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CogSci 2025

How important is language for human-like intelligence?

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

symposium

### 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.

Speakers often communicate imprecisely, using expressions that are strictly speaking false yet felicitous. The degree to which imprecision is tolerated in discourse is governed by the Standard of Precision (SoP). While it is known that contextual factors can modulate the SoP, less is known about the discourse dynamics of imprecision. Previous accounts have claimed that implicit negotiations of the SoP are unidirectional: they only work upward, not downward. Here, we investigated whether parallel asymmetries arise when comprehenders adapt their SoPs in response to an interlocutor’s precision preferences. Results show bidirectional SoP adaptation effects: exposure to lower standards increased tolerance for imprecision, while exposure to higher standards reinforced stricter thresholds. These updates persisted beyond the dialogue, suggesting that exposure to (im)precise speakers modulates not only interpretations within a discourse, but also beyond the conversation. More broadly, our study provides a novel framework for studying real-time dynamic meaning negotiations during conversation.

Meaning adaptation in the discourse dynamics of imprecision

Children are keen explorers of the outside world: They systematically explore surprising findings and test hypotheses during play. However, less is known about whether toddlers are similarly driven to explore and learn about the self. The present work adapts classic exploratory play paradigms to ask whether toddlers are intrinsically motivated to explore their own competence. In Experiment 1, we selected Montessori practical life toys that were verified to be developmentally appropriate and equally appealing to toddlers (N = 24, ages 24-35 months). In Experiment 2, 2-year-olds (N = 49, ages 24-35 months) played with these toys along with a parent. Toys were presented in pairs. In each pair, the parent guided the toddler’s hand while playing with one toy, which provided confounded evidence about the toddler’s competence, and took turns playing the other toy independently, which provided unconfounded evidence. When given a chance to freely explore the toys on their own, toddlers first approached the confounded toy, which suggests that toddlers sought to resolve uncertainty about their competence. As a further test of this idea, Experiment 3 (N = 11, ongoing) asks whether toddlers’ exploration is modulated by task difficulty. Preliminary results suggest that toddlers explore confounded toys more often for more challenging tasks compared to easier ones. Together, our work provides insights into children’s early motivation to understand the self, and this understanding is an important first step for researchers, educators, and parents to better encourage and scaffold this motivation throughout development.

Are two-year-olds intrinsically motivated to explore their own competence?

Hippocampal subregions and medial prefrontal cortex may differentially shape memory precision in the mature brain: While anterior and posterior hippocampus may store the general themes and specific details of an episode, respectively, medial prefrontal cortex may instead connect related memories. However, given continued change to the functionality of these regions beyond childhood, it is unclear how children’s memory precision is influenced by these same mechanisms. We characterized how hippocampal subregions and medial prefrontal cortex separately and in tandem encourage memory precision in childhood versus adulthood. Children (7-9 years old) and adults studied scene photographs and then performed a recognition test that included both the studied scenes and highly similar lures. Behaviourally, adults had more precise memories than children in that they were better able to discriminate studied scenes from lures. At the neural level, anterior hippocampus and medial prefrontal cortex were differently engaged during this memory formation: Anterior hippocampus engagement was related to subsequent memory across age groups, while children showed greater medial prefrontal cortex engagement than adults overall when studying scenes. Considering individual differences in engagement revealed further developmental differences. Children showed evidence for a trade-off in their reliance on posterior hippocampus versus medial prefrontal cortex during precise memory formation, suggesting competition between these regions. By contrast, the same structures in adults played a more cooperative role in supporting memory precision. These findings suggest that the relationship between posterior hippocampus and medial prefrontal cortex reverses over development to yield adult-like memory precision, such that these regions work in opposition in childhood before becoming specialized to cooperatively encourage precision in adulthood.

Trade-offs in posterior hippocampus versus medial prefrontal cortex mechanisms underlie memory precision in childhood

The ability to combine existing concepts into novel ideas stands as a fundamental hallmark of human intelligence. Recent advances in Vision-Language Models (VLMs) like GPT-4V and DALLE-3 have sparked debate about whether their outputs reflect combinational creativity—defined by M. A. Boden (1998) as synthesizing novel ideas through combining existing concepts—or sophisticated pattern matching of training data. Drawing inspiration from cognitive science, we investigate the combinational creativity of VLMs from the lens of concept blending. We propose the Identification-Explanation-Implication (IEI) framework, which decomposes creative processes into three levels: identifying input spaces, extracting shared attributes, and deriving novel semantic implications. To validate this framework, we curate CreativeMashup, a high-quality dataset of 666 artist-generated visual mashups annotated according to the IEI framework. Through extensive experiments, we demonstrate that in comprehension tasks, the best VLMs have surpassed average human performance while falling short of expert-level understanding; in generation tasks, incorporating our IEI framework into the generation pipeline significantly enhances the creative quality of VLMs' outputs. Our findings establish both a theoretical foundation for evaluating artificial creativity and practical guidelines for improving creative generation in VLMs.

