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Great expectations: A predictive processing account of automobile driving

Johan Engström ; Jonas Bärgman (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Daniel Nilsson (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Bobbie Seppelt ; Gustav Markkula ; Giulio Francesco Bianchi Piccinini (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Trent Victor (Institutionen för tillämpad mekanik, Fordonssäkerhet)
Theoretical Issues in Ergonomics Science (1463-922X). p. 1-39. (2017)
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Predictive processing, expectancy, driving, driver behaviour, perception, action



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Denna post skapades 2017-05-09.
CPL Pubid: 249183

 

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Institutioner (Chalmers)

Institutionen för tillämpad mekanik, Fordonssäkerhet (2005-2017)

Ämnesområden

Kognitionsforskning
Transport
Farkostteknik

Chalmers infrastruktur