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A goal-centric outlook on learning.

Gaia Molinaro1, Anne G E Collins2

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

Trends in Cognitive Sciences
|September 11, 2023
PubMed
Summary
This summary is machine-generated.

Goals fundamentally shape learning and decision-making by influencing how we perceive information, actions, and outcomes. Understanding goal selection is key to advancing computational theories of learning in both humans and AI.

Keywords:
abstractioncomputational modelingdecision-makinggoalslearningmotivationreinforcement learningrewards

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Computational models of learning often treat goals as fixed inputs.
  • Empirical evidence suggests goals significantly impact cognitive processes.

Purpose of the Study:

  • To review how goals influence learning and decision-making.
  • To highlight the importance of studying goal selection in cognitive theories.
  • To propose a goal-centric framework for understanding learning.

Main Methods:

  • Literature review of empirical findings on goal-directed cognition.
  • Synthesis of research on goal value attribution and exploration strategies.
  • Framing goal selection within a hierarchical decision-making context.

Main Results:

  • Goals shape representations of inputs, actions, and outcomes.
  • Goal setting critically influences states, actions, and rewards in learning.
  • Principles of goal value attribution and exploration are synthesized.

Conclusions:

  • Studying goal selection is crucial for advancing learning theories.
  • A goal-centric perspective offers a more complete understanding of learning in biological and artificial agents.