Pragmatic Language Interpretation as Probabilistic Inference

Loopholes: A window into value alignment and the communication of meaning

Intentional misunderstandings take advantage of the ambiguity of language to do what someone said, instead of what they actually wanted. These purposeful misconstruals or loopholes are a familiar facet of fable, law, and everyday life. Engaging with loopholes requires a nuanced understanding of goals (your own and those of others), ambiguity, and social alignment. As such, loopholes provide a unique window into the normal operations of cooperation and communication. Despite their pervasiveness and utility in social interaction, research on loophole behavior is scarce. Here, we combine a theoretical analysis with empirical data to give a framework of loophole behavior. We first establish that loopholes are widespread, and exploited most often in equal or subordinate relationships (Study 1). We show that people reliably distinguish loophole behavior from both compliance and non-compliance (Study 2), and that people predict that others are most likely to exploit loopholes when their goals are in conflict with their social partner's and there is a cost for non-compliance (Study 3). We discuss these findings in light of other computational frameworks for communication and joint-planning, as well as discuss how loophole behavior might develop and the implications of this work for human-machine alignment.

The alignment model of indirect communication

Speakers often choose utterances under uncertainty about the potential opinion of the listener. In this case, utterances that do not signal the speaker's opinion directly may allow the speaker to avoid possible conflict: saying that an election outcome is interesting rather than amazing, even if the speaker is truly excited about it, may give her an option to retreat if it turns out that the listener's opinion is the opposite. By enhancing the Rational Speech Act framework with a turn-taking pragmatic system, we develop a model of indirect communication that is able to (1) rationalize the choice of indirect utterances when speakers' opinions do not align; (2) capture complex reasoning about the true interlocutor's opinion when facing indirect utterances and responses. The model has several novel features: in addition to standard informativeness goals, speaker choices factor in potential divergences of opinions between conversation partners. The listener model further considers multi-turn dialogues rather than isolated utterances: it is able to derive that an utterance like "interesting" can be interpreted positively or negatively depending on preceding discourse. The model, though complex, makes novel, non-trivial qualitative predictions, which are supported by data from three behavioral experiments reported here.

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks

Humans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science. Existing approaches have been successful at explaining how humans generalize rapidly in controlled settings but are usually too restrictive to tractably handle naturalistic data. We show that learning from limited naturalistic data is possible with an approach that bridges the divide between two popular modeling traditions: Bayesian models and neural networks. This approach distills a Bayesian model's inductive biases-the factors that guide generalization-into a neural network that has flexible representations. Like a Bayesian model, the resulting system can learn formal linguistic patterns from limited data. Like a neural network, it can also learn aspects of English syntax from naturally-occurring sentences. Thus, this model provides a single system that can learn rapidly and can handle naturalistic data.

Learning Loopholes: The Development of Intentional Misunderstandings in Children

What do children do when they do not want to obey but cannot afford to disobey? Might they, like adults, feign misunderstanding and seek out loopholes? Across four studies (N = 723; 44% female; USA; majority White; data collected 2020-2023), we find that loophole behavior emerges around ages 5 to 6 (Study 1, 3-18 years), that children think loopholes will get them into less trouble than non-compliance (Study 2, 4-10 years), predict that other children will be more likely to exploit loopholes when goals conflict (Study 3, 5-10 years), and are increasingly able to generate loopholes themselves (Study 4, 5-10 years). This work provides new insights on how children navigate the gray area between compliance and defiance and the development of loophole behavior across early and middle childhood.

Hunting for Paradoxes: A Research Strategy for Cognitive Science

How should we identify interesting topics in cognitive science? This paper suggests that one useful research strategy is to hunt for, and attempt to resolve, paradoxes: that is, apparent or real contradictions in our understanding of the mind and of thought. The rationale for this strategy is the assumption that our current thinking, and our various partial theories, of any topic are typically ill-defined, inconsistent or both. Thus, contradictions and confusions abound. Isolating paradoxes helps us expose vagueness and contradictions and demands that we formulate our ideas more precisely. From this point of view, finding a robust and puzzling contradiction in our current thinking should be celebrated as an achievement in itself. Ideally, of course, we then make further progress by clarifying how the paradox may be resolved, by clarifying our theories or finding new data that may decide between inconsistent assumptions. This approach is illustrated through examples from the author's research over several decades, which seems in retrospect to involve a repeated, if largely unwitting, application of this strategy.

Effects of Individual Differences and Prosodic Focus on the Interpretation of Quantity Scalar Terms in Mandarin-Speaking 3- to 8-Year-Olds

PURPOSE

This study focuses on examining how individual differences, including biological, linguistic, and cognitive traits, and prosodic focus affect the computation biases and reaction time (RT) associated with quantity scalar terms in Mandarin-speaking children aged 3-8 years.

METHOD

The participants of this study were 27 Mandarin-speaking children aged 3-8 years. They completed a computer-based sentence evaluation task, and their receptive vocabulary, nonverbal IQ, and theory of mind (ToM) skills were assessed. Additionally, parents provided insights into their children's executive functions, including working memory, planning, regulation, and inhibition abilities, through a questionnaire reflecting daily performance.

RESULTS

Mandarin-speaking 3- to 8-year-olds showed pervasive quantifier semantic biases versus bimodally distributed ad hoc semantic/pragmatic biases. Their quantifier pragmatic bias increased with age, working memory, and planning abilities but decreased with first-order ToM. In contrast, their ad hoc pragmatic bias improved with second-order ToM, working memory, planning, and inhibition abilities but decreased with age and receptive vocabulary. Prosodic focus reduced the number of hesitators and minimized the RT differences between hesitators and pragmatic/semantic responders.

