United States

**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://s3.eu-west-1.amazonaws.com/underline.prod/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.

The ability to represent semantic structure in the environment — objects, parts, and relations — is a core aspect of human visual perception and cognition. Here we leverage recent advances in program synthesis to develop an algorithm for learning the part-based structure of drawings as represented by graphics programs. This algorithm iteratively learns a library of abstract subroutines that can be used to more compactly represent a set of drawings by capturing common structural elements. Our experiments explore how this algorithm exploits statistical reg- ularities across drawings to learn new subroutines. Together, these findings highlight the potential for understanding human visual concept learning via program-like abstractions.

Learning part-based abstractions for visual object concepts

poster

Biologically Plausible Spiking Neural Networks for Perceptual Filling-In

Application of machine learning to signal entrainment identifies predictive processing in sign language.

Perceptual Sensitivity to an Artificial Co-Actor in Competitive 2D Pong

Inferring Actions, Intentions, and Causal Relations in a Deep Neural Network

Comparing Markov and quantum random walk models of categorization decisions

Seeing in the dark: Testing deep neural network and analysis-by-synthesis accounts of 3D shape perception with highly degraded images

Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus

Knowledge-Gap Awareness as Mediating Cognitive Mechanism in Tool-Mediated Learning in Computer Science: A Multi-Method Experimental Study

Meaning in brains and machines: Internal activation update in large-scale language model partially reflects the N400 brain potential

Predicting Learning and Retention of a Complex Task Using a Cognitive Architecture

https://d3smihljt9218e.cloudfront.net/transcript/17150/subtitle/861bd3fc4b6993a150f25f8105d3ce18.vtt

English

Learning part-based abstractions for visual object concepts

Downloads

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Biologically Plausible Spiking Neural Networks for Perceptual Filling-In

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