Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach

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. Author summary Affective states, such as happiness, are key to well-being. They are thought to reflect characteristics of the environment such as the availability of reward and the inevitability of punishment. However, there is a lack of agreement about: (i) the time scales over which these characteristics are measured; (ii) how and in what combinations actual or expected outcomes influence affect; (iii) how affect itself influences decision-making. A particular stance on the last issue underpins the judgement bias task, which, by measuring an individual's willingness to make 'optimistic' or 'pessimistic' choices that are rendered risky by perceptual ambiguity, is one of the few cross-species tests for affect. Here we apply a novel computational analysis to a novel judgement bias task to examine all three issues. We reveal a rich interplay between affect and rewards, punishments, and uncertainty.