Netherlands

Does grammatical gender influence the semantic perception of inanimate nouns? We examined word embeddings from distributional semantics models, representing meanings in a vector space. In 26 gendered languages and non-gendered English, we measured the meaning similarity of inanimate nouns to gendered anchor nouns like &#39;male&#39; and &#39;female.&#39; In gendered languages, noun meanings aligned more with the anchor noun congruent with grammatical gender. This effect persisted when comparing the same noun across languages (e.g., &#39;cucchiaio&#39; vs &#39;cuchara&#39; vs &#39;spoon&#39;). We propose that grammatical gender introduces a gender bias into lexical semantics through distributional similarities with anchor words, revealing masculine/feminine features even without direct sensorimotor experience. This suggests that embodiment in language processing may become statistically embedded in word usage patterns.

**Authors:**

Luca Onnis: University of Oslo; Alfred Lim: University of Nottingham Malaysia

CogSci 2024

Distributed semantic representations of inanimate nouns are gender biased in gendered languages

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Does grammatical gender influence the semantic perception of inanimate nouns? We examined word embeddings from distributional semantics models, representing meanings in a vector space. In 26 gendered languages and non-gendered English, we measured the meaning similarity of inanimate nouns to gendered anchor nouns like 'male' and 'female.' In gendered languages, noun meanings aligned more with the anchor noun congruent with grammatical gender. This effect persisted when comparing the same noun across languages (e.g., 'cucchiaio' vs 'cuchara' vs 'spoon'). We propose that grammatical gender introduces a gender bias into lexical semantics through distributional similarities with anchor words, revealing masculine/feminine features even without direct sensorimotor experience. This suggests that embodiment in language processing may become statistically embedded in word usage patterns.

**Authors:**

Luca Onnis: University of Oslo; Alfred Lim: University of Nottingham Malaysia

poster

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**<br>
Recordings of the invited program are now available. You can access them by clicking on the 'Schedule' icon on the left. Select view by week and navigate to the day and time slot of the recording you wish to view. Click on the time slot to access the recording.

Recordings available (click on each recording to view):

**Thursday, July 25**<br>
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)<br>
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)<br>
1415: [Invited Symposium: Dynamics between minds and the environment](https://underline.io/events/465/sessions?eventSessionId=18016&searchGroup=lecture)<br>
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)<br>

**Friday, July 26**<br>
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)<br>
1030: [Rumelhart Symposium: Childhood as exploration](https://underline.io/events/465/sessions/18038/lecture/100346-childhood-as-exploration)<br>
1415: [Elman Prize Symposium](https://underline.io/events/465/sessions?eventSessionId=18053&searchGroup=lecture)<br>
1600: [Invited Symposium: Dynamics between minds](https://underline.io/events/465/sessions?eventSessionId=18065&searchGroup=lecture)<br>
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)<br>

**Saturday, July 27**<br>
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)<br>
1000: [Glushko Talks](https://underline.io/events/465/sessions?eventSessionId=18086&searchGroup=lecture)<br>
1500: [Invited Symposium: Dynamics within minds](https://underline.io/events/465/sessions?eventSessionId=18100&searchGroup=lecture)<br>


#### **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**<br>
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**<br>
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.
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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.

We propose a generative modeling framework of task-oriented language production in aphasia. The model assumes a task, such as describing a picture. We estimate the prior distribution over intended sentences, given the task, using utterances from unimpaired speakers given the same task. We define a generative model of operations, such as semantic and phonological errors, retracing, and filled pauses that intervene on the intended sentence to incrementally yield an observed utterance. We present a basic proof of concept implemented in the Gen probabilistic programming language, using one task from AphasiaBank. Given incremental observations, a particle filter estimates probabilities for latent variables (such as the error type at a given word or the speaker’s intended sentence) for which ground truth annotations are present. Our approach combines a model of production with inferences that reflect rational comprehension of impaired language. Future work will address issues of scalability and efficient inference.

