Netherlands

Often the only source of information for learning a word is its surrounding language context. For example, even without seeing a rambutan, one can learn that it is a fruit just from hearing “I like sweet, juicy rambutans”. What processes foster learning words from context? We investigated candidate processes that can unfold when the context precedes a new word and can foster learning via prediction, versus when the context occurs after and can only be used retroactively. We particularly sought to illuminate a role for working memory in linking a new word to the meaning implied by its context. Experiment 1 probed word learning during reading with eye tracking, and Experiment 2 probed word learning from speech. We found convergent evidence that regardless of whether the context precedes or follows a new word, word learning depends on maintaining the context in working memory while linking it to a new word.

**Authors:**

Layla Unger: University of York; Vladimir Sloutsky: The Ohio State University

CogSci 2024

Dynamic Processes of Learning Words from Context

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

technical paper

The 46th Annual Meeting of the Cognitive Science Society was an in-person meeting held in Rotterdam, The Netherlands at the Postillion Hotel & Conference Centre.

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**Thursday, July 25** 
0900: [Keynote Speaker Morgan Barense on Enhancing real-world event memory](https://underline.io/events/465/sessions/17997/lecture/99084-dynamics-between-minds-and-the-environment) 
1000: [Gleitman Award Winner Isabelle Dautriche on Language Foundations: Insights from acquisition, communication, cognition, and more](https://underline.io/events/465/sessions/18000/lecture/99085-gleitman-talk) 
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture) 
1700: [Rumelhart Prize Presentation Speaker Alison Gopnik on Exploit, explore, empower: Three ages and three intelligences](https://underline.io/events/465/sessions/18028/lecture/99161-rp1-rumelhart-prize-presentation) 

**Friday, July 26** 
0900: [C.L. de Carvalho-Heineken Prize Keynote Speaker Kia Nobre on Focusing in memory](https://underline.io/events/465/sessions/18035/lecture/99162-hp1-c-l-de-carvalho-heineken-prize-keynote-address) 
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration) 
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture) 
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture) 
1745: [Keynote Speaker Andrea E. Martin on Neural dynamics encode the structure and statistics of language](https://underline.io/events/465/sessions/18077/lecture/99271-neural-dynamics-encode-the-structure-and-statistics-of-language) 

**Saturday, July 27** 
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1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture) 
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture) 


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#### **Letter from the President**

Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
their experience inspires them to join our future meetings for many years to come.

I want to thank this year’s conference co-chairs, Larissa K Samuelson, Stefan Frank, Mariya Toneva, Allyson Mackey, and Eliot Hazeltine, for putting together an amazing series of invited talks and symposia around the theme of Dynamics of Cognition. The co-chairs also deserve credit for handling a record-breaking number of submissions and producing a truly exciting conference program 

Beyond the invited talks and symposia dedicated to this year’s conference theme, I would like to highlight the presentations and events honoring the 
Rumelhart, Elman, Gleitman and C .L . de Carvalho Heineken prize winners, including the Rumelhart reception on Thursday. As you may know, we are recording all of the invited talks and symposia, and all prize-winners’ talks, and will be making this content available after the conference. 

The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
Society is deeply grateful to Rick Dale and Andrea Bender, who serve as editors of the two Society journals, Cognitive Science and Topics in Cognitive Science respectively. We especially acknowledge our editors’ generosity in donating their stipends each year to D&I and outreach efforts. 

This year’s Annual Meeting depends critically on the support of Marischal De Armond and his team at Podium Conference Specialists: Sharon Zwack, Cendrine De Vis, Sarah-Kate Burke, and Rachel Waller worked tirelessly to secure the venue and handled a multitude of logistical and organizational issues, from arranging coffee breaks and receptions to making sure that a huge number of posters fits comfortably into the available space. Finally, and importantly, I would like to acknowledge the Cognitive Science Society Executive Officer, Erica Wojcik, for managing the complex and ever expanding sphere of our Society’s activities.

I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

![](https://assets.underline.io/markdown_image/1/image/3c66d0e21fff30e204df1138ffd15173.jpeg)

**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

**CODE OF CONDUCT** 
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**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Learning names for novel objects has been shown to be impacted by the context in which they appear. Manipulations of context, therefore, provide a key pathway to explore these learning dynamics. Here we use a neural process model that instantiates the details of ‘context’ to generate novel, counterintuitive predictions about how similarity in object properties influence learning. Specifically, we use a dynamic field model, WOLVES, to simulate and predict learning in a cross-situational word learning task in two conditions: one where the two objects presented on each learning trial are metrically similar in a property (‘NEAR’) and another condition where the two objects are always dissimilar (‘FAR’). WOLVES predicts—counterintuitively—that participants should learn better in the ‘NEAR’ condition (where objects are potentially confusable) than in ‘FAR’ condition (where objects are distinctive). We then tested this prediction empirically, finding support for the novel prediction. This study shows the utility of process models which instantiate the details of ‘context’ during learning and provides support for WOLVES. We know of no other theory of cross-situational word learning that captures these novel findings.

