Engström, J, Bärgman, J, Nilsson, D et al. (4 more authors) (2018) Great expectations: A predictive processing account of automobile driving. Theoretical Issues in Ergonomics Science, 19 (2). pp. 156-194. ISSN 1463-922X
Abstract
Predictive processing has been proposed as a unifying framework for understanding brain function, suggesting that cognition and behaviour can be fundamentally understood based on the single principle of prediction error minimisation. According to predictive processing, the brain is a statistical organ that continuously attempts get a grip on states in the world by predicting how these states cause sensory input and minimising the deviations between the predicted and actual input. While these ideas have had a strong influence in neuroscience and cognitive science, they have so far not been adopted in applied human factors research. The present paper represents a first attempt to do so, exploring how predictive processing concepts can be used to understand automobile driving. It is shown how a framework based on predictive processing may provide a novel perspective on a range of driving phenomena and offer a unifying framework for traditionally disparate human factors models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis in Theoretical Issues in Ergonomics Science on 18th April 2017, available online: http://www.tandfonline.com/10.1080/1463922X.2017.1306148. |
Keywords: | Predictive processing, expectancy, driving, driver behaviour, perception, action |
Dates: |
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Institution: | The University of Leeds |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Apr 2017 16:17 |
Last Modified: | 30 May 2018 14:03 |
Published Version: | https://doi.org/10.1080/1463922X.2017.1306148 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/1463922X.2017.1306148 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:114453 |