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Purposive Learning01:22

Purposive Learning

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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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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning.

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Humans select goals based on latent learning progress, an internal estimate of learning, not just performance changes. This finding advances understanding of human goal selection and artificial intelligence development.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Humans are goal-directed agents, but the mechanisms of goal selection are not fully understood.
  • Learning progress, or performance change, is a known factor in goal selection for humans and AI.
  • The role of internal, or latent, learning progress in human goal selection requires further investigation.

Purpose of the Study:

  • To investigate the influence of latent learning progress on human goal selection.
  • To explore how individuals estimate learning without immediate performance feedback.
  • To identify factors contributing to inter-individual differences in goal selection.

Main Methods:

  • A hierarchical reinforcement learning task was designed for human participants (N = 175).
  • Participants repeatedly selected their own goals and learned goal-conditioned policies.
  • Behavioral data and computational modeling were used to analyze goal selection strategies.

Main Results:

  • Latent learning progress significantly influences human goal selection.
  • Inter-individual differences in goal selection were observed and partially explained by environmental structure recognition.
  • Participants could estimate learning progress internally, independent of immediate performance changes.

Conclusions:

  • Latent learning progress is a key driver of human goal selection, complementing observed performance.
  • Understanding latent learning can lead to personalized learning systems and more human-like AI.
  • Individual differences in recognizing environmental structure impact goal-selection strategies.