**Authors:**

Thomas Clark: Massachusetts Institute of Technology; Edward Gibson: Massachusetts Institute of Technology; Roger Levy: Massachusetts Institute of Technology

Inferring errors and intended meanings with a generative model of language production in aphasia

The idea of curriculum learning, whereby a model is first exposed to simpler examples before an increase in complexity, has long fascinated the AI community. Unfortunately, the experimental successes of curriculum learning have been mixed, particularly applied to natural language, where a vast body of literature appears to evidence its failures. However, recent work has shown that language models trained on transcribed-child-directed-speech (CDS) learn more grammar compared to those trained on Wikipedia. To a lesser extent, the same trend has been observed through training on transcribed speech and simple text data. Motivated by these findings, we revisit the idea of curriculum learning starting from CDS, before moving to simple data, and finally finishing with complex long form text. Unfortunately, through experimentation with an array of model and training step sizes, only in the smallest models trained for the least steps does curriculum learning show any advantage over random sampling.

**Authors:**

Mattia Opper: University of Edinburgh; Sydelle de Souza: University of Edinburgh

Starting Small, After All? Curriculum Learning with Child-Directed Speech

Chain-of-Thought (CoT) has brought a fresh perspective to improve the reasoning ability of large language models (LLMs). To relieve the burden of manual design in CoT, Zero-shot CoT has pioneered a direct interaction with LLMs. Based on it, researchers attempt to optimize reasoning paths through various prompting approaches like reflection, selection, and planning. However, few studies have focused on the possibility of combining all these strategies through a cognitive theory. Inspired by experiential learning, this paper proposes a new zero-shot prompting method based on Kolb’s reflective cycle, named Self-Hint prompting. Specifically, Self-Hint prompting introduces an automated iterative interaction approach to simulate the conscious reflection process, which uses intermediate observations as hints to guide LLMs. We have conducted comprehensive experiments on various math reasoning benchmarks. The empirical results on GPT models demonstrate the effectiveness of our method. Proposed Self-Hint prompting consistently outperforms other zero-shot baselines.

**Authors:**

Jindou Chen: Shanghai Jiao Tong University; Jidong Tian: Shanghai Jiao Tong University; Yaohui Jin: Artificial Intelligence Institute

Self-Hint Prompting Improves Zero-shot Reasoning in Large Language Models via Reflective Cycle

Humans collaborate to improve productivity and collective outcomes, but people don't always exert maximal effort towards accomplishing collaborative goals. Instead, individuals often expend less effort in group settings, a phenomenon known as social loafing that is traditionally viewed as detrimental to productivity. However, theories from multiprocessing computer systems suggest that this behavior might be a rational response to the diminishing returns from division of labor when scaling group size. Here, we examine how considerations of efficiency, analogous to principles in multiprocessing, affect the perceived acceptability of withholding effort during a collaborative task. We conducted experiments varying workload and group size across scenarios in which all agents except for one are actively contributing to a common goal. We then compare human judgments to a model inspired by computational efficiency in multiprocessing systems. We find that people are systematically influenced by task efficiency in addition to social norms when judging social loafing.

**Authors:**

Elizabeth Mieczkowski: Princeton University; Cameron Rouse Turner: Princeton University; Natalia A Vélez: Princeton University; Tom Griffiths: Princeton University 

Many Hands Don't Always Make Light Work: Explaining Social Loafing via Multiprocessing Efficiency

This paper reports on two studies examining participants’ identification accuracy in discriminating real faces from realistic “artificial” faces created through the Artificial Intelligence (AI) system StyleGAN. Across the two studies, two different sets of participants (N = 400) attempted to distinguish 24 real from 24 AI-generated images. Both sets of participants exhibited poor discrimination accuracy and a bias to report all images as real (Study 2). We examined other possible influencing factors were examined, such as smile intensity (Study 1) and age-congruence between participants and faces (Study 2). Implications for future research, and for understanding the potential societal impacts of AI-generated online content are discussed.

**Authors:**

Sean Duffy: Rutgers University Camden ; Antoine August: Rutgers University - Camden; Kate Wisniewski: Rutgers University - Camden

Discriminating real from A.I.-generated faces: Effects of emotion, gender, and age.

