Effect of Apparent Depth in Peripheral Target Detection in Driving under Focused and Divided Attention
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Bibliographic record
Abstract
The ability to detect events in the visual periphery is crucial to driving safely. The useful field of view (UFOV) task provides an index of the spatial extent of peripheral vision under focused and divided attention. Previous research reported reduced UFOV at greater perceived distances in driving (Andersen et al., 2011; Pierce & Andersen, 2014); however, these studies used long stimulus durations, making it difficult to compare directly with the traditional UFOV task (Sekuler & Ball, 1988; Sekuler, Bennett & Mamelak, 2000), which correlates with critical aspects of driving performance (Owsley et al., 1998; Ball et al., 1993). Furthermore, previous studies on the depth effect in driving assessed performance only under divided attention. The current study adapts the traditional 2D UFOV task to a computer-rendered 3D environment to examine whether apparent depth affects the detection of brief peripheral targets, under focused and divided attention, and with target retinal image size matched across depth. In the central task, participants tried to maintain a constant distance from a speed-varying lead car, indicated when the lead car's image size matched that of a surrounding size-invariant box. In the peripheral task, participants detected targets appearing at one of several possible locations on the left or right side at two apparent distances, implied via simulated forward motion and pictorial cues. The central and peripheral tasks were completed separately under focused attention, and then, simultaneously under divided attention. We tested 24 participants and found they responded more accurately to near than far targets at larger eccentricities under focused and divided attention. Another 24 participants, tested in a second experiment with different target appearance probabilities, showed similar results. Thus, our data suggest that apparent depth influenced the detection of briefly flashed peripheral targets. These results are generally consistent with previous research, and have important implications for understanding the mechanisms modulating the UFOV. Meeting abstract presented at VSS 2017
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it