Netherlands

Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry. Recent research in cognitive science suggests neural networks do not share this capacity, concluding that human geometric abilities come from discrete symbolic structure in human mental representations. However, progress in artificial intelligence (AI) suggests that neural networks begin to demonstrate more human-like reasoning after scaling up standard architectures in both model size and amount of training data. In this study, we revisit empirical results in cognitive science on geometric visual processing and identify three key biases in geometric visual processing: a sensitivity towards complexity, regularity, and the perception of parts and relations. We test tasks from the literature that probe these biases in humans and find that large pre-trained neural network models used in AI demonstrate more human-like abstract geometric processing.

**Authors:**

Declan I. Campbell: Princeton University; Sreejan Kumar: Princeton Neuroscience Institute; Tyler Giallanza: Princeton Neuroscience Institute; Jonathan Cohen: Princeton University; Tom Griffiths: Princeton University 

CogSci 2024

Human-Like Geometric Abstraction in Large Pre-trained Neural Networks

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

We present an intentional agent implemented as a neural dynamic model that controls a simulated robot and is continuously coupled to the sensory-motor surface while carrying out pick and place action plans. In a hierarchical structure, higher-level intentions guide lower-level motor behavior. Intentions are represented as action phrases that can be flexibly composed out of a set of atomic action and object concepts. Action plans are implemented as partially ordered sequences which represent dependencies between action phrases. Intentions steer behavior through sequences of low-level movement primitives, while guiding search for target and reference objects in the scene. The model is inspired by proposals for shared neural and structural basis of language and action (Pastra & Aloimonos, 2012). We demonstrate a neural process model that represents intentions as compositional structures, and address how that representational framework may guide action with some of the remarkable flexibility found in human goal-directed motor behavior.

**Authors:**

Stephan Sehring: Ruhr-Universität Bochum; Richard Julius Paul Koebe: Institut für Neuroinformatik; Sophie Aerdker: Ruhr-Universität Bochum; Gregor Schöner: Ruhr-Universität Bochum

A Neural Dynamic Model Autonomously Drives a Robot to Perform Structured Sequences of Action Intentions

In classroom interactions that take place over video conferencing platforms, teachers and students continue to gesture, but their bodies are neither physically copresent nor fully visible to each other. Do instructor gestures help learning in this context, as has been found for in-person learning and for video-based learning in lab experiments? Across all conditions, we show professors lecturing spontaneously with unscripted co-speech gestures. In some conditions, we edit this recording to mimic the Zoom lecture experience, cropping the video so only the top half of the professor's gesture space is available, or removing the video altogether. Results show that participants paid significantly more visual attention to the partial gesture condition than to stimuli where the gesturing was fully visible, and they scored significantly higher on an immediate comprehension test if they had seen lectures in the partially visible condition. We discuss these unexpected findings as potential effects of the reduction in intercorporeality in video-mediated learning. This work raises further questions of mechanism for gesture’s role in learning.

**Authors:**

Elena C Cuffari: Franklin and Marshall College; Lauren H. Howard: Franklin & Marshall College; Ayako Belka: Franklin and Marshall College; Alonna Reilly: Franklin and Marshall College; Kyle M Samson: Franklin & Marshall College; Jingyi Sun: Franklin and Marshall College; WEI HUANG: University of Michigan Ann Arbor; Zeyi Zang: University of Illinois Urbana-Champaign

A Little Goes a Long Way: How Gesture Visibility in Video Lectures Impacts Attention and Learning

We investigated the emotion-based modulation in the attentional mechanism by presenting angry and happy faces simultaneously in the extrafoveal vision. In a letter discrimination task at the fixation, pairs of task-irrelevant happy and angry faces were displayed peripherally (≥5° away from the fixation) to study the valence-facilitated attentional capture under mutual competition for processing resources. Selective orienting was assessed using eye movement measures such as the probability of first fixation on these emotional face images. Results revealed a higher probability of first fixation for happy faces relative to angry faces. Processing of affective stimuli in the extrafoveal indicates early occurring covert orienting of attention followed by overt attention in the foveal vision. The attentional capture advantage by happy faces occurred in the absence of differences in arousal level and other perceptual properties. We propose that happy faces have a unique capacity to capture attention when competing with angry faces.

