Great expectations: a predictive processing account of automobile driving
DOI: 10.1080/1463922x.2017.1306148
archive: archived pipeline: cataloged verified
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Summary
This theoretical paper proposes applying the predictive processing framework to automobile driving, aiming to bridge a gap between basic neuroscience and applied human factors research. The authors argue that traditional information processing models, which view cognition as a feed-forward stream from sensory input to action, are insufficient for understanding complex driving behaviors. Instead, they adopt predictive processing, which posits that the brain is a statistical organ that continuously generates predictions of sensory input and minimizes the resulting prediction errors. This framework offers a unified perspective on disparate human factors models by treating perception and action as intertwined processes serving the single goal of minimizing surprise or prediction error. The paper outlines three principal mechanisms of predictive processing: active inference, precision weighting, and model tuning. Active inference explains how the brain resolves prediction errors either by updating internal predictions (perception) or by acting to change sensory input to match predictions (action). For example, a driver braking to maintain a fixed distance from a slowing lead vehicle is acting to cancel a visual looming prediction error. Precision weighting relates to the confidence in predictions, scaling the influence of prediction errors; this mechanism is linked to attention and cognitive control, allowing drivers to prioritize reliable sensory data (e.g., lane markings in fog) or shift control between automated lower-level responses and flexible higher-level planning. Model tuning refers to the gradual adjustment of the generative model over time through learning, where repeated exposure to statistical regularities strengthens specific predictions, leading to automaticity and skilled behavior. The authors apply these concepts to various driving phenomena, including reactions to unexpected hazards, visual scanning at intersections, and interactions with automated driving functions. They illustrate how hierarchical generative models allow higher-level contextual predictions (e.g., "approaching an intersection") to disambiguate lower-level sensory signals. The paper demonstrates that predictive processing can explain how drivers handle uncertainty, such as performing epistemic actions (e.g., checking mirrors) to increase prediction precision before executing pragmatic actions (e.g., overtaking). Furthermore, it connects the framework to established concepts like situation awareness and bounded rationality, suggesting that skilled driving emerges from generative models that are tuned to minimize prediction error and complexity. The significance of this work lies in its potential to provide a novel, unifying theoretical foundation for human factors research in driving. By reframing driver behavior as expectation-driven active engagement with the environment, the predictive processing framework integrates perception, action, attention, and learning into a single coherent model. This approach not only offers new insights into traditional driving phenomena but also provides a robust basis for understanding human-machine interaction in increasingly automated vehicles. The authors conclude that adopting this framework can help resolve inconsistencies in existing models and guide future research into driver behavior and ergonomics.
Key finding
The predictive processing framework provides a unifying theoretical perspective for understanding automobile driving by explaining driver behavior through the continuous minimization of sensory prediction errors.
Methodology
review
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 openalex_abstract on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 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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- Theoretical Contribution: theory or model, computational model