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The color red has both positive and negative associations. In research, its evolutionary salience has the potential to bias performance through attentional processes. The present study investigated whether color had an effect on numerosity estimation and associated metacognitive judgments. 55 participants estimated the number of dots (7, 17 or 23) in given dot arrays (in green, blue, or red), rated their confidence in their estimates, and finally indicated the magnitude and direction of their estimation errors. Results showed that participants’ numerosity estimates were more precise in red dots compared to blue, while green was not different from the other two colors. The stimulus color did not affect the accuracy of numerosity estimations or the metric-error-monitoring performance. We conclude a red advantage over blue in numerosity estimation in terms of estimation precision. We interpret these findings through reduced trial-to-trial variability in visual attention for red stimuli.

CogSci 2023

Red stimuli increases precision in numerosity estimation

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)
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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.

It is rather uncontroversial that gender should have no influence on treating others as equal epistemic agents. However, is this view reflected in practice? This paper aims to test whether the gender of the testifier and the accused of assault is related to the perception of a testimony’s reliability and the guilt of the potential perpetrator. Two experiments were conducted, in which the subjects (n = 361, 47% females, 53% males) assessed the reliability of the testifier and the guilt of the potential perpetrator in four scenarios of assault accusation, in which the only difference was the gender of the people presented. During the study, we have observed dependencies of gender and ascription of reliability, and, to a lesser extent, of guilt. The results of our research may constitute an argument for the existence of different epistemic status endowed on people depending on their gender and existing gender stereotypes.

Gender Differences in Reliability and Guilt Ascriptions in the Context of Violent Crimes

It can be difficult to uncover the truth when presented with two conflicting beliefs. Adults integrate beliefs in a rational way by placing more weight on beliefs help with high confidence than beliefs with low confidence. Here, we investigate the development of this rational integration in 5- to 10-year-old children. Children at all ages preferred an integrated belief when two informants presented beliefs with equal confidence, but selected the higher confidence belief if confidence differed. We interpret this as evidence that children use expressed confidence to inform rational decision-making, consistent with the principles of rational cue combination.

Children Leverage Confidence to Rationally Integrate Beliefs

Previous research assessing people’s sense of correlation used scatterplots as stimuli and asked participants to estimate the correlation of said scatterplots. This method has consistently shown that people tend to underestimate the correlation of a scatterplot (e.g., guessing the correlation is 0.25 when the actual correlation is 0.5). However, it is unclear whether this underestimation is perceptual or reflective of having a poor internal representation of different correlations. We investigated this question by flipping the task: instead of estimating the correlation from a scatterplot, participants drew a scatterplot based on a given correlation. They drew 20 points to represent the correlation coefficients: 0, 0.25, 0.5, 0.75, and 1. While the drawn correlations of 0, 0.75 and 1 were quite accurate, the drawn correlations of 0.25 and 0.5 were much higher than the requested correlations. This pattern is consistent with previous research, suggesting the underestimation may not be perceptual.

Performance of drawing scatterplots based on given correlations

Since the landmark paper of Spivey et al. (2005), there has been an explosion in the use of continuous trajectories for studying decision-making. In these studies, the participant makes their decision by reaching to a target. By extracting trajectory-based measures, it is possible to observe how the decision unfolds over time. However, studies have shown that these trajectories may not actually continuously track the decision-making process, but rather result from several discrete submovements executed intermittently. Due to this, conclusions of papers using purely kinematic measures may be affected by methodological details and motor control properties of the arm. This study presents simulations of trajectories using motor control models and demonstrates the relationship between non-decision-related parameters and outcome measures typically used. We also comment on the implications of these findings for using kinematic measures in decision-making and make recommendations for how the decision variable can be more appropriately sampled.

The effect of kinematics on standard measures used in quantifying decision making using arm movements: a simulation study

Recent experimental work has provided evidence for pure reasoning in infancy, a capacity permitting flexible integration of multiple sources of information to form rational expectations about novel events (Teglas et al., 2011). However, the neural underpinnings of this capacity have remained elusive. In this work, we present the first ecologically rational, neural-level account of these findings on pure reasoning in human infants. Our work bridges two dominant approaches in computational developmental psychology, i.e. neural-network models and Bayesian modeling, substantiating the view that intuitive physics in infancy might, at least partly, involve heuristics: a set of simple, fast, resource efficient, approximation algorithms that yield sufficiently good results.

