AI Article Synopsis
- A new proposal suggests that a computational understanding of the self involves recognizing one's own body in space and time, framing self-representation as a complex computational challenge for human-like agents.
- Researchers conducted 'self-finding' tasks using simple video games, where 124 players needed to identify themselves among multiple options to succeed.
- The study found that human players perform nearly perfectly at self-orientation, while popular deep reinforcement learning algorithms struggle, indicating that self-orientation enables humans to adapt effectively to new environments.
Article 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.
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| http://dx.doi.org/10.1038/s41562-023-01696-5 | DOI Listing |
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