Differential effects of partial foreknowledge on efficiency and switch costs of saccadic eye movements
Bibliographic record
Abstract
Foreknowledge can be used to optimize behavioural responses to stimuli. Most previous reports have studied the effects of complete foreknowledge, in which all aspects of an upcoming trial are known. However, these have left open the question of which aspects of a trial are most useful to know in advance, in terms of reducing error rate and latency. In the current study we systematically investigated different types of ‘partial foreknowledge’ — that is, for stimulus, task or response, but not all three — in a saccade paradigm. Partial foreknowledge blocks contained predictable ‘ABBA’ sequences of either stimulus location, task-set (prosaccade or antisaccade) or response direction, while the other two trial parameters followed a random sequence. These blocks were compared to blocks with no foreknowledge or complete foreknowledge of all three parameters. 10 healthy subjects performed the experiment. As output variables we measured saccadic inverse efficiency score (reaction time divided by accuracy rate) and switch cost, which is the difference in scores between trials in which a parameter value changed and trials in which it was repeated. The results showed that the effects on saccadic efficiency of prosaccades and antisaccades were not equivalent for all three types of foreknowledge. While stimulus foreknowledge had no effect on efficiency, task foreknowledge had some effect and response foreknowledge was as effective as complete foreknowledge. Foreknowledge effects on switch costs followed a similar pattern in general, but were not specific for switching of the trial attribute for which foreknowledge was available. We conclude that partial foreknowledge shows a ‘distal-to-proximal’ gradient effect on efficiency, most consistent with preparatory activation of a motor schema in advance of the stimulus, with consequent benefits for both switched and repeated trials.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".