A Bayesian formulation of behavioral control.

Area of Science:

• Cognitive Psychology
• Computational Psychiatry
• Behavioral Neuroscience

Background:

• Helplessness is a core concept in understanding depression, influencing cognitive theories, animal models, and treatments.
• Existing definitions of helplessness and behavioral control lack universal acceptance in psychology and psychiatry.
• Formal engineering definitions of control do not fully encompass intuitive psychological concepts.

Purpose of the Study:

• To formalize the concept of controllability using probabilistic methods.
• To investigate the role of reinforcement learning in optimizing behavior within affectively charged environments.
• To explore how normative inference processes may lead to maladaptive beliefs associated with helplessness and depression.

Main Methods:

• Formalizing controllability based on prior distributions over affectively charged environments.
• Applying reinforcement learning principles to model behavior optimization.
• Analyzing normative inference processes to understand belief formation.

Main Results:

• A novel formalization of controllability is proposed, linking it to probabilistic characteristics of environments.
• Reinforcement learning frameworks are shown to be relevant for understanding behavioral optimization in the context of control.
• Maladaptive beliefs, characteristic of depression, can emerge from standard inference mechanisms.

Conclusions:

• The formalized concept of controllability offers a new perspective on helplessness.
• The findings provide a computational framework for understanding depression and its animal models.
• This work bridges theoretical psychology, computational methods, and neurobiological models of depression.