How Do Drivers Mitigate the Effects of Naturalistic Visual Complexity? On Attentional Strategies and Their Implications under a Change Blindness Protocol

Autor/inn/en	Kondyli, Vasiliki; Bhatt, Mehul; Levin, Daniel; Suchan, Jakob
Titel	How Do Drivers Mitigate the Effects of Naturalistic Visual Complexity? On Attentional Strategies and Their Implications under a Change Blindness Protocol
Quelle	In: Cognitive Research: Principles and Implications, 8 (2023), Artikel 54 (30 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kondyli, Vasiliki)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s41235-023-00501-1
Schlagwörter	Motor Vehicles; Visual Perception; Attention; Spatial Ability; Safety; Coping; Eye Movements; Familiarity; Environmental Influences; Context Effect + Suchen Sie Ihr Suchwort? Motor vehicle; Kraftwagen; Visuelle Wahrnehmung; Aufmerksamkeit; Räumliches Vorstellungsvermögen; Sicherheit; Bewältigung; Augenbewegung; Environmental influence; Umwelteinfluss
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 "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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01