Visual expectation paradigm and keypress identification compared: Mapping emotion category boundaries
Bibliographic record
Abstract
Some physical continua (e.g. color, speech sounds, and central to the current study, emotional facial expressions) are perceptually grouped into discrete categories. We tested the category boundary for four continua of 11 morphed photographs representing physically equidistant images with “happy” and “sad” as endpoints. Here, we compare results from a 2AFC keypress identification to categories derived from participant's anticipatory eye movement data. Experiment 1 relied on an identification task in which participants saw continuum endpoint images (Unambiguous images) and selected “happy” or “sad” via keypress. Then they were asked to make the same decision for the five middle images (Ambiguous images) from the same continuum. Participants clearly categorized emotion images as happy or sad except for one image which elicited chance performance, suggesting a category boundary at this location. In Experiment 2, naïve participants saw a subset of these same stimuli in a visual expectation paradigm. During a training phase, the emotion (happy or sad) of an Unambiguous image displayed in the center of the screen predicted the location of a visual reward (e.g. a happy image was followed by a cartoon to the left, a sad image by a cartoon to the right). This visual reward appeared 750 ms after the central face image, allowing anticipatory eye movements to be recorded with an eye-tracker. After training, participants were presented with the five Ambiguous images and anticipatory eye movements were recorded. Experiments 1 and 2 revealed similar results: The middle images of the continuum had the most variable responses. The presence of a category boundary was further suggested by the greater number of non-responses (participants failing to show anticipatory eye movements) for images near the expected category boundary. The visual expectation paradigm is a viable methodology for the study of category boundaries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".