United States

Infants’ attention to faces is a critical component of social development, yet little is known about how face looking unfolds during real-world interactions. Using head-mounted eye tracking and computer vision, we investigated the availability of faces in infants’ field of view and their face-looking behaviors while playing with caregivers. Faces were visible only 23% of the time, and even then, infants looked at them only 19% of the time. Time series analyses revealed that face visibility is unrelated to face looking; Infants orient their head and body to bring a face into view when they intend to look at it, rather than looking because a face is present. Face looking is related to the face’s visual properties and is only slightly influenced by parents’ face looking. These findings highlight the active nature of infant face looking and suggest that it is shaped by both visual features and infants’ own interest.

CogSci 2025

Using Head-Mounted Eye Tracking to Examine Infant Face Looking During Naturalistic Freeplay

cognitive development

social cognition

eye tracking

perception

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

poster

### 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.
<br><br>

#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

A large body of research suggests that approach-related emotional states are lateralized to the left prefrontal cortex (Harmon-Jones et & Gable, 2018). However, because affective motivation and valence have often been entangled in experimental designs, it is unclear which construct drives this laterality. In one fMRI study designed to dissociate motivation and valence, Berkman and Lieberman (2010) found that approach motivation was more left-lateralized than avoidance motivation in the dorsolateral prefrontal cortex (DLPFC), controlling for valence.  Our study did not replicate this key finding from Berkman and Lieberman (2010).  Furthermore, whereas Berkman and Lieberman (2010) found that individuals' trait approach motivation predicted the laterality of approach-related DLPFC activity, we found that trait approach motivation predicted the laterality of positive valence, controlling for motivation.  Overall, our results do not provide any clear support for the 'textbook' model of affective motivation in the frontal lobes.

Replication of Prefrontal Asymmetry in Approach-Avoidance Motivation in fMRI

Vague temporal adverbials, such as ”recently," "just" and ”long time ago,” describe the temporal distance between a past event and the utterance time, but leave the exact duration underspecified. 
In this paper, we introduce a factorized model that captures the semantics of these adverbials as probabilistic distributions. These distributions are composed with event-specific distributions to yield a contextualized meaning for an adverbial applied to a specific event. 
We fit the model's parameters using existing data capturing judgements of native speakers regarding the applicability of these vague temporal adverbials to events that took place a given time ago. Comparing our approach to a non-factorized model based on a single Gaussian distribution for each pair of event and temporal adverbial, we find out that, while both models have similar predictive power, our model is preferable in terms of Occam’s razor, as it is simpler and has a better extendability.

A Factorized Probabilistic Model of the Semantics of Vague Temporal Adverbials Relative to Different Events

The Tritone Paradox is an auditory illusion in which a sequence of two complex tones is perceived as either ascending or descending, depending on the individual. It presents an interesting phenomenon for investigating pitch perception in contexts. however, no neurophysiological study has been conducted. This study identified event-related potential (ERP) correlates of pitch judgments under different pitch contexts. Twenty-seven participants judged whether the tritone pair was perceived as ascending or descending after listening to a sequence of ascending or descending tone pairs. Cortical auditory evoked responses to the second tone of the tritone pair were compared across contexts. In the Rise context, standard stimuli evoked larger responses at Fp1; in the Fall context, deviant stimuli elicited stronger responses across all sites. These results suggest that frontal and central brain regions are involved in processing ambiguous pitch stimuli, and that ERP responses reflect the interaction between stimulus context and perceptual.

Neural responses of Interval Judgment in the Tritone Paradox 

To learn about the world, people must gather information from external sources (e.g., books, the internet, other people). People prefer sources that have been reliable in the past—but how do we seek information when uncertain about a source’s quality? We test the hypothesis that these decisions are governed by an explore-exploit tradeoff. Prior work has researched this in reward search. Here, we adapt the tradeoff to information search. We propose that people weigh exploring an information source that is more uncertain with exploiting an information source that is more informationally promising. In one experiment (N = 171), we found evidence for both random and directed exploration in people’s choices between information sources. However, people explored uncertain information sources even without the possibility of future exploitation (differing from typical reward search). People may be broadly interested in learning about sources’ quality, and they explore to gain this information.

Learning how to learn: Evidence for an explore-exploit tradeoff in information search 

Cognitive functioning across development has predominantly been assessed through task performance. However, the role of cognitive effort infants and young children exert has been largely neglected in understanding cognitive functioning. In a large longitudinal sample (YOUth cohort, N = 2241) of infants and young children aged 5, 10 and 36 months, we extracted dynamic baseline-corrected pupil responses to measure cognitive effort during a gap-overlap eye-tracking task. Results revealed a shift from predominantly reactive effort when infants were younger to more preparatory effort in older children, especially for the more demanding condition. Moreover, preparatory effort in infancy predicted cognitive effort in childhood. These findings underscore the importance of measuring cognitive effort in addition to task performance to capture a more complete picture of early cognitive development.