CONCLUSIONS

Children show a higher overall pragmatic bias in ad hoc compared to quantifier scalar terms, alongside notable individual differences. Quantifier and ad hoc scalar terms appear to have different initial interpretations, with the former leaning toward a semantic interpretation and the latter toward a pragmatic one. Prosodic focus reduced hesitation and encouraged further processing, although it did not significantly alter interpretation biases. Future studies should employ larger sample sizes and implicit measures to further explore these findings.

Generics and Quantified Generalizations: Asymmetry Effects and Strategic Communicators

Generic statements ('Tigers have stripes') are pervasive and developmentally early-emerging modes of generalization with a distinctive linguistic profile. Previous experimental work suggests that generics display a unique asymmetry between the prevalence levels required to accept them and the prevalence levels typically implied by their use. This asymmetry effect is thought to have serious social consequences: if speakers use socially problematic generics based on prevalence levels that are systematically lower than what is typically inferred by their recipients, then using generics will likely exacerbate social stereotypes and biases. This paper presents evidence against the popular hypothesis that this asymmetry effect is unique to generics. Correcting for various shortcomings of previous studies, we found a generalized asymmetry effect across generics and various kinds of explicitly quantified statements ('most', 'some', 'typically', 'usually'). In addition, to better understand the conditions under which generalized asymmetry effects may exacerbate biases, we examine whether speakers choose generalizing sentences based simply on their acceptance conditions, or are systematically sensitive to the implications likely drawn by their typical recipients. In support of the latter view, we found that, in neutral or cooperative scenarios, speakers reliably choose generalizing sentences whose implied prevalence levels closely match the actual ones. In non-cooperative scenarios, many speakers exploit asymmetry effects to further their own goals by choosing generalizing sentences that are strictly true but likely to mislead their recipients. These results refine our understanding of the source of asymmetry effects and the conditions under which they may introduce biased beliefs into social networks.

Tracking minds in communication

How does social cognition help us communicate through language? At what levels does this interaction occur? In classical views, social cognition is independent of language, and integrating the two can be slow, effortful, and error-prone. But new research into word level processes reveals that communication is brimming with social micro-processes that happen in real time, guiding even the simplest choices like how we use adjectives, articles, and demonstratives. We interpret these findings in the context of advances in theoretical models of social cognition and propose a communicative mind-tracking framework, where social micro-processes are not a secondary process in how we use language - they are fundamental to how communication works.

Expectation violations signal goals in novel human communication

Communication, often grounded in shared expectations, faces challenges when a Sender and Receiver lack a common linguistic background. Our study explores how people instinctively turn to the fundamental principles of the physical world to overcome such barriers. Specifically, through an experimental game in which Senders convey messages via trajectories, we investigate how they develop novel strategies without relying on common linguistic cues. We build a computational model based on the principle of expectancy violations and a set of common universal priors derived from movement kinetics. The model replicates participant-designed messages with high accuracy and shows how its core variable-surprise-predicts the Receiver's physiological and neuronal responses in brain areas processing expectation violations. This work highlights the adaptability of human communication, showing how surprise can be a powerful tool in forming new communicative strategies without relying on common language.

Tool evolution as a prerequisite for consciousness

Within the concept of the extended mind, the active modification of external objects, externalizations, is seen as an auxiliary means to adapt to the environment. Toolmaking and use are advanced stages of externalizations that evolve. All past or present tools can, theoretically, be precisely assigned a location in an evolutionary tree with predecessors and progeny. Tools are reliably replicated, modified, and selected by their ability to facilitate human needs. Tool evolution, therefore, fulfills Darwinian criteria where the material tool is the phenotype and the instruction to build it is the code. The ostensive triangle consisting of a pointing individual, an observing individual, and a pointed-at object or tool is the germ cell of social transmission of instructions. Tool-building instructions ultimately can be reduced to distinct sequences of motor acts that can be recombined and are socially transmitted. When executed, they replicate tools for the reward of convenience or improved fitness. Tools elicit affordances relating to their use that synchronize different individuals' perceptions, result in psychological "understanding," and thereby modify social networks. Massive tool fabrication as present today in the "tool-sphere" has, therefore, accelerated prosociality and over time led to the acquisition of an individual's third person perspective. The entangled biological evolution accelerated the ongoing cumulative cultural evolution by selecting traits facilitating social transmission. In this context, tool evolution and the corresponding acquired individual instructional content is a precondition to the emergence of higher cognition and "consciousness." A neuroscience investigating externalizations as the starting point of this process is urgently needed.

Pragmatics as Social Inference About Intentional Action

Pragmatic inferences are based on assumptions about how speakers communicate: speakers are taken to be cooperative and rational; they consider alternatives and make intentional choices to produce maximally informative utterances. In principle, this analysis applies to linguistic but also non-linguistic communicative actions, but this prediction is typically only tested in children and not in more systematic implicature contexts. We test key implications of this view across six online experiments with American English speaking adults (total N = 231). Experiments 1A and 1B showed that participants made pragmatic inferences based on different types of communicative actions, some being non-linguistic. In Experiment 2, pragmatic inferences were found to be conditional on the speaker's epistemic states. Finally, Experiments 3A to 3C showed that pragmatic inferences were more likely to be made when the communicative action was produced intentionally. Taken together, these results strengthen the view that pragmatics includes social inference about cooperative communication over intentional actions, even non-linguistic actions.

