- Title
- Evidence accumulation modelling in the wild: understanding safety-critical decisions
- Creator
- Boag, Russell J.; Strickland, Luke; Heathcote, Andrew; Neal, Andrew; Palada, Hector; Loft, Shayne
- Relation
- Trends in Cognitive Sciences Vol. 27, Issue 2, p. 175-188
- Publisher Link
- http://dx.doi.org/10.1016/j.tics.2022.11.009
- Publisher
- Cell Press
- Resource Type
- journal article
- Date
- 2023
- Description
- Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research.
- Subject
- evidence accumulation; computational cognitive model; decision making; human factors; performance and safety; applied cognition
- Identifier
- http://hdl.handle.net/1959.13/1478981
- Identifier
- uon:50257
- Identifier
- ISSN:1364-6613
- Language
- eng
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