ArticleQuarterly journal of experimental psychology (2006)2024
What can evidence accumulation modelling tell us about human social cognition?
Article in Quarterly journal of experimental psychology (2006), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Assessing underlying processes of implicit weight bias: a diffusion model analysis.Scientific reports · 2026Article
- Eye movements, not reaction times, reveal anticipatory attentional bias in childhood social anxiety disorder.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Covert neural and autonomic signatures of shared perception.Social cognitive and affective neuroscience · 2026Article
- Mindfulness training enhances face working memory: evidence from the drift-diffusion model.NPJ science of learning · 2025Article
- Uncovering the cognitive mechanisms underlying the gaze cueing effect.Quarterly journal of experimental psychology (2006) · 2024Article
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2 authors.
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Abstract
Evidence accumulation models are a series of computational models that provide an account for speeded decision-making. These models have been used extensively within the cognitive psychology literature to great success, allowing inferences to be drawn about the psychological processes that underlie cognition that are sometimes not available in a traditional analysis of accuracy or reaction time (RT). Despite this, there have been only a few applications of these models within the domain of social cognition. In this article, we explore several ways in which the study of human social information processing would benefit from application of evidence accumulation modelling. We begin first with a brief overview of the evidence accumulation modelling framework and their past success within the domain of cognitive psychology. We then highlight five ways in which social cognitive research would benefit from an evidence accumulation approach. This includes (1) greater specification of assumptions, (2) unambiguous comparisons across blocked task conditions, (3) quantifying and comparing the magnitude of effects in standardised measures, (4) a novel approach for studying individual differences, and (5) improved reproducibility and accessibility. These points are illustrated using examples from the domain of social attention. Finally, we outline several methodological and practical considerations, which should help researchers use evidence accumulation models productively. Ultimately, it will be seen that evidence accumulation modelling offers a well-developed, accessible, and commonly understood framework that can reveal inferences about cognition that may otherwise be out of reach in a traditional analysis of accuracy and RT. This approach, therefore, has the potential to substantially revise our understanding of social cognition.
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