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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".