How do drivers mitigate the effects of naturalistic visual complexity? : On attentional strategies and their implications under a change blindness protocol
(2023) In Cognitive Research: Principles and Implications 8(1). p.54-54- Abstract
How do the limits of high-level visual processing affect human performance in naturalistic, dynamic settings of (multimodal) interaction where observers can draw on experience to strategically adapt attention to familiar forms of complexity? In this backdrop, we investigate change detection in a driving context to study attentional allocation aimed at overcoming environmental complexity and temporal load. Results indicate that visuospatial complexity substantially increases change blindness but also that participants effectively respond to this load by increasing their focus on safety-relevant events, by adjusting their driving, and by avoiding non-productive forms of attentional elaboration, thereby also controlling... (More)
How do the limits of high-level visual processing affect human performance in naturalistic, dynamic settings of (multimodal) interaction where observers can draw on experience to strategically adapt attention to familiar forms of complexity? In this backdrop, we investigate change detection in a driving context to study attentional allocation aimed at overcoming environmental complexity and temporal load. Results indicate that visuospatial complexity substantially increases change blindness but also that participants effectively respond to this load by increasing their focus on safety-relevant events, by adjusting their driving, and by avoiding non-productive forms of attentional elaboration, thereby also controlling "looked-but-failed-to-see" errors. Furthermore, analyses of gaze patterns reveal that drivers occasionally, but effectively, limit attentional monitoring and lingering for irrelevant changes. Overall, the experimental outcomes reveal how drivers exhibit effective attentional compensation in highly complex situations. Our findings uncover implications for driving education and development of driving skill-testing methods, as well as for human-factors guided development of AI-based driving assistance systems.
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- author
- Kondyli, Vasiliki LU ; Bhatt, Mehul ; Levin, Daniel and Suchan, Jakob
- publishing date
- 2023-08-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Humans, Automobile Driving, Visual Perception, Educational Status
- in
- Cognitive Research: Principles and Implications
- volume
- 8
- issue
- 1
- pages
- 54 - 54
- publisher
- Springer
- external identifiers
-
- pmid:37556047
- scopus:85167370133
- ISSN
- 2365-7464
- DOI
- 10.1186/s41235-023-00501-1
- language
- English
- LU publication?
- no
- additional info
- © 2023. The Psychonomic Society.
- id
- 0cf47c8d-6f14-4d1e-a850-41da6dbbbce8
- date added to LUP
- 2024-12-18 15:05:40
- date last changed
- 2025-07-17 21:10:02
@article{0cf47c8d-6f14-4d1e-a850-41da6dbbbce8, abstract = {{<p>How do the limits of high-level visual processing affect human performance in naturalistic, dynamic settings of (multimodal) interaction where observers can draw on experience to strategically adapt attention to familiar forms of complexity? In this backdrop, we investigate change detection in a driving context to study attentional allocation aimed at overcoming environmental complexity and temporal load. Results indicate that visuospatial complexity substantially increases change blindness but also that participants effectively respond to this load by increasing their focus on safety-relevant events, by adjusting their driving, and by avoiding non-productive forms of attentional elaboration, thereby also controlling "looked-but-failed-to-see" errors. Furthermore, analyses of gaze patterns reveal that drivers occasionally, but effectively, limit attentional monitoring and lingering for irrelevant changes. Overall, the experimental outcomes reveal how drivers exhibit effective attentional compensation in highly complex situations. Our findings uncover implications for driving education and development of driving skill-testing methods, as well as for human-factors guided development of AI-based driving assistance systems.</p>}}, author = {{Kondyli, Vasiliki and Bhatt, Mehul and Levin, Daniel and Suchan, Jakob}}, issn = {{2365-7464}}, keywords = {{Humans; Automobile Driving; Visual Perception; Educational Status}}, language = {{eng}}, month = {{08}}, number = {{1}}, pages = {{54--54}}, publisher = {{Springer}}, series = {{Cognitive Research: Principles and Implications}}, title = {{How do drivers mitigate the effects of naturalistic visual complexity? : On attentional strategies and their implications under a change blindness protocol}}, url = {{http://dx.doi.org/10.1186/s41235-023-00501-1}}, doi = {{10.1186/s41235-023-00501-1}}, volume = {{8}}, year = {{2023}}, }