Probing and Inducing Combinational Creativity in Vision-Language Models

Differential Case Marking (DCM) refers to the selective use of grammatical case marking based on semantic, pragmatic, or other factors. The emergence of DCM has been studied in artificial language learning experiments with human participants, which were specifically aimed at disentangling the effects of learning from those of communication (Smith & Culbertson, 2020). Meanwhile, multi-agent reinforcement learning frameworks based on neural networks have gained significant interest to simulate the emergence of human-like linguistic phenomena. In this study, we employ such a framework in which agents first acquire an artificial language before engaging in communicative interactions, enabling direct comparisons to human results. Using a very generic communication optimization algorithm and neural-network learners that have no prior experience with language or semantic preferences, our results demonstrate that learning alone does not lead to DCM, but when agents communicate, differential use of markers arises. This supports Smith & Culbertson (2020)'s findings highlighting the critical role of communication in shaping DCM and showcases the potential of neural-agent models to complement experimental research on language evolution.

Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents

Despite large inter-individual differences in experience and conceptual structures, humans converge on referents largely underspecified by the signals exchanged during communication. Many neural networks have become extremely sensitive to context-dependent relationships between signals, but remain relatively blind to their referents outside the signal space. Here, we study how human interlocutors dynamically coordinate both their signal and referential spaces over extended communicative interactions. We identify a latent control parameter, representational complexity, that may regulate referential coordination in human communication. Using a custom hierarchical Transformer model, we generate movement- and interaction-level embeddings from neurotypical (NT) and autistic (ASC) dyads engaged in an experimental semiotic task. By leveraging ASC-related communicative variance, we identify changes in parameters that track the representational dimensionality of signals and referents as communication unfolds. Movement-level embeddings (within-trial dependencies) could not differentiate the two groups, indicating comparable communicative behaviors. In contrast, interactionlevel embeddings (across-trial dependencies) distinguished ASC from NT dyads with high accuracy. Crucially, representational complexity, i.e. dyadic alignment in the interaction-level intrinsic dimensionality used to encode communicative histories, tracked referential coordination demands, with greater misalignment in ASC dyads under referential volatility. These findings suggest that referential alignment is an interaction-level process, driven by the dynamic adaptation of representational complexity, rather than statistical relationships between signals alone.

Alignment of Representational Complexity as a Latent Control Parameter in Referential Communication

Humans can make moral inferences from multiple sources of input. In contrast, automated moral inference in artificial intelligence typically relies on language models with textual input. However, morality is conveyed through modalities beyond language. We present a computational framework that supports moral inference from natural images, demonstrated in two related tasks: 1) inferring human moral judgment toward visual images and 2) analyzing patterns in moral content communicated via images from public news. We find that models based on text alone cannot capture the fine-grained human moral judgment toward visual stimuli, but language-vision fusion models offer better precision in visual moral inference. Furthermore, applications of our framework to news data reveal implicit biases in news categories and geopolitical discussions. Our work creates avenues for automating visual moral inference and discovering patterns of visual moral communication in public media.

Visual moral inference and communication

Humans use clocks to objectively measure their subjective temporal experiences. But can the spatial properties in a clock distort our experience of time ? This study examines how spatial boundaries in an analog clock influence prospective temporal judgements. We found that when the second hand crossed more boundaries, it distorted participants’ spatial memories, causing them to overestimate the arc traced by the second hand. However, this distortion in spatial memory did not significantly influence participants’ temporal judgments. Besides boundary crossing, our study examined and replicated the influence of speed on temporal judgment, with faster speeds of the second hand being associated with a dilated temporal experience.

Second Hand Effects: Exploring Spatial Influences on Temporal Judgments in Clocks

As the use of naturalistic speech data of children’s language
experiences increases, the temporal dynamics of the speech
environment becomes a more obvious aspect of speech to
investigate. To connect the temporal dynamics in child-
centered speech to existing experimental work showing that the
temporal presentation of items has measurable effects on
learning, it is important to develop measures that quantify
temporal patterns in speech. The present work explores one
such measure, the burstiness metric, and investigates word
burstiness and its relationship with frequency, its behavior
across different timescales, and whether it can be used to
quantify constructs of massed and spaced orders. Our findings
suggest that while related, word burstiness is not an index of
frequency, that burstiness varies across both words of different
lexical classes and timescales, and that it does not appropriately
capture massed and spaced temporal patterns. We discuss
implications for how this measure may be used for child-
centered audio.

The Applications and Limitations of the Burstiness Metric in Investigating the Temporal Distribution of Words in Child-Centered Audio

Today's leading AI systems have achieved one of the field's oldest dreams and promises: They can take in any language as input and produce reasonable responses – often very much like the responses that a reasonable (and knowledgeable and helpful) person would produce.  Yet the processes at work inside these systems, and how they are built, do not (at least obviously) have much in common with the mechanisms or origins of the human mind.  What would it take to build a model with something like the input-output behavior of ChatGPT but whose inner workings actually instantiated a theory of human cognition – and even our best current scientific theory?  I will discuss several possible routes to this goal, and the challenges and opportunities they present. I will argue that now more than ever is the time for a bidirectional exchange between the fields of AI and Cog Sci – fields that grew up together starting in the 1950s, but have followed very different trajectories recently.  AI tools and techniques have much to offer cognitive theories, but cognitive science has just as much if not more to offer back to AI.  Understanding and using AI tools in a framework guided by foundational thinking in cognitive science represents the best hope to deliver on the theoretical goals, dreams, and promises of both fields. 

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