Optimism as a prior belief about the probability of future reward.
Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.
Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom.
Aistis
A
Stankevicius
Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zürich and ETH Zürich, Zürich, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zürich, Switzerland; Gatsby Computational Neuroscience Unit and Wellcome Trust Neuroimaging Centre, UCL, London, United Kingdom.
Quentin J M
QJ
Huys
Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom.
Aditi
A
Kalra
Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom.
Peggy
P
Seriès
D000293:Adolescent; D000328:Adult; D059031:Anticipation, Psychological / Q000502:physiology*; D002755:Choice Behavior / Q000502:physiology*; D003198:Computer Simulation; D003469:Culture; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D012201:Reward; D055815:Young Adult