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The ability to represent and reason about other people’s perspectives (‘social cognition’, or ‘Theory of Mind’ [ToM]) plays an important role in daily life. It is not currently clear how different types of interaction may impact subsequent perspective taking, and whether this differs with age. Here, we examine how experiences of social inclusion vs. social exclusion (manipulated using the Cyberball task) influence later perspective-taking efficiency, in younger (19-30 years; N = 100; M = 24.69) and older (65-80 years; N = 100; M = 69.63) participants. As expected, significant age differences were found in perspective-taking abilities, with younger participants out-performing older participants. This was specific to false-belief trials, with no difference in memory trials performance. Counter to hypothesized, no interaction between impact of social experience (inclusion vs. exclusion) and age was found, indicating that both younger and older adults are similarly impacted by social experiences. Implications will be discussed.

CogSci 2023

The Impact of Social Inclusion vs. Exclusion on Subsequent Social Cognition Abilities in Younger and Older Adults

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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)
<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.

People care about others’ welfare to varying degrees, captured by the welfare tradeoff ratio (WTR), or the weight placed on another person’s welfare compared to one’s own. People can infer another person’s WTR toward themselves and reciprocate by adjusting their own WTR toward that person. However, the evolutionary origin of such a capacity is unclear. In games with perfect information, a heuristic strategy with tit-for-tat-like reciprocity is unbeatable, and the additional computation of inferring the opponent’s WTR confers no benefit. Here we show that in games with asymmetric information, where the actor has more accurate information about the payoff structure than the observer, the heuristic strategy is prone to errors from misperception, while reciprocity based on the inference of WTRs is robust. These findings suggest that asymmetric information about social decisions, a realistic modification to the game environment, may have contributed to the evolution of people’s understanding of others’ WTRs.


The evolution of reciprocity based on welfare tradeoff ratios in games with asymmetric information

Children's perception of gender stereotypes influences their gender role representations, leading to preferences regarding gender-toy associations and gender-based beliefs. Exposure to literary stories may shape children’s gender-role representation by either enforcing stereotypes or countering them. The current study investigates one type of literature that young children are often exposed to: folklore stories with goddesses, which contain abundant and varied gender information across different cultures. Twenty-eight 4-to-5-year-olds were randomly assigned to hear either a gender role typical or atypical goddess story. Toy stereotyping ratings and confidence levels of responses were obtained before and after the story exposure. This is ongoing study provides a preliminary summary of investigating the impact of story reading on young children’s gender stereotype representation. It also provides insight regarding the need for and ways to measure impacts of story-reading.

Story Reading and Children’s Gender Stereotype Representation

We rated video recordings of participants playing a charades-like semiotic game, where they took turns miming stories and picking the sequence of pictures that best suited what was mimed. The stories had one (male or female) or two characters (male and female). Although there was no pressure to disambiguate between the characters, most of the participants did so by producing iconic or indexical gender markers. Our analysis shows that when the gender of the characters in a story (the referent environment) matched the gender of the participants (the physical environment), the participants were more likely to use indexes rather than icons (p < 0.05, β = 1.4247). These results complement research on the role of the environment in the emergence of language-like structures and their systematicity in silent gesture tasks (e.g. Nölle et al. 2018).


Does the environment shape gender marking in mimed stories?

Past studies have shown that adult listeners can classify gender from children’s speech – long before anatomical differences between the sexes emerge. This suggests that gender differences in speaking style are learned early in life through socialization. Yet, no study to date has asked whether children also perceive gender cues in child speech. In Study 1, we found that 3.5- to 6-year-olds were able to detect gender from their age-matched peers’ speech, and that their performance was moderated by parents’ gender attitudes. In Study 2, we examine whether the effect of parental socialization is mirrored in children's production. We predicted that how easy children's gender can be perceived from their speech is moderated by parents’ gender attitudes. Our findings support our prediction. Taken together, our results demonstrate that sensitivity to gender cues emerges early in life, and that parental socialization contributes to the development of both gendered speech perception and production.

