title: Mood as Representation of Momentum.
abstract: Experiences affect mood, which in turn affects subsequent experiences. Recent studies suggest two specific principles. First, mood depends on how recent reward outcomes differ from expectations. Second, mood biases the way we perceive outcomes (e.g., rewards), and this bias affects learning about those outcomes. We propose that this two-way interaction serves to mitigate inefficiencies in the application of reinforcement learning to real-world problems. Specifically, we propose that mood represents the overall momentum of recent outcomes, and its biasing influence on the perception of outcomes 'corrects' learning to account for environmental dependencies. We describe potential dysfunctions of this adaptive mechanism that might contribute to the symptoms of mood disorders.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK. Electronic address: e.eldar@ucl.ac.uk.
authors.forename: Eran
authors.initials: E
authors.lastname: Eldar
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK.
authors.forename: Robb B
authors.initials: RB
authors.lastname: Rutledge
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK.
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Dolan
authors.affiliation: Princeton Neuroscience Institute and Psychology Department, Princeton University, Princeton, NJ 08544, USA.
authors.forename: Yael
authors.initials: Y
authors.lastname: Niv
mesh_terms: D000339:Affect; D006801:Humans; D007858:Learning; D012054:Reinforcement, Psychology; D012201:Reward
keywords: Mood; decision making; reinforcement learning