Abstract
While attention has consistently been shown to be biased toward threatening objects in experimental settings, our understanding of how attention is modulated when the observer is in an anxious or aroused state and how this ultimately affects behavior is limited. In real-world environments, automobile drivers can sometimes carry negative perceptions toward bicyclists that share the road. It is unclear whether bicyclist encounters on a roadway lead to physiological changes and attentional biases that ultimately influence driving behavior. Here, we examined whether participants in a high-fidelity driving simulator exhibited an arousal response in the presence of a bicyclist and how this modulated eye movements and driving behavior. We hypothesized that bicyclists would evoke a robust arousal and orienting response, the strength of which would be associated with safer driving behavior. The results revealed that encountering a bicyclist evoked negative arousal by both self-report and physiological measures. Physiological and eye-tracking measures were themselves unrelated, however, being independently associated with safer driving behavior. Our findings offer a real-world demonstration of how arousal and attentional prioritization can lead to adaptive behavior.
Highlights
The world is filled with vast amounts of information in a dynamically changing environment
We first evaluated whether the bicyclist evoked negative arousal by measuring physiological changes upon viewing the bicyclist compared to baseline periods of the task (Fig. 2)
Our correlation matrix and randomization test allowed us to investigate whether the collection of acquired measures reliably supports our aforementioned hypothesis that the bicyclist elicits a negative arousal response leading to increased attention to the bicyclist and compensatory driving behaviors
Summary
The world is filled with vast amounts of information in a dynamically changing environment. Attention is the cognitive process that selectively filters incoming sensory information to determine which stimuli are represented in the brain (Desimone & Duncan, 1995). Learned associations between stimuli and reward (e.g., Anderson et al, 2011; Della Libera & Chelazzi, 2006; Engelmann & Pessoa, 2007; Kiss et al, 2009; Navalpakkam et al, 2010) and punishment (e.g., Chubala & Smith, 2009; Koster et al, 2004; Schmidt et al, 2015a, 2015b), along with implicit learning of statistical relationships among sensory cues (e.g., Chun & Jiang, 1998; Fiser & Aslin, 2001; Frost et al, 2015; Turk-Browne et al, 2005), have been shown to influence the control of attention
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