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Evidence accumulation modelling in the wild: understanding safety-critical decisions
Russell J Boag1, Luke Strickland2, Andrew Heathcote3
1School of Psychological Sciences, University of Western Australia, Crawley, WA 6009, Australia.
Evidence accumulation models (EAMs) help understand cognitive processes behind decisions and response times. Their application is expanding from simple tasks to complex real-world domains like air-traffic control.
Area of Science:
- Cognitive psychology and neuroscience
- Human Factors research
Background:
- Evidence accumulation models (EAMs) are computational cognitive models.
- Historically limited to simple decision tasks.
- Recently applied to applied domains.
Purpose of the Study:
- Discuss the application of EAMs in applied domains.
- Explain how EAMs help understand cognitive adaptation to task demands and interventions.
- Argue for wider adoption of cognitive modeling in Human Factors research.
Main Methods:
- Computational cognitive modeling.
- Application of EAMs to complex behavioral data.
Main Results:
- EAMs provide insight into cognitive processes in applied domains (e.g., air-traffic control, driving, image discrimination, maritime surveillance).
- EAMs help understand cognitive system adaptation to task demands and automation.
Conclusions:
- EAMs are valuable tools for understanding human decision-making and cognitive processes.
- Wider adoption of EAMs in Human Factors research is recommended.
- EAMs offer insights into how cognitive systems adapt to complex environments and interventions.
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