**Authors:**

Lijiya Chacko: IIT Bombay; Rashmi Gupta: IIT Bpmbay

The attentional system is tuned to initially orient to happy faces when competing with angry faces: An eye-tracker investigation

Human learners acquire and integrate multiple linguistic regularities simultaneously over developmental time. Understanding how this process works remains a fundamental research challenge. It’s been proposed that systems with many redundant, overlapping, and intercorrelated features—often known as systematicity—aid in learning. By using high-density eye-tracking and combining two established research paradigms—implicit artificial language learning and visual statistical learning—we demonstrate how the property of systematicity guides attention and leads to enhanced learning in real-time. Preliminary results (N = 35) indicate that participants in the High Systematicity condition not only demonstrated superior overall learning performance but also enhanced proficiency in generative rule-specific tasks, compared to participants in the Low Systematicity condition. We hypothesize that many intercorrelated cues more effectively guide learners' attention to the relational structures of the system. Ongoing eye-tracking analysis will provide fine-grained evidence for this hypothesis with broad implications for learning in many other domains.

**Authors:**

Lucile Vleugels: University of Colorado, Boulder; Lei Yuan: University of Colorado, Boulder

Finding Structure in Real Time: An Eye Tracking Study on the Statistical Learning of Multiple Linguistic Structures Simultaneously

Human communication benefits from concurrent multimodal information, e.g., seeing and hearing speakers talk. Familiarization with the face of a talking speaker later helps voice
recognition in auditory-only conditions, e.g., on the phone.
Based on human neuroscience findings, we hypothesized that this so-called ’face-benefit’ develops from two factors. (1.) Generative models in the brain integrate information from different modalities into multimodal contexts to better predict the trajectory of the speech signal. (2.) Once a context, e.g., a speaker, is sufficiently learned, the model substitutes absent sensory inputs, e.g., their face, with predictions via simulation.
We trained a deep generative model for multimodal predictions on synthetic data of audio-visual talking faces. Subsequently, we tested the model’s ability to recognize familiar and unfamiliar speakers from auditory-only data. We plan to compare different processing pathways to analyze which model structure best explains human behavioral data of previous experiments on voice identity recognition.

**Authors:**

Christian Gumbsch: TUD Dresden University of Technology; Martin V. Butz: University of Tuebingen; Katharina von Kriegstein: TU Dresden University of Technology

Modeling auditory voice recognition improvements by face simulation

The McGurk effect is a demonstration of the multimodal nature of speech perception; listening to /b/ while watching visual mouth movements for /g/ is expected to result in a “fusion” perception of /d/. A majority of studies on the effect use isolated syllables, whereas our goal was to enhance ecological validity by examining word stimuli. We varied task (forced-choice vs. open-ended) and stimuli (words vs. non-words) between participants. In the word condition, all three stimuli formed words (e.g., beer/deer/gear), and in the non-word condition, the B, D, or G stimulus was a word while the other two were nonwords (e.g., besk/desk/gesk). Fusion responses were much lower than in previous studies, but importantly, participants showed the most fusion responses when the D stimulus was a word and B and G were non-words. These results challenge assumptions about the underlying mechanisms of the McGurk effect, arguing against a purely perceptual illusion.

**Authors:**

Laura Getz: University of San Diego

Did you say Beer, Deer, or Gear? Exploring the McGurk effect using word stimuli

We tailor the explanations we give depending on the person asking for them – you would explain why an event happened differently depending on which of the contributing causes the listener already knows. While significant prior work focuses on how causal structure in the world influences explanation, we focus on how explanation production is modulated by listener belief. We propose a computational model framing explanation as rational communication about causal events, using a recursive theory-of-mind and language production framework to choose amongst possible explanatory utterances that minimize the divergence between speaker and listener belief about a why an event happened. We evaluate our model using some partial observer stimuli, which manipulate the listener’s stated prior knowledge about an event, and find that our model well-predicts human judgements about which of several contributing causes is the best explanation for a speaker to provide by modeling their communicative value to the listener.

