Context-dependent modulation of spatial attention: prioritizing behaviourally relevant stimuli
Bibliographic record
Abstract
Human attention can be guided by semantic information conveyed by individual objects in the environment. Over time, we learn to allocate attention resources towards stimuli that are behaviourally relevant to ongoing action, leading to attention capture by meaningful peripheral stimuli. A common example includes, while driving, stimuli that imply a possibly hazardous scenario (e.g. a pedestrian about to cross the road) warrant attentional prioritization to ensure safe proceedings. In the current study, we report a novel phenomenon in which the guidance of attention is dependent on the stimuli appearing in a behaviourally relevant context. Using a driving simulator, we simulated a real-world driving task representing an overlearned behaviour for licensed drivers. While driving, participants underwent a peripheral cue-target paradigm where a roadside pedestrian avatar (target) appeared following a cylinder cue. Results revealed that, during simulated driving conditions, participants (all with driver's licenses) showed greater attentional facilitation when pedestrians were oriented towards the road compared to away. This orientation-specific selectivity was not seen if the 3-D context was removed (Experiment 1) or the same visual scene was presented, but participants' viewpoints remained stationary (Experiment 2), or an inanimate object served as a target during simulated driving (Experiment 3). This context-specific attention modulation likely reflects drivers' expertise in automatically attending to behaviourally relevant information in a context-dependent manner.
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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 | 0.002 |
| 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 | 0.001 |
| 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".