Temporal dynamics in the expansion and contraction of the attentional window
Bibliographic record
Abstract
The vast amount of visual information in the world necessitates a selective mechanism that limits visual processing to objects or locations of interest. Visual attention fulfils this selective function, and may be allocated with varying degrees of success over tasks, space, and time. We propose a qualitative model that accounts for the modulation of the spatial extent of the focus of attention across time, and we test that model in a series of experiments. Specifically, we employed the Attentional Blink (AB) and Lag-1 sparing, to test the spatiotemporal modulations of attention. When two sequential targets are inserted in a rapid stream of distractors, perception of the second target is impaired at short inter-target lags (AB deficit). Paradoxically, this deficit disappears when the second target appears directly after the first (Lag-1 sparing). Lag-1 sparing always occurs when the two targets appear in the same spatial location, but occurs to targets in different spatial locations only if the focus of attention encompasses both locations. Given this, the incidence and magnitude of Lag-1 sparing provides a sensitive measure of the degree to which the focus of attention encompasses the location of the second target. The present research utilized two simultaneous distractor streams to measure our ability to shift and expand spatial attention over time. Two main findings emerged: first, when the second target appeared directly after the first, there was a progressive transition from Lag-1 sparing to AB deficit as the SOA between successive items was increased. This provides a measure of the spatiotemporal modulations of the focus of attention. Second, the change from Lag-1 sparing to AB deficit was related linearly to SOA. This strongly suggests that the spatial extent of attention varies linearly over time and that the expanding and shrinking of the focus of attention may be analog in nature.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".