**Authors:**

Ajaz Ahmad Bhat: University Brunei Darussalam; John Spencer: University of East Anglia; Larissa K Samuelson: University of East Anglia

Similarity in object properties supports cross-situational word learning: Predictions from a dynamic neural model confirmed

Speech perception requires inferring category membership from varied acoustic cues, with listeners adeptly adjusting cue utilization upon encountering novel speech inputs. This adaptivity has been examined through the dimension-based statistical learning (DBSL) paradigm, which reveals that listeners can quickly de-emphasize secondary cues when cue correlations deviate from long-term expectations, resulting in cue-reweighting. Although multiple accounts of cue-reweighting have been proposed, direct comparisons of these accounts against human perceptual data are scarce. This study evaluates three computational models–cue normalization, Bayesian ideal adaptor, and error-driven learning–against classic DBSL findings to elucidate how cue reweighting supports adaptation to new speech patterns. These models differ in how they map cues onto categories for categorization and in how recent exposure to atypical input patterns influences this mapping. Our results show that both the error-driven learning and ideal adaptor models effectively capture the key patterns of cue-reweighting phenomena, whereas prelinguistic cue normalization does not. This comparison not only highlights the models' relative efficacy but also advances our understanding of the dynamic processes underlying speech perception adaptation.

**Authors:**

Yiming Lu: University of California, Irvine; Xin Xie: University of California, Irvine

Modeling cue re-weighting in dimension-based statistical learning

Compositional generalization that requires production and comprehension of novel _structures_ through observed constituent parts has been shown to be challenging for even very powerful neural network models of language. However, one of the test cases that poses the greatest difficulty---generalization of modifiers to unobserved syntactic positions---has not been empirically attested in human learners under the same exposure conditions assumed by these tests. In this work, we test adult human learners on whether they generalize or withhold the production of modification in novel syntactic positions using artificial language learning. We find that adult native speakers of English are biased towards producing modifiers in unobserved positions (therefore producing novel structures), even when they only observe modification in a single syntactic position, and even when the knowledge of their native language actively biases them against the plausibility of the target structures.

**Authors:**

Najoung Kim: Boston University; Paul Smolensky: Johns Hopkins University

Structural Generalization of Modification in Adult Learners of Artificial Language

The computational principles that underlie human concept learning have been debated in the literature for decades. Here, we formalize and test a new perspective that is grounded in rate-distortion theory (RDT), the mathematical theory of optimal (lossy) data compression, which has recently been gaining increasing popularity in cognitive science. More specifically, we characterize optimal conceptual systems as solutions to a special type of RDT problem, show how these optimal systems can generalize to unseen examples, and test their predictions for human behavior in three foundational concept-learning experiments. We find converging evidence that optimal compression may account for human concept learning. Our work also lends new insight into the relation between learnability and compressibility; integrates prototype, exemplar, and Bayesian approaches to human concepts within the RDT framework; and offers a potential theoretical link between concept learning and other cognitive functions that have been successfully characterized by efficient compression.

**Authors:**

Nathaniel Imel: University of California Irvine; Noga Zaslavsky: UCI

Optimal compression in human concept learning

This paper explores the use of linguistic strategies, specifically discourse markers like ’but’, to express contrasts between expectations and reality when faced with unexpected events. The study concentrates on Denial of Expectation (DofE), the
most powerful form of contrast, which arises when the expected value based on background assumptions is not met. The main focus of this paper is to model DofE as a weighted homogeneous relationship between object properties. The aim is to predict DofE for numerical properties in specific contexts. I aim to address a gap in previous models by considering the role of context. This is achieved by analyzing contrastive sentences from German car and motorcycle reviews. The research presents the concept of expectation intervals for scalar properties. These intervals align with expectations and exceeding them triggers a potential contrast. The study incorporates causality, expected behavior, and a shift function in selecting contrastive pairs, transforming the conditions into an algorithm.

