The relationship between line bisection performance and emotion processing: Where do you draw the line?
Bibliographic record
Abstract
A recent study demonstrated that higher accuracy on a line bisection task related to greater ratings of evocative impact from paintings. The authors suggested that line bisection accuracy may act as a "barometer" for both visuospatial and emotion processing, likely as a function of overlapping neural correlates in the right temporoparietal region. We suggest and test an alternative explanation: that visuospatial bias interacted with asymmetries in the paintings and the rating scales to produce the apparent relationship between emotion and visuospatial functions. In the present study, using both visual-analogue and numeric rating scales, the relationship between line bisection performance and ratings of paintings (evocative impact, aesthetics, novelty, technique, and closure) was examined in a young adult sample. We demonstrate that left-hand line bisection bias direction, not line bisection accuracy, is related to most ratings, and that line bisection bias interacts with stimulus orientation (non-mirrored/mirrored) and rating scale direction (ascending/descending) in such a way that can explain the results of the previous study. We conclude that the line bisection task appears to be a sensitive measure of visuospatial attentional biases, which can influence ratings of asymmetrical paintings, and may affect how individuals perceive stimuli in their environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".