Affective bias - a propensity to focus on negative information at the expense of positive information - is a core feature of many mental health problems. However, it can be caused by wide range of possible underlying cognitive mechanisms. Here we illustrate this by focusing on one particular behavioural signature of affective bias - increased tendency of anxious/depressed individuals to predict lower rewards - in the context of the Signal Detection Theory (SDT) modelling framework. Specifically, we apply this framework to a tone-discrimination task (Aylward et al., 2019a), and argue that the same behavioural signature (differential placement of the 'decision criterion' between healthy controls and individuals with mood/anxiety disorders) might be driven by multiple SDT processes. Building on this theoretical foundation, we propose five experiments to test five hypothetical sources of this affective bias: beliefs about prior probabilities, beliefs about performance, subjective value of reward, learning differences, and need for accuracy differences. We argue that greater precision about the mechanisms driving affective bias may eventually enable us to better understand and treat mood and anxiety disorders.
Affective Bias Through the Lens of Signal Detection Theory