Keywords: contrast; computational and cognitive modeling; discourse analysis

**Authors:**

Marie Christin Walch: Leibniz Universität Hannover

Predicting the Unexpected - Analysis and Modeling of the Denial of Expectation

Intentional visual imagery is a component of numerous aspects of cognition. Related to visual imagery, mental simulation plays a role in embodied theories of language comprehension that propose activation of modality-specific regions of the brain takes place as part of people understanding language. The extent to which the processes underlying conscious, voluntary visual imagery versus less conscious, more automatic mental simulation overlap is unclear. We investigated this issue by having aphantasics (people who are unable to experience conscious voluntary visual imagery) and control participants perform a property verification task in which they were asked whether a property is a physical part of an object (e.g., lion-tail). We manipulated the false trials in that the two words either were associated (semantically related) but did not form an object-part combination (monkey-banana), or were not associated (apple-cloud). Solomon and Barsalou (2004) demonstrated that word association influenced responses when the words in the false trials were not associated, whereas when they were related, perceptual measures most strongly influenced the results, indicating mental simulation. Here control participants and aphantasics demonstrated similar evidence of the use of both mental simulation and word association when verifying whether the words formed an object-part combination. These results provide evidence that visual imagery and mental simulation are at least somewhat separable cognitive processes.

**Authors:**

Laura J. Speed: Radboud University; Ken McRae: University of Western Ontario

Dissociating mental imagery and mental simulation: Evidence from aphantasia

Entities in the spatial domain (objects) and the temporal domain (events) are characterized by parallel distinctions that are supported by a shared notion of individuation that runs across domains. This work investigates whether conceptual considerations of individuation are language-independent. We test speakers of English, which uses count-mass syntax and telicity to mark linguistic individuals in the nominal and verbal domain respectively, and Mandarin, which lacks these linguistic features. Our results throw light onto the nature of entity categories in the human mind: both English-speaking and Mandarin-speaking viewers process individuated and non-individuated entities differently, with only the former having a well-defined (temporal/spatial) structure with integrally-ordered, distinct parts. Crucially, these features of non-linguistic individuation are conceptualized in similar ways cross-linguistically and are potentially universal.

**Authors:**

Sarah Hye-yeon Lee: University of Pennsylvania; Anna Papafragou: Unversity of Pennsylvania

Object-Event Correspondences Across Languages

Food neophobia – the reluctance to eat novel food – has been associated with poorer performance in category-based tasks within the food domain among preschoolers. This research aims to unravel this negative relationship and determine if this association is specific to food items or reflects general cognitive rigidity in considering alternative ways to represent entities. In study 1, 123 children between 3 and 6 years were tested on an inductive reasoning task, comparing food and animals. In study 2, 112 children aged 4 to 6 engaged in a cross-categorization task comparing food, animals and artifacts. Results indicated that neophobic children exhibited poorer induction and cross-categorization performance in all domains compared to their neophilic counterparts. These findings highlight the importance of child characteristics in shaping the general development of category-based abilities and suggest that food neophobia, rather than a fear of novelty, reflects instead difficulties in changing perspectives once items have been classified.

**Authors:**

Foinant Damien: Université de Bourgogne; Lafraire Jérémie: Institut Lyfe Research and Innovation Center; Jean-Pierre Thibaut: Université de Bourgogne

Preschoolers’ Neophobia Influences Category-based Abilities Beyond the Food Domain

Innovation in children is typically studied by examining their capacity to create novel tools. However, innovation also involves recognising the future utility of a solution. Across two experiments, we examined children’s capacity to recognise and construct a tool for future uses. Experiment One presented 3- to 5-year-olds (N=55) with a future-directed problem-solving task. When given a tool construction opportunity in anticipation of returning to the task, only 5-year-olds made the correctly shaped tool above chance levels. Experiment Two assessed 3- to 7-year-olds’ (N=92) capacity to build a tool with future, as well as present, utility in mind. Age was positively associated with constructing a tool of greater utility than necessary to solve the present task. Children’s propensity to construct longer tools was associated with their capacity to prepare for two alternative possibilities on a secondary task, suggesting performance on our innovation task reflects emerging future-oriented cognition.

**Authors:**

Zoe Ockerby: The University of Queensland; Thomas Suddendorf: University of Queensland; Jonathan Redshaw: University of Queensland

Innovating for the future: When do children begin to recognise and manufacture solutions to future problems?

Question asking is a key tool for learning, especially in childhood. However, formulating good questions is challenging. In any given situation, many questions are possible but only few are informative. In the present work, we investigate two ways 5- to 10-year-olds and adults simplify the challenge of formulating questions: by reusing previous questions, and by remixing components of previous questions to form new questions. Our experimental results suggest that children and adults reuse and remix questions and adaptively modulate reuse depending on how informative a question will be in a particular situation. This work shows that task-relevant experience asking questions provides fodder for future questions, simplifying the challenge of inquiry and enabling effective learning.

**Authors:**

Emily G Liquin: New York University; Marjorie Rhodes: New York University; Todd M Gureckis: New York University

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