Canada

Facebook sends users friend suggestions when they have friends in common with others. However, how do people actually consider mutual friendships when inferring whether two individuals are friends? We examined how people (N=352) rely on numerical factors when judging whether individuals are friends based on their mutual connections. Participants saw two target individuals and their friends within a group, and judged if the targets were friends with each other. Experiment 1 manipulated the number of mutual friends and the number of friends each target had. Experiment 2 manipulated the proportion of these two factors. People were more likely to infer friendships when I) targets had more mutual friends; II) targets had fewer friends each; and III) there was a high proportion of mutual friends, which are all factors that impact the likelihood of targets’ friendship. In sum, people consider relevant numerical information about mutual friends when making friendship judgments.


CogSci 2022

Inferring friendships from mutual connections

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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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)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

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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. 





How much information can people gain from being lied to? We propose that people can infer the truth from false messages if two preconditions are met: (1) bigger lies are more costly, and (2) speakers have known, directional deception goals. We tested this with a marble-flipping task in which a judge tried to accurately estimate the number of sampled marbles, while a sender attempted to make the judge over- or underestimate. The sender could produce larger lies about the number of marbles drawn by physically clicking marbles along a lower or higher cost function. We found that judges took into consideration both the senders' goals and costs to correct for bias introduced by senders' lies. Our paradigm allows us to show that a large amount of the variation can be explained by people correcting others' lies based on the lies that they themselves would produce.


Inferring truth from lies

Humans’ ability to attribute mental states to agents has been hypothesized to underpin our unique social behaviors. However, questions remain about the extent to which our representational Theory of Mind (ToM) is shared with non-human primates (NHPs). Here, we present a set of computational models each built to formalize a different representational theory of a foundational ToM component—understanding what others can see—and compare each model’s performance to that of NHPs across a range of previously published perspective-taking experiments. Our results show that multiple competing theories can account for NHPs’ perspective-taking abilities, including both human-like ToM and less complex mentalistic theories, but not simpler, non-mentalistic theories. This work supports the idea that NHPs may reason about others’ mental states when assessing their visual perspectives, and provides promising avenues for future work using computational modeling to determine if and how NHPs represent more complex mental states (e.g., ignorance, belief).

Multiple representational theories explain non-human primate perspective-taking: Evidence from computational modeling

Other people's mental states---what they want, what they know, and how they combine the two to act---are structured by the experiences that they've had. In line with this, we propose that inferences about other people's experiences are a central, but often neglected, aspect of human Theory of Mind. We explore this idea by presenting and testing a computational model that jointly infers others' desires, knowledge, and experience. We find that, by focusing inferences on others' experience, our model can make richer inferences about other's knowledge than would be otherwise possible. Our model quantitatively fits participant judgments on two experiments above an and beyond an alternative model. Overall, our work extends the richness of human Theory of Mind judgements that can be formalized as Bayesian inference over a generative model.

Integrating Experience into Bayesian Theory of Mind

Social learning is essential to survival. It is likely to evolve when it is more
efficient than asocial, trial-and-error learning. The consensus in cultural
evolutionary theory holds that some amount of environmental variability and
uncertainty about the best decisions are necessary for social learning to evolve.
However, current models for the evolution of social learning tend to conflate forms
of uncertainty, and rarely consider different ones in tandem. Moreover, many models
are limited by considering only two possible behaviors and environmental states. Here
we use evolutionary agent-based modeling to identify the complex ways in which
different forms of uncertainty affect social learning. We model a
time-varying environment with dozens of possible behaviors performed by agents
engaging in individual and social learning. We show that ambiguous payoffs, larger
possible decision sets, and shorter agent lifespans sometimes increase social
learning prevalence, as expected. However we also find 
which concrete uncertainty conditions cause evolution to select against social learning.


