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Our choices often require us to prioritize some features of our rich sensory experience over others. Past work suggests that humans solve this problem by focusing on relevant information while discarding that which is irrelevant. Yet learning which features to prioritize requires extensive experience. Moreover, features that are irrelevant now may become relevant in the future. One way to address these issues is by sampling individual richly encoded experiences from episodic memory. Here we hypothesize that episodic memory is used to guide decisions based on multiple features of past events. We test this hypothesis using an experiment in which participants made choices about the value of features that were present in multiple past experiences. We find evidence suggesting that participants used episodic memories to flexibly access features of past events during decision making. Overall, these results suggest that episodic memory promotes adaptive decisions when knowledge of multiple features is necessary.

**Authors:**

Jonathan Nicholas: New York University; Marcelo G Mattar: New York University

CogSci 2024

Humans use episodic memory to access features of past experience for flexible decision making

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 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.

**ON-DEMAND PROGRAM ACCESS AFTER THE CONFERENCE** 
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Recordings available (click on each recording to view):

**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** 
0900: [Keynote Speaker Gregor Schöner on How higher cognition emerges from the dynamics of strongly interacting neural populations](https://underline.io/events/465/sessions/18083/lecture/99273-dynamics-within-the-mind) 
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) 


#### **CogSci 2024 Program Book**
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**The Program Book can be downloaded [here](https://drive.google.com/file/d/1yRVp1cBqRnAbdYi4GXWV_pZeyWNsRquv/view?usp=sharing).**

#### **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** 
By attending the CogSci 2024 Conference, you are required to adhere to the society’s [Code of Conduct](https://drive.google.com/file/d/16-6KkptF0Gn3ZYGJDlpqpwTdImw45Ng0/view?usp=drive_link).

**ABOUT THE COGNITIVE SCIENCE SOCIETY** 
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.

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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.

This paper tests the similarity in neural responses across repeated words and morphosyntactic features both within and between two languages. Prior work using priming has revealed robust cross-linguistic lexical effects and effects for shared grammatical form, such as argument structure; these methods have been less successful when applied to morphosyntactic features. Combining machine-learning based neural decoding with EEG data collected from Korean-English bilinguals we, first, replicate prior work showing successful classification of lexical items from EEG signals. We then extend this to demonstrate successful classification of morphosyntactic features of number and tense. Finally, we find that EEG decoding in one language does not successfully generalize to another, even when temporal differences are considered. Taken together, these results point to stable EEG representations for lexical items and morphosyntactic features, but suggest that these representations are different between the two languages investigated here.

**Authors:**

Jeonghwa Cho: University of Michigan; Jonathan R. Brennan: University of Michigan

Neural decoding of words and morphosyntactic features within and across languages

The ability to exert cognitive control is central to human brain function, facilitating goal-directed task performance. However, humans exhibit limitations in the duration over which they can exert cognitive control---a phenomenon referred to as cognitive fatigue. This study explores a computational rationale for cognitive fatigue in continual learning scenarios: cognitive fatigue serves to limit the extended performance of one task to avoid the forgetting of previously learned tasks. Our study employs a meta-learning framework, wherein cognitive control is optimally allocated to balance immediate task performance with forgetting of other tasks. We demonstrate that this model replicates common patterns of cognitive fatigue, such as performance degradation over time and sensitivity to reward. Furthermore, we discuss novel predictions, including variations in cognitive fatigue based on task representation overlap. This approach offers a novel perspective on the computational role of cognitive fatigue in neural systems.

