Familiarity wins over novelty: A persistent attentional bias toward regularities
Bibliographic record
Abstract
The visual environment is often stable, but some aspects may change over time. For example, the furniture in the room may be re-arranged, and new furniture may replace old ones. The challenge for the mind is thus to update knowledge about the environment in light of new information. Here we examine whether the attentional bias to regularities can be shifted in the presence of new stimuli. The experiment consists of two halves. In the first half, observers viewed four simultaneous streams of shapes. The stream in one 'structured' location contained triplets, the shapes in one 'random' location were randomized, and a gray square appeared in each of the two 'neutral' locations. Occasional search arrays were presented where the target appeared randomly at one of the four locations. In the second half, everything was the same except that the stream in the structured or the random location may change. Several changes occurred across four conditions: (1) the structured stream became random; (2) the random stream became structured; (3) the random stream now contained new random shapes; and (4) the random stream now contained new structured shapes. In the baseline condition, no change ever occurred. We found that in all conditions, during the first half of the experiment, target discrimination was reliably faster for targets at structured vs. random or neutral locations, suggesting that attention was drawn to the structured location. In the second half, regardless of condition, target discrimination was again reliably faster for targets at structured vs. random or neutral locations. This suggests that attention persisted at the previously structured location, even though the stream was no longer structured, or newly structured stream or new shapes emerged in another location. These findings reveal the robustness and persistence of the attentional bias to regularities even in the presence of novel stimuli. Meeting abstract presented at VSS 2014
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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 | 0.006 |
| 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 | 0.001 |
| 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".