**Authors:**

Yuka Machino: Massachusetts Institute of Technology; Ron Shprints: Massachusetts Institute of Technology; Max Siegel: MIT; Lionel Wong: Massachusetts Institute of Technology; Josh Tenenbaum: MIT

Listener Knowledge Structures Commonsense Explanation

The Event Segmentation Theory suggests that people naturally divide everyday experiences into distinct units, with event boundaries serving as anchors in long-term memory and aiding recall. These boundaries are ubiquitous in children’s daily experiences and may significantly influence learning. This study investigated how event boundaries affect novel category learning in young children. Specifically, 23 English-speaking three-year-olds learned novel object categories under two conditions. In the event boundary condition, objects were moved across two different background contexts, whereas in the control condition, they remained within the same backgrounds. We hypothesized that presenting objects across an event boundary would enhance generalization. Unexpectedly, both conditions yielded similar performance. An order effect emerged, with initially introduced categories showing better performance, suggesting the impact of task structure and children’s differing interpretations of event boundaries, particularly among females. This finding opens avenues for further investigation into the role of event boundaries in early category learning.

**Authors:**

Alice Xu: University of California, Los Angeles; James Stigler: University of California, Los Angeles; Catherine Sandhofer: University of California, Los Angeles

The Effect of Event Boundaries on 3-Year-Olds’ Novel Category Learning

Studies of children's number knowledge have revealed their awareness of a variety of cognitive strategies supporting counting, even for sets that they do not or cannot count. For instance, even if a set is too large to count, they judge that adding, then removing an object yields the same cardinality. Very young children understand that the counting routine involves establishing one-to-one correspondence, even when they themselves cannot count. Such studies have focused on what children know about number. We focus instead on what children understand about properties of sets that make them more or less easy to count. We varied the size, numerosity, density, and layout of set elements across 12 trials, such that one set in each pair was easier to track and remember. Preschoolers succeeded on some but not all trials, showing early sensitivity and learning opportunities regarding how set features influence counting difficulty.

**Authors:**

Karla E Perez: MIT; Max Siegel: MIT; Nicole Coates: Massachusetts Institute of Technology; Josh Tenenbaum: MIT; Laura Schulz: Massachusetts Institute of Technology

Knowing What Counts for Counting

Infants develop the ability to anticipate action goals during their first year, as shown by anticipatory gaze behavior. As they grow older, this is first evident for most familiar actions and agents, e.g., human hands performing a reaching action; later also for unusual agents (e.g., mechanical claws). We argue that this ability emerges as infants attempt to segment the world they observe into events---to infer the currently unfolding events and to predict their consequences for minimizing anticipated uncertainty. We propose a computational model that explains this development from a functional, algorithmic perspective, CAPRI2 (Cognitive Action PRediction And Inference in Infants). Our model integrates proposals about the development of object files, event files, and physical reasoning abilities into a learning and probabilistic planning-as-inference framework. While observing goal-directed, or arbitrary, interactions between two objects (i.e., potential agent and patient), CAPRI2's active inference processes infer both maximally consistent event interpretations and motor actions (here, eye fixations), where the latter are executed in the service of further minimizing current and anticipated uncertainties. As a result, CAPRI2 models typical developmental patterns of infants' anticipatory gaze behavior in an emergent manner. In particular, to successfully model the emergent developmental pattern, our model suggests that infants activate object event files, implicitly reason about object interactions in an event-oriented manner, infer consistent interpretations of their observations, and control their gaze shifts to minimize anticipated uncertainty. We propose that these mechanisms, as reflected in our model, may constitute fundamental building blocks for developing goal-predictive capacities in infants.

**Authors:**

Johanna Katharina Theuer: University of Tübingen; Nadine Nicole Koch: University of Stuttgart; Christian Gumbsch: TUD Dresden University of Technology; Birgit Elsner: University of Potsdam; Martin V. Butz: University of Tuebingen

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A Neural Dynamic Model Autonomously Drives a Robot to Perform Structured Sequences of Action Intentions

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