Self-orienting in human and machine learning

Julian Freitas (), Ahmet Kaan Uğuralp, Zeliha Oğuz-Uğuralp, L. A. Paul, Joshua Tenenbaum and Tomer D. Ullman
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Julian Freitas: Harvard Business School
Ahmet Kaan Uğuralp: Bilkent University
Zeliha Oğuz-Uğuralp: Bilkent University
L. A. Paul: Yale University
Joshua Tenenbaum: MIT
Tomer D. Ullman: Harvard University

Nature Human Behaviour, 2023, vol. 7, issue 12, 2126-2139

Abstract: Abstract A current proposal for a computational notion of self is a representation of one’s body in a specific time and place, which includes the recognition of that representation as the agent. This turns self-representation into a process of self-orientation, a challenging computational problem for any human-like agent. Here, to examine this process, we created several ‘self-finding’ tasks based on simple video games, in which players (N = 124) had to identify themselves out of a set of candidates in order to play effectively. Quantitative and qualitative testing showed that human players are nearly optimal at self-orienting. In contrast, well-known deep reinforcement learning algorithms, which excel at learning much more complex video games, are far from optimal. We suggest that self-orienting allows humans to flexibly navigate new settings.

Date: 2023
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DOI: 10.1038/s41562-023-01696-5

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