Netherlands

Self-regulated learning influences students’ learning behaviors and is a significant academic performance factor. Resource management strategy based on self-regulated learning theory is an important indicator for students to demonstrate self-regulatory skills. However, current self-regulated learning and resource management strategy assessments still rely on subjective evaluations and self-assessments, which are time-consuming and laborious. Therefore, we propose a novel method combined with mobile sensing by collecting detailed learning strategy subscales and objective mobile sensing data from N = 211 college students to explore a new approach to assessing self-regulated learning and resource management strategy. To our knowledge, we are the first to propose a mobile sensing approach for assessing self-regulated learning and learning strategies. The method studies the associations between the learning strategy subscales and these daily behavior patterns and presents a set of features for behavior patterns from mobile sensing data. These features represent the self-regulated learning skills of college students.

**Authors:**

Tongyu Zhao: University of Minho; Jiaying Gao: School of Artificial intenlligence; Yu Feng: University of Minho; Yatong Zu: Jilin University; Adriano Tavares: University of Minho; Tiago Gomes: University of Minho; Hao Xu: Jilin University

CogSci 2024

Behavioral Sensing: An Exploratory Study to Assess Self-Regulated Learning and Resource Management strategy of University Students using Mobile Sensing

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** 
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** 
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.

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.

poster

Electroencephalography (EEG) and function Near Infrared Spectroscopy (fNIRS), both portable and non-invasive, enhance brain-computer interface (BCI) performance by integrating their spatial and temporal benefits when combined together. However, the fusion of these two signals still faces challenges. To fully unitize the complementarity of EEG and fNIRS for improved performance in EEG-fNIRS BCI, we propose an EEG-fNIRS fusion network based on end-to-end mutual information learning, named EFMLNet. In the model, EEG and fNIRS data are fed into their respective feature extractors for the extraction of temporal and spatial information. Furthermore, their complementary information is fused by two parallel mutual learning modules. Classification experiments were conducted on two public BCI datasets, including motor imagery (MI) and mental arithmetic (MA) tasks. The cross-subject classification accuracy achieved on the MI and
MA datasets was 71.52% and 69.16%, respectively, surpassing the performance of most existing fusion methods and showing promise for real-time hybrid BCI systems.

**Authors:**

Lina Qiu: South China Normal University; Weisen Feng: South China Normal University; Zuorui Ying: South China Normal University; Jiahui Pan: South China Normal University

EFMLNet: Fusion Model Based on End-to-End Mutual Information Learning for Hybrid EEG-fNIRS Brain-Computer Interface Applications

Currently, emotion recognition based on electroencephalograms (EEGs) has a wide range of applications. Although many approaches have been proposed for automatic emotion recognition with favorable performance, there are still several challenges: (1) how to sufficiently model the long- and short-term temporal feature discrepancies and redundant spatial information of EEGs and (2) how to alleviate the negative impact of the ambiguity of emotion classes. To tackle these issues, we propose the CSET-CCA, a novel framework for EEG-based emotion recognition. The feature extractor of this model combines the 1D convolutional neural network (CNN), channel Squeeze-and-Excitation (SE) module and transformer. It can extract the temporal features of EEG signals from local and global perspectives and select the critical channels in emotion recognition. Moreover, to adaptively perceive the confusion degrees of classes and increase the model's attention on confusing emotion classes, we design class confusion-aware (CCA) attention. We evaluate the CSET-CCA with the SEED and SEED-V datasets. The experimental results show that the proposed approach outperforms state-of-the-art methods.

**Authors:**

Jiahui Pan: South China Normal University; Chenyu Bai: South China Normal University

EEG-Based Emotion Recognition via Convolutional Transformer with Class Confusion-Aware Attention

High-dimensional deep neural network representations of images
and concepts can be aligned to predict human annotations
of diverse stimuli. However, such alignment requires the costly
collection of behavioral responses, such that, in practice, the
deep-feature spaces are only ever sparsely sampled. Here, we
propose an active learning approach to adaptively sample experimental
stimuli to efficiently learn a Bayesian matrix factorization
model with deep side information. We observe a
significant efficiency gain over a passive baseline. Furthermore,
with a sequential batched sampling strategy, the algorithm is
applicable not only to small datasets collected from traditional
laboratory experiments but also to settings where large-scale
crowdsourced data collection is needed to accurately align the
high-dimensional deep feature representations derived from
pre-trained networks. This provides cost-effective solutions for
collecting and generating quality-assured predictions in largescale
behavioral and cognitive studies.

**Authors:**

Yangyang Yu: Stevens Institute of Technology; Jordan W. Suchow: Stevens Institute of Technology

Actively learning a Bayesian matrix fusion model with deep side information

Asking informative questions requires mastery of language, reasoning under uncertainty, and search under cognitive constraints. How do people navigate these computational tradeoffs? We model human question-asking in an adaptation of Battleship that admits a variety of creative questions. Our language-informed program sampling (LIPS) model uses large language models (LLMs) to sample k natural language questions and translate them into symbolic programs that are scored based on expected information gain. We find that with only k=5 samples, this simple Monte Carlo maximization strategy yields informative questions well-calibrated to humans in the Battleship domain. In contrast, pure LLMs struggle to ground questions in the board state; notably, GPT-4V provides no improvement over non-visual baselines. Our results illustrate how cognitive models of informative question-asking can leverage the statistics of language to capture human priors, while highlighting some shortcomings of LLMs as grounded reasoners.