It Starts at Home: The Role of Parental Socialization on the Development of Gendered Speech

During referential communication, speaker choices regarding the syntactic encoding of their expressions can modulate the linear ordering of the properties necessary to identify the referent. We investigated whether such syntactic choices are influenced by the informativity of these properties in a given visual context, as quantified by Referential Entropy Reduction (RER). In two experiments, a maze-based sentence completion task was used to examine whether informativity of a particular property (animal or action) influenced the decision to produce pre- versus post-nominal modifications when describing animal-performing-action referents in a visual scene. While many participants used a fixed strategy, informativity did significantly influence linearization for the remaining participants, consistent with a maximal informativity strategy in which the high RER property is encoded first. This suggests that speakers who vary their encodings are indeed sensitive to the informativity of properties in a visual scene, preferring syntactic linearization in which informative properties appear early.


Does informativity modulate linearization preferences in reference production?

This paper presents a challenge to enactivist approaches to cognition (e.g. Ward, D., Silverman, D. & Villalobos, M. 2017): how can enactivists integrate data and results from research methods used in developmental psychology and animal cognition studies that heavily rely on representations, given enactivism's aversion to representations? I discuss theoretical commitments behind forms of looking time studies that have been used to probe into the cognitive abilities of infants and animals, focusing on the Violation of Expectation (VoE) paradigm (Ginnobili & Olmos 2021), to illustrate why these methods might pose an especially difficult problem for enactivists and their re-interpretation of cognitive science in terms of dynamic coupling between organism and environment. I argue that despite the lack of clarity regarding how the notion of expectation should be applied to the minds of neonates and nonhuman animals, there is an inherently representational aspect to expectation, given that it embodies satisfaction conditions.


Expectation, Representation, and Enactivism

Recent research has identified intertemporal impulsivity as a critical cognitive variable for explaining the autocatalytic nature of socioeconomic status. But how exactly this relationship transpires has not been clearly identified, with several possible cognitive mechanisms having been proposed in the literature. We designed an experimental paradigm in which participants farmed crops under budgetary constraints and intermittently faced random resource demands. We discovered that, as a result of unpredictable resource shocks, people's preferences shifted from long-term choices to short-term ones. We also found people's self-reported sense-of-control scores to be predictive of the magnitude of their preference shifts. On the basis of these results, we argue that steep inter-temporal discounting arises as a rational adaptation to persistently experiencing long-term planning failures due to unpredictable resource shocks.


Unpredictability shortens planning horizons

Decision-making models hold a vital role in the field cognitive science, serving as a means of describing and predicting human behavior. While classical models with similar assumptions are frequently favored, there is no guarantee they provide the best accounts of behavior. Here, we evaluate BEAST, a model that has demonstrated extraordinary predictive capabilities in diverse settings, but was excluded from a recent large-scale comparison of models due to its complexity. Our evaluation of the model's performance on a large collection of experiments of decisions under risk shows it provides excellent predictions in some domains. We further show how BEAST can be adapted to increase its predictive power in contextualized settings. Our results highlight the importance of considering a more inclusive approach toward complex models to deepen our understanding of the psychological mechanisms underlying human decision making behavior.


The Importance of Complex Models in Decision Making Research: An Empirical Analysis using BEAST

Face stereotypes are prevalent and consequential. In this paper, we investigate an experience sampling account of face stereotyping. In two experiments, we show that, in an experience-sampling-based Trust Game, participants in the role of an investor were much more likely to play the game with trustees with trustworthy faces than those with untrustworthy faces (Experiment 1). Crucially, this endogenous experience sampling bias has amplified the behavioral consequences of the stereotype of facial trustworthiness. In contrast, when the information was directly described with no sampling constraint for the participants, the face stereotype had a very limited effect on investment decisions (Experiment 2). We conclude by suggesting that the description-experience gap paves a promising avenue for studying sampling-based accounts in social cognition and behavior.


A description-experience gap in face stereotyping

Computational cognitive modeling is an important tool for understanding the processes that support human and animal decision-making. Choice data in sequential decision-making tasks are always noisy, and separating noise from signal can improve the quality of computational modeling. Currently, most models assume that noise is static, typically by including a parameter (e.g., uniform ε or logistic β) to estimate the noise level. However, this assumption is not guaranteed to hold -- for example, an agent can lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Assuming that noise is static could bias parameter and model identification. Here, we propose a new method to dynamically infer noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (for example, attentive and noisy). Using four empirical datasets with diverse behavioral and modeling features, we demonstrate that our method improves model fit and that it can be easily incorporated into existing fitting procedures, including maximum likelihood estimation and hierarchical Bayesian modeling.


Next from CogSci 2023

The evolution of reciprocity based on welfare tradeoff ratios in games with asymmetric information