The DOI matches its Crossref record.
Effects of experience and processing demands on visual information acquisition in drivers
Abstract
This study investigated the differences between novices and experienced drivers in their distribution of visual attention under different levels of cognitive load imposed by different types of road, and as reflected in their visual search strategies. The task involved a 20-min drive on various roads while the drivers' eye movements were recorded. The measures taken included fixation durations, as an indicator of the time taken to assimilate fixated objects, and the variance of fixation co-ordinates to describe the spread of search in both the horizontal and vertical axes. Differences were found between novices and experienced drivers according to the type of road being driven. The results suggested that experienced drivers select visual strategies according to the complexity of the roadway, and that the strategies of novices are too inflexible to meet changing demands.
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Cited by
A selective review of selective attention research from the past century
Driver, J. (2001) · British Journal of Psychology · doi:10.1348/000712601162103 · Cited by 658
Multimodal behavioral and physiological signals as indicators of cognitive load
Zhou, J., Yu, K., Chen, F., Wang, Y., & Arshad, S. Z. (2018) · The Handbook of Multimodal-Multisensor Interfaces: Foundations, User Modeling, and Common Modality Combinations - Volume 2 · doi:10.1145/3107990.3108002 · Cited by 25
Metadata
- Authors
- CRUNDALL, D. E., & UNDERWOOD, G.
- Venue
- Ergonomics
- Publisher
- Informa UK Limited
- Published
- 1998-04
- Volume
- 41
- Issue
- 4
- Pages
- 448-458
- ISSN
- 0014-0139 (ISSN-L)
- 1366-5847
- OpenAlex
- W2112665064
- Cited by
- 425
- References
- 0
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
