Analyzing the Relationship between Behavioral Measures of the Attentional Networks Performance and Self-Report Data of Attention-Related Driving Errors.

LÓPEZ-RAMÓN, F.; CASTRO, C.; ROCA, J; LEDESMA, R.; LUPIAÑEZ, J

Conferencia; . 2nd International Conference on Driver Distraction and Inattention,; 2011

Driving is a complex activity that demands attention from competing tasks. Thediversion of attention, or driver distraction and inattention, can produce errorsand may cause failures in performance while driving.Based on Posner´s theoretical framework, several tests were created for obtainingbehavioral measures of the functioning of the attentional networks: alerting,orienting and executive control and their interactions (e.g., the ANTI test,and the ANTI-V test, that additionally measures vigilance).However, an approach that combines self-report data about the propensity toattentionrelated driving errors and the behavioral measurement of the threeAttentional Networks performance of drivers is still missing.Therefore, this research aims to explore the relationship between the behavioralmeasures of the attentional functioning (measured by ANTI and ANTI-Vbehavioral tasks) related to self-report data of personal proneness to distractibilitywhile driving (assessed by the Driver Behavioural Questionnaire [DBQ] andthe Attention-Related Driving Errors Scale [ARDES]).Preliminary results obtained with ARDES questionnaire and ANTI task suggestthat the group with greater propensity to experience attention-related errorsmay be more at risk when driving. That is, their attentional performance showeda slowdown in the overall functioning of the attentional networks (that can alsobe considered as an attentional measure of vigilance) and less endogenouspreparation for high priority warning signs. But the results also showed that theyare less at risk in situations in which they can deal with response conflict in thepresence of valid cues (e.g., traffic signs). It can be inferred that the group proneto commit attention-related errors shows a particular combination of attentionalnetworks functioning that could, in some situations, compensate their deficitwith a better response conflict.