On the determination of eye gaze and arrow direction: Automaticity reconsidered.
Bibliographic record
Abstract
It is a widely held view that the determination of eye gaze direction is "automatic" in various senses (e.g., innate; informationally encapsulated; triggered without intent). The determination of arrow direction is also held to be automatic (following a certain amount of learning) despite not being innate. The present experiments evaluate the automaticity assumption of both eyes and arrows in terms of an interference criterion. The results of 10 experiments support the inference that explicit judgements of eye gaze direction, when participants respond with a lateralized key press, are (a) neither automatic in the strong sense (they are interfered with by an uninformative, incongruent arrow in the display) and (b) nor are they are automatic in a weaker sense (uninformative, incongruent arrows interfere more strongly with the determination of eye gaze direction than uninformative, incongruent eyes interfere with the arrow direction task). However, the determination of arrow direction is also not strongly automatic, given that it is interfered with by irrelevant eyes. At least with respect to an interference criterion, the determination of eye gaze direction appears less prepotent than the determination of arrow direction, which itself is only weakly automatic. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| 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".