How do drivers mitigate the effects of naturalistic visual complexity?
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-08-01
|
| Series: | Cognitive Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41235-023-00501-1 |
| _version_ | 1797454182420054016 |
|---|---|
| author | Vasiliki Kondyli Mehul Bhatt Daniel Levin Jakob Suchan |
| author_facet | Vasiliki Kondyli Mehul Bhatt Daniel Levin Jakob Suchan |
| author_sort | Vasiliki Kondyli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:33:35Z |
| format | Article |
| id | doaj.art-dc9a791f61ff46a7a22f7c4eef0884c7 |
| institution | Directory Open Access Journal |
| issn | 2365-7464 |
| language | English |
| last_indexed | 2024-03-09T15:33:35Z |
| publishDate | 2023-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cognitive Research |
| spelling | doaj.art-dc9a791f61ff46a7a22f7c4eef0884c72023-11-26T12:08:21ZengSpringerOpenCognitive Research2365-74642023-08-018113010.1186/s41235-023-00501-1How do drivers mitigate the effects of naturalistic visual complexity?Vasiliki Kondyli0Mehul Bhatt1Daniel Levin2Jakob Suchan3CoDesign Lab EU – codesign-lab.org, Örebro UniversityCoDesign Lab EU – codesign-lab.org, Örebro UniversityVanderbilt UniversityGerman Aerospace Center - DLR, Institute of Systems Engineering for Future MobilityAbstract 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.https://doi.org/10.1186/s41235-023-00501-1Visual perceptionChange blindnessVisuospatial complexityAttentional strategiesNaturalistic observationEveryday driving |
| spellingShingle | Vasiliki Kondyli Mehul Bhatt Daniel Levin Jakob Suchan How do drivers mitigate the effects of naturalistic visual complexity? Cognitive Research Visual perception Change blindness Visuospatial complexity Attentional strategies Naturalistic observation Everyday driving |
| title | How do drivers mitigate the effects of naturalistic visual complexity? |
| title_full | How do drivers mitigate the effects of naturalistic visual complexity? |
| title_fullStr | How do drivers mitigate the effects of naturalistic visual complexity? |
| title_full_unstemmed | How do drivers mitigate the effects of naturalistic visual complexity? |
| title_short | How do drivers mitigate the effects of naturalistic visual complexity? |
| title_sort | how do drivers mitigate the effects of naturalistic visual complexity |
| topic | Visual perception Change blindness Visuospatial complexity Attentional strategies Naturalistic observation Everyday driving |
| url | https://doi.org/10.1186/s41235-023-00501-1 |
| work_keys_str_mv | AT vasilikikondyli howdodriversmitigatetheeffectsofnaturalisticvisualcomplexity AT mehulbhatt howdodriversmitigatetheeffectsofnaturalisticvisualcomplexity AT daniellevin howdodriversmitigatetheeffectsofnaturalisticvisualcomplexity AT jakobsuchan howdodriversmitigatetheeffectsofnaturalisticvisualcomplexity |