Dynamic workload measurement and modeling: Driving and conversing
DOI: 10.1037/xap0000431
archive: archived pipeline: cataloged verified
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Summary
This study investigates the cognitive mechanisms underlying performance decrements in the Detection Response Task (DRT), an international standard for measuring driver cognitive load. While DRT response times (RTs) reliably increase under secondary task load, it remains unclear whether this slowing results from reduced information processing rates, increased response caution, or longer non-decision processing times. The authors specifically examine whether conversing with a passenger in the vehicle or via a hands-free cell phone affects these distinct cognitive components during simulated driving. The researchers conducted an experiment with 40 undergraduate participants who performed a simulated driving task on a high-fidelity simulator while completing the DRT. The DRT required participants to press a button upon detecting a peripheral light. Cognitive load was manipulated across three within-subject conditions: driving alone (baseline), driving while conversing with a passenger seated in the car, and driving while conversing with a passenger in a separate room via hands-free cell phone. Participants engaged in casual conversation, avoiding discussion of the driving task. The authors analyzed the data using hierarchical Bayesian methods, fitting 22 variations of the single-bound diffusion model to the RT distributions. These models allowed for variability in drift rate (processing speed), response threshold (caution), and non-decision time, with model selection based on the Watanabe-Akaike Information Criterion (WAIC). The results demonstrated that drivers exhibited significantly slower DRT response times when conversing, either with an in-car passenger or via cell phone, compared to the baseline condition. There was no significant difference in RTs between the two conversation types. Model fitting revealed that the slowing was not caused by a decrease in the rate of information processing (drift rate), which remained constant across conditions. Instead, the best-fitting model indicated that the increased response times were mediated by an increase in the response threshold, reflecting greater caution. Non-decision times showed no significant group-level differences, though individual variability existed. The authors propose that drivers strategically raise their response thresholds to prioritize the primary driving task and avoid response conflicts when cognitive load increases. These findings challenge the prevailing view that DRT sensitivity to cognitive load stems from a direct depletion of shared processing resources. Instead, the study suggests that DRT slowing is an indirect result of strategic adjustments in response caution. This implies that drivers may consciously or unconsciously adopt a more conservative response strategy for secondary tasks when their primary task demands increase. The results highlight the importance of distinguishing between processing capacity limitations and strategic threshold adjustments when interpreting cognitive load metrics in safety-critical environments like driving.
Key finding
Drivers showed elevated DRT RT relative to non-drivers, and within both members of the dyad RT was higher when speaking than when listening, with conversational turn-taking producing reciprocal workload trade-offs. Unlike Tillman et al. (2017), who attributed dual-task cost solely to increased response threshold, LBAO modelling here showed that conversing while driving altered both the response threshold and the rate of evidence accumulation, consistent with a combined response-caution and capacity-sharing account; aggregating across speaking and listening in earlier work likely masked the drift-rate effect.
Methodology
simulator
Sample size: N=44 (22 dyads, 23 female, M age=21.1, SD=3.4), University of Utah undergraduates
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 (3 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-06 |
| archive | failed | pmc | — | — | 8 | 2026-06-04 |
| extract | success | pdf_extracted | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-04 |
| promote | success | — | — | — | 2 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: theory or model, computational model