The multitasking motorist and the attention economy

Strayer, DL; Getty, D; Biondi, F; Cooper, JM · 2021 · publications_jsonl

DOI: 10.1037/0000208-007

archive: archived pipeline: cataloged verified

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Summary

This chapter examines the relationship between the "attention economy" and driving performance, specifically focusing on how concurrent non-driving activities impair a motorist’s situation awareness. The authors argue that while some driving aspects are automatic, safe operation requires focused attention to manage dynamic, information-dense environments. The core problem addressed is inattentive driving, where limited attentional resources are diverted from the primary task of driving to secondary tasks, such as smartphone use, leading to degraded performance and increased crash risk. To explain these mechanisms, the authors utilize the SPIDER model, an acronym for Scanning, Predicting, Identification, Decision-making, and Executing a Response. These processes constitute the mental operations required to maintain situation awareness. The text reviews empirical evidence demonstrating that secondary tasks impair each SPIDER component: visual scanning becomes concentrated and less comprehensive; hazard prediction via anticipatory glances is reduced; identification suffers from inattentional blindness, where drivers fail to process visible objects; decision-making becomes unsafe, such as misjudging gaps in traffic; and response execution is delayed, particularly under high perceptual load. The authors emphasize that these impairments are not instantaneous but follow dynamic fluctuations. Situation awareness diminishes during a secondary task and recovers slowly afterward, often following a power function. For instance, sending a voice-texted message may degrade situation awareness for approximately 57 seconds, including a "technology hangover" period after the task ends. The paper further extends this framework to vehicle automation, specifically Level 2 and Level 3 semi-autonomous systems. The authors contend that automation shifts driving from a control task to a monitoring task, a role at which humans are notoriously poor due to vigilance decrements and under-stimulation. This leads to the "paradox of automation," where increased trust in reliable systems causes drivers to disengage, resulting in a loss of situation awareness. When automation disengages, drivers face a critical recovery period during which they may lack the situational context necessary to safely resume control. The authors cite the 2016 Tesla Model S crash as a tragic example of this phenomenon, where the driver’s prolonged disengagement and overreliance on automation prevented a timely reaction to an obstacle. The significance of this work lies in its integration of cognitive psychology with traffic safety, highlighting that distraction effects persist well beyond the duration of the secondary task. The authors conclude that understanding the temporal dynamics of situation awareness loss and recovery is critical for traffic safety. They warn that current semi-autonomous technologies, by reducing driver engagement, may inadvertently increase risk by pushing drivers out of the loop, necessitating further research into the attentional demands of human-automation interaction.

Key finding

Distraction harms driving by degrading SPIDER-related processes, lowering situation awareness in a continuous loss/recovery curve that persists 20-60 s past task offset; the same framework predicts vehicle automation will produce analogous loss of situation awareness during monitoring and a recovery cost at takeover.

Methodology

theoretical

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via tag_papers on 2026-05-30 (4 acquisition events logged).

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

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• situational awareness
• temporal
• manual
• vigilance
• visual
• peripheral attention

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: theory or model, conceptual framework