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It is a well-known observation that people tend to dislike risky situations that could potentially lead to a loss, a phenomenon that is called loss aversion. This is often explained using valuation bias, i.e., the subjective value of losses is larger than the subjective value of gains of equal magnitude. However, recent studies using the drift-diffusion model have shown that a pre-valuation bias towards rejection is also a primary determinant of loss-averse behavior. It has large contributions to model fits, predicts a key relationship between rejection rates and response times, and explains the most individual heterogeneity in the rejection rates of participants. We analyzed data from three previously published experiments using the drift- diffusion model and found that these findings generalize to them. However, we found that valuation bias plays the most important role in predicting how likely a person is to accept a given gamble. Our findings also showed that a person&#39;s loss aversion parameter, lambda, which captures their propensity to avoid losses is closely related to valuation bias. These results combined highlight the importance of valuation bias in understanding people&#39;s choice patterns. Finally, using the leaky, competing, accumulator model, we show that valuation bias may be linked to people paying more attention to losses as compared to gains. This finding provides hints about the cognitive underpinnings of valuation bias.

CogSci 2023

Using Computational Models to Understand the Role and Nature of Valuation Bias in Mixed Gambles

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

It is a well-known observation that people tend to dislike risky situations that could potentially lead to a loss, a phenomenon that is called loss aversion. This is often explained using valuation bias, i.e., the subjective value of losses is larger than the subjective value of gains of equal magnitude. However, recent studies using the drift-diffusion model have shown that a pre-valuation bias towards rejection is also a primary determinant of loss-averse behavior. It has large contributions to model fits, predicts a key relationship between rejection rates and response times, and explains the most individual heterogeneity in the rejection rates of participants. We analyzed data from three previously published experiments using the drift- diffusion model and found that these findings generalize to them. However, we found that valuation bias plays the most important role in predicting how likely a person is to accept a given gamble. Our findings also showed that a person's loss aversion parameter, lambda, which captures their propensity to avoid losses is closely related to valuation bias. These results combined highlight the importance of valuation bias in understanding people's choice patterns. Finally, using the leaky, competing, accumulator model, we show that valuation bias may be linked to people paying more attention to losses as compared to gains. This finding provides hints about the cognitive underpinnings of valuation bias.

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. 
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.

Complex decision making (DM) requires coordination of elementary information processes subserved by a distributed network of brain areas. Computational models help to understand these processes, but most of the existing models focus on simulating only one of the many parallel operations. One existing spiking neural (SNN) model (Duggins, Krzeminski, Eliasmith, Wichary, 2020) attempts to simulate DM holistically, however it does not take into account significant influence of emotional context of DM. To address this limitation, we propose to examine the impact of arousal-related neural gain modulation on DM using the mentioned SNN model. In this study we outline the methodology and perform successful in-silico validation of global gain modulation hypothesis with the SNN model of DM. To perform the simulation, we use a well-studied multi-attribute choice task and we validate simulation results against human behavioral data.

Modeling the impact of arousal on decision making with spiking neural networks

How do high trusters cognitively process integrative information regarding others while interacting with them? This exploratory study investigated the difference between high and low trusters in influencing top-down and bottom-up processes to score points in the ultimatum game against a computer agent. We designed an experiment wherein (1) participants expected their partners to be humans or agents (top-down processes) and (2) agents used one of three different types of algorithmic behavior (bottom-up processes) to propose and respond. We found that high trusters made rational proposals when they believed their opponents were human and when their opponents’ behaviors were ambiguous; they also modulated each other’s points when they believed their opponents were human and when their opponents’ behaviors were altruistic. Results suggest that, in interpersonal communication, high trusters flexibly use both types of cognitive processing based on reciprocity when developing representations of others and deciding on a response.

Behavioral characteristics in general trust: an exploratory laboratory-based analysis using the ultimatum game

Many real-life decisions, such as booking a vacation or selecting a partner, involve relatively cost-free sampling of options up to a terminal decision point, beyond which the choice becomes costly to reverse. Such problems can be formulated as optimal stopping problems (OSPs), such as the famous secretary problem. Although human behavior on optimal stopping problems has been studied extensively, much of the literature has focused on the behavior of individual decision-makers operating using a binary payoff function. In this study, we use an OSP with a continuous payoff function to study how individuals’ decisions differ from the collective decision of groups of three members working together. An independent threshold model offered the best explanation for the behavior of both individuals and groups. We found groups performed significantly better than individuals, with individuals consistently waiting too long to make a choice relative to the optimal strategy. Finally, we also found a lack of long-term learning in OSPs for groups, a trend previously documented in individuals.

Groups are Better than Individuals at Solving Optimum Stopping Problems

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.

Measuring the time utility of mental effort

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.

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Modeling the impact of arousal on decision making with spiking neural networks

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