Predictive processing: Shedding light on the computational processes underlying motivated behavior

Area of Science:

• Computational Neuroscience
• Cognitive Science
• Behavioral Economics

Background:

• Motivation research traditionally focuses on drives and rewards.
• Predictive processing offers a computational framework for understanding perception and action.
• Bridging these fields can illuminate the mechanisms of motivated behavior.

Purpose of the Study:

• To integrate the predictive processing framework with theories of motivation.
• To computationally model motivated behavior by decomposing expected free energy.
• To explore how intrinsic and extrinsic values guide environmental interaction.

Main Methods:

• Decomposition of expected free energy into its constituent value components.
• Application of predictive processing principles to motivational constructs.
• Theoretical modeling of adaptive behavior.

Main Results:

• Expected free energy can be meaningfully decomposed into epistemic and instrumental affordances.
• This decomposition provides a computational basis for understanding intrinsic and extrinsic motivation.
• The framework offers insights into how individuals adaptively interact with their environment.

Conclusions:

• The predictive processing framework offers a powerful lens for understanding motivation.
• Decomposing expected free energy into intrinsic and extrinsic values advances computational models of behavior.
• This approach enhances our understanding of adaptive environmental engagement.