Mental distress through the prism of predictive processing theory

Area of Science:

• Cognitive Neuroscience
• Psychiatry
• Computational Psychology

Background:

• Mental distress and psychopathology, particularly affective and motivational disorders, are complex conditions.
• Existing explanations often lack a unified mechanistic model.
• Predictive processing theory offers a framework for understanding cognition and behavior.

Purpose of the Study:

• To review predictive processing theory's perspective on goals and affect.
• To elucidate the development of mental distress and psychopathology.
• To integrate classical factors like uncertainty and control into a mechanistic model.

Main Methods:

• Review of predictive processing theory literature.
• Analysis of goals and affect within the predictive processing framework.
• Integration of classical etiological factors into a mechanistic model.

Main Results:

• Predictive processing theory provides a mechanistic account of adaptive and maladaptive cognition and behavior.
• Classical factors such as uncertainty and control are integrated into this model.
• The model highlights the role of model-dependent value construction and validation.

Conclusions:

• Predictive processing offers novel implications for treating mental disorders.
• Treatment implications include focusing on the construction of value, model validation through evidence, and learning adaptive beliefs.
• This approach can help relieve suffering in affective and motivational disorders.