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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Humans excel at self-orientation, a key computational process for recognizing oneself in space and time. This ability, crucial for flexible navigation, surpasses current AI algorithms in simple self-finding tasks.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • A computational concept of self involves an agent recognizing its own body representation in a specific time and place.
  • This self-representation is framed as a self-orientation process, posing a significant computational challenge for artificial agents.
  • Self-orientation is fundamental for agents to effectively interact with and navigate their environment.

Purpose of the Study:

  • To investigate the computational process of self-orientation in human agents.
  • To compare human self-orientation capabilities with those of deep reinforcement learning algorithms.
  • To explore the role of self-orientation in enabling flexible navigation.

Main Methods:

  • Development of 'self-finding' tasks using simple video games.
  • Recruitment of 124 human players to identify themselves within the game environment.
  • Quantitative and qualitative assessments of player performance in self-orientation tasks.
  • Evaluation of established deep reinforcement learning algorithms on the same tasks.

Main Results:

  • Human players demonstrated near-optimal performance in self-orientation tasks.
  • Deep reinforcement learning algorithms performed significantly below optimal levels.
  • A notable discrepancy exists between human and AI capabilities in self-orientation, despite AI's success in complex games.

Conclusions:

  • Humans possess a highly effective mechanism for self-orientation.
  • Current deep reinforcement learning approaches are not optimized for self-orientation.
  • Self-orientation is proposed as a critical component for enabling flexible environmental navigation in agents.