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An outstanding issue in cognitive science is whether the computational principles that apply to causal reasoning also guide the way that participants encode the relations among events in memory. The outcome density effect is a behavioral pattern in causal reasoning in which participants’ causal ratings linearly decrease as the base rate of the effect also decreases, even while contingency remains 0. It is key evidence for Bayesian models of causal reasoning as it reflects decreasing uncertainty. We queried whether it may also, separately, affect memory for events. We measured both recall and causal ratings in a causal learning task to test whether the outcome density manipulation affects causal judgment, recall, or both. We replicated the outcome density effect on causal judgment, lending support to Bayesian models, but found that memory instead exhibited a U-shaped relationship with base rate, and therefore, causal judgment and memory had divergent signatures.

CogSci 2022

Patterns of Causal Judgements Diverge from Patterns of Recall: a Test of the Outcome Density Effect

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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


Children are capable of using the causal properties of objects to determine category membership. Can they also recognize causal system categories, or abstract patterns of causation that apply across phenomena (Rottman et al., 2012)? For example, do children recognize that one causal chain (e.g. the transmission of salmonella across species) shares the same underlying structure as a chain in another domain (e.g. heat transfer between objects)? If so, recognizing that superficially distinct events share the same underlying causal system may facilitate knowledge transfer. In Study 1, 6- to 7-year-olds and adults, but not 4 to 5-year-olds, are capable of identifying simple machines that share the same causal structure. In ongoing work, we control for low-level perceptual similarities and explore whether adults and children recognize similar causal structures among unique mechanical systems. We also consider whether their recognition of causal systems can be used to support knowledge transfer.

Children’s Recognition of Shared Causal Structure in Mechanical Systems

An accurate mental model of the partner's behavior is fundamental for efficient cooperation. The theory of mind demonstrates that humans are able to create such a model from repeated interactions with their human partners. However, it is an open question whether humans are also willing and capable of taking the perspective of artificial agents and creating similar mental models of agent behavior. We developed a repeated cooperative task that allows us to investigate the process that guides the formation of a specific partner model by repeatedly asking for a prediction of an artificial agent's actions. We found that humans learn to anticipate the artificial partners' behavior if it is goal-directed. An inability to explicitly explain the partner's behavior suggests that this is an implicit learning process. The role of the acquisition of task knowledge in the model of the other agent's behavior is discussed.

Thinking into a machine's mind - Anticipation of an agent's behavior in a cooperative game

Language studies in Asperger Syndrome (AS) report problems in intention interpretation, figurative language and pragmatic abilities. Those abilities require to differentiate constructional and contextual meaning. Previous research use a functional framework to look for literal and figurative language processes. Method. We use Conceptual Metaphor Theory (CMT) and Steen's five steps analysis to compare metaphorical expression frequencies in AS and Typical Development (TD) from an experiential-based perspective. We documented the conceptual metaphors (A is B form), and the metaphor's types: Structural, Orientational, and Ontological. We applied three tasks to elicit speech: (a) conversation task, (b) narration task, and (c) description task. Results. Our Mexico City’s data indicates that AS children are able to produce common metaphorical expressions at the same levels as TD children, at equal ages in Spanish. We found both populations use intentional and contextualized metaphor expressions, and the metaphors are mostly conventionalized expressions previously not considered.


Frequencies of Metaphorical Expressions in Asperger Syndrome and Typical Development

Prior work suggests children understand how speech conveys information and influences others’ minds. Although these studies have focused on communication under ideal conditions, auditory noise plagues the real world, often corrupting the transmission of information. The current study examines how children reason about the impact of auditory noise on communication. Children (N=72, Age:3;0-5;11) watched scenarios where a teacher tells a learner about two toys, but loud auditory noise masks one of the explanations. When asked which toy the learner wants to hear about again, children were more likely to select the noise-masked toy when the learner knew about neither toy (No Knowledge) than when he already knew about the masked toy (Partial Knowledge). However, their preference for the masked toy also increased with age in both conditions. Overall, these results demonstrate children's developing understanding of when and how communication affects listeners' knowledge and information-seeking behaviors.

Young children's reasoning about the epistemic consequences of auditory noise

Apperly and Butterfill (2009) argue that adult theory of mind (ToM) requires two parallel systems. One system, efficient but inflexible, enables rapid judgements by operating without explicit modeling of beliefs, while a separate, effortful system, enables richer predictions over more complex belief encodings. Here, we agree with their qualitative distinction but propose a different model: a single process, but with effortful re-representation leading to two phases of ToM reasoning. Efficient reasoning, in our view, occurs over representations that include actions, but not necessarily explicit belief states. Effortful reasoning, then, involves re-representation of these initial encodings in order to handle errors, resolve real-world conflicts, and fully account for others’ belief states. We present an implemented computational model, based in memory retrieval and structural alignment, that illustrates our approach.