Beyond what was said: Neural computations underlying pragmatic reasoning in referential communication

The ability to infer a speaker's utterance within a particular context for the intended meaning is central to communication. Yet, little is known about the underlying neurocomputational mechanisms of pragmatic inference, let alone relevant differences among individuals. Here, using a reference game combined with model-based functional magnetic resonance imaging (fMRI), we showed that an individual-level pragmatic inference model was a better predictor of listeners' performance than a population-level model. Our fMRI results showed that Bayesian posterior probability was positively correlated with activity in the ventromedial prefrontal cortex (vmPFC) and ventral striatum and negatively correlated with activity in dorsomedial PFC, anterior insula (AI), and inferior frontal gyrus (IFG). Importantly, individual differences in higher-order reasoning were correlated with stronger activation in IFG and AI and positively modulated the vmPFC functional connectivity with AI. Our findings provide a preliminary neurocomputational account of how the brain represents Bayesian belief inferences and the neural basis of heterogeneity in such reasoning.

Giving Generic Language Another Thought

According to an influential research program in cognitive science, philosophy, and linguistics, there is a deep, special connection between generics and pernicious aspects of social cognition, such as stereotyping. Specifically, generics are thought to exacerbate our propensity to essentialize, lead us to overgeneralize based on scarce evidence and to other epistemically dubious patterns of inference. Recently, however, several studies have put empirical and theoretical pressure on some of the main tenets of this research program. The goal of this paper is to bring these results together in a comprehensive narrative and systematically evaluate their impact on the hypothesis that generics have a uniquely problematic effect on our social and cognitive capacities.

Contextualizing predictive minds

The structure of human memory seems to be optimized for efficient prediction, planning, and behavior. We propose that these capacities rely on a tripartite structure of memory that includes concepts, events, and contexts-three layers that constitute the mental world model. We suggest that the mechanism that critically increases adaptivity and flexibility is the tendency to contextualize. This tendency promotes local, context-encoding abstractions, which focus event- and concept-based planning and inference processes on the task and situation at hand. As a result, cognitive contextualization offers a solution to the frame problem-the need to select relevant features of the environment from the rich stream of sensorimotor signals. We draw evidence for our proposal from developmental psychology and neuroscience. Adopting a computational stance, we present evidence from cognitive modeling research which suggests that context sensitivity is a feature that is critical for maximizing the efficiency of cognitive processes. Finally, we turn to recent deep-learning architectures which independently demonstrate how context-sensitive memory can emerge in a self-organized learning system constrained by cognitively-inspired inductive biases.

Do Languages Have Exclusive Disjunctions?

Most natural languages have more than one linguistic form available to express disjunction. One of these forms is often reported by native speakers to be more exclusive than the other(s) and, in recent years, it has been claimed that some languages may in fact have dedicated exclusive disjunctions. In this paper, we report on a series of experiments testing this claim across five languages of primary interest. Results show important variation in the rates of exclusive interpretation associated with the different particles used to express disjunction in these languages. Crucially, our findings show that, while complex disjunctions are usually perceived as more exclusive than their simple counterparts cross-linguistically, even the most exclusive disjunctions remain ambiguous between an inclusive and an exclusive interpretation. We discuss what factors may play a role in accounting for the gradient exclusivity effects observed in our data and how to model these effects in pragmatic and grammatical accounts of scalar implicatures.

The Psychology of Framing: How Everyday Language Shapes the Way We Think, Feel, and Act

When we use language to communicate, we must choose what to say, what not to say, and how to say it. That is, we must decide how to frame the message. These linguistic choices matter: Framing a discussion one way or another can influence how people think, feel, and act in many important domains, including politics, health, business, journalism, law, and even conversations with loved ones. The ubiquity of framing effects raises several important questions relevant to the public interest: What makes certain messages so potent and others so ineffectual? Do framing effects pose a threat to our autonomy, or are they a rational response to variation in linguistic content? Can we learn to use language more effectively to promote policy reforms or other causes we believe in, or is this an overly idealistic goal? In this article, we address these questions by providing an integrative review of the psychology of framing. We begin with a brief history of the concept of framing and a survey of common framing effects. We then outline the cognitive, social-pragmatic, and emotional mechanisms underlying such effects. This discussion centers on the view that framing is a natural-and unavoidable-feature of human communication. From this perspective, framing effects reflect a sensible response to messages that communicate different information. In the second half of the article, we provide a taxonomy of linguistic framing techniques, describing various ways that the structure or content of a message can be altered to shape people's mental models of what is being described. Some framing manipulations are subtle, involving a slight shift in grammar or wording. Others are more overt, involving wholesale changes to a message. Finally, we consider factors that moderate the impact of framing, gaps in the current empirical literature, and opportunities for future research. We conclude by offering general recommendations for effective framing and reflecting on the place of framing in society. Linguistic framing is powerful, but its effects are not inevitable-we can always reframe an issue to ourselves or other people.

Ambivalence by design: A computational account of loopholes

Loopholes offer an opening. Rather than comply or directly refuse, people can subvert an intended request by an intentional misunderstanding. Such behaviors exploit ambiguity and under-specification in language. Using loopholes is commonplace and intuitive in everyday social interaction, both familiar and consequential. Loopholes are also of concern in the law, and increasingly in artificial intelligence. However, the computational and cognitive underpinnings of loopholes are not well understood. Here, we propose a utility-theoretic recursive social reasoning model that formalizes and accounts for loophole behavior. The model captures the decision process of a loophole-aware listener, who trades off their own utility with that of the speaker, and considers an expected social penalty for non-cooperative behavior. The social penalty is computed through the listener's recursive reasoning about a virtual naive observer's inference of a naive listener's social intent. Our model captures qualitative patterns in previous data, and also generates new quantitative predictions consistent with novel studies (N = 265). We consider the broader implications of our model for other aspects of social reasoning, including plausible deniability and humor.

