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Superposition mechanism as a neural basis for understanding others
Wataru Noguchi1, Hiroyuki Iizuka2,3, Masahito Yamamoto2,3
1Faculty of Information Science and Technology, Hokkaido University, Sapporo, 060-0814, Japan. w.noguchi@ist.hokudai.ac.jp.
Scientific Reports
|February 22, 2022
Summary
Artificial agents can develop concepts of "self" and "other" using a novel superposition mechanism in deep neural networks. This predictive learning approach enables social cognition without innate frameworks, advancing understanding in cognitive science.
Area of Science:
- Cognitive Science
- Neuroscience
- Artificial Intelligence
Background:
- Social cognition research dominates neuroscience, psychology, cognitive science, and philosophy.
- Dominant frameworks like theory-theory (TT) and simulation theory (ST) lack explanations for acquiring "self" and "other" concepts.
Purpose of the Study:
- To investigate if artificial agents can develop internal representations of "self" and "other" without pre-given frameworks.
- To demonstrate a novel mechanism for enabling social cognition in artificial agents.
Main Methods:
- Utilized deep neural networks with a proposed superposition mechanism for predictive learning.
- Trained an artificial agent to acquire concepts of "self" and "other" through this mechanism.
Main Results:
- The artificial agent developed shared spatial representations of "self" and "other".
- The agent demonstrated perspective-taking abilities and mirror-neuron-like neural network activities.
- The proposed superposition mechanism proved essential for developing "self" and "other" concepts.
Conclusions:
- Social cognition can emerge in artificial agents via predictive learning and a superposition mechanism.
- This approach offers a pathway for developing social cognition without innate "self" and "other" frameworks, challenging existing theories.
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