Efficient and Effortful Theory of Mind Reasoning in the AToM Cognitive Model

As efficient systems of communication, languages are usually expected to map meanings to forms in a one-to-one way, using for example the same affix form (e.g., -s in English) every time a particular meaning is intended (e.g., plural number), and placing affixes with the same meaning consistently in the same position (e.g., always suffixal). Forms and positional rules extending over contexts with a common meaning (e.g., plural in 1PL, 2PL, 3PL) are thus considered natural, and those extending over contexts with no consistent common meaning (e.g., 1PL and 3SG) are considered unnatural. Natural patterns are most common cross-linguistically, and most learnable in experiments; however, little is yet know about differences between unnatural classes. In this study we explore syncretism (i.e., use of the same form in different functions) and affix position in the domain of person and number agreement in verbs, both cross-linguistically and in artificial language learning experiments. Results from the two approaches and both phenomena converge in finding a gradient of (un)naturalness. Rather than a dichotomous natural/unnatural distinction, we found that both cross-linguistic frequency and learnability are proportional to the amount of shared feature values among the contexts requiring the same form or position. We argue that a cognitive bias towards similarity-based structure explains our experimental results and could be driving the patterns observed in natural languages.

A Naturalness Gradient Shapes the Learnability and Cross-Linguistic Distribution of Morphological Paradigms

Words in human languages cluster together in phonological neighborhoods more closely than would be expected by chance. But why? One explanation is that large neighborhoods are directly selected for, possibly because they scaffold word learning and production. But it's also possible that they emerge as a byproduct of other constraints or selection pressures operating over real lexica. We advance one such selection pressure as a candidate explanation. A pressure to avoid overloading unique wordforms with homophones may lead to clusters of words that are not identical but similar. Using simulated baselines, we test the viability of this alternative account. We find that a pressure against loading too many meanings on unique wordforms––paired with the phonotactics of a target language––produces lexica with neighborhoods that are at least as large on average as those in real lexica. This does not rule out the possibility of a pro-neighborhood pressure, but it does demonstrate the viability of a parsimonious alternative account based on a pressure against homonymy for which there is independent evidence.

Can a pressure against homophones explain phonological neighborhoods?

Recent experimental and computational modelling work has found that languages are shaped by the referential context in which they operate. Wray and Grace (2007) argue that even compositionality, traditionally regarded as a universal and fundamental feature of human languages, may have only culturally evolved in response to changing social contexts. But how can the referential contexts of individual interactions come to shape the level of compositionality in the language of an entire community? To explore this question, we propose an iterated hierarchical Bayesian model that shows how partner-specific linguistic innovations can be generalized as community-wide features via a context-sensitive pathway. Our simulations show that the degree of compositionality that evolves in the language of a community depends on the communicative needs of its members, but also on the degree of user uncertainty over the nature of those needs, and on the level of heterogeneity in the community's needs.

The complexity of a language is shaped by the communicative needs of its users and by the hierarchical nature of their social inferences

Typological data shows a tendency for languages to exhibit harmonic (i.e. consistent) ordering between heads and dependents. However, some categories seem to contradict this tendency. Here we investigate one such case, the order of the noun with respect to two dependents—adjectives, which tend to follow the noun and genitives which precede. We report two silent gesture experiments examining (i) whether there are cognitive biases favouring postnominal adjective and prenominal genitive order in a single trial judgement task, and (ii) if those preferences continue to influence order when participants learn a complete word order system. Our results shed light on how biases for individual categories of elements interact with biases that affect the wider linguistic system. While participants strongly prefer postnominal adjectives and prenominal genitives when these are judged in isolation, when they learn a system of ordering, these biases are obscured and (at least in some cases) harmony emerges.

The Influence of Category-specific and System-wide Preferences on Cross-Linguistic Word Order Patterns

In an experimental study, we investigated how social interaction dynamics affect collective creativity. Pairs of participants collaborated in a computer game, creating “beautiful and interesting” shapes by moving tiles on a large touchscreen. We identified naturally emerging interaction styles by applying k-means clustering on participants’ tile moves. The game allowed us to quantify the unfolding creative process in a well-defined search space. Pairs characterized by a single dominating member tended to visit fewer areas of the solution space, stay there longer and created on average more (but less original) shapes. In contrast, pairs that took turns with every tile move tended to explore more, stayed in each area of the solution space for less time and created fewer (but more original) shapes. While previous literature found conflicting effects of ‘creating with another’, the current paper suggests naturally emerging interaction styles as a differentiating factor underlying how collective creativity unfolds.

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Children’s Recognition of Shared Causal Structure in Mechanical Systems

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