**Authors:**

Yujun Li: Peking University; Rodrigo Carrasco-Davis: UCL; Younes Strittmatter: Brown University; Stefano Sarao Mannelli: UCL; Sebastian Musslick: Brown University

A meta-learning framework for rationalizing cognitive fatigue in neural systems

Social media is often used as a platform where individuals engage in debate regarding topics that are important to them. Not all arguments are equally convincing, and whilst a given argument may be persuasive to some people, it is often seen as inadequate by others. We are interested in both the individual and argument level differences that make ‘everyday’ arguments such as those on social media persuasive. In a replication of our Everyday Belief Bias Task (Deans-Browne & Singmann, 2024), we investigate this question using a paradigm that consists of two parts. In the first part, we measure participant’s individual beliefs about eight claims each referring to a political topic (e.g., Abortion should be legal). In the second part, participants rated an argument for each of these claims that was deemed as either good, inconsistent (containing internal inconsistencies), or authority-based (being centered around appeals to authority). We replicated the belief consistency effect – participants preferred arguments that were also in line with their beliefs. We also found that authority-based arguments were rated as worse than inconsistent arguments, and that both types of arguments were rated as worse than good arguments. The implications are first that people do not evaluate arguments independently of the background beliefs held about them. Secondly, people are willing to ignore inconsistencies in arguments more than they are willing to accept the endorsement of authority figures as adequate evidence for arguments.

**Authors:**

Calvin Deans-Browne: University College London; Alexandra Băitanu: University College London; Yuliya Dubinska Ms: UCL; Henrik Singmann: UCL

Inconsistent Arguments are Perceived as Better Than Appeals to Authority: An Extension of the Everyday Belief Bias

The complexity of everyday choices make them difficult to formally study. We address this challenge by constructing a dataset of over 100K real-life decision problems based on a combination of social media and large-scale survey data. Using large language models (LLMs) for automated coding, we are able to extract hundreds of choice attributes at play in these problems and map them onto a common representational space. This representation allows us to quantify both the content (e.g. broader themes) and the structure (e.g. specific tradeoffs) inherent in everyday choices. We also present subsets of these decision problems to human participants, and find consistency in choice patterns, allowing us to predict naturalistic choices with established decision models. Overall, our research provides new insights into the attributes and tradeoffs that underpin important life choices. In doing so, our work shows how LLM-based structure extraction can be used to study real-world cognition and behavior.

**Authors:**

Sudeep Bhatia: University of Pennsylvania; Simon T. van Baal: University of Vienna; Lukasz Walasek: University of Warwick

The Structure of Everyday Choice: Insights from 100K Real-life Decision Problems

Content-curating algorithms provide a crucial service for social media users by surfacing relevant content, but they can also bring about harms when their objectives are misaligned with user values and welfare. Yet, potential behavioral consequences of this alignment problem remain understudied in controlled experiments. In a preregistered, two-wave, collaborative filtering experiment, we demonstrate that small changes to the metrics used for sampling and ranking posts affect the beliefs people form. Our results show observable differences in two types of outcomes within statisticized groups: belief accuracy and consensus. We find partial support for hypotheses that the recently proposed approaches of "bridging-based ranking" and "intelligence-based ranking" promote consensus and belief accuracy, respectively. We also find that while personalized, engagement-based ranking promotes posts that participants perceive favorably, it simultaneously leads those participants to form more polarized and less accurate beliefs than any of the other algorithms considered.

**Authors:**

Jason W. Burton: Copenhagen Business School; Stefan M Herzog: Max Planck Institute for Human Development; Philipp Lorenz-Spreen: Max Planck Institute for Human Development

Simple changes to content curation algorithms affect the beliefs people form in a collaborative filtering experiment

Category fluency is a widely studied cognitive task. Two major competing accounts have been proposed as the underlying retrieval mechanism: an optimal foraging process deliberately searching through memory (Hills et al., 2012) and a random walk sampling from a semantic network (Abbott et al., 2015). Evidence for both accounts has centered around predicting human patch switches, where both existing models of category fluency produce paradoxically identical results. We begin by peeling back the assumptions made by existing models, namely that each named exemplar only depends on the previous exemplar, by (i) adding an additional bias to model the category transition probability directly and (ii) relying on a large language model to predict based on the full prior exemplar sequence. Then, we present evidence towards resolving the disagreement between different models of foraging by reformulating them as sequence generators. For evaluation, we compare generated category fluency runs to a bank of humanwritten sequences by utilizing a metric based on n-gram overlap. We find that category switch predictors do not necessarily produce human-like sequences; rather, the additional biases used by the Hills et al. (2012) model are required to improve generation quality, which is further improved by our category modification. Even generating exclusively with an LLM requires an additional global cue to trigger the patch switching behavior during production. Further tests on only the search process on top of the semantic network highlight the importance of deterministic search in replicating human behavior.

