title: Algorithms for survival: a comparative perspective on emotions.
abstract: The nature and neural implementation of emotions is the subject of vigorous debate. Here, we use Bayesian decision theory to address key complexities in this field and conceptualize emotions in terms of their relationship to survival-relevant behavioural choices. Decision theory indicates which behaviours are optimal in a given situation; however, the calculations required are radically intractable. We therefore conjecture that the brain uses a range of pre-programmed algorithms that provide approximate solutions. These solutions seem to produce specific behavioural manifestations of emotions and can also be associated with core affective dimensions. We identify principles according to which these algorithms are implemented in the brain and illustrate our approach by considering decision making in the face of proximal threat.
authors.affiliation: Division for Clinical Psychiatry Research, Psychiatric Hospital, University of Zurich; at the Neuroscience Centre Zurich, University of Zurich, 8032 Zurich, Switzerland; and at the Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK.
authors.forename: Dominik R
authors.initials: DR
authors.lastname: Bach
authors.affiliation: Gatsby Computational Neuroscience Unit, University College London, London W1T 4JG, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Dayan
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003657:Decision Making; D004644:Emotions / Q000502:physiology*; D006801:Humans; D013534:Survival / Q000502:physiology / Q000523:psychology*