Evidence accumulation modelling in the wild: understanding safety-critical decisions. Issue 2 (February 2023)

• Record Type: Journal Article
• Authors: Boag, Russell J.; Strickland, Luke; Heathcote, Andrew; Neal, Andrew; Palada, Hector; Loft, Shayne
• Is Part Of: Trends in cognitive sciences. Volume 27:Issue 2(2023)
• Page Start: 175
• Page End: 188
• Publication Date: 2023-02
• Subjects: evidence accumulation -- computational cognitive model -- decision making -- human factors -- performance and safety -- applied cognition; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2022.11.009
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract: 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. Highlights: Across the diverse domains of air-traffic control, driving, forensic and medical image discrimination, and maritime surveillance, evidence accumulation models (EAMs) provide a coherent account of the latent cognitive mechanisms that underlie safety-critical decisions. EAM accumulation rates can be used to measure cognitive capacity and to identify when task demands compromise performance and safety. EAM threshold, bias, and rate parameters can be used to identify proactive and reactive cognitive control strategies that human operators use to meet expected and unexpected changes in task demands. Human Factors practitioners can use EAM analyses to improve human operator training and work design, including informing the design of automated support tools. Theoreticians gain from understanding how cognitive theories (instantiated in EAMs) generalise to more complex work tasks.