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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&#39; goals and costs to correct for bias introduced by senders&#39; lies. Our paradigm allows us to show that a large amount of the variation can be explained by people correcting others&#39; lies based on the lies that they themselves would produce.


CogSci 2022

Inferring truth from lies

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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.


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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.
 
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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. 





The present study investigated whether making judgments of learning (JOLs) can enhance the learning of the studied materials (backward effect) and newly studied materials (forward effect) in inductive learning. In two experiments, participants studied various butterfly species through Sections A and B. After learning Section A, participants made either target-present JOLs, target-absent JOLs, or merely restudied Section A (Experiment 1). We also manipulated whether participants make an explicit retrieval attempt before making JOLs (Experiment 2). Then all participants learned Section B and took a final test for both sections. The results revealed that simply making target-present or target-absent JOLs did not produce learning benefits. However, when people made the target-absent JOLs combined with explicit retrieval attempts they outperformed the other groups in both sections, showing backward and forward effects of JOLs. In conclusion, JOLs seem to be effective only when learners actively retrieve the learned information while making metacognitive judgments.

Do Judgments of Learning Improve Inductive Learning?

People reciprocate another person’s altruistic or spiteful intentions toward themselves over repeated interactions, but it is unclear how such other-regarding intentions are represented in people’s minds and how the dynamics of reciprocity unfold. Recent work formalizes other-regarding intentions as welfare tradeoff ratios (WTR) and demonstrates that people reciprocate by adjusting their own WTR in response to the perceived WTR of another person. However, due to the complexity and inaccuracy of existing WTR measurements, it is still infeasible to study the long-term dynamics of people’s WTR adjustment beyond a few trials. Here we develop an experimental paradigm based on nonlinear continuous decomposed games that precisely reveals to the participant the opponent's WTR and measures the participant’s WTR in a single trial. We find that participants are sensitive to the opponent’s WTR and adjust their WTR accordingly. This experimental framework enables the fine-grained investigation of the long-term dynamics of people’s intention-based reciprocity.

Studying the long-term dynamics of reciprocity based on welfare tradeoff ratios

Making inferences about claims we do not have direct experience with is a common feature of everyday life. In these situations, it makes sense to consult others: an apparent consensus may be a useful cue to the truth of a claim. This strategy is not without its challenges. The utility of a consensus should depend in part on the sources of evidence that underlie it. If each person based their conclusion on independent data then the fact that they agree is informative. If, instead, everyone relied on the same primary source, the consensus is less meaningful. However, the extent to which people are actually sensitive to this kind of source independence is still unclear. Here, we present the results of three experiments that examine this issue in a social media setting, by varying the sources of primary data cited via retweets. In each experiment, participants rated their agreement with 12 different claims before and after reading four tweets that were retweeted on the basis of either the same or different primary data. We found that people were sensitive to source independence only when it was clear that the tweeters had relied on the primary data to reach their conclusion. Implications for existing research are discussed.

Source independence affects argument persuasiveness when the relevance is clear

Social environments often impose tradeoffs between pursuing personal goals and maintaining a favorable reputation. We studied how individuals navigate these tradeoffs using Reinforcement Learning (RL), paying particular attention to the role of social value orientation (SVO). We had human participants play an interated Trust Game against various software opponents and analyzed the behaviors. We then incorporated RL into two cognitive models, trained these RL agents against the same software opponents, and performed similar analyses. Our results show that the RL agents reproduce many interesting features in the human data, such as the dynamics of convergence during learning and the tendency to defect once reciprocation becomes impossible. We also endowed some of our agents with SVO by incorporating terms for altruism and inequality aversion into their reward functions. These prosocial agents differed from proself agents in ways that resembled the differences between prosocial and proself participants. This suggests that RL is a useful framework for understanding how people use feedback to make social decisions.

Reinforcement Learning, Social Value Orientation, and Decision Making: Computational Models and Empirical Validation

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.

Leveraging psychometrics of rational inattention to estimate individual differences in the capacity for cognitive control

Language use can be characterised as transparent, stating facts about the world, or non-transparent, requiring additional meaning to be inferred. The challenge faced by addressees is recognizing when language use is transparent or not. The current study investigates two factors that may influence how readily participants interpret utterances as instances of transparent or non-transparent language use; speaker knowledgeability and utterance form. When utterances involved negation participants were more likely to recognize this as non-transparent language use and infer that the situation is usually different. Whereas speaker knowledge did not influence how utterances were understood.

Effects of negation and knowledgeability on pragmatic inferences

Politeness is a social linguistic phenomenon. Modeling polite language production and understanding is difficult, as it may contradict conversational maxims and is shaped by extralinguistic social influences, such as the speaker-hearer relationship. This paper extends Yoon et al.'s (2016) Rational Speech Act-based model of politeness by mapping speaker-hearer relationship influences to the utility weights of the model and instantiates it in German. Three online experiments, for empirical analysis and collection of behavioural data for model training and evaluation, are presented. These confirm the influence of the speaker-hearer relations on indirect politeness. Furthermore, two versions of the model are trained and evaluated to find out which part of the model is better suited for the integration of social influences. Overall, both model versions yielded similar results and were able to predict the meaning of polite speech acts.


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Do Judgments of Learning Improve Inductive Learning?

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