The Individual and Combined Effects of Spatial Context and Feature Cues in Visual Search
Bibliographic record
Abstract
When performing a visual search the entire visual array is typically considered relevant, and all of it must be included in search parameters. There is evidence however, that people are capable of narrowing attention to isolated spatial areas and/or visual features when those features are task relevant. In the present study we examined these two search strategies by examining their effects both individually and in combination. This allowed us to assess the manner in which the application of multiple parameters, as opposed to the use of a single parameter alone, affects visual search. Search parameters were communicated by visual cues that defined the spatial context and/or a relevant visual feature of the target. Participants were shown a random array of 36 letters and instructed to search for the target (N or X) and to indicate by button-press which target was present in that trial. Each trial was preceded by one of four cues: a featural cue, a spatial cue, a combined featural and spatial cue or no cue. These indicated, respectively, the target colour, approximate target location, target colour and location, and nothing. Analyses revealed that response times decreased for featural and spatial cue trials with respect to no cue trials, and that the combined presentation of featural and spatial cues produced a greater decrease in response times than either cue alone. These results support the assertion that people are capable of using both spatial context and visual features to improve the efficiency of their searches, possibly by directing their attention first to the relevant spatial context and then to the relevant features within it.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".