Learning from conditional probabilities

Bayesianism, that is, the formal capturing of belief in terms of probabilities, has had a major impact in cognitive science. Decades of research have examined lay reasoners' learning and reasoning with probabilities. The bulk of that research has concerned the response to new evidence. That response will depend on the conditional probabilities a reasoner assumes, yet little research has addressed the question of how reasoners respond when they are provided with new conditional probabilities. Furthermore, there are not just open empirical questions as to how lay reasoners actually respond, there are also open questions about how they should respond. This is illustrated by philosophical debate about the so-called Judy Benjamin Problem. In this paper, we present experiments on belief revision problems in which the new information is a conditional probability. More specifically, we investigate two versions of these problems. One where basic probability theory (as the core of what it means 'to be Bayesian') provides a single correct answer, and one where that answer is under-constrained. The former provide a new type of evidence on the longstanding question of human probabilistic reasoning skill. The latter informs debate on how to expand the Bayesian toolbox to deal with the issues raised by the Judy Benjamin Problem.

Pre-Stimulus Activity of Left and Right TPJ in Linguistic Predictive Processing: A MEG Study

BACKGROUND

The left and right temporoparietal junctions (TPJs) are two brain areas involved in several brain networks, largely studied for their diverse roles, from attentional orientation to theory of mind and, recently, predictive processing. In predictive processing, one crucial concept is prior precision, that is, the reliability of the predictions of incoming stimuli. This has been linked with modulations of alpha power as measured with electrophysiological techniques, but TPJs have seldom been studied in this framework.

METHODS

The present article investigates, using magnetoencephalography, whether spontaneous oscillations in pre-stimulus alpha power in the left and right TPJs can modulate brain responses during a linguistic task that requires predictive processing in literal and non-literal sentences.

RESULTS

Overall, results show that pre-stimulus alpha power in the rTPJ was associated with post-stimulus responses only in the left superior temporal gyrus, while lTPJ pre-stimulus alpha power was associated with post-stimulus activity in Broca's area, left middle temporal gyrus, and left superior temporal gyrus.

CONCLUSIONS

We conclude that both the right and left TPJs have a role in linguistic prediction, involving a network of core language regions, with differences across brain areas and linguistic conditions that can be parsimoniously explained in the context of predictive processing.

Building machines that learn and think with people

What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and we propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.

How rational inference about authority debunking can curtail, sustain, or spread belief polarization

In polarized societies, divided subgroups of people have different perspectives on a range of topics. Aiming to reduce polarization, authorities may use debunking to lend support to one perspective over another. Debunking by authorities gives all observers shared information, which could reduce disagreement. In practice, however, debunking may have no effect or could even contribute to further polarization of beliefs. We developed a cognitively inspired model of observers' rational inferences from an authority's debunking. After observing each debunking attempt, simulated observers simultaneously update their beliefs about the perspective underlying the debunked claims and about the authority's motives, using an intuitive causal model of the authority's decision-making process. We varied the observers' prior beliefs and uncertainty systematically. Simulations generated a range of outcomes, from belief convergence (less common) to persistent divergence (more common). In many simulations, observers who initially held shared beliefs about the authority later acquired polarized beliefs about the authority's biases and commitment to truth. These polarized beliefs constrained the authority's influence on new topics, making it possible for belief polarization to spread. We discuss the implications of the model with respect to beliefs about elections.

(Mal)adaptive Mentalizing in the Cognitive Hierarchy, and Its Link to Paranoia

Humans need to be on their toes when interacting with competitive others to avoid being taken advantage of. Too much caution out of context can, however, be detrimental and produce false beliefs of intended harm. Here, we offer a formal account of this phenomenon through the lens of Theory of Mind. We simulate agents of different depths of mentalizing within a simple game theoretic paradigm and show how, if aligned well, deep recursive mentalization gives rise to both successful deception as well as reasonable skepticism. However, we also show that if a self is mentalizing too deeply - hyper-mentalizing - false beliefs arise that a partner is trying to trick them maliciously, resulting in a material loss to the self. Importantly, we show that this is only true when hypermentalizing agents believe observed actions are generated intentionally. This theory offers a potential cognitive mechanism for suspiciousness, paranoia, and conspiratorial ideation. Rather than a deficit in Theory of Mind, paranoia may arise from the application of overly strategic thinking to ingenuous behaviour.

Author Summary

Interacting competitively requires vigilance to avoid deception. However, excessive caution can have adverse effects, stemming from false beliefs of intentional harm. So far there is no formal cognitive account of what may cause this suspiciousness. Here we present an examination of this phenomenon through the lens of Theory of Mind - the cognitive ability to consider the beliefs, intentions, and desires of others. By simulating interacting computer agents we illustrate how well-aligned agents can give rise to successful deception and justified skepticism. Crucially, we also reveal that overly cautious agents develop false beliefs that an ingenuous partner is attempting malicious trickery, leading to tangible losses. As well as formally defining a plausible mechanism for suspiciousness, paranoia, and conspiratorial thinking, our theory indicates that rather than a deficit in Theory of Mind, paranoia may involve an over-application of strategy to genuine behaviour.

forager: a Python package and web interface for modeling mental search

Analyzing data from the verbal fluency task (e.g., "name all the animals you can in a minute") is of interest to both memory researchers and clinicians due to its broader implications for memory search and retrieval. Recent work has proposed several computational models to examine nuanced differences in search behavior, which can provide insights into the mechanisms underlying memory search. A prominent account of memory search within the fluency task was proposed by Hills et al. (2012), where mental search is modeled after how animals forage for food in physical space. Despite the broad potential utility of these models to scientists and clinicians, there is currently no open-source program to apply and compare existing foraging models or clustering algorithms without extensive, often redundant programming. To remove this barrier to studying search patterns in the fluency task, we created forager, a Python package ( https://github.com/thelexiconlab/forager ) and web interface ( https://forager.research.bowdoin.edu/ ). forager provides multiple automated methods to designate clusters and switches within a fluency list, implements a novel set of computational models that can examine the influence of multiple lexical sources (semantic, phonological, and frequency) on memory search using semantic embeddings, and also enables researchers to evaluate relative model performance at the individual and group level. The package and web interface cater to users with various levels of programming experience. In this work, we introduce forager's basic functionality and use cases that demonstrate its utility with pre-existing behavioral and clinical data sets of the semantic fluency task.