Changes in cognitive effort across infancy and early childhood

Repeated social coordination is a crucial aspect of daily life in which individuals strategically distribute labor and resources, often to accomplish complex tasks and goals. However, social coordination is also very challenging because humans often have competing interests, especially when successful coordination persistently leaves one party better off, entrenching inequality. Here we use a novel task, the Asymmetric Social Exchange (ASE) Game, to study how individuals learn to coordinate with different kinds of social partners and how individual trait variability on key social dimensions related to negative evaluation (i.e., social anxiety), impacts compliance with disadvantageous conventions (N = 675). Using two kinds of Bayesian models, one that learns from experience and one that builds a causal model of others' hidden motivations, we show that differences in coordination strategies arise from both individual learning differences and from expressed social preferences. Further, we find that social anxiety increases compliance with disadvantageous payoffs.

Solving strategic social coordination via Bayesian learning

The causal world is highly inconsistent. While past research demonstrates even young learners’ ability to reason from probabilistic evidence, there has been little examination of how learners reason about the nature of causal inconsistencies. For instance, what do we think about why variations occur? Four- to six-year-olds (N=90) watched either a person or machine repeatedly act in one of two possible ways. At test, the opposite behavior occurred, and children were asked whether the change was due to an internal disposition/capacity for variation or constraints in the external context. Results reveal a significant bias towards internal explanations of agents’ inconsistencies (70%, p=0.01, two-tailed binomial) and for external explanations of machines in older (63%, p=0.09, two-tailed binomial), but not younger (50%) children – suggesting young learners consider both inherent and contextual sources of variation in causal relationships and gradually develop complex expectations about the different likelihoods of these sources depending on domain.

Disposition or Disruption: How do young learners explain inconsistent causal evidence?

Several cognitive theories explain successful learning of spatial visualization skills such as mental rotation. Spatial learning may occur through domain-specific changes to spatial transformation ability (in parietal cortex), embodied sensory-motor changes (in premotor cortex), or domain-general changes to executive functions (in prefrontal cortex). To evaluate these hypotheses, we analyzed brain activity during mental rotation using fMRI in 60 adults who completed 9 weeks of spatial visualization training. Mental rotation robustly activated bilateral parietal, premotor, and prefrontal areas both before and after training, with increased activity in the intraparietal sulcus, premotor cortex, and dorsolateral prefrontal cortex after training. However, improvements in spatial visualization and transfer to geometric reasoning were coupled with parietal and premotor changes, and not prefrontal changes. These results support the hypothesis that spatial learning is driven primarily by refinement of spatial transformation and sensory-motor imagery, although other brain regions may adapt secondarily as a byproduct of learning.

Cognitive and neural mechanisms of spatial learning and transfer in adults

The ability to learn new visual concepts from limited examples is a hallmark of human cognition. While traditional category learning models represent each example as an unstructured feature vector, compositional concept learning is thought to depend on (1) structured representations of examples (e.g., directed graphs consisting of objects and their relations) and (2) the identification of shared relational structure across examples through analogical mapping. Here, we introduce Probabilistic Schema Induction (PSI), a prototype model that employs deep learning to perform analogical mapping over structured representations of only a handful of examples, forming a compositional concept called a schema. In doing so, PSI relies on a novel conception of similarity that weighs object-level similarity and relational similarity, as well as a mechanism for amplifying relations relevant to classification, analogous to selective attention parameters in traditional models. We show that PSI produces human-like learning performance and outperforms two controls: a prototype model that uses unstructured feature vectors extracted from a deep learning model, and a variant of PSI with weaker structured representations. Notably, we find that PSI’s human-like performance is driven by an adaptive strategy that increases relational similarity over object-level similarity and upweights the contribution of relations that distinguish classes. These findings suggest that structured representations and analogical mapping are critical to modeling rapid human-like learning of compositional visual concepts, and demonstrate how deep learning can be leveraged to create psychological models.

Few-Shot Learning of Visual Compositional Concepts through Probabilistic Schema Induction

Understanding latent variables and their causal mechanisms is central to psychological theory, yet most latent variable models in psychology have largely remained correlational. This work attempts to address three pivotal issues: identifying useful information from observational data that reveal latent causal factors, developing algorithms to leverage this distributional information, ensuring the identifiability of the recovered latent factors and their causal structure. We introduce a generalizable framework for discovering hidden causal structures from observed distributions in psychometric data. Applied to survey datasets on personality traits, teacher burnout, and multitasking behavior, our method uncovers hidden causal factors and their intricate interactions. Additionally, our findings offer an alternative perspective on psychometric scoring, grounded in the strength of the learned causal relations. These insights contribute to behavioral modeling and measurement and await further confirmatory studies to validate their implications for psychological science.

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Replication of Prefrontal Asymmetry in Approach-Avoidance Motivation in fMRI

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Next from CogSci 2025

Replication of Prefrontal Asymmetry in Approach-Avoidance Motivation in fMRI

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Joint 3D Face Reconstruction and Dense Face Alignment via Deep Face Feature Alignment

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