Proactive control prevents salience-driven attention capture in a cued go/no-go search task
Bibliographic record
Abstract
According to salience-driven selection theory, observers automatically orient attention to the most salient stimulus in the visual field unless that stimulus falls outside of the currently monitored region (i.e., the attentional window). Recently, we introduced a go/no-go search paradigm to test this theory in a novel way (Tay et al., 2022; doi: 10.1037/xhp0000972) Participants viewed arrays of 16 blue items or 16 yellow items and indicated whether arrays of one colour (Go trials) contained an orientation singleton. No response was required for displays containing other-colour items (No-Go trials). The singleton elicited an event-related potential (ERP) component associated with attentional selection—the N2pc—on Go trials but not on No-Go trials, which is inconsistent with salience-driven selection theory. Here, we designed a cued Go/No-Go search task to determine whether distractor-suppression processes can be engaged proactively on a trial-by-trial basis. Each search display contained 16 blue bars that were all oriented horizontally or vertically on randomly intermixed trials. On 50% of trials, one bar was rotated 90° to produce an orientation singleton. Approximately one second before the search display, an array-wide square or circle was presented for 100 ms to indicate whether a response (singleton present or absent) was required for the upcoming array. Thus, participants had ample time to engage inhibitory control processes prior to display onset on No-Go trials. Unsurprisingly, the singleton elicited an N2pc over the posterior scalp on Go trials. On No-Go trials, the same singleton elicited a long-lasting distractor positivity (PD) that began 150–200 ms after array onset. Anterior-scalp ERPs indicated that participants did not passively ignore the search array on No-Go trials. This suggests that observers can decide in advance, and on a trial-by-trial basis, to search for a singleton or to prevent such search from occurring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".