Children use disagreement to infer what happened

In a rapidly changing and diverse world, the ability to reason about conflicting perspectives is critical for effective communication, collaboration, and critical thinking. The current pre-registered experiments with children ages 7 to 11 years investigated the developmental foundations of this ability through a novel social reasoning paradigm and a computational approach. In the inference task, children were asked to figure out what happened based on whether two speakers agreed or disagreed in their interpretation. In the prediction task, children were provided information about what happened and asked to predict whether two speakers will agree or disagree. Together, these experiments assessed children's understanding that disagreement often results from ambiguity about what happened, and that ambiguity about what happened is often predictive of disagreement. Experiment 1 (N = 52) showed that children are more likely to infer that an ambiguous utterance occurred after learning that people disagreed (versus agreed) about what happened and found that these inferences become stronger with age. Experiment 2 (N = 110) similarly found age-related change in children's inferences and also showed that children could reason in the forward direction, predicting that an ambiguous utterance would lead to disagreement. A computational model indicated that although children's ability to predict when disagreements might arise may be critical for making the reverse inferences, it did not fully account for age-related change.

Demonstratives as attention tools: Evidence of mentalistic representations within language

Linguistic communication is an intrinsically social activity that enables us to share thoughts across minds. Many complex social uses of language can be captured by domain-general representations of other minds (i.e., mentalistic representations) that externally modulate linguistic meaning through Gricean reasoning. However, here we show that representations of others' attention are embedded within language itself. Across ten languages, we show that demonstratives-basic grammatical words (e.g., "this"/"that") which are evolutionarily ancient, learned early in life, and documented in all known languages-are intrinsic attention tools. Beyond their spatial meanings, demonstratives encode both joint attention and the direction in which the listener must turn to establish it. Crucially, the frequency of the spatial and attentional uses of demonstratives varies across languages, suggesting that both spatial and mentalistic representations are part of their conventional meaning. Using computational modeling, we show that mentalistic representations of others' attention are internally encoded in demonstratives, with their effect further boosted by Gricean reasoning. Yet, speakers are largely unaware of this, incorrectly reporting that they primarily capture spatial representations. Our findings show that representations of other people's cognitive states (namely, their attention) are embedded in language and suggest that the most basic building blocks of the linguistic system crucially rely on social cognition.

Interactions between Stress Levels and Hormonal Responses Related to Sports Performance in Pro Women's Basketball Team

The testosterone to cortisol ratio (T:C ratio) is a measure of whether elite athletes are recovering from their training. This study described this hormone balance stress in elite women's basketball. (1) Objectives: to analyse the fluctuation of T:C ratio over a 16-week period and explore itis relation to their athletic performance. The participants characteristics were: (height: 177.6 ± 6.4 cm; body mass: 77.808 ± 12.396 kg age: 26.0 ± 5.9 years; and a playing experience of 14.7 ± 2.9 years with 5.0 ± 1.2 years at the elite level. The T:C ratio at Time 1 is: 4.0 ± 2.4 (n = 12); and at Time 2 is: 5.1 ± 4.3 (n = 12). (2) Methods: during 16 weeks of competition, participants underwent analysis of blood samples to assess various biochemical parameters including hormone levels. In addition, their athletic performance was assessed with the following tests: jumping (SJ, CMJ, ABK, DJ); throwing test with a medicine ball (3 kg); Illinois COD agility test; sprint repeatability with change of direction; 20-m speed test without change of direction; and Yo-yo intermittent endurance test IET (II). (3) Results: The main alterations observed were an increase in T levels (1.687%) and a decrease in C levels (-7.634%) between moments, with an improvement (26.366%) in the T:C ratio. Improvements were also observed in some of the tests developed, such as jumping (SJ: 11.5%, p = 0.029; CMJ: 10.5%, p = 0.03; DJ: 13.0%, p = 0.01), upper body strength (MBT: 5.4%, p = 0.03), translation ability (20 m: -1.7%), repeated sprint ability (RSA: -2.2%), as well as intermittent endurance test (Yy (IET): 63.5%, p = 0.01), with significant changes in some of the performance tests. (4) Conclusions: T:C ratio may differ in a manner unrelated to training volume, showing some variation. These results may be attributed to the accumulation of psychophysiological stress during the season.

Preschoolers' Comprehension of Functional Metaphors

Previous work suggests that preschoolers often misunderstand metaphors. However, some recent studies demonstrate that preschoolers can represent abstract relations, suggesting that the cognitive foundations of metaphor comprehension may develop earlier than previously believed. The present experiments used novel paradigms to explore whether preschoolers (N = 200; 4-5 years; 100 males, 100 females; predominantly White) can understand metaphors based on abstract, functional similarities. In Experiment 1, preschoolers and adults (N = 64; 18-41 years; 25 males, 39 females; predominantly White) rated functional metaphors (e.g., "Roofs are hats"; "Tires are shoes") as "smarter" than nonsense statements (e.g., "Boats are skirts"; "Pennies are sunglasses") in a metalinguistic judgment task (d = .42 in preschoolers; d = 3.06 in adults). In Experiment 2, preschoolers preferred functional explanations (e.g., "Both keep you dry") over perceptual explanations (e.g., "Both have pointy tops") when interpreting functional metaphors (e.g., "Roofs are hats") (d = .99). In Experiment 3, preschoolers preferred functional metaphors (e.g., "Roofs are hats") over nonsense statements (e.g., "Roofs are scissors") when prompted to select the "better" utterance (d = 1.25). Moreover, over a quarter of preschoolers in Experiment 1 and half of preschoolers in Experiment 3 explicitly articulated functional similarities when justifying their responses, and the performance of these subsets of children drove the success of the entire sample in both experiments. These findings demonstrate that preschoolers can understand metaphors based on abstract, functional similarities.

