Shell games: Location and object-based inhibitory mechanisms in individual and social action contexts
Bibliographic record
Abstract
Directing one's attention to a stimulus leads to the activation of inhibitory mechanisms for that stimulus. These inhibitory mechanisms must be overcome when that stimulus becomes a target once more resulting in longer reaction times to previously attended stimuli relative to other stimuli. This phenomenon of inhibition of return (IOR) is an efficient search mechanism that prevents individuals from reinvestigating a previously searched area/stimulus. Interestingly, the mechanisms of IOR seem to be shared across people (social IOR). The present experiment aimed to replicate and extend the study conducted by Tipper et al. (1994) to examine the IOR effects that emerge in individual and social tasks with static or dissociable stimuli. Based on previous findings, we expected a more pronounced IOR effect in the static condition for the same object and location than in dissociable conditions in which the potential target objects moved and switched locations after the initial response. We expected this finding because IOR is thought to be achieved through combined object and location inhibitory mechanisms. Furthermore, we predicted that the results obtained in the individual condition would be similar to the joint condition. The results showed an IOR effect when the same target occurred in the same location, but an IOR effect was not observed when the object moved in the dissociable condition. Importantly, this pattern of effects was observed in the individual and joint conditions. The findings can be explained in terms of object-based and location-based inhibitory mechanisms that compete with one another when objects switch locations.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".