Statistics Learning of Target Regularities in a Pop-out Search: Behavioral Performance and Neural Mechanisms
Bibliographic record
Abstract
The study examined human performance and related neural mechanisms in a pop-out search with different probabilities of target location and the relation between the target location and feature. In a search array, we introduced the binding relation between two target features and two kinds of location probability. Moreover, in the second half of the experiment, such a probability pattern for location/feature binding was reversed. Behavioral results revealed successful statistical learning of probability for both absolute target location and target’s location-feature binding indicated by faster RTs in the high-probability conditions for both location and location-feature binding. Moreover, the learning benefit for the probability of location-feature binding acquired during 1st (training) phase was still expressed in the 2nd (reversal) phase despite the actual binding probability was reversed. ERP results suggested that both the attentional selection and response selection process were affected by such learning revealed in the difference in N2pc and LRP amplitudes between the two conditions with different binding probability in the reversal phase. An expectation to the high probability for location-feature binding was also suggested from time-frequency analysis and Multi-Variate Pattern Classification (MVPC) indicated by larger alpha ERD magnitude and lower decoding accuracy, respectively, when the target appeared at the high-binding location in the training phase instead of the reversal phase. Overall, we have demonstrated behavioral evidence and 4 EEG markers for the associative learning of the probability of relation between location and feature of target in a pop-out search.
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 | 0.003 |
| 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".