A Hierarchical Bayesian Model of Adaptive Teaching

How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.

Teaching Without Thinking: Negative Evaluations of Rote Pedagogy

When people make decisions, they act in a way that is either automatic ("rote"), or more thoughtful ("reflective"). But do people notice when others are behaving in a rote way, and do they care? We examine the detection of rote behavior and its consequences in U.S. adults, focusing specifically on pedagogy and learning. We establish repetitiveness as a cue for rote behavior (Experiment 1), and find that rote people are seen as worse teachers (Experiment 2). We also find that the more a person's feedback seems similar across groups (indicating greater rote-ness), the more negatively their teaching is evaluated (Experiment 3). A word-embedding analysis of an open-response task shows people naturally cluster rote and reflective teachers into different semantic categories (Experiment 4). We also show that repetitiveness can be decoupled from perceptions of rote-ness given contextual explanation (Experiment 5). Finally, we establish two additional cues to rote behavior that can be tied to quality of teaching (Experiment 6). These results empirically show that people detect and care about scripted behaviors in pedagogy, and suggest an important extension to formal frameworks of social reasoning.

Meaning as mentalization

The way we establish meaning has been a profound question not only in language research but in developmental science as well. The relation between linguistic form and content has been loosened up in recent pragmatic approaches to communication, showing that code-based models of language comprehension must be augmented by context-sensitive, pragmatic-inferential mechanisms to recover the speaker's intended meaning. Language acquisition has traditionally been thought to involve building a mental lexicon and extracting syntactic rules from noisy linguistic input, while communicative-pragmatic inferences have also been argued to be indispensable. Recent research findings exploring the electrophysiological indicator of semantic processing, the N400, have raised serious questions about the traditional separation between semantic decoding and pragmatic inferential processes. The N400 appears to be sensitive to mentalization-the ability to attribute beliefs to social partners-already from its developmental onset. This finding raises the possibility that mentalization may not simply contribute to pragmatic inferences that enrich linguistic decoding processes but that the semantic system may be functioning in a fundamentally mentalistic manner. The present review first summarizes the key contributions of pragmatic models of communication to language comprehension. Then, it provides an overview of how communicative intentions are interpreted in developmental theories of communication, with a special emphasis on mentalization. Next, it discusses the sensitivity of infants to the information-transmitting potential of language, their ability to pick up its code-like features, and their capacity to track language comprehension of social partners using mentalization. In conclusion, I argue that the recovery of meaning during linguistic communication is not adequately modeled as a process of code-based semantic retrieval complemented by pragmatic inferences. Instead, the semantic system may establish meaning, as intended, during language comprehension and acquisition through mentalistic attribution of content to communicative partners.

Genies, lawyers, and smart-asses: Extending proxy failures to intentional misunderstandings

We propose that the logic of a genie - an agent that exploits an ambiguous request to intentionally misunderstand a stated goal - underlies a common and consequential phenomenon, well within what is currently called proxy failures. We argue that such intentional misunderstandings are not covered by the current proposed framework for proxy failures, and suggest to expand it.

Minimizing Entropy and Complexity in Creative Production from Emergent Pragmatics to Action Semantics

New insights into intractable industrial challenges can be revealed by framing them in terms of natural science. One intractable industrial challenge is that creative production can be much more financially expensive and time consuming than standardized production. Creative products include a wide range of goods that have one or more original characteristics. The scaling up of creative production is hindered by high financial production costs and long production durations. In this paper, creative production is framed in terms of interactions between entropy and complexity during progressions from emergent pragmatics to action semantics. An analysis of interactions between entropy and complexity is provided that relates established practice in creative production to organizational survival in changing environments. The analysis in this paper is related to assembly theory, which is a recent theoretical development in natural science that addresses how open-ended generation of complex physical objects can emerge from selection in biology. Parallels between assembly practice in industrial production and assembly theory in natural science are explained through constructs that are common to both, such as assembly index. Overall, analyses reported in the paper reveal that interactions between entropy and complexity underlie intractable challenges in creative production, from the production of individual products to the survival of companies.

Bayesian Reinforcement Learning With Limited Cognitive Load

All biological and artificial agents must act given limits on their ability to acquire and process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent's learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This body of work provides an account of capacity-limited Bayesian reinforcement learning, a unifying normative framework for modeling the effect of processing constraints on learning and action selection. Here, we provide an accessible review of recent algorithms and theoretical results in this setting, paying special attention to how these ideas can be applied to studying questions in the cognitive and behavioral sciences.