**Authors:**

David Heineman: Georgia Institute of Technology; Reba Koenen: Georgia Institute of Technology; Sashank Varma: Georgia Tech

Towards a path dependent account of category fluency

This study introduces a novel approach for quantifying individual differences in print exposure through the integration of distributional semantics with the Author Production Test (APT). By employing the Universal Sentence Encoder to generate vector representations of authors from their works, we constructed 'participant vectors' reflecting the aggregated author vectors individuals produced in the APT and 'genre vectors' capturing the representative characteristics of each literary genre. By analyzing the cosine similarities between participant and genre vectors, we objectively estimated individuals' genre preferences. The results demonstrated a significant correlation between these objective measures and self-reported genre preferences, particularly for older frequent readers, highlighting the method's effectiveness. Our findings offer a promising avenue for the objective measurement of print exposure, with potential implications for developing personalized models of lexical behavior.

**Authors:**

Mengyang Qiu: Trent University; Nichol Castro: University at Buffalo; Brendan Johns: McGill University

Estimating Type of Print Exposure across Aging through Author Production

Temporal language grounding (TLG) aims to localize query-related events in videos, which explores how to cognize relationships of video content with language descriptions. According to selective visual attention mechanism in cognitive science, people’s cognition and understanding of what happens often rely on dynamic foreground information in the video. Nonetheless, background usually predominates the scenes so that query-related visual features and irrelevant ones are confused. Thus, we propose a Foreground Enhanced Network (FEN) to diminish the background effect from two aspects.
FEN at first in spatial dimension explicitly models the evolving foreground in video features by removing relatively unchanged background content. Besides, we propose a progressive contrastive sample generation module to gradually learn the differences between the predicted proposal and its elongated proposals that include the former as a portion, thereby distinguishing similar neighborhood frames. Experiments on two common-used datasets show the efficacy of our model.

**Authors:**

Hongzhou Wu: NUDT; Xuechen Zhao: National University of Defense and Technology; Yifan Lyu: Institute of Software, Chinese academy of sciences; Xiang Zhang: National University of Defense Technology

Foreground Enhanced Network for Weakly Supervised Temporal Language Grounding

We conduct tests of a hybrid-similarity exemplar model on its ability to account for the context-dependent memorability of items embedded in high-dimensional category spaces. According to the model, recognition judgments are based on the summed similarity of test items to studied exemplars. The model allows for the idea that “self-similarity” among objects differs due to matching on highly salient distinctive features. Participants viewed a study list of rock images belonging to geologically defined categories where the number of studied items from each category was manipulated, and their old-new recognition performance was then tested. With a minimum of parameter estimation, the model provided good accounts of changing levels of memorability due to contextual effects of category size, within- and between-category similarity, and the presence of distinctive features. We discuss future directions for improving upon the current predictions from the model.

**Authors:**

Robert Nosofsky: Indiana University; Adam Osth: University of Melbourne

Hybrid-Similarity Exemplar Model of Context-Dependent Memorability

People rely on communicative cues when assessing others’ knowledge levels about a topic. Speech fluency has been shown to inform these assessments (Brennan & Williams, 1995), however little is known whether co-speech gestures also impact how we judge others’ level of expertise. To address this, we showed 42 participants (Mage=21.05) short videos of speakers in four conditions (fluent or disfluent speech with gestures, fluent or disfluent speech without gestures). Participants then provided FOAK (feelings of another’s knowing) judgements of the speaker. A mixed effects regression analysis, with conditions as fixed and trial and subjects as random effects, revealed that fluent speech elicited higher FOAK ratings than disfluent speech, p<.01. Surprisingly gestures did not affect FOAK ratings. This is a first study to suggest, fluency can be a more prominent cue than gestures when assessing others’ knowledge levels.

**Authors:**

Can Avcı: Koç University; Terry Eskenazi: Koc University; Tilbe Göksun: Koç University

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Neural decoding of words and morphosyntactic features within and across languages

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