Bibliographic record
Abstract
The purpose of this study was to examine factors that influence how people look at objects they will have to act upon while watching others interact with them first. We investigated whether including different types of task-relevant information into an observational learning task would result in participants adapting their gaze towards an object with more task-relevant information. The participant watched an actor simultaneously lift and replace two objects with two hands then was cued to lift one of the two objects. The objects had the potential to change weight between each trial. In our cue condition, participants were cued to lift one of the objects every single time. In our object condition, the participants were cued equally to act on both objects; however, the weights of only one of the objects would have the potential to change. The hypothesis in the cue condition was that the participant would look significantly more at the object being cued. The hypothesis for the object condition was that the participant would look significantly more (i.e. adapt their gaze) at the object changing weight. The rationale behind this is that participants will learn to allocate their gaze significantly more towards that object so they can gain information about its properties (i.e. weight change). Pending results will indicate whether or not this occurred, and has implications for understanding eye movement sequences in visually guided behaviour tasks. The outcome of this study also has implications for the mechanisms of eye gaze with respect to social learning tasks.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".