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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
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Efficiency and prioritization of inference-based credit assignment.

Rani Moran1, Peter Dayan2, Raymond J Dolan1

  • 1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, London WC1B 5EH, UK; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK.

Current Biology : CB
|April 22, 2021
PubMed
Summary
This summary is machine-generated.

Learning to value environmental states involves credit assignment (CA). Inferring hidden states, though cognitively demanding, selectively impairs CA for observable outcomes, challenging normative theories.

Keywords:
cognitive mapscontrolcredit-assignmentdecision makinginferenceintrinsic value of informationmodel-basedmodel-freereinforcement learningreward

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Area of Science:

  • Cognitive neuroscience
  • Decision-making
  • Reinforcement learning

Background:

  • Organisms adapt by learning state values, crucial for reward approach and punishment avoidance.
  • Credit assignment (CA) updates state values based on feedback, but humans infer values for unobserved states using cognitive maps.
  • The impact of inference on CA, particularly for hidden versus observable states, remains unclear.

Purpose of the Study:

  • To investigate how cognitive inference influences credit assignment (CA).
  • To determine if CA is differentially affected for hidden versus observable states during inference.

Main Methods:

  • A lottery choice task where participants inferred hidden states based on choice-state associations.
  • Manipulation of state observability (fully observable vs. one hidden state).
  • Measurement of state-value updates to assess CA.

Main Results:

  • Performing inference significantly reduced state-value updates.
  • This reduction in CA was selectively applied to the observable outcome, not the inferred hidden state.
  • Findings deviate from normative theories of credit assignment.

Conclusions:

  • Cognitive inference imposes a cost that impairs credit assignment.
  • The impairment is specific to observable states, suggesting a novel mechanism in decision-making.
  • Results offer insights into the functional operation of cognitive maps in value-based learning.