial dependence depends on the response to the stimulus, rather than on the perception. In this study, we aimed to separate the effect of the stimulus and that of the response by direct experimental manipulation. Specifically, we compared the magnitude of the serial dependence between trials in which participants only saw the dots in the previous trials and trials in which participants answered the number of dots in the previous trials.

Impact of past answer on serial dependence in numerosity perception

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.

Effects of learning and feedback on risk preferences

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

Next from CogSci 2023

Belief Attitude and Belief Updating