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The empirical measurement of mental effort is an important problem in the cognitive sciences. Recently, researchers have adopted econometric tools to attempt to characterize mental effort in terms of monetary costs foregone. Such efforts yield a very helpful calculation device - a money utility of mental effort. However, since the opportunity cost of applying mental effort in any given situation is measured with respect to time rather than money in most ecologically reasonable settings, it is even more desirable to obtain a measure of the time utility of mental effort. In the absence of direct measurements of mental effort, such a task has proved econometrically challenging. We use a recently developed direct measure of mental effort to characterize its time utility, finding that it is approximately linear in effort. We discuss some implications of this result for current theories of mental effort, as well as for practical applications.

CogSci 2023

Measuring the time utility of mental effort

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





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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. 
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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 [**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.

Decades of research have established that people’s performance can suffer when they need to quickly switch between tasks, and that they can reduce these performance costs the more time they have to prepare. Two major theories have attempted to explain how people actively prepare for tasks over time, debating whether these task state transitions are discrete or gradual. We attempted to bring clarity to this debate by developing new statistical methods for single-trial modeling of task state transitions, which we use in a task that combines the strengths of cued and predictable task-switching. We found that participants’ behavior was best explained as a hybrid between discrete and gradual transitions. Over the preparation period, participants discretely transitioned from an unprepared state into a dynamic, increasingly prepared state.These findings provide a new account of cognitive flexibility, paving the way for mechanistic models of task-switching.

Continuous and Discrete Transitions during Task-Switching

There are often systematic differences between description- and experienced-based risky choices. For description-based choices, individuals are more risk-seeking for losses than for gains. Nevertheless, for experience-based choices, such a trend is reversed when they encounter alternatives associated with extreme outcomes (i.e., the best and worst). One reason is that extreme outcomes are more salient in memory, potentially due to their distinctiveness and proximity to the edges of the outcome distribution. Ludvig and colleagues (2018) have suggested that proximity to edges (not distinctiveness) results in memory bias for extreme outcomes. The current study replicates and extends upon Ludvig et al. (2018)’s work to examine the effects of increasing distances (addition, scaling) between neighboring outcomes. The results are consistent with those observed in the previous study, thereby further supporting the claim that extreme outcomes are prominent in both memory and choice because they are close to the edges of the experienced distribution.

How close is close enough? How even neighbors of extreme choices drive risky choice

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.

Red stimuli increases precision in numerosity estimation

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.

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Continuous and Discrete Transitions during Task-Switching

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