Word embeddings trained on large scale text corpora are central to modern natural language processing and are also important as cognitive models and tools in psycholinguistic research. 
An important alternative to these text-based models are embeddings derived from word association norms. Recently, these association-based embeddings have been shown to outperform text-based word embeddings of comparable complexity (such as GloVE, word2Vec & fastText) in semantic similarity rating tasks. Here we evaluate English and Rioplatense Spanish association-based embeddings derived from the Small World of Words (SWOW) project on the Google Analogy set and the Bigger Analogy Test Set. We also developed a small analogy set that focuses on semantic relationships, such as event knowledge and category-exemplar relationships such as prototypicality. SWOW-derived word embeddings perform similarly as traditional text-based word embeddings in semantic analogies, and outform them in some categories. These results illustrate relevant s

**Authors:**

Rocío Belén Lión: Universidad de la República; Hernan Quintero: Universidad de la República; Juan C Valle-Lisboa: Universidad de la República; Alvaro Cabana: Universidad de la República

Evaluating word association-derived word embeddings on semantic analogies

Early language development, and vocabulary size specifically, is a predictor of well-being later in life, such as emotional development and academic achievement. Many successful vocabulary interventions for young children involve sharing a book with a caregiver, because storybooks are a good source of vocabulary that one might not otherwise encounter in everyday life.. With the advent of Large Language Models (LLM), automatically generating stories has become a feasible way to tailor materials to the needs and interests of individual learners. Here we evaluate 1) whether parents of preschoolers find automatically generated stories containing specific vocabulary target words acceptable, and 2) whether preschoolers can learn these target words from being read the automatically generated stories. We find that parents overall consider automatically generated stories engaging, age- appropriate, and educational. In addition, children successfully learn the target words in the storybooks(compared to control words drawn from books not read). We conclude with a discussion on future work to improve the effectiveness of automatically generated stories to support robust vocabulary learning.

**Authors:**

Jennifer Weber: University of Colorado, Boulder; Maria Valentini: University of Colorado Boulder; Téa Wright: University of Colorado Boulder; Katharina von der Wense: University of Colorado Boulder; Eliana Colunga: University of Colorado Boulder

Evaluating LLMs as Tools to Support Early Vocabulary Learning

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.

**Authors:**

Yun-Shiuan Chuang: University of Wisconsin - Madison; Agam Goyal: University of Wisconsin - Madison; Nikunj Harlalka: University of Wisconsin - Madison; Siddharth Suresh: University of Wisconsin-Madison; Robert Hawkins: University of Wisconsin-Madison; Sijia Yang: University of Wisconsin - Madison; Dhavan Shah: University of Wisconsin - Madison; Junjie Hu: University of Wisconsin-Madison; Timothy T Rogers: University of Wisconsin- Madison

Simulating Opinion Dynamics with Networks of LLM-based Agents

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

**Authors:**

Maria Vorobeva: Carleton University; Suhaylah B Carim Bacor: Carleton University; Mary Alexandria Kelly: Carleton University

Investigating Expert and Novice Programming Differences on Problems of Varying Complexity

Sketch mapping is a method used to investigate an individual's cognitive map of the surrounding environment. Although the method of sketch mapping is used in numerous experiments to investigate people's spatial perception, there is no comprehensive method to analyze sketch maps. Most methods are quantitative and limited to counting features or determining the (metric) spatial distortion in sketch maps. This paper aims to bridge this gap by proposing a comprehensive sketch map analysis method evaluating the completeness, level of generalization, and qualitative spatial accuracy. Our approach can assist researchers in geography, psychology, and education in systematically evaluating spatial knowledge, independent of specific research questions and experimental scenarios.

**Authors:**

Angela Schwering: University of Muenster; Jakub Krukar: University of Muenster; Charu Manivannan: Institute for geoinformatics, University of Muenster

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Inferring errors and intended meanings with a generative model of language production in aphasia

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