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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.


CogSci 2023

A description-experience gap in face stereotyping

technical paper

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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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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.


A generalized method for dynamic noise inference in modeling sequential decision-making

Despite evidence that learners are storing a lot more than simple words, it is still unclear what determines whether a phrase is stored holistically. For example, storage could be driven by either phrasal frequency or by the mutual predictability of a phrase's component parts . Further, the processing consequences of holistic storage are also unclear. Given that sentence processing is incremental, how does recognition of individual words give rise to recognition of holistically stored phrases? The present study examines these questions. Specifically, participants are presented with sentences that contain compound nouns in locally plausible or locally implausible contexts. We examine whether participants are able to overcome local implausibility effects more easily if the compound nouns are highly predictable. We find that predictability does not overcome local implausibility effects, suggesting that either predictability is not driving holistic storage or that holistic storage driven by predictability does not facilitate comprehension in our task.


Does Predictability Drive the Holistic Storage of Compound Nouns?

What determines children's production of syntactic alternations? This study takes up this question with the double PP construction in English as the test case (e.g., write [on the paper] [with this new pen]). Leveraging data of spontaneous child-parent interactions, we investigate the roles of dependency length and parent input frequency, with the latter being operationalized as lexical frequency and contextual predictability. We found that when child and parent data was combined, all three factors turned out to have significant predictive power, with dependency length having the most pronounced role. Results from the developmental trajectory of DLM as well as logistic regression analysis suggest that child production of constituent ordering preferences starts becoming more parent-like after the age of 30 months.


Double PP Constituent Ordering Preferences in English Early Child Language

Generative syntax and a consistently ordered count routine are both understood to have central roles in learning a number system. However, there has been little experimental exploration of how the diversity of each of these features alters the inductive landscape of number learning, as most empirical work has been constrained to correlational studies. We present a causal manipulation both of syntactic structures and counting procedures, using an artificial language paradigm. Our findings suggest that (1) learners have a greater facility with conjunctive over multiplicative rules of composition, (2) counting procedures help learners to recall words independently of syntax, and (3) predictable syntax helps learners to wield numerical concepts, independently of - and possibly despite - counting routines.


How does the syntax of counting affect learnability? Evidence from artificial language learning.

Languages exhibit a tremendous amount of variation in how they organise and order morphemes within words; however, regularities are also found. For example, gender and number inflectional morphology tend to appear together within a single affix, and when they appear in two separate affixes, gender marking tends to be placed closer to the stem than number. Formal theories of gender and number have been designed (in part) to explain these tendencies. However, determining whether the abstract representations hypothesised by these theories indeed drive the patterns we find cross-linguistically is difficult, if not impossible, based on the natural language data alone. In this study we use an artificial language learning paradigm to test whether the inferences learners make about the order of gender and number affixes—in the absence of any explicit information in the input—accord with formal theories of how they are represented. We test two different populations, English and Italian speakers, with substantially differ- ent gender systems in their first language. Our results suggest a clear preference for placing gender closest to the noun across these populations, across different types of gender systems, and across prefixing and suffixing morphology. These results expand the range of behavioural evidence for the role of cognitive representations in determining morpheme order.


Speakers' cognitive representations of gender and number morphology shape cross-linguistic tendencies in morpheme order

When faced with uncertainty, human observers maximize performance by integrating sensory information with learned task-relevant regularities. Does this behavior similarly occur in social settings? Here, we used a probabilistic decision-making task to explore how human behavior in an intergroup context is influenced by readily available but task-irrelevant social information (in the form of group membership) versus task-relevant reward information that could be learned over time. Across two experiments, we show that participants learned and utilized task-relevant regularities to inform their choices. We also show that human observers are not universally biased towards utilizing social information in all settings––participants learned to disregard social information when not relevant to the task at hand. However, learning about the utility of social information had a long-term influence on observers’ ability to subsequently learn and utilize available sources of information. Real-world intergroup contexts typically encompass situations and stimuli that have been previously experienced by the observer. Our findings highlight the powerful influence of learning in such contexts.


Learning and Information Use in an Intergroup Context

Research on social motor coordination (SMC), defined as the intentional or unintentional coordination of movement between individuals in a social setting, has been linked to greater feelings of rapport and social connectedness. Here, we further investigated this relationship using a silent disco paradigm where groups of individuals listened to either the same music (the same track at the same time) or different music (different tracks at any one time). Visual information was also manipulated by having groups listened and danced with curtains separating them closed (2 minutes) or open (10 minutes). Head movements were recorded with a wireless motion tracking system, attached to the silent disco headphones. Rapport and social connectedness measures were obtained using questionnaires completed after participation in the silent disco. Results showed that groups that listened to the same music exhibited a greater degree of SMC than groups that listened to different music. Greater degrees of SMC were also observed when group members were able to see one another. Finally, greater SMC was associated with increased self-other overlap and increased perceived quality of interaction.


Social connection on the dance floor: Movement coordination in small group silent disco leads to greater self-other overlap

Helping is an inherently cooperative behavior, but the cognitive mechanisms underlying this behavior remain relatively underexplored. In this paper, we introduce a novel gamified paradigm for understanding a variety of cognitive behaviors associated with helping. Principals are assigned secret goals in a block-based grid (e.g., move all blue blocks to room C), and helpers can either pass their turn or make a move that could help the principal. We show that principals make useful and pragmatic first moves and helpers accumulate evidence over time before initiating a helpful move. We also introduce a preliminary set of computational models based on recursive pragmatic inference and utility maximization that attempt to account for these behavioral findings.


Help me help you: A computational model for goal inference and action planning

Solving problems with others not only reduces the time required to complete a challenge but may also enable the discovery of novel strategies that qualitatively change how a problem is approached. At the dyadic level, the laboratory-based ‘shepherding task’ demonstrated that, when tasked to contain evasive agents to a centralized location, some participants discover a non-obvious but optimal strategy to solve the task. This paper quantified the interactions between participants engaged in the task using Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA), applied to each participant’s gaze and hand movements. The results demonstrated that strategy discoverers exhibited greater amounts of incidental coupling than non-discoverers prior to discovery. Once discovered, the strategy reduced the strength of coupling between participants, indicating that the strategy also reduced coordination demands. Future work will investigate whether differences in problem-solving can be attributable to differences in the perceptual features participants use which scaffold the discovery of task-optimal solutions.


Incidental Coupling of Perceptual-Motor Behaviors Associated with Solution Insight during Physical Collaborative Problem-Solving

This symposia brings together researchers, with backgrounds in both academia and industry, to discuss how to best foster theory development, basic science, and tackle applied problems.

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A generalized method for dynamic noise inference in modeling sequential decision-making