Large Language Models: A Historical and Sociocultural Perspective

This letter explores the intricate historical and contemporary links between large language models (LLMs) and cognitive science through the lens of information theory, statistical language models, and socioanthropological linguistic theories. The emergence of LLMs highlights the enduring significance of information-based and statistical learning theories in understanding human communication. These theories, initially proposed in the mid-20th century, offered a visionary framework for integrating computational science, social sciences, and humanities, which nonetheless was not fully fulfilled at that time. The subsequent development of sociolinguistics and linguistic anthropology, especially since the 1970s, provided critical perspectives and empirical methods that both challenged and enriched this framework. This letter proposes that two pivotal concepts derived from this development, metapragmatic function and indexicality, offer a fruitful theoretical perspective for integrating the semantic, textual, and pragmatic, contextual dimensions of communication, an amalgamation that contemporary LLMs have yet to fully achieve. The author believes that contemporary cognitive science is at a crucial crossroads, where fostering interdisciplinary dialogues among computational linguistics, social linguistics and linguistic anthropology, and cognitive and social psychology is in particular imperative. Such collaboration is vital to bridge the computational, cognitive, and sociocultural aspects of human communication and human-AI interaction, especially in the era of large language and multimodal models and human-centric Artificial Intelligence (AI).

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Are you thinking what I'm thinking? Perspective-taking in a language game

Many theories of communication claim that perspective-taking is a fundamental component of the successful design of utterances for a specific audience. In three experiments, we investigated perspective-taking in a constrained communication situation: Participants played a word guessing game where each trial required them to select a clue word to communicate a single target word to their partner. In many cases, the task requires participants to take the perspective of their partner when generating, evaluating, and selecting potential clue words. For example, if the target word was 'heart', the first word that came to mind might be 'love', but this would not in fact be a very useful clue word. Instead, a word like 'cardiovascular' is much more likely than 'love' to make the partner guess 'heart'. Pairs of participants took turns giving and receiving clues to guess target words, receiving feedback after each trial. In Experiment 1, participants appeared unable to improve their perspective-taking over repeated interactions, despite a baseline performance that suggested strong perspective-taking abilities. In Experiment 2, which included extensive feedback after each trial and only target words for which good clues existed and which required perspective-taking, some measures of perspective-taking showed modest improvements. In Experiment 3, which was conducted online, we used Experiment 2 feedback with Experiment 1 target words. As in Experiment 1, participants did not improve over the course of the game in Experiment 3. The results of these three experiments show quite strong limits on people's ability to adapt and improve perspective-taking without the context provided by interaction history and growing common ground.

Storytelling as Inverse Inverse Planning

Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by this "journey?" Recently, literary theorist Karin Kukkonen proposed that storytelling is "probability design:" the art of giving an audience pieces of information bit by bit, to craft the journey of their changing beliefs about the fictional world. A good "probability design" choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as inverse inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary "naïve planning." Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.

An Information-Theoretic Account of Availability Effects in Language Production

I present a computational-level model of language production in terms of a combination of information theory and control theory in which words are chosen incrementally in order to maximize communicative value subject to an information-theoretic capacity constraint. The theory generally predicts a tradeoff between ease of production and communicative accuracy. I apply the theory to two cases of apparent availability effects in language production, in which words are selected on the basis of their accessibility to a speaker who has not yet perfectly planned the rest of the utterance. Using corpus data on English relative clause complementizer dropping and experimental data on Mandarin noun classifier choice, I show that the theory reproduces the observed phenomena, providing an alternative account to Uniform Information Density and a promising general model of language production which is tightly linked to emerging theories in computational neuroscience.

Using contrastive inferences to learn about new words and categories

In the face of unfamiliar language or objects, description is one cue people can use to learn about both. Beyond narrowing potential referents to those that match a descriptor (e.g., "tall"), people could infer that a described object is one that contrasts with other relevant objects of the same type (e.g., "the tall cup" contrasts with another, shorter cup). This contrast may be in relation to other objects present in the environment (this cup is tall among present cups) or to the referent's category (this cup is tall for a cup in general). In three experiments, we investigate whether people use such contrastive inferences from description to learn new word-referent mappings and learn about new categories' feature distributions. People use contrastive inferences to guide their referent choice, though size - and not color - adjectives prompt them to consistently choose the contrastive target over alternatives (Experiment 1). People also use color and size description to infer that a novel object is atypical of its category (Experiments 2 and 3): utterances like "the blue toma" prompt people to infer that tomas are less likely to be blue in general. However, these two inferences do not trade off substantially: people infer a described referent is atypical even when the descriptor was necessary to establish reference. We model these experiments in the Rational Speech Act (RSA) framework and find that it predicts both of these inferences. Overall, people are able to use contrastive inferences from description to resolve reference and make inferences about a novel object's category, letting them learn more about new things than literal meaning alone allows.

An individual differences perspective on pragmatic abilities in the preschool years

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities. RESEARCH HIGHLIGHTS: Individual differences in pragmatic abilities are important to understanding variation in language development. Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures. We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability. We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency-based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition.

Priming scalar and ad hoc enrichment in children

Sentences can be enriched by considering what the speaker does not say but could have done. Children, however, struggle to derive one type of such enrichments, scalar implicatures. A popular explanation for this, the lexical alternatives account, is that they do not have lexical knowledge of the appropriate alternatives to generate the implicature. Namely, children are unaware of the scalar relationship between some and all. We conducted a priming study with N = 72 children, aged 5;1 years, and an adult sample, N = 51, to test this hypothesis. Participants were exposed to prime trials of strong, alternative, or weak sentences involving scalar or ad hoc expressions, and then saw a target trial that could be interpreted in either way. Consistent with previous studies, children were reluctant to derive scalar implicatures. However, there were two novel findings. (1) Children responded with twice the rate of ad hoc implicature responses than adults, suggesting that the implicature was the developmentally prior interpretation for ad hoc expressions. (2) Children showed robust priming effects, suggesting that children are aware of the scalar relationship between some and all, even if they choose not to derive the implicature. This suggests that the root cause of the scalar implicature deficit is not due to the absence of lexical knowledge of the relationship between some and all.

