Netherlands

Early word learning involves mapping individual words to their meanings and building organized semantic representations among words. Previous corpus-based studies (e.g., using text from websites, newspapers, child-directed speech corpora) demonstrated that linguistic information such as word co-occurrence alone is sufficient to build semantically organized word knowledge. The present study explored two new research directions to advance understanding of how infants acquire semantically organized word knowledge. First, infants in the real world hear words surrounded by contextual information. Going beyond inferring semantic knowledge merely from language input, we examined the role of extra-linguistic contextual information in learning semantic knowledge. Second, previous research relies on large amounts of linguistic data to demonstrate in-principle learning, which is unrealistic compared with the input children receive. Here, we showed
that incorporating extra-linguistic information provides an efficient mechanism through which semantic knowledge can be acquired with a small amount of data infants perceive in everyday learning contexts, such as toy play.

**Authors:**

Jane Yang: University of Texas at Austin; Yayun Zhang: Max Planck Institute for Psycholinguistics; Chen Yu: University of Texas at Austin

CogSci 2024

Learning semantic knowledge based on infant real-time attention and parent in-situ speech

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


CogSci 2021

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.

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**<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**
<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**<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.
<br><br>

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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.

In our cross-cultural corpus study of 332 comics, we asked whether animacy preference plays a role in comics. Are animates or inanimates more or less grammatically marked compared to each other, similarly to differential marking modulated by animacy in grammars of many languages? Following Opfer (2002), we considered the animacy preference as the expectation that only animates move in a goal-directed way. We focused on two visual morphological markings that indicate motion in comics and differ in their goal-directedness: the goal-directed motion lines (trailing a moving entity) and the non-goal-directed circumfixing lines (surrounding an entity). We found that inanimates are more marked by motion lines than animates in our data, while there is no difference between the two groups with circumfixing lines. This indicates that inanimates need to be marked by motion lines in order to signal their goal-directed movement, which is otherwise unexpected. We call this the principle of “Mark the unexpected!”.

**Authors:**

Ana Krajinovic: Tilburg University; Irmak Hacımusaoğlu: Tilburg University; Bruno Cardoso: Tilburg University; Neil Cohn: Tilburg University

Mark the unexpected! Animacy preference and motion marking in visual language

During sentence processing, comprehenders have to maintain a mapping between lexical items and their position in the sentence (syntactic position). We propose a model of morpheme-position binding in working memory, based on models such as 'serial-order-in-a-box' and its SOB-complex-span version. Like those working memory models, our sentence processing version derives a range of attested memory interference effects from the process of item-position binding. We present simulation results capturing similarity-based interference and item-distortion. These two major classes of interference effects have not received a unified account before, and are not fully captured by cue-based retrieval models.

**Authors:**

Maayan Keshev: The Hebrew University of Jerusalem; Mandy Cartner: Tel Aviv University; Aya Meltzer-Asscher: Tel Aviv University; Brian Dillon: University of Massachusetts Amherst

A working memory model of sentence processing as binding morphemes to syntactic positions

In this paper, we report three reading time (RT) experiments (one using self-paced reading and two using A-Maze) that tested the cognitive mechanisms underlying the processing of classifier-noun dependencies in Mandarin Chinese (MC). We leveraged prenominal relative clauses and the contrast between general and specific classifiers in MC, which offered a good testing ground for existing theories of sentence processing. Results from the A-Maze experiments showed both locality and expectation effects. More importantly, we observed an interaction between locality and expectation in the way of Information Locality (Futrell, 2019; Futrell, Gibson, & Levy, 2020): Expectation-driven facilitation was highly constrained by locality effects. To capture the results, we implemented a resource-rational Lossy-Context Surprisal model (Hahn et al., 2022) for MC, which successfully replicated the key patterns in the A-Maze experiments.

**Authors:**

Hailin Hao: University of Southern California; Yang Yang: Guangdong University of Foreign Studies; Michael Hahn: Saarland University

Information Locality in the Processing of Classifier-Noun Dependencies in Mandarin Chinese

Computational psycholinguistics has traditionally employed a complexity metric called Node Count, which counts the number of syntactic nodes representing syntactic structures and predicts processing costs in human sentence processing. However, Node Count does not dissociate distinct syntactic operations deriving those syntactic structures, so that how much processing cost each syntactic operation induces remains to be investigated. In this paper, we introduce a novel complexity metric dubbed Composition Count, which counts the number of syntactic operations deriving syntactic structures, allowing us to understand the computational system of human sentence processing from the derivational, not representational, perspective. Specifically, employing Combinatory Categorial Grammar (CCG) which is equipped with multiple syntactic operations and thus suitable for the purpose here, we investigate (i) how much distinct syntactic operations of CCG contribute to predicting human reading times, and (ii) whether the same holds across languages. The results demonstrate that distinct syntactic operations of CCG have independent and cross-linguistic contributions to predicting human reading times, while Node Count turns out not to be robust cross-linguistically. In conclusion, these results strongly suggest the importance of Composition Count to dissociate distinct syntactic operations, not whole syntactic representations, and understand the computational system of human sentence processing.

