Spatial metaphors in thinking about other people
Bibliographic record
Abstract
Spatial metaphors contribute to our capacity for abstract thought. Consistent with this idea, it has been shown that processing semantic information (related to valence, power, etc.) can bias performance in a spatial task. Advancing this line of work, the present study examined whether spatial metaphors have a role in thinking about other people. Participants read short vignettes about academic performance, health or social life, which described students in superior and inferior states. In Experiment 1, after reading each vignette, participants were explicitly asked to assign a location to each protagonist using a pen-and-paper task. Findings from this experiment provided initial indication that thinking about the