Netherlands

The theory of language prediction posits a competitive preactivation of semantic (meaning) and form (sound/grapheme) information, aiding in the selection of the most likely lexical candidate. Hypothetically, multiple semantic and form cohorts are preactivated before the actual lexical candidate is activated. This study explores this by examining young adults reading constrained sentences (discretely), with simultaneous electroencephalographic recording. Representational similarity analysis was conducted to assess word-specific, semantic-related, and form-related pair of sentences (focusing on the word preceding the expected word). To examine the temporality, cluster permutation and divergence point analyses were performed. The results indicated a semantic coactivation effect occurring before the phonological one and the recovery of the specific words. However, despite the phonological information being recovered before the word specific information, there were no significant differences in temporality. These findings indicate a semantic coactivation process for meaning selection during prediction, with form coactivation dependent on the expected word&#39;s selection.

**Authors:**

Armando Quetzalcóatl Angulo Chavira: UNAM; Alejandra Mitzi Castellón-Flores: Universidad Nacional Autónoma de México; Elia Haydee Carrasco-Ortíz: Universidad Autónoma de Queretaro; Natalia Arias-Trejo: Universidad Nacional Autónoma de Mexico, UNAM

CogSci 2024

Temporal Dynamics of Semantic and Form preactivation in Lexical Selection: An EEG Study

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**
<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/cble/" seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>


**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.

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

It looks like you are not registered for this event. 

To access the site please register [**here**](https://cognitivesciencesociety.org/registration/). 

Please register!

The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

The theory of language prediction posits a competitive preactivation of semantic (meaning) and form (sound/grapheme) information, aiding in the selection of the most likely lexical candidate. Hypothetically, multiple semantic and form cohorts are preactivated before the actual lexical candidate is activated. This study explores this by examining young adults reading constrained sentences (discretely), with simultaneous electroencephalographic recording. Representational similarity analysis was conducted to assess word-specific, semantic-related, and form-related pair of sentences (focusing on the word preceding the expected word). To examine the temporality, cluster permutation and divergence point analyses were performed. The results indicated a semantic coactivation effect occurring before the phonological one and the recovery of the specific words. However, despite the phonological information being recovered before the word specific information, there were no significant differences in temporality. These findings indicate a semantic coactivation process for meaning selection during prediction, with form coactivation dependent on the expected word's selection.

**Authors:**

Armando Quetzalcóatl Angulo Chavira: UNAM; Alejandra Mitzi Castellón-Flores: Universidad Nacional Autónoma de México; Elia Haydee Carrasco-Ortíz: Universidad Autónoma de Queretaro; Natalia Arias-Trejo: Universidad Nacional Autónoma de Mexico, UNAM

poster

Though working memory deals with different types of contents, the vast majority of studies on working memory updating have been conducted on non-sense syllables and numbers. The present study aims to understand the updating process of words, particularly, whether word length increases the response latencies for updating. The study hypothesized updating of longer words to be more time consuming than shorter words. A modified version of the working memory updating paradigm proposed by Artuso & Palladino (2011) is used for the study. A within-subject experimental design was employed. Repeated measures ANOVA of response latencies across conditions of 3,4 and 5 letter word updating, found no significant differences in reaction times on the basis of word length. The involvement of chunking and other long term memory processes can be cited as the reason for this.

**Authors:**

Thara Baburaj: Indian Institute of Technology, Guwahati

Effect of word length on updating working memory contents

An open question in the developmental causal learning literature concerns how children’s beliefs about causal systems impact their inferences. This study investigated how 4- and 5-year-olds’ causal beliefs related to their “backwards blocking” abilities, as well as whether associative learning or Bayesian inference better explained their judgements. Children were taught either that two causes together produced a larger effect than that produced by each individually or that they produced the same size effect as that produced by either one. A third group received no training. Results indicated that 4-year-olds engaged in backwards blocking only after additivity training and that their inferences mainly matched an associative model. In contrast, 5-year-olds consistently engaged in backwards blocking and produced responses that largely matched a Bayesian model. These findings suggest that the effect of children's beliefs about causal systems on their inferences undergoes a developmental progression and implicate the role of multiple cognitive mechanisms.

