Modelling mood updating: a proof of principle study

Area of Science:

• Computational psychiatry
• Neuroscience
• Affective science

Background:

• Mood is hypothesized as an expectation of interoceptive consequences of actions.
• This aligns with brain function models minimizing sensory prediction error (free energy).

Purpose of the Study:

• To present a computational model of mood updating.
• To explore how altered prior beliefs influence mood in psychiatric conditions.

Main Methods:

• Utilizing a partial observable Markov decision process.
• Modeling mood changes in response to environmental stressors by minimizing prediction error.

Main Results:

• Demonstrated how altering prior beliefs can model mood states in depression, mania, and anxiety.
• Provided a computational framework for understanding mood and psychopathology.

Conclusions:

• These models offer a computational account of mood and related disorders.
• The approach can generate hypotheses for experimental research and predict disease trajectories.