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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Embodied decisions as active inference.

Matteo Priorelli1,2, Ivilin Peev Stoianov1, Giovanni Pezzulo1

  • 1Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.

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Summary
This summary is machine-generated.

Embodied decisions involve parallel processing and action feedback, unlike traditional serial models. Active inference naturally explains these complex, real-world choices by balancing accuracy and opportunity risk.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Sciences

Background:

  • Traditional decision-making models view choices as serial processes where evidence accumulation precedes action.
  • Embodied decisions in dynamic environments contrast this, involving parallel perception-action loops and action-related feedback.
  • Existing models struggle to capture the interplay between action dynamics and choice selection in real-world scenarios.

Purpose of the Study:

  • To demonstrate how active inference can naturally explain the characteristics of embodied decision-making.
  • To investigate the feedback mechanisms between motor planning and motor inference in embodied choices.
  • To explore the normative principles underlying embodied decisions in ecological contexts.

Main Methods:

  • Utilized active inference, a framework that unifies perception and action through free-energy minimization.
  • Developed simulations to model the continuous feedback loop between motor planning and motor inference.
  • Analyzed simulation outcomes to identify emergent properties of embodied decision-making.

Main Results:

  • Active inference simulations reproduced key signatures of embodied decisions, including parallel processing and action-feedback effects.
  • Demonstrated that optimizing embodied choices necessitates a continuous feedback loop between action dynamics and belief updating.
  • Identified a normative balance between decision accuracy and the risk of missing opportunities in simulated ecological settings.

Conclusions:

  • Active inference provides a unified framework for understanding both traditional and embodied decision-making.
  • Embodied decisions are characterized by a dynamic interplay between perception, action planning, and action execution.
  • The framework highlights the adaptive advantage of balancing decisional accuracy with the efficient exploitation of opportunities.