**Authors:**

Kohei Kajikawa: University of Tokyo; Ryo Yoshida: University of Tokyo; Yohei Oseki: University of Tokyo

Dissociating Syntactic Operations via Composition Count

Both prediction and working memory constraints have been established as key factors in characterizing incremental sentence processing difficulty. Here we investigate the less explored question: Whether and how predictive expectation and working memory interact with each other using data from naturalistic reading time corpora. We provide broad-coverage evaluations of two hypotheses that make divergent predictions regarding the interaction of expectation and memory constraints: the Information Locality and Prediction Maintenance hypotheses. We first confirmed the predictions of both expectation- and working memory-based theories. Regarding their interactions, we find support the Information Locality hypothesis: Strong mutual predictability can enhance locality effects. We argue that future theory building in sentence processing should therefore take into consideration both prediction and memory constraints, as well as their potential interaction.

**Authors:**

Hailin Hao: University of Southern California; Himanshu Yadav: Indian Institute of Technology Kanpur; Elsi Kaiser: University of Southern California

Naturalistic Reading Time Data Support Information Locality

How do humans decide what to look at and when to stop looking? The Rational Action, Noisy Choice for Habituation (RANCH) model formulates looking behaviors as a rational information acquisition process. RANCH instantiates a hypothesis about the perceptual encoding process using a neural network-derived embedding space, which allows it to operate on raw images. In this paper, we show that the model not only captures key looking time patterns such as habituation and dishabituation, but also makes fine-grained, out-of-sample predictions about magnitudes of dishabituation to previously unseen stimuli. We validated those predictions experimentally with a self-paced looking time task in adults (N = 468). We also show that model fits are robust across parameters, but that assumptions about the perceptual encoding process, the learning process and the decision process are all critical for predicting human performance.

**Authors:**

Anjie Cao: Stanford ; Gal Raz: MIT; Rebecca Saxe: MIT; Michael C. Frank: Stanford University

Predicting graded dishabituation in a rational learning model using perceptual stimulus embeddings

Differentiating letter shapes accurately is an increasingly crucial competence. Are letters as distinctive as they could be? We used a unique dataset of crowdsourced letter descriptions across 43 writing systems to produce a comprehensive typology of letter shapes for these diverse scripts. We extracted from 19,591 letter classifications, contributed by 1,683 participants, enough features to provide a unique description of all letters in each system. We show that scripts, compared to phoneme inventories, are feature-extensive: they use additional features to do what could be done with a lower number of features, used more efficiently. Compared to 516 phoneme inventories from the P-base dataset, our 43 scripts have lower feature economy (fewer symbols for a given number of features) and lower feature informativeness (a less balanced distribution of feature values). Letter shapes, we argue, having more degrees of freedom than speech sounds, use features in a more wasteful way.

**Authors:**

Yoolim Kim: Wellesley College; Marc Allassonnière-Tang: CNRS; Helena Miton: Santa Fe Institute; Olivier Morin: PSL University

Letter shapes phonology: Feature economy and informativeness in 43 writing systems

Here we used deep neural network + hidden Markov model (DNN+HMM) to provide a computational account for the relationship among holistic processing (HP), face scanning pattern and face recognition performance. The model accounted for the positive associations between HP and eyes-focused face scanning pattern/face recognition performance observed in the literature regardless of the version of the composite task used to measure HP. Interestingly, we observed a quadratic relationship between HP and face scanning pattern, where models being highly eyes-focused or highly nose-focused had lower HP. By inspecting fixation locations and associated attention window size in the model and XAI methods, we found that the eyes- and nose-focused models both developed local and holistic internal representations during training, and their difference was in the temporal dynamics of how these representations were used. Our findings demonstrated how computational modeling could unravel the mechanisms underlying cognition not readily observable in human data.

**Authors:**

Wei Xing: University of Hong Kong; Yueyuan Zheng: University of Hong Kong; Antoni B. Chan: City University of Hong Kong; Janet Hsiao: Hong Kong University of Science & Technology

Is Holistic Processing Associated with Face Scanning Pattern and Performance in Face Recognition? Evidence from Deep Neural Network with Hidden Markov Modeling

In this paper, we test the effect of manipulating discrimination difficulty on subsequent generalization of learning and in particular, on the peak shift effect. Participants learned a discrimination where one stimulus led to an outcome (S+) and another stimulus led to no outcome (S-). Difficulty was manipulated by varying the degree of similarity between the S+ and S- across groups (easy/medium/hard). In contrast to similar studies in animals, we found that increasing the difficulty of the discrimination resulted in less peak shift. Using a hierarchical mixture model, we characterize the effects of discrimination difficulty on relational- and similarity based responding, and show for the first time, a similar mixture of responding on stimulus identification gradients. We conclude that peak shift on generalization and identification measures can be explained by mixtures of participants responding in different ways.

**Authors:**

Jessica C. Lee: University of Sydney; Tamara Cahyadi: Macquarie University; Peter Lovibond: University of New South Wales; René Schlegelmilch: University of Bremen

Effects of Discrimination Difficulty on Peak Shift and Generalization

We propose that kinds relate to particular things either constitutively or contingently. Taxonomic categories of animals and artifacts constitutively relate their members: DOG and CAR group things by aspects of the forms of their matter; the forms that make them things instead of stuff. Categories of things in roles or with diseases contingently relate to their members: LAWYER and DIABETIC group things by forms other than the forms that make them things. We confirm this distinction in five experiments with American adults.

**Authors:**

Alexander Noyes: New York University; Katherine Ritchie: University of California, Irvine; Marjorie Rhodes: New York University

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Mark the unexpected! Animacy preference and motion marking in visual language

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