Associative learning between target and distractor layout and location probability cueing in the same visual search task
Bibliographic record
Abstract
The contextual cueing effect (CCE) is a phenomenon that shows that our brains can take advantage of invariant contextual information in our environment to help us locate targets or relevant information more efficiently. In a seminal study by Chun and Jiang (1998), participants searched for a target letter “T” among “L” distractors. Unbeknownst to the participants, some trials had repeated configurations, while others had novel ones. Participants found the “T” faster in repeated configurations, showing implicit learning. Classical studies demonstrated learning of only single context-target pairing. However, recent research (Wang et al., 2020) shows that learning could also happen for repeated contexts paired with one of multiple (e.g., 4) target locations. In the current study, we intended to examine such learning at the individual scene level by producing matching target eccentricity between a pair of repeated and novel scenes. We varied the magnitude of four target eccentricities by producing equal spacing (in Experiment 1) or variable spacing (in Experiments 2 and 3) of both repeated and novel scenes. Experiment 1 showed comparable learning for different target locations with different eccentricities except for targets with the smallest eccentricity. In Experiment 2, we compared conditions with targets concentrated on the larger versus smaller eccentricity range in a between-subject design, and we found that at least when the target appeared in a large eccentricity, CCE was larger when the target appeared in the distribution condition with larger eccentricity bias than distribution with low eccentricity bias. However, this trend appeared present even in the classical contextual cueing paradigm with one target paired with one repeated context. In Experiment 3, we performed the same manipulation of eccentricity distribution in the classical contextual cueing paradigm and found the effect seen in Experiment 2 was not robust. These results suggest that when a given target could be paired with multiple repeated contexts, the learning of target-context association is more flexible and can be modulated by the target's location probability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".