Seeing What Matters: Attentional (MIS-) Alignment between Humans and AI in VR-Simulated Prediction of Driving Accidents
Bibliographic record
Abstract
This study explores how human and AI visual attention differ in a short-term prediction task, particularly in the moments before an accident is about to happen. Since real-world studies of this kind would pose ethical and safety risks, we employed virtual reality (VR) to simulate an accident scenario. In the scenario, the driver approaches a fork in the road, knowing that one path would lead off a cliff crashing the car fatally-as the fork comes closer, the other, safe, path is suddenly blocked by trees, forcing the driver to make a split-second decision where to go. A total of $N=71$ drivers completed the task, and we asked another $N=30$ observers to watch short video clips leading up to the final event and to predict which way the driver would take. We then compared both prediction accuracy as well as attention patterns-how focus is distributed across objects-with AI systems, including vision language models (VLMs) and vision-only models. We found that overall, prediction performance increased as the accident time point approached; interestingly, humans fared better than AI systems overall except for the final time period just before the event. We also found that humans adapted their attention dynamically, shifting focus to important scene elements before an event, whereas AI attention remained static, overlooking key details of the scene. Importantly, as the accident time point approached, human-AI attentional alignment decreased, even though both types of models improved in prediction accuracy. Despite distinct temporal trajectories-vision-only models declining from an early advantage and VLMs peaking in the middle-both models achieved low to zero alignment with human attention. These findings highlight a critical dissociation: AI models make accurate predictions, but rely on visual strategies diverging from human processing, underscoring a gap between explainability and task performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".