EEG signatures of contextual influences on visual search with real scenes
Bibliographic record
Abstract
Abstract The use of scene context is a powerful way by which biological organisms guide and facilitate visual search. Although many studies have shown enhancements of target-related electroencephalographic activity (EEG) with synthetic cues, there have been fewer studies demonstrating such enhancements during search with scene context and objects in real world scenes. Here, observers covertly searched for a target in images of real scenes while we used EEG to measure the steady state visual evoked response to objects flickering at different frequencies. The target appeared in its typical contextual location or out of context while we controlled for low-level properties of the image including target saliency against the background and retinal eccentricity. A pattern classifier using EEG activity at the relevant modulated frequencies showed target detection accuracy increased when the target was in a contextually appropriate location. A control condition for which observers searched the same images for a different target orthogonal to the contextual manipulation, resulted in no effects of scene context on classifier performance, confirming that image properties cannot explain the contextual modulations of neural activity. Pattern classifier decisions for individual images was also related to the aggregated observer behavioral decisions for individual images. Together, these findings demonstrate target-related neural responses are modulated by scene context during visual search with real world scenes and can be related to behavioral search decisions. Significance Statement Contextual relationships among objects are fundamental for humans to find objects in real world scenes. Although there is a larger literature understanding the brain mechanisms when a target appears at a location indicated by a synthetic cue such as an arrow or box, less is known about how the scene context modulates target-related neural activity. Here we show how neural activity predictive of the presence of a searched object in cluttered real scenes increases when the target object appears at a contextual location and diminishes when it appears at a place that is out of context. The results increase our understanding of how the brain processes real scenes and how context modulates object processing.
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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 | 0.001 |
| 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) | 0.001 | 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".