Citation

Robbins CJ, Allen HA, Miller KA, Chapman P. PLoS One 2019; 14(9): e0222905.

Affiliation

School of Psychology, University of Nottingham, Nottingham, England, United Kingdom.

Copyright

(Copyright © 2019, Public Library of Science)

DOI

10.1371/journal.pone.0222905

PMID

31545850

Abstract

Motorcyclists are involved in an exceptionally high number of crashes for the distance they travel, with one of the most common incidents being where another road user pulls out into the path of an oncoming motorcycle frequently resulting in a fatal collision. These instances have previously been interpreted as failures of visual attention, sometimes termed 'Look but Fail to See' (LBFTS) crashes, and interventions have focused on improving drivers' visual scanning and motorcycles' visibility. Here we show from a series of three experiments in a high-fidelity driving simulator, that when drivers' visual attention towards and memory for approaching vehicles is experimentally tested, drivers fail to report approaching motorcycles on between 13% and 18% of occasions. This happens even when the driver is pulling out into a safety-critical gap in front of the motorcycle, and often happens despite the driver having directly fixated on the oncoming vehicle. These failures in reporting a critical vehicle were not associated with how long the driver looked at the vehicle for, but were associated with drivers' subsequent visual search and the time that elapsed between fixating on the oncoming vehicle and pulling out of the junction. Here, we raise the possibility that interference in short-term memory might prevent drivers holding important visual information during these complex manoeuvres. This explanation suggests that some junction crashes on real roads that have been attributed to LBFTS errors may have been misclassified and might instead be the result of 'Saw but Forgot' (SBF) errors. We provide a framework for understanding the role of short-term memory in such situations, the Perceive Retain Choose (PRC) model, as well as novel predictions and proposals for practical interventions that may prevent this type of crash in the future.

Language: en