Please use this identifier to cite or link to this item:
https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/703
Title: | Affective bias as a rational response to the statistics of rewards and punishments |
Authors: | Browning, Michael |
Keywords: | Depressive Disorders |
Issue Date: | Oct-2017 |
Citation: | Erdem Pulcu, Michael Browning. Affective bias as a rational response to the statistics of rewards and punishments. eLife 2017;6:e27879 |
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. |
Description: | Open Access. Creative Commons License |
URI: | https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/703 |
ISSN: | 2050-084X |
Appears in Collections: | Depressive Disorders |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
elife-27879-v3.pdf | Main article | 1.64 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.