Acting and anticipating: Impact of outcome-compatible distractor depends on response selection efficiency.
Bibliographic record
Abstract
Action selection is thought to involve selection of the action's sensory outcomes. This notion is supported when encountering a distractor that resembles a learned response-outcome biases response selection. Some evidence, however, suggests that a larger contribution of stimulus-based response selection leaves little role for outcome-based selection, especially in forced-choice tasks with easily identifiable target stimuli. In the present study, we asked whether the contribution of outcome-based selection depends on the ease and efficiency of stimulus-based selection. If so, then efficient stimulus-based response selection should reduce the impact of an irrelevant distractor that resemble a response-outcome. We manipulated efficiency of stimulus-based selection by varying the spatial relationship between stimulus and response (Experiment 1) and by varying stimulus discriminability (Experiments 2). We hypothesized that with efficient stimulus-based selection, outcome-based processes will play a weaker role in response selection, and performance will be less susceptible to outcome-compatible or -incompatible distractors. By contrast, when stimulus-based selection is relatively inefficient, outcome-based processes will play a stronger role in response selection, and performance should be more susceptible to outcome-compatible or -incompatible distractors. Confirming our predictions, our results showed stronger impact of the distractors when stimulus-based response selection was relatively inefficient. Finally, results of a control experiment (Experiment 3) suggested that learning the consistent response-outcome mapping is necessary for obtaining the effect of these distractors. We conclude that outcome-based processes do contribute to response selection in forced-choice tasks, and that this contribution varies with the efficiency of stimulus-based response selection. (PsycINFO Database Record
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".