Learned action effects modulate salience in space: Evidence for the preactivation theory
Bibliographic record
Abstract
Performing an action can reduce sensitivity to the sensory outcomes associated with the action. One explanation of this sensory attenuation effect, the preactivation account, proposes that action performance raises the activity of the internal representation of the actions sensory outcome, and that this heightened activity influences how incoming stimulus is processed. In particular, the stimulus-driven raise in neural activity is reduced for stimuli consistent with the activated representation, due to preactivation, compared with inconsistent stimuli that have no such preactivation. In this way, the stimulus-driven raise in neural activity hinders detecting a stimulus consistent with a learned action outcome. To test this account, we used a spatial attentional cuing phenomenon known as the attentional repulsion effect. In Experiment 1 we confirmed that when a cue is consistent with a learned action-outcome its effective salience is reduced (i.e., a smaller attentional repulsion effect) compared with a cue that is inconsistent with the action-outcome. Critically, the attentional repulsion effect paradigm allowed us to test the effect of action-induced activation of cue representation on the distribution of salience in space when action effects were no longer present. This was done In Experiments 2 and 3, we found that actions increase the salience of action-outcome locations even in the absence of action-outcomes. In other words, through learned action effects, we were able to generate attentional repulsion effects without the presence of the peripheral cues. These findings provide strong support for the preactivation account of action-induced sensory attenuation. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".