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A self-supervised domain-general learning framework for human ventral stream representation

Authors: Talia Konkle; George A. Alvarez;

A self-supervised domain-general learning framework for human ventral stream representation

Abstract

Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find that category information implicitly emerges in the local similarity structure of this feature space. Further, these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world.

It is unknown whether object category learning can be formed purely through domain general learning of natural image structure. Here the authors show that human visual brain responses to objects are well-captured by self-supervised deep neural network models trained without labels, supporting a domain-general account.

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Microsoft Academic Graph classification: Computer science Feature vector computer.software_genre Domain (software engineering) Similarity (psychology) Natural (music) Structure (mathematical logic) business.industry Representation (systemics) Object (computer science) Human visual system model Artificial intelligence business computer Natural language processing

Keywords

Science, General Physics and Astronomy, Learning algorithms, Article, General Biochemistry, Genetics and Molecular Biology, Deep Learning, Humans, Visual Pathways, Object vision, Visual Cortex, Brain Mapping, Multidisciplinary, Q, Brain, Reproducibility of Results, General Chemistry, Magnetic Resonance Imaging, Pattern Recognition, Visual, Perception, Neural Networks, Computer

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    35
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
35
Top 1%
Average
Top 10%
Green
gold
Funded by
NSF| COMPCOG: Intuitive Physics without Intuition or Physics: Leveraging Deep Neural Networks to Model Human Physical Reasoning
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1946308
  • Funding stream: Directorate for Social, Behavioral & Economic Sciences | Division of Behavioral and Cognitive Sciences
,
NSF| CAREER: The Tuning and Topography of the Ventral Visual Stream
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1942438
  • Funding stream: Directorate for Social, Behavioral & Economic Sciences | Division of Behavioral and Cognitive Sciences
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