Affect-congruent attention modulates generalized reward expectations

Abstract

Positive and negative affective states are respectively associated with optimistic and pessimistic expectations regarding future reward. One mechanism that might underlie these affect-related expectation biases is attention to positive- versus negative-valence stimulus features (e.g., attending to the positive reviews of a restaurant versus its expensive price). Here we tested the effects of experimentally induced positive and negative affect on feature-based attention in 120 participants completing a compound-generalization task with eye-tracking. We found that participants' reward expectations for novel compound stimuli were modulated by the affect induction in an affect-congruent way: positive affect increased reward expectations for compounds, whereas negative affect decreased reward expectations. Computational modelling and eye-tracking analyses each revealed that these effects were driven by affect-congruent changes in participants' allocation of attention to high- versus low-value features of compound stimuli. These results provide mechanistic insight into a process by which affect produces biases in generalized reward expectations.

On the misery of cognitive effort

“…A negative effect of cognitive effort on mood is consistent with both the view of cognitive effort as computational cost, and of momentary mood ratings as running estimates of reward rates (Bennett et al, 2021;Rutledge et al, 2014). In terms of the former, we have noted previous failures to show an obvious energetic correlate of cognitive effort expenditure (Kurzban, 2010;Madsen et al, 1995).…”

Mouse behaviour on the trial-unique non-matching-to-location (TUNL) touchscreen task reflects a mixture of distinct working memory codes and response biases

“…The one caveat to this otherwise consistent result comes from Dataset 3: although model M9 was still the best-fitting model overall as measured by the WAIC statistic, model M6 provided a statistically equivalent fit to data when model fit uncertainty was accounted for (i.e., the difference in WAIC values between M6 and M9 in Dataset 3 was smaller than the standard error of this difference; cf. Bennett et al, 2021; Weber et al, 2022).…”

Neurocomputational Mechanisms Underlying Maladaptive Self-Belief Formation in Depression

“…The stronger neural response to negative prediction errors is in line with a general neural sensitivity to negative (social) prediction errors in the insula 13,106,107 , as well as a stronger insula reactivity specifically in samples with depression 64 or social anxiety 63,108 . This effect could be explained by an attentional bias in individuals with depression 98,109 in an affect-congruent way 28 , with either a faster orientation 99 or longer maintenance of attention to negative feedback 110,111 . Since the insula reactivity to more negative prediction errors is associated with less positive affect during task performance in the present experiment, the detected group difference might also reflect stronger emotional responsivity in individuals with depression.…”

“…The experience of prediction errors is also an affective phenomenon 25,26 , which is of particular importance in the context of belief updating, especially in depression. Negative affect is one of the central depressive symptoms 27 , and the individual's current affective state shapes expectations 28 , which thereby contribute to the formation of self-beliefs 29 . This, in turn, shapes how the individual feels in a particular situation, which results in a recursive influence of affect and belief updating on each other 29,30 .…”