Self-orienting in human and machine learning.

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.