A Role for Set in the Control of Automatic Spatial Response Activation
Bibliographic record
Abstract
Spatial stimulus-response (S-R) compatibility effects are widely assumed to reflect the automatic activation of a spatial response by the spatial attributes of a stimulus. The experiments reported here investigate the role of the participant's set in enabling or interacting with this putatively automatic spatial response activation. Participants performed a color discrimination task (Experiment 1) or a localization task (Experiment 2). In each experiment, two different S-R mappings were used and a task-cue indicated the appropriate mapping on each trial. S-R compatibility and the time between the task-cue and target were manipulated, and compatibility effects were assessed as a function of (a) the time between the task-cue and the stimulus, and (b) whether the S-R mapping repeated or switched on consecutive trials. Critically, whether response mappings repeated or switched on consecutive trials determined the relation between compatibility effects and the time between task-cue and stimulus. These results are discussed in terms of an interaction between automatic spatial response activation and the participant's set.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".