Bibliographic record
Abstract
The purpose of this thesis was to investigate how the presence of non-target objects can influence the planning of a movement towards a remembered target location. One specific aim was to examine how the temporal effects of the task could affect movement planning. The final aim of this thesis was to examine whether or not the mere presence of extrinsic cues can suppress the encoding of intrinsic cues.\nIt was found that when non-target objects are presented simultaneously with the target, interference occurs; however, if the non-target objects are presented at least 250 ms in advance of the targets performance improved. The results also revealed that uncertainty regarding trial type altered participants’ response strategy. It appears as though when participants can anticipate when the response is required, they plan the movement as the trial progresses, however, it appears as though when there is uncertainty participants either suppress their movement plan or hold the representation of target location and only plan the movement when uncertainty has been resolved. Furthermore, the results of Experiments 3 and 4 indicated that participants automatically encode target location within an extrinsic reference frame when non-target objects are available. The principal conclusion was that movement planning is clearly affected by the presence of non-target objects.
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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 | 0.015 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".