Citation

Pulcu E, Browning M. Elife 2017; 6: e27879.

Affiliation

Oxford Health NHS Foundation Trust, Oxford, United Kingdom.

Erratum On

Elife 2017;6:.

Copyright

(Copyright © 2017, dLife Sciences Plublications, Ltd)

DOI

10.7554/eLife.27879

PMID

28976304

PMCID

PMC5633345

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.

Language: en

Keywords

computational Modelling; depression; human; learning; neuroscience; norepinepherine; pupilometry