Abstract
Links between affective states and risk-taking are often characterised using summary statistics from serial decision-making tasks. However, our understanding of these links, and the utility of decision-making as a marker of affect, needs to accommodate the fact that ongoing (e.g., within-task) experience of rewarding and punishing decision outcomes may alter future decisions and affective states. To date, the interplay between affect, ongoing reward and punisher experience, and decision-making has received little detailed investigation. Here, we examined the relationships between reward and loss experience, affect, and decision-making in humans using a novel judgement bias task analysed with a novel computational model. We demonstrated the influence of within-task favourability on decision-making, with more risk-averse/‘pessimistic’ decisions following more positive previous outcomes and a greater current average earning rate. Additionally, individuals reporting more negative affect tended to exhibit greater risk-seeking decision-making, and, based on our model, estimated time more poorly. We also found that individuals reported more positive affective valence during periods of the task when prediction errors and offered decision outcomes were more positive. Our results thus provide new evidence that (short-term) within-task rewarding and punishing experiences determine both future decision-making and subjectively experienced affective states.
Highlights
There is a traditional gulf in the field of decision-making between one-shot tasks, in which each trial is independent of the others, and ongoing tasks, in which individuals are supposed to apply what they learn from the consequences of their actions in earlier trials to later trials
The influence of the consequences of previous decisions in the task on subsequent affect and choices needs to be investigated. We examined this relationship between psychophysical decision-making, decision-outcomes and self-reported affect in human participants using a novel ‘go’/‘stay’ judgement bias task design in which either reward or loss magnitude was systematically varied across trials, so that there were epochs of high and low value potential outcomes
In order to examine the relationships between rewards and punishments, affective states and risky decision-making, we asked human participants to perform a novel version of a go/no-go judgement bias task, whilst providing reports on their affective states
Summary
There is a traditional gulf in the field of decision-making between one-shot tasks, in which each trial is independent of the others (i.e., there is assumed to be no influence of previous outcomes or stimuli on current decisions), and ongoing tasks, in which individuals are supposed to apply what they learn from the consequences of their actions in earlier trials to later trials. The distinction is blurred—adaptation paradigms use repeated stimulus presentations to ‘set’ the state for one-shot psychophysical examinations [3]; participants perform exquisite Bayesian learning in stop signal reaction time tasks, despite the instructed independence of the trials [4]. This distinction is pertinent in research where decisions on serial trials yielding reward or loss/punishment are used to probe the influence of affective states and disorders on risk-taking and reward sensitivity. Within-task experience may alter local affective states and influence future decisions and, summary statistics of task performance, with knock-on effects for our understanding of the relationship between affect and decision-making and the utility of decision-making markers of affective states or disorders
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