ArticleComputational psychiatry (Cambridge, Mass.)2021
Affective Bias Through the Lens of Signal Detection Theory.
Article in Computational psychiatry (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Type-1 and type-2 decisions feature computational noise of similar magnitude.Communications psychology · 2026Article
- Cognition in Treatment-Resistant Depression: Obstacles and Challenges?Advances in experimental medicine and biology · 2026Review
- Physically stressed bees expect less reward in an active choice judgement bias test.Proceedings. Biological sciences · 2024Article
- Dorsal Anterior Cingulate Cortex Coordinates Contextual Mental Imagery for Single-Beat Manipulation during Rhythmic Sensorimotor Synchronization.Brain sciences · 2024Article
- Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making.Computational psychiatry (Cambridge, Mass.) · 2024Article
- Perspective Taking and Memory for Self- and Town-Related Information in Male Adolescents and Young Adults.Cognitive development · 2023Article
- Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling.Biological psychiatry · 2023Review
- Computational perspectives on human fear and anxiety.Neuroscience and biobehavioral reviews · 2023Review
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Authors and funding
2 authors.
Funding
Abstract
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 show how to apply this framework to measure affective bias and compare it to the behaviour of an optimal observer. We also show how to extend the framework to make predictions about bias when the individual holds incorrect assumptions about the decision context. 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 the mechanisms underlying mood and anxiety disorders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.