Subjective expectancy and inhibition of return: A dissociation in a non-spatial two-alternative forced choice task
Bibliographic record
Abstract
"Inhibition of Return (IOR) is conventionally defined by slow responses to targets that appear at the same location as a prior attentional cue, relative to a condition in which targets appear at a different location from a prior attentional cue (Posner & Cohen, 1984). A number of recent studies have extended the study of IOR to non-spatial orienting tasks (Law, Pratt, & Abrams, 1995; Hu, Samuel, & Chan, 2011; Spadaro, He, & Milliken, 2012), which is consistent with the view that a fundamental process that favours the perceptual encoding of new events is responsible for IOR. However, an alternative account of IOR is that participants expect uncued targets to appear more often than cued targets even when these two target types are equiprobable. The aim of the current study was to examine directly the relation between performance and subjective expectancy in a task known to produce repetition benefits under one set of conditions, and IOR-like effects under another set of conditions. The performance measure (i.e. RTs) showed either repetition benefits or IOR-like effects depending on whether or not an intervening event was introduced. Interestingly, participants reported that they expected uncued targets more often than cued targets across both conditions, a result that is inconsistent with the view that repetition effects generally, and IOR-like effects specifically, are directly related to subjective expectancy."
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".