Affective bias as a rational response to the statistics of rewards and punishments

Abstract

Affective bias, the tendency to differentially prioritise the processing of negative relative to positive events, is commonly observed in clinical and non-clinical populations. However, why such biases develop is not known. Using a computational framework, we investigated whether affective biases may reflect individuals’ estimates of the information content of negative relative to positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of distinct positive and negative outcomes which may bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel approach to treatment development.

Neural arbitration between social and individual learning systems

“…Thus, in stable compared to volatile environments, the propensity for arbitration in favor of the more precise information source increases. Numerous studies have demonstrated an important role of volatility in higher level learning ( Behrens et al, 2007 ; Behrens et al, 2008 ; Nassar et al, 2010 ; Iglesias et al, 2013 ; Vossel et al, 2014 ; Diaconescu et al, 2017 ; Pulcu and Browning, 2017 ), in-keeping with the present findings.…”

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

“…Overall, the present results add to the growing literature showing associations between psychopathology and learning under uncertainty. Previous studies using computational approaches have largely focused on learning about rewards and losses 10 – 12 , 27 , 42 , or perceptual learning 9 , and those that have used more aversive paradigms (using outcomes intended to evoke subjective anxiety), such as learning to predict electric shocks, have been limited by small samples 5 , 15 , 18 , 43 . While there is a rich literature using simple fear conditioning paradigms to investigate aversive learning in individuals with anxiety disorders 44 , 45 , these tasks typically do not manipulate uncertainty, as was the intention in our task.…”

Anxiety, Depression, and Decision Making: A Computational Perspective

“…There is no difference between high-and low-anxious individuals in mean learning rate, and high-anxious individuals do not show impaired prediction error generation-modulation of both pupil dilation and next-trial reaction time by outcome surprise (i.e., the unsigned prediction error) is unaffected by trait anxiety (Browning et al 2015). The association between anxiety and impoverished adaptation of learning rate has been shown to also hold in the case of reward loss but not reward gain (Pulcu & Browning 2017).…”