**Authors:**

Rebecca Beaton: Vanderbilt University ; Deon T. Benton: Vanderbilt University

“Dancing on the ceiling”: The role of different forms of thinking on retrospective reevaluation in children

Generalizations in thought and language are powerful tools to share information agents need to predict and control their environments. However, some generalizations are restricted to “sociocultural bubbles” (e.g. “women have trouble getting tenure in math”). How do we communicate such patterns? We examined how 4-7-year-olds (N=45) and adults (N=159) respond to context cues signaling that the speaker uses a generic generalization to convey a broad vs. contextually-restricted regularity. Adults endorsed generics flexibly, tracking context cues (p<.001), but younger children struggled, over-attributing socially contingent properties to the group beyond the “bubble”, on par with context-general regularities (p=.421). This reveals a troubling discrepancy between children and adults’ interpretations of generics, opening doors for miscommunication. When adults highlight problematic patterns with the hope of promoting social change, children may perceive their assertions as claims about group’s broad, unalterable attributes. We discuss strategies to mitigate this in educational and family communication settings.

**Authors:**

Alejandro Curiel: California State University East Bay; Danisha Watson: California State University East Bay; Virginia Valerio-Lambert: California State University East Bay; Justin Miranda: California State University East Bay; Jocelyn Celaya: California State University East Bay; Sabria Hinton: California State University East Bay; Katherine Ritchie: University of California, Irvine; Ny Vasil: California State University East Bay

Generic Language as a Vehicle for Socially-Contingent Generalizations

The human ability to learn and compose conceptual operations is foundational to making flexible generalizations, such as creating new dishes from known cooking processes. Beyond naive chaining of functions, there is evidence from the linguistic literature that people can learn and apply context-sensitive, interactive rules, such that output production depends on context changes induced by different function orderings. Extending the investigation into the visual domain, we developed a function learning paradigm to explore the capacity of humans and neural network models in learning and reasoning with compositional functions under varied interaction conditions. Following brief training on individual functions, human participants were assessed on composing two learned functions, in ways covering four main interaction types, including instances in which the application of the first function creates or removes the context for applying the second function. Our findings indicate that humans can make zero-shot generalizations on novel visual function compositions across interaction conditions, demonstrating sensitivity to contextual changes. A comparison with a neural network model on the same task reveals that, through the meta-learning for compositionality (MLC) approach, a standard sequence-to-sequence Transformer can approximate a strong function learner, and also mimic human error patterns with additional fine-tuning.

**Authors:**

Yanli Zhou: New York University; Brenden Lake: NYU; Adina Williams: Meta Platforms Inc.

Compositional learning of functions in humans and machines

The influence exerted by no longer relevant intentions that have been successfully executed or cancelled is called aftereffects. The current study investigated the effect of encoding a secondary intention on the aftereffects of non-relevant prospective intentions. The study used an active phase-finished phase paradigm with participants randomly assigned to either experimental or control conditions. In the experimental condition, participants encoded a secondary intention in the finished phase of the task. In the control condition, participants did not encode any additional instructions. Commission errors and response latencies were analysed in the finished phase for fulfilled intentions or encoded but unfulfilled intentions. Independent sample t-tests found significant (p<0.05) differences between experimental and control groups. Suspended cues displayed a higher accessibility due to anticipatory monitoring and pending response action, and also resulted in more commission errors in comparison to repeat cues.

**Authors:**

Snigdha Ayyagari: Indian Institute of Technology Guwahati

Encoding a Secondary Intention can Increase Aftereffects in Prospective Memory

As mental representations are standardly thought to underlie all cognitive processes, a major goal of cognitive science has been to uncover representations. Methods for representation learning from behavioral data often model choice or reaction time data alone, but not jointly, leaving out potentially useful information. Here we develop two models of choice and RT in the odd-one-out task, including one based on the Linear Ballistic Accumulator. Parameter recovery simulations show joint modeling of choice and RT with LBA recovers representations more accurately than modeling choice alone with softmax. However, on two empirical datasets of images and words, joint models performed no better than choice-only models, despite a significant correlation of reaction time with two measures of similarity and choice difficulty in both datasets. We speculate on reasons for the unrealized promise of joint modeling of RT and choice in representation learning.

