Cognitive Bias in Ambiguity Judgements: Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans.
Positive and negative moods can be treated as prior expectations over future delivery of rewards and punishments. This provides an inferential foundation for the cognitive (judgement) bias task, now widely-used for assessing affective states in non-human animals. In the task, information about affect is extracted from the optimistic or pessimistic manner in which participants resolve ambiguities in sensory input. Here, we report a novel variant of the task aimed at dissecting the effects of affect manipulations on perceptual and value computations for decision-making under ambiguity in humans. Participants were instructed to judge which way a Gabor patch (250ms presentation) was leaning. If the stimulus leant one way (e.g. left), pressing the REWard key yielded a monetary WIN whilst pressing the SAFE key failed to acquire the WIN. If it leant the other way (e.g. right), pressing the SAFE key avoided a LOSS whilst pressing the REWard key incurred the LOSS. The size (0-100 UK pence) of the offered WIN and threatened LOSS, and the ambiguity of the stimulus (vertical being completely ambiguous) were varied on a trial-by-trial basis, allowing us to investigate how decisions were affected by differing combinations of these factors. Half the subjects performed the task in a 'Pleasantly' decorated room and were given a gift (bag of sweets) prior to starting, whilst the other half were in a bare 'Unpleasant' room and were not given anything. Although these treatments had little effect on self-reported mood, they did lead to differences in decision-making. All subjects were risk averse under ambiguity, consistent with the notion of loss aversion. Analysis using a Bayesian decision model indicated that Unpleasant Room subjects were ('pessimistically') biased towards choosing the SAFE key under ambiguity, but also weighed WINS more heavily than LOSSes compared to Pleasant Room subjects. These apparently contradictory findings may be explained by the influence of affect on different processes underlying decision-making, and the task presented here offers opportunities for further dissecting such processes.
Gatsby Computational Neuroscience Unit, UCL, London W1T 4JG, United Kingdom.
Kiyohito
K
Iigaya
Centre for Behavioural Biology, School of Veterinary Science, University of Bristol, Langford, BS40 5DU, United Kingdom.
Aurelie
A
Jolivald
Gatsby Computational Neuroscience Unit, UCL, London W1T 4JG, United Kingdom.
Wittawat
W
Jitkrittum
School of Experimental Psychology, University of Bristol, Bristol, BS8 1TU, United Kingdom.
Iain D
ID
Gilchrist
Gatsby Computational Neuroscience Unit, UCL, London W1T 4JG, United Kingdom.
Peter
P
Dayan
Centre for Behavioural Biology, School of Veterinary Science, University of Bristol, Langford, BS40 5DU, United Kingdom.
Elizabeth
E
Paul
Centre for Behavioural Biology, School of Veterinary Science, University of Bristol, Langford, BS40 5DU, United Kingdom.
Michael
http://orcid.org/0000-0002-5302-1871
M
Mendl
D000293:Adolescent; D000328:Adult; D000339:Affect / Q000502:physiology*; D000465:Algorithms; D001499:Bayes Theorem; D002755:Choice Behavior / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D004644:Emotions / Q000502:physiology; D005260:Female; D006801:Humans; D007600:Judgment / Q000502:physiology*; D008297:Male; D010775:Photic Stimulation; D011597:Psychomotor Performance / Q000502:physiology; D012201:Reward; D055815:Young Adult