Visual Information Requirements for Dismounted Soldier Target Acquisition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We conducted an empirical investigation of the visual information requirements for target detection and threat identification decisions in the dismounted soldier context. Forty soldiers viewed digital photographs of a person standing against a forested background. The soldiers made two-alternative detection decisions requiring them to determine whether the target was present in the scene, and two-alternative threat identification decisions that required discrimination of the objects held by the target, the clothing worn by the target, and target postures. The images were presented to subjects on a computer display, and variation in the apparent target distance was simulated through digital image magnification and by varying the viewing distance to the display. Image resolution was degraded progressively by spatial frequency filtering and we estimated the resolution threshold in each task. These threshold values were compared with the historical Johnson criteria for predicting imaging device performance. Our data are broadly consistent with the previously reported values, though our threat identification decisions required subjects to perceive information with a larger spatial scale than the Johnson criterion for identification of standing human targets. In a second experiment, we employed a four-alternative identification decision and found results that were consistent with those from Experiment 1. We also confirmed that the spatial scale of visual information used for target acquisition is highly task-specific, and provided a novel demonstration of changes in visual information requirements as a function of target range. These findings pose challenges for models of target acquisition with imaging devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it