Recent progress in artificial intelligence has renewed interest in
building machines that learn like animals. Almost all of the
work comparing learning across biological and artificial systems
comes from studies where animals and machines received
different training data, obscuring whether differences between
animals and machines emerged from differences in learning
mechanisms versus training data. We present an experimental
approach—a “newborn embodied Turing Test”—that allows
newborn animals and machines to be raised in the same
environments and tested with the same tasks, permitting direct
comparison of their learning abilities. To make this platform, we
first collected controlled-rearing data from newborn chicks, then
performed “digital twin” experiments in which machines were
raised in virtual environments that mimicked the rearing
conditions of the chicks. We found that 1) machines (deep
reinforcement learning agents with intrinsic motivation) can
spontaneously develop visually guided preference behavior, akin
to imprinting in newborn chicks, and 2) machines are still far
from newborn-level performance on object recognition tasks.
Almost all of the chicks developed view-invariant object
recognition, whereas the machines tended to develop
view-dependent recognition. Moreover, the learning outcomes
(developmental pathways) were far more constrained in the
chicks versus machines. Ultimately, we anticipate that this
approach will help researchers develop embodied AI systems
that learn like newborn animals.




A newborn embodied Turing test for view-invariant object recognition

What are the computational foundations of social grouping?
Traditional approaches to this question have focused on verbal
reasoning or simple (low-dimensional) quantitative models. In
the real world, however, social preferences emerge when
high-dimensional learning systems (brains and bodies) interact
with high-dimensional sensory inputs during an animal’s
embodied interactions with the world. A deep understanding of
social grouping will therefore require embodied models that
learn directly from sensory inputs using high-dimensional
learning mechanisms. To this end, we built artificial neural
networks (ANNs), embodied those ANNs in virtual fish bodies,
and raised the artificial fish in virtual fish tanks that mimicked
the rearing conditions of real fish. We then compared the social
preferences that emerged in real fish versus artificial fish. We
found that when artificial fish had two core learning
mechanisms (reinforcement learning and curiosity-driven
learning), artificial fish developed fish-like social preferences.
Like real fish, the artificial fish spontaneously learned to prefer
members of their own group over members of other groups. The
artificial fish also spontaneously learned to self-segregate with
their in-group, akin to self-segregation behavior seen in nature.
Our results suggest that social grouping can emerge in embodied
agents from three ingredients: (1) reinforcement learning, (2)
intrinsic motivation, and (3) early social experiences with
in-group members. This approach lays a foundation for reverse
engineering animal-like social behavior with image-computable
models, bridging the divide between high-dimensional sensory
inputs and social preferences.

Parallel development of social preferences in fish and machines

In recent years, the brain and cognitive sciences have made great strides developing a mechanistic understanding of object recognition in mature brains. Despite this progress, fundamental questions remain about the origins and computational foundations of object recognition. What learning algorithms underlie object recognition in newborn brains? Since newborn animals learn largely through unsupervised learning, we explored whether unsupervised learning algorithms can be used to predict the view-invariant object recognition behavior of newborn chicks. Specifically, we used feature representations derived from unsupervised deep neural networks (DNNs) as inputs to cognitive models of categorization. We show that features derived from unsupervised DNNs make competitive predictions about chick behavior compared to supervised features. More generally, we argue that linking controlled-rearing studies to image-computable DNN models opens new experimental avenues for studying the origins and computational basis of object recognition in newborn animals.

Modeling Object Recognition in Newborn Chicks using Deep Neural Networks

Poster Session 1

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
<br><br>

#### **CogSci 2023 Program Book**
<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/tzgk/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>
<br>

CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.


<i>**Session Chair: **Amilcar Cardoso</i><br><br>
<i>**Technical Session Chair: **Merle Kortendieck</i><br><br>
**Session Description: **<br>The session is comprised of 15 minute talks followed by up to 5 minutes of live Q&A time for each presenter. <br>

IP12: Neural networks & imaging

technical paper

### 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.

Donsuk Lee

3

1

SHORT BIO

Presentations

A newborn embodied Turing test for view-invariant object recognition

Parallel development of social preferences in fish and machines

Modeling Object Recognition in Newborn Chicks using Deep Neural Networks

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES