Bow Your Head in Shame, or, Hold Your Head Up with Pride: Semantic Processing of Self-Esteem Concepts Orients Attention Vertically
Bibliographic record
Abstract
Embodied cognition holds that abstract concepts are grounded in perceptual-motor simulations. If a given embodied metaphor maps onto a spatial representation, then thinking of that concept should bias the allocation of attention. In this study, we used positive and negative self-esteem words to examine two properties of conceptual cueing. First, we tested the orientation-specificity hypothesis, which predicts that conceptual cues should selectively activate certain spatial axes (in this case, valenced self-esteem concepts should activate vertical space), instead of any spatial continuum. Second, we tested whether conceptual cueing requires semantic processing, or if it can be achieved with shallow visual processing of the cue words. Participants viewed centrally presented words consisting of high or low self-esteem traits (e.g., brave, timid) before detecting a target above or below the cue in the vertical condition, or on the left or right of the word in the horizontal condition. Participants were faster to detect targets when their location was compatible with the valence of the word cues, but only in the vertical condition. Moreover, this effect was observed when participants processed the semantics of the word, but not when processing its orthography. The results show that conceptual cueing by spatial metaphors is orientation-specific, and that an explicit consideration of the word cues' semantics is required for conceptual cueing to occur.
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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 | 0.003 |
| 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) | 0.002 | 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".