Some forms of uncertainty may suppress the evolution of social learning

There is much diversity in how people form theories and perspectives about situations. There are many instances today in which people may want others to question their perspectives as a first step to changing them. Aspects of counterfactual thinking such as mutability and availability may be involved in questioning a perspective. We explore this in an exploratory study (n = 80) using ambiguous social judgment scenarios. We performed path analysis on the data to compare four models, three of which are based on counterfactual generation. The best fitting model showed that availability was a mediator for mutability to predict our measure of questioning an initial perspective.

Changing Perspectives: Examining Factors Related To Counterfactual Thinking In Ambiguous Social Judgments

Ownership is pervasive across human societies but invisible to the naked eye, and so people need to infer who owns what. Given its ubiquity, one might expect that people's intuitive theories of ownership would converge. Yet, ownership disputes are common, raising the question of whether these intuitions might actually vary across people. We explored this question using a set of simple, parametrically varied object-transfer events where participants determined how much two agents owned an object. Participants showed strong consensus about ownership in some events, but opposing intuitions in others. Subject-level analyses suggest that these disagreements reflect two overarching intuitive theories, one where intentions are central to ownership, and another where physical possession is prioritized. Our results suggest that people have stable but different intuitive theories of ownership, which stem from their beliefs about the relationship between mental states and possession.


People Have Systematically Different Intuitions about Ownership even in Seemingly Simple Cases

People’s expectations play an important role in their reactions to events. There is often disappointment when events fail to meet expectations and a thrill to having one’s expectations exceeded (e.g. Mellers et al., 1997). Here, we examined how expectations influenced people's reactions to events with serious moral consequences, and their moral decisions concerning such situations. Participants judged pairs of events where a victim experienced a similar harm, but where victims were at different prior risk of being harmed (e.g. someone killed by a drunk driver on a Friday night versus a Saturday morning). People found unexpected harms to be more upsetting and they were more likely to choose to prevent an unexpected harm than an expected one. Further studies explored other factors that might have influenced judgments beyond expectations. This bias may have pernicious moral consequences, potentially leading to reduced concern for victims in most need of help.

Expectations bias moral judgments

The legal system is concerned with interpreting and applying existing laws to real world circumstances. In the U.S., the final arbiters of these laws are the nine justices of the U.S. Supreme Court. Over the past few decades, political leanings have played an increasing role in how these justices are appointed. As such, it seems apt to investigate the role that moral reasoning plays in their world views and their decision making. The present study uses corpus statistics and moral foundation theory to explore the types of arguments brought up by different justices during the 2004-06 terms. More importantly, the study investigates how the use of moral language by advocates arguing in front of the court influences the vote of the justices. The results show that increased use of moral language and rhetoric, especially loyalty, can affect the outcome of the case.

Moral Foundations in Supreme Court Oral Arguments

Although language is critical to supporting morality within society, it is not clear how moral language itself evolved. We investigate the evolution of moral semantics, hypothesizing that words evolved to take on moral meanings from concrete experiences through metaphorization. We test this hypothesis by analyzing moral semantic change in words from the Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. In contrast with the observation that words become concrete over time, we demonstrate that moral words in the English lexicon undergo concrete-to-abstract shifts, reflecting systematic metaphorical mappings to the moral domain. Our results provide large-scale evidence for the role of metaphor in the historical development of the English moral lexicon.

Evolution of moral semantics through metaphorization

Recent years have witnessed significant advances in our understanding of bounds on rationality in both cognitive psychology and economics. These two fields have been making separate progress, but time is ripe for unifying these efforts. In this article, we introduce recently developed economic tools, themselves rooted in the psychometric tradition, to quantify individual differences in the capacity for cognitive control. These
tools suggest that a reliable assessment of the capacity for cognitive control may be accomplished by examining task performance as a function of reward. We demonstrate through simulation studies that an incentive-informed measure of task performance does a better job of recovering individual differences in one’s capacity for cognitive control, compared to the commonly used congruency effect. Furthermore, we show that the economic approach can be used to predict control-dependent behavior across different task settings. We conclude by discussing future directions for the fruitful integration of behavioral economics and cognitive psychology with the aim of improved measurement of individual differences in the capacity for cognitive control.

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Inferring truth from lies

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