2017/02/10 by Johan Engström, Gustav Markkula, Trent Victor +1 · 287 citations
Engineering · Health Professions · Neuroscience · Psychology · #Artificial intelligence #Cognition #Cognitive load #Cognitive psychology #Computer science #Control (management) #Human-Automation Interaction and Safety #Neuroscience #Older Adults Driving Studies #Psychology #Traffic and Road Safety
paper · doi:10.1177/0018720817690639
published in Human Factors The Journal of the Human Factors and Ergonomics Society 59(5), 734-764 (SAGE Publishing)
openalex publication_date 2017/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
OBJECTIVE: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. BACKGROUND: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. METHOD: Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. RESULTS: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. CONCLUSION: Effects of cognitive load on driving are strongly selective and task dependent. APPLICATION: The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.