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Computational Psychiatry & Decision-making

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Abstracts

  • pdf A valuation framework for emotions applied to depression and recurrence
  • Huys QJM
  • In: Computational Psychiatry: New Perspectives on Mental Illness, edited by A. D. Redish and J. A. Gordon. Strüngmann Forum Reports, vol. 20, J. Lupp, series editor. Cambridge, MA: MIT Press
  • The burden of depression is substantially aggravated by relapses and recurrences, and these become more inevitable with every episode of depression. This chapter first describes how computational psychiatry can provide a normative framework for emotions that might provide an integrative approach to core cognitive components of depression and relapse. At the heart of this account is the notion that emotions effectively imply a valuation, and that they are therefore amenable to description and dissection by reinforcement-learning methods. It is argued that cognitive accounts of emotion can be viewed in terms of model-based valuation, and that automatic emotional responses relate to model-free valuation and the innate recruitment of fixed behavioural patterns. The model-based view captures phenomena such as helplessness, hopelessness, attributions and stress sensitization. Considering it in more atomic algorithmic detail opens up the possibility of viewing rumination and emotion regulation in this same normative framework, too. The chapter then briefly outlines the problem of treatment selection for relapse and recurrence prevention, and then suggests ways in which the computational framework of emotions might help in improving this. The discussion closes with a very brief general overview over what we can hope to gain from computational psychiatry.