Freely selected and forced responses quickly bind to the target location, but not to the target identity, that activates them in a visuo-spatial task
Bibliographic record
Abstract
Target objects are identified by analyzing their features (i.e., colour, shape, size, etc.) separately and automatically; hence, post-identification, must later bind together to form a comprehensive object. This study looked into whether target identities and/or locations bind to their assigned responses. Subjects experienced a visuo-spatial task involving paired sequential trials; first the 'prime' and then the 'probe'. Correct manual responses were determined by target location, some locations allowing for a 'free choice' of two permissible outputs, while other locations required a 'forced choice' response. Critical free choice trials involved a competition between a former prime target response and a control response, and involved between-hand or within-hand finger responses. Targets appeared randomly at 'free choice' and 'forced choice' locations. When the prime target's location was repeated on the probe, subjects showed a significant bias toward choosing the just-executed prime response, indicating that self-selected prime trial responses strongly bind to the target-occupied locations generating their execution; however, they did not bind to the target's identity. In this way, freely chosen prime responses behaved like responses that are predetermined (Hommel, 2007) by the prime target; binding to relevant (location), but not to irrelevant, target properties (Hommel, 2004). When the prime-probe target location changed, the free choice bias reversed; subjects preferred the control response, presumably because this avoided a location-response binding violation. Longer latencies for the just-executed versus the control responses supported a violation aversion selection force.Acknowledgments: This work was supported by a grant from the Natural Sciences and Engineering Research Council of Canada to the second author.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".