Dynamic Modeling of Visual Search
Bibliographic record
Abstract
In 1998/1999, three participants trained for up to 74-h-long sessions to find a target present on half the trials in visual displays of 1, 2, or 4 initially novel objects. There were four targets and four foils that never changed. Displays occurred simultaneously, or the objects occurred successively, or the four features of each object occurred successively. When successive, the SOAs were short (17, 33, or 50 ms), so the displays appeared simultaneous, making it likely that the search strategy was the same in all conditions. A 2004 publication examined only the simultaneous condition and found evidence suggesting serial search as well as some small amount of automatic attention to targets and occasional early or late termination of search. A 2021 publication examined only the displays with single objects, obtaining evidence for dynamic perception of features. These studies drew conclusions from modeling subtle aspects of the response time distributions; extending such modeling to all conditions would have been complex making it difficult to understand the main processes at work. Here, we present a simple way to extend the 2021 model to the conditions with multiple item displays. It is a hybrid model with parallel automatic processing of features from all display items, processing that finishes during the first comparison, combined with serial comparisons that terminate when a target is found, or when none is found. When objects occur sequentially, there is a tendency to compare first the first object presented that probability rising with SOA. This model gives a good qualitative account of the accuracy and median response times from all the conditions. This success suggests that a more complex model incorporating the dynamic processes of the 2021 model would provide an excellent quantitative account for the accuracy and response time distributions for all the conditions of this visual search study.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".