Spatial Bias induced by Semantic Valence: Evidence From Eye Movement Trajectories
Bibliographic record
Abstract
Concepts of positive and negative valence are metaphorically structured in space (e.g., happy is up, sad is down). In fact, coupling a conceptual task (e.g., evaluating words as positive or negative) with a visuospatial task (e.g., identifying stimuli above or below fixation) often gives rise to metaphorical congruency effects. For instance, after reading a positive concept, a visual target above fixation is processed more efficiently than one below fixation. Recent studies, however, have challenged the idea that up and down spatial codes are automatically activated by valence concepts. Instead, it is possible that tasks requiring upward and downward attentional orienting artificially emphasize the link between valence and space. Here, we address the question as to whether the up and down spatial codes can be activated in a task that does not require attentional orienting along the vertical axis. To uncouple the valence axis from the spatial response axis we measured saccadic trajectory deviations, with the assumption that fast saccades deviate toward the salient segment of space. Participants read a single word at fixation, referring to a positive (e.g., ‘happy’), negative (e.g., ‘sad’), or neutral concept (e.g., ‘table). A peripheral visual target then appeared to the left or right, and participants made speeded saccadic responses to the target (unless the preceding word referred to a piece of furniture). Examining saccadic trajectories revealed patterns of deviation along the vertical axis consistent with the metaphorical congruency account; larger saccadic deviations upward were found after positive concepts compared to negative concepts. Importantly, placing task-irrelevant distractors above and below fixation did not modulate the pattern of deviations. These results suggest that metaphorical congruency effects between valence and space are not an artificial product of specific experimental tasks. That is, semantic processing of valence may automatically recruit spatial features along the vertical axis. Meeting abstract presented at VSS 2013
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".