United States

CogSci 2025

Moral and Affective Psychophysics: Or, How (Not) to Study Subjective Magnitude

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

invited talk

### 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** 
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. 
 
* **Conference Creation** 
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.
 
* **Journal Creation** 
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.
 

#### 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.

Studies of segmental co-occurrence constraints have consistently
produced evidence consistent with a cross-linguistic
anti-similarity bias for consonants (e.g. Frisch et al. (2004),
Pozdniakov & Segerer (2007), Walter (2010), Doucette et al.
(2024)). The current study tests whether, in spite of this universal
similarity avoidance bias, there are cross-linguistically
consistent featural harmony biases the world’s lexicons. In
particular, we test for the presence of a nasal consonant harmony
bias, given the fact that categorical nasal consonant harmony
is attested in multiple language families and that nasal
harmony is phonetically-motivated. A Bayesian negative binomial
model of 91 typologically diverse languages’ type frequencies
for two-consonant words shows evidence of a weak
but reliable cross-linguistic bias in favor of nasal harmony,
as well as a comparable bias in favor of voicing harmony.
The findings also show patterns consistent with a similarityavoidance
bias, most notably a strong cross-linguistic bias
against coronal harmony. Taken together, these findings support
the notion that similarity-based co-occurrence constraints
may be feature dependent in cross-linguistically consistent
ways, and more generally that featural dimensions are relevant
for understanding the role of segmental redundancy in lexicons.

Sub-phonemic featural dimensions mediate consonantal co-occurrence biases in a cross-linguistically consistent manner

When addressing infants, parents often exaggerate positive affect in both their faces and speech. While these cues have been theorized to support learning, their frequency alongside linguistic input and the information they convey in children’s real-world experiences remain unclear. We analyzed ~377 hours of egocentric at-home videos from infants (5–28 months) to examine affective cues in faces and language surrounding early-learned words. Using automated tools, we tagged happy affect in the utterance in which each word was embedded and in co-occurring faces. Faces were visible in only 13.5% of word instances, and even fewer displayed happy affect. However, more (vs. less) positive words tended to co-occur with happier faces. Linguistic context, in contrast, conveyed stronger positive affect and more reliably aligned with word valence than facial affect. These findings suggest that facial and linguistic affect may serve distinct roles in infants' learning environments.

Exploring the affective structure of children's early language environment through egocentric video

The most commonly used measure of spatial cognition to assess both adults and children is mental rotation. However, little is known about the cognitive strategies that children use to solve this task. Understanding how and when children may employ different mental rotation strategies can illuminate the development of mental rotation ability and help clarify previous mixed findings on the developmental trajectory of children's mental rotation skills. Thus, in this study, we investigated what strategies children use in a new mental rotation task and whether their strategy use would be influenced by the test instructions. In Experiment 1, we found that the types of strategies children used in our mental rotation test differed from strategies reported in previous research, suggesting that strategy use is dependent on test design. In Experiment 2, we found that children's strategy use can be malleable; changing the test instructions reduced one type of erroneous strategy, flipping. Our findings suggest that different tests labeled “mental rotation tests” may actually be measuring different abilities.

The Malleability of Children's Mental Rotation Strategies: What do children's mental rotation tests really measure?

A key feature of human theory-of-mind is the ability to attribute beliefs to other agents as mentalistic explanations for their behavior. But given the wide variety of beliefs that agents may hold about the world and the rich language we can use to express them, which specific beliefs are people inclined to attribute to others? In this paper, we investigate the hypothesis that people prefer to attribute beliefs that are good explanations for the behavior they observe. We develop a computational model that quantifies the explanatory strength of a (natural language) statement about an agent's beliefs via three factors: accuracy, informativity, and causal relevance to actions, each of which can be computed from a probabilistic generative model of belief-driven behavior. Using this model, we study the role of each factor in how people selectively attribute beliefs to other agents. We investigate this via an experiment where participants watch an agent collect keys hidden in boxes in order to reach a goal, then rank a set of statements describing the agent's beliefs about the boxes' contents. We find that accuracy and informativity perform reasonably well at predicting these rankings when combined, but that causal relevance is the single factor that best explains participants' responses.

Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality

Humans can generalise from past experiences to novel situations as well as revise prior knowledge to flexibly adapt to changing contexts and goals. The representational geometry framework formalises how information is structured in the brain and suggests that abstraction involves a trade-off between generalisation and flexibility. However, how task representations evolve across learning and relate to behaviour remains unclear. Here, we tested the hypothesis that representational compression of task representations across learning underlies this flexibility impairment. Using an extra-dimensional shifting task, we manipulated the pretraining length to control the degree of compression. In both humans and artificial neural networks, longer pretraining was associated with decreased flexibility. Network dynamics indicated that greater compression incurred a higher representational reorganisation cost, limiting flexibility. Introducing an auxiliary reconstruction loss maintained higher dimensionality and mitigated the flexibility impairment. These findings suggest representational compression constrains flexibility, and preserving representational richness enhances flexibility.

The Effect of Representational Compression on Flexibility Across Learning in Humans and Artificial Neural Networks

Theory of Mind enables us to attribute mental states like beliefs and desires. We use it cooperatively, but we also use it adversarially, as when we lie. Prior work has shown people use Theory of Mind to craft lies to be believable to their audience, based on their audience's beliefs. But we usually also know something about our audience's desires. In this work, we ask a new question: Do people cater to their audience's desires by telling them what they want to hear? We propose that people expect others to be wishful thinkers---allowing their desires to color their beliefs---and exploit this by tailoring lies to audience desires. We implement this theory as a computational model and test it against human behavior in a novel task. This model quantitatively captures people's patterns of lying---both at the population and subject levels. This work advances our understanding of social cognition in adversarial interactions.

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