**Authors:**

Gabriel Grand: MIT; Valerio Pepe: Havard University; Jacob Andreas: MIT; Josh Tenenbaum: MIT

Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling

Child-directed speech (CDS) is a particular type of speech that adults use when addressing young children. Its properties also change as a function of extralinguistic factors, such as age of the child being addressed. Access to large amounts of representative CDS would be useful for child language research, such as computational models of language acquisition. In this study, we describe an approach to model age-dependent linguistic properties of CDS using a language model (LM) trained on CDS transcripts and ages of the recipient children, as obtained from North American English corpora of the CHILDES database. The created LM can then be used to stochastically generate synthetic CDS transcripts in an age-appropriate manner. We systematically compare linguistic properties of the generated CDS against the real CDS addressed at children of different ages. The results show that the LM manages to capture age-dependent changes in CDS, except for a slight difference in the effective vocabulary size. We also provide a systematic characterization of age-dependent linguistic properties of CDS in CHILDES, illustrating how all measured aspects of the CDS change with children’s age.

**Authors:**

Okko Räsänen: Tampere University; Daniil Kocharov: Tampere University

Age-Dependent Analysis and Stochastic Generation of Child-Directed Speech

Computational models have long been used in Cognitive Science, but most research has used models trained on text. With recent advances in Computer Vision, new research is expanding to visually informed models. We explore the potential of such models to account for psycholinguistic data on the task of naming visually presented stimuli, from naming norms. We compare the performance of three representative models on norms including stimuli in the form of line drawings, colored drawings, and realistic photos: a supervised object classifier, a self-supervised Computer Vision model, and the state-of-the-art language and vision model CLIP. Results show that the visual encoder of CLIP consistently outperforms the other computer vision models and generalizes well across image types. The linguistic encoder of CLIP in turn outperforms all other models and achieves good overall accuracy. All in all, our results show promise for the use of language and vision models in Cognitive Science.

**Authors:**

Zhirui Chen: University of Amsterdam; Andreas Mädebach: Universidad Pompeu Fabra; Eleonora Gualdoni: Universitat Pompeu Fabra; Gemma Boleda: Universitat Pompeu Fabra

On the Use of Language and Vision Models for Cognitive Science: The Case of Naming Norms

Large Language Models (LLMs) have driven extraordinary improvements in NLP. However, it is unclear how such models represent lexical concepts--i.e., the meanings of the words they use. We evaluate the lexical representations of GPT-4, GPT-3, and Falcon-40B through the lens of HIPE theory, a concept representation theory focused on words describing artifacts (such as “mop”, “pencil”, and “whistle”). The theory posits a causal graph relating the meanings of such words to the form, use, and history of the referred objects. We test LLMs with the stimuli used by Chaigneau et al. (2004) on human subjects, and consider a variety of prompt designs. Our experiments concern judgements about causal outcomes, object function, and object naming. We do not find clear evidence that GPT-3 or Falcon-40B encode HIPE’s causal structure, but find evidence that GPT-4 does. The results contribute to a growing body of research characterizing the representational capacity of LLMs.

**Authors:**

Sam Musker: Brown University; Ellie Pavlick: Brown University

Testing Causal Models of Word Meaning in LLMs

Implicit biases are a common source of prejudicial decision-making in society. While use of language models might be intended to eliminate human bias and prevent instances of harmful prejudice, they are trained on human-generated linguistic data and thus inherit human-like biased attitudes. We conducted a narrative review of implicit attitudes in linguistic models, drawing on literature from artificial intelligence, social psychology, and cognitive science. Findings suggest an important link between statistical patterns in language and the implicit biases displayed by people (drawn from experimental data). While several efforts have been made to capture the levels of bias in language models, there is no contribution yet that focuses on the causal nature of the relationship between language and implicit bias in language models. This literature review highlights the state of the art in this growing field, identifies gaps in the literature and showcases challenges for further research in the future.

**Authors:**

Kevin Kiy: Maynooth University; Dermot Lynott: Maynooth University; Diarmuid O'Donoghue: Maynooth University

Implicit Bias in Language Models – A Narrative Literature Review with Systematic Elements

Emergent communication (EC) is the field that seeks to understand the mechanisms behind the emergence and evolution of natural language. In EC, the de facto standard has been using sequential architectures that have not explicitly incorporated the “tree-structured hierarchy” inherent in human language. This study utilizes a stack-based model called RL-SPINN, which learns tree structures through reinforcement learning without ground-truth parsing data, and acquires sentence representations according to these structures. We use this model as the basis for the understanding agents and investigate the extent to which the inductive bias of an architecture that explicitly utilizes tree structures affects the emergent language. The experimental results show that the emergent language generated by the RL-SPINN-based model exhibits higher communication accuracy than those generated by other baselines in some settings. This work is the first to focus on the tree-structured hierarchy of language and suggests new directions for future research in EC.

**Authors:**

Daichi Kato: The University of Tokyo; Ryo Ueda: The University of Tokyo; Jason Naradowsky: University of Tokyo; Yusuke Miyao: The University of Tokyo

Emergent Communication with Stack-Based Agents

Executive functions guide episodic memory to retrieve information essential for adaptive behavior. The prefrontal cortex achieves this by influencing hippocampal processing through anatomical projections targeting the entorhinal cortex and area CA1. However, most computational models of the hippocampus overlook this cognitive control, either neglecting it or implementing implausible direct connections to the hippocampus. This paper explores the contextual control of associative memory implemented by modern Hopfield networks, within a hippocampus-inspired autoencoder. Our experiments underscore the importance of proximity between prefrontal afferences and the locus of memory storage for efficient contextual modulation of episodic memory, challenging the standard model of hippocampal processing. These findings not only advance our understanding of higher-level cognition but also provide design principles for more adaptive machine learning algorithms.

**Authors:**

Hugo Chateau-Laurent: Inria centre at the university of Bordeaux; Frederic Alexandre: Inria centre at the university of Bordeaux

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