People can use the placement of objects to infer communicative goals

Beyond words and gestures, people have a remarkable capacity to communicate indirectly through everyday objects: A hat on a chair can mean it is occupied, rope hanging across an entrance can mean we should not cross, and objects placed in a closed box can imply they are not ours to take. How do people generate and interpret the communicative meaning of objects? We hypothesized that this capacity is supported by social goal inference, where observers recover what social goal explains an object being placed in a particular location. To test this idea, we study a category of common ad-hoc communicative objects where a small cost is used to signal avoidance. Using computational modeling, we first show that goal inference from indirect physical evidence can give rise to the ability to use object placement to communicate. We then show that people from the U.S. and the Tsimane'-a farming-foraging group native to the Bolivian Amazon-can infer the communicative meaning of object placement in the absence of a pre-existing convention, and that people's inferences are quantitatively predicted by our model. Finally, we show evidence that people can store and retrieve this meaning for use in subsequent encounters, revealing a potential mechanism for how ad-hoc communicative objects become quickly conventionalized. Our model helps shed light on how humans use their ability to interpret other people's behavior to embed social meaning into the physical world.

Information-theoretic principles in incremental language production

I apply a recently emerging perspective on the complexity of action selection, the rate-distortion theory of control, to provide a computational-level model of errors and difficulties in human language production, which is grounded in information theory and control theory. Language production is cast as the sequential selection of actions to achieve a communicative goal subject to a capacity constraint on cognitive control. In a series of calculations, simulations, corpus analyses, and comparisons to experimental data, I show that the model directly predicts some of the major known qualitative and quantitative phenomena in language production, including semantic interference and predictability effects in word choice; accessibility-based ("easy-first") production preferences in word order alternations; and the existence and distribution of disfluencies including filled pauses, corrections, and false starts. I connect the rate-distortion view to existing models of human language production, to probabilistic models of semantics and pragmatics, and to proposals for controlled language generation in the machine learning and reinforcement learning literature.

Socially intelligent machines that learn from humans and help humans learn

A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human-machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

Active Iterative Social Inference in Multi-Trial Signaling Games

Human behavioral choices can reveal intrinsic and extrinsic decision-influencing factors. We investigate the inference of choice priors in situations of referential ambiguity. In particular, we use the scenario of signaling games and investigate to which extent study participants profit from actively engaging in the task. Previous work has revealed that speakers are able to infer listeners' choice priors upon observing ambiguity resolution. However, it was also shown that only a small group of participants was able to strategically construct ambiguous situations to create learning opportunities. This paper sets to address how prior inference unfolds in more complex learning scenarios. In Experiment 1, we examine whether participants accumulate evidence about inferred choice priors across a series of four consecutive trials. Despite the intuitive simplicity of the task, information integration turns out to be only partially successful. Integration errors result from a variety of sources, including transitivity failure and recency bias. In Experiment 2, we investigate how the ability to actively construct learning scenarios affects the success of prior inference and whether the iterative settings improve the ability to choose utterances strategically. The results suggest that full task engagement and explicit access to the reasoning pipeline facilitates the invocation of optimal utterance choices as well as the accurate inference of listeners' choice priors.

Conventionality determines the time course of indirect replies comprehension: An ERP study

Indirect language comprehension requires decoding both the literal meaning and the intended meaning of an utterance, in which pragmatic inference is involved. This study tests the role of conventionality in the time course of indirect reply processing by comparing conventional and non-conventional indirect replies with direct reply, respectively. We constructed discourses which consist of a context and a dialogue with one question (e.g., May I buy a necklace for you) and one reply (e.g., I really have too many). The reply utterance was segmented into three phrases and presented orderly for EEG recording, e.g., with the subject as the first phrase (e.g., I), the adverbial as the second phrase (e.g., really), and the predicate as the third phrase (e.g., have too many). Our results showed that for conventional indirect replies, the second phrase elicited a larger anterior negativity, and the third phrase elicited a larger anterior N400 compared with those in direct replies. By contrast, for the non-conventional indirect reply, only the third phrase elicited a larger late negativity than the direct replies. These findings suggest that conventionality determines the time course of the pragmatic inferences for the most relevant interpretation during indirect replies comprehension.

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein-Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters' emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.

Formalising social representation to explain psychiatric symptoms

Recent work in social cognition has moved beyond a focus on how people process social rewards to examine how healthy people represent other agents and how this is altered in psychiatric disorders. However, formal modelling of social representation has not kept pace with these changes, impeding our understanding of how core aspects of social cognition function, and fail, in psychopathology. Here, we suggest that belief-based computational models provide a basis for an integrated sociocognitive approach to psychiatry, with the potential to address important but unexamined pathologies of social representation, such as maladaptive schemas and illusory social agents.

Show or tell? Exploring when (and why) teaching with language outperforms demonstration

People use a wide range of communicative acts across different modalities, from concrete demonstrations to abstract language. While these modalities are typically studied independently, we take a comparative approach and ask when and why one modality might outperform another. We present a series of real-time, multi-player experiments asking participants to teach concepts using either demonstrations or language. Our first experiment (N=416) asks when language might outperform demonstration. We manipulate the complexity of the concept being taught and find that language communicates complex concepts more effectively than demonstration. We then ask why language succeeds in this setting. We hypothesized that language allowed teachers to reference abstract object features (e.g., shapes and colors), while demonstration teachers could only provide concrete examples (specific positive or negative objects). To test this hypothesis, our second experiment (N=568) ablated object features from the teacher's interface. This manipulation severely impaired linguistic (but not demonstrative) teaching. Our findings suggest that language communicates complex concepts by directly transmitting abstract rules. In contrast, demonstrations transmit examples, requiring the learner to infer the rules.