Evidence accumulation modelling in the wild: Understanding safety-critical decisions
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 applic...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
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Elsevier
2022
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| Online Access: | http://purl.org/au-research/grants/arc/DP200101842 http://hdl.handle.net/20.500.11937/89740 |
| _version_ | 1848765279813763072 |
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| author | Boag, Russell Strickland, Luke Heathcote, Andrew Neal, Andrew Palada, Hector Loft, Shayne |
| author_facet | Boag, Russell Strickland, Luke Heathcote, Andrew Neal, Andrew Palada, Hector Loft, Shayne |
| author_sort | Boag, Russell |
| building | Curtin Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:32:44Z |
| format | Journal Article |
| id | curtin-20.500.11937-89740 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:32:44Z |
| publishDate | 2022 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-897402024-01-24T00:18:14Z Evidence accumulation modelling in the wild: Understanding safety-critical decisions Boag, Russell Strickland, Luke Heathcote, Andrew Neal, Andrew Palada, Hector Loft, Shayne 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/89740 10.1016/j.tics.2022.11.009 http://purl.org/au-research/grants/arc/DP200101842 http://purl.org/au-research/grants/arc/DP210100313 http://creativecommons.org/licenses/by-nc-nd/4.0/ Elsevier fulltext |
| spellingShingle | Boag, Russell Strickland, Luke Heathcote, Andrew Neal, Andrew Palada, Hector Loft, Shayne Evidence accumulation modelling in the wild: Understanding safety-critical decisions |
| title | Evidence accumulation modelling in the wild: Understanding safety-critical decisions |
| title_full | Evidence accumulation modelling in the wild: Understanding safety-critical decisions |
| title_fullStr | Evidence accumulation modelling in the wild: Understanding safety-critical decisions |
| title_full_unstemmed | Evidence accumulation modelling in the wild: Understanding safety-critical decisions |
| title_short | Evidence accumulation modelling in the wild: Understanding safety-critical decisions |
| title_sort | evidence accumulation modelling in the wild: understanding safety-critical decisions |
| url | http://purl.org/au-research/grants/arc/DP200101842 http://purl.org/au-research/grants/arc/DP200101842 http://hdl.handle.net/20.500.11937/89740 |