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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Humans can navigate complex graph structures acquired during latent learning.

Milena Rmus1, Harrison Ritz2, Lindsay E Hunter3

  • 1Department of Psychology, University of California, Berkeley, USA.

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Humans can infer complex graph structures from disordered experiences, demonstrating latent learning. This ability to assemble knowledge networks is linked to model-based reinforcement learning and planning.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Humans represent knowledge in associative networks, similar to graph structures.
  • Artificial agents with graph structures show enhanced compositionality and transfer learning.
  • Previous research showed humans learn graph structures via direct experience or reward covariation.

Purpose of the Study:

  • To investigate if humans can learn graph structures from disjoint, disordered experiences (latent learning).
  • To explore the connection between acquiring graph-structured knowledge and model-based reinforcement learning.
  • To determine if graph structure acquisition is a core computation for planning and reasoning.

Main Methods:

  • Participants learned graph structures from fragmented experiences with randomized edges.
  • Shortest-path distances were assessed across inferred graph structures.
  • Correlation analysis between graph reasoning and model-based reinforcement learning propensity.

Main Results:

  • Humans can infer and assemble graph structures from disordered, latent experiences.
  • The ability to reason about graph structures correlates with model-based reinforcement learning.
  • This suggests latent graph learning is a general cognitive ability.

Conclusions:

  • Humans possess a latent learning capability to infer graph-structured knowledge from fragmented experiences.
  • Acquiring graph representations is fundamental for forward planning and reasoning.
  • This ability may underpin model-based reinforcement learning and other complex cognitive functions.