VPNeural Network Modeling of Pure Reasoning in Preverbal Infants

Children rely on their approximate number system (ANS) to guess quantities even before they are exposed to formal mathematics. As they develop, children improve their performances on estimation tasks as well as formal mathematics tasks. Studies have shown that the improvements of these two tasks are due to the development of children's executive functions. We aim to investigate the influence of attention control and visual processing on the development of children's ANS. In this experiment, children from the age of 3 to 6 were recruited to do an estimation task while eye-tracked. Our analysis suggests that children's estimates improve under the long display-time condition and with greater visual fixation. The result from our Bayesian hierarchical regression modeling demonstrates that young children significantly underestimate dots in their peripheral vision, which leads them to underestimate, especially for large number under short-time condition when they could only foveate a limited number of dots. Children from older age groups displayed a significant improvement in estimating peripheral dots, contributing to a more accurate mean and standard deviation for estimates.

Children’s Estimation of Peripheral Information Drives Improvements in Approximate Number Sense

Individuals teach others about the world through language and demonstration. When might one of these modalities be more effective than the other? In this work, we study the factors that modulate the effectiveness of language vs. demonstration through the lens of multi-agent systems. We train neural network agents to teach via language or demonstration in a grounded communication task, manipulating 1) the inherent difficulty of the task and 2) the competence of the teacher. We find that teaching by demonstration is more effective in the simplest settings, but language is more effective as task difficulty increases, due to its ability to generalize more effectively to unseen scenarios. Overall, these results provide converging evidence for a tradeoff between language and demonstration as teaching modalities, and make the novel predictions that demonstration may be optimal for easy tasks, while language enables generalization in more challenging settings.

Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems

Active learning techniques are educational tools that engage students with hands-on and interactive learning experiences. However, despite proven pedagogical advantages, active learning activities can be difficult to implement in traditional university classroom setups. We present a newly developed template for an educational escape room that can be used in a class of any size or discipline, does not require any special equipment, and is supplemented by free and accessible technology. We describe implementation of the escape room template in a plot-driven, dynamic sleep laboratory escape room, using puzzles to reinforce lecture content about circadian rhythms and sleep cycles in a large introductory neuroscience course of 300 students. This proof-of-concept innovative educational escape room framework combines escape room principles, technology, and physical paper elements to provide a new way of engaging students with complex course concepts in an interactive way, connecting best practices in education, applied neuroscience, and computer science.

An Innovative Educational Escape Room Framework for Active Learning in Classrooms

Computer programs correspond to instructions that tell a computer how to perform a task. Because computers do not understand human languages, programs have to be written in a specialized programming language. Such languages have unique syntax and semantics that on the surface appear quite different from human languages. To foster understanding what a program means, novices need to translate the programming language instructions into a familiar human language. We developed a tutoring system that provides translation activities, from the language Python into plain English, and feedback on student answers. One version of the tutoring system provides open-ended input boxes that students type translations into. A second version instead asks students to generate answers to the translation using drop-down menus. The latter provides more scaffolding but also may encourage shallow behaviors such as guessing and checking. To shed light on how these designs influence learning, we evaluate them with novice programmers. Here, we report on the results of the evaluation and next steps.

Supporting Learning of Programming through Translation Activities in a Computer Tutor

One possibility that may facilitate learning difficult tasks is building a specific curriculum, i.e., to start with simpler tasks and continue with increasing task difficulty. While such curriculum learning has long been considered and investigated in educational settings, little research has been conducted on curriculum learning in well-controlled experimental settings. To fill this gap, we investigated curriculum learning in a perceptual decision-making task. Across two experiments, we tested whether different training conditions (ascending difficulty, descending difficulty, random difficulty, easy difficulty, and difficult difficulty) affected participants' performance in a subsequent testing phase. We found that participants trained on a sequence of varying difficulty (i.e., ascending difficulty, descending difficulty, and random difficulty) outperformed others from easy-only and difficult-only training. However, we found no differences during testing between the conditions with varying difficulty. These results suggest that varying the difficulty during training yields advantages when learning the random-dot-motion task.

Next from CogSci 2023

Gender Differences in Reliability and Guilt Ascriptions in the Context of Violent Crimes

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