**Authors:**

Russell Richie: University of Pennsylvania; Nehal Ajmal: Vassar College; Martin N Hebart: Max Planck Institute for Human Cognitive and Brain Sciences

Unrealized promise of joint modeling of choice and reaction time in improving representation learning

This study introduces an innovative approach to readability assessment, integrating cognitive science principles with artificial intelligence to evaluate text comprehensibility. Traditional methods of determining text readability have largely focused on surface-level features, neglecting educational complexity and curriculum alignment of the content. This study proposes a novel method that employs large language models (LLMs) to assess text difficulty by considering content depth and its alignment with educational standards. By leveraging the extensive knowledge encapsulated in LLMs, the method evaluates whether the content of the text corresponds to a specific educational level, ranging from elementary to university. Our readability assessment method provides a more nuanced understanding of text accessibility. The difficulty of the text content is assessed using a combination of language resources to measure the amount of scientific knowledge contained in the text. It promises to enhance educational resources' alignment with learners' capabilities, facilitating more effective learning experiences.

**Authors:**

Yo Ehara: Tokyo Gakugei University

Advanced Readability Estimation through Educational Content Complexity

Language is fundamental in human cognition and communication, helping us to encode the world around us. Adjectives represent a linguistic form used extensively, particularly in the social domain. Adjectives vary in both their valence and their breadth (e.g., “punctual” is narrower than “dependable”). Variations in adjectival breadth have not been studied extensively but may have significant consequences across various domains. The present study explores how subtle distinctions in adjective use may relate to descriptions of experiences that people share. To assess linguistic breadth in online communication, we examine whether online reviews with different star ratings are associated with differences in adjectival breadth. Through an analysis of over 200,000 reviews from Amazon digital music (Study 1) and Yelp restaurants (Study 2), we find evidence that linguistic desirability and breadth of adjectives in reviews positively correlate with their ratings. Specifically, higher-rated reviews tend to use broader and more desirable adjectives. However, this relationship varies between product categories, with high-rated music reviews showing increased linguistic breadth and desirability, while top-rated restaurant reviews demonstrate a decrease in breadth. This paper contributes to understanding linguistic breadth in social media contexts, highlighting how subtle language variations in evaluations can reflect different cognitive and communicative processes.

**Authors:**

Lin Lin: University of California, Los Angeles; Rick Dale: University of California, Los Angeles (UCLA); Steve Stroessner: University of California, Los Angeles (UCLA)

Breadth of the Stars: Exploring Adjectival Breadth in Online Reviews

(I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context;

**Authors:**

Abhishek Agrawal: Aix-Marseille Université; Benoit Favre: Aix-Marseille University; Abdellah Fourtassi: Aix-Marseille University

Analysing Communicative Intent Coordination in Child-Caregiver Interactions

Large Language models (LLM) exhibit human-like proficiency in various tasks such as translation, question answering, essay writing, and programming. Emerging research explores the use of LLMs in collective problem-solving endeavors, such as tasks where groups try to uncover clues through discussions. Although prior work has investigated individual problem-solving tasks, leveraging LLM-powered agents for group consensus and decision-making remains largely unexplored. This research addresses this gap by (1) proposing an algorithm to enable free-form conversation in groups of LLM agents, (2) creating metrics to evaluate the human-likeness of the generated dialogue and problem-solving performance, and (3) evaluating LLM agent groups against human groups using an open source dataset. Our results reveal that LLM groups outperform human groups in problem-solving tasks. LLM groups also show a greater improvement in scores after participating in free discussions. In particular, analyses indicate that LLM agent groups exhibit more disagreements, complex statements, and a propensity for positive statements compared to human groups. The results shed light on the potential of LLMs to facilitate collective reasoning and provide insight into the dynamics of group interactions involving synthetic LLM agents.

**Authors:**

Yinuo Du: Software and Societal Systems Department; Prashanth Rajivan: University of Washington; Cleotilde Gonzalez: Carnegie Mellon University

Premium content

Downloads

Next from CogSci 2024

Effect of word length on updating working memory contents

Similar lecture

Unveiling the Path to Phonological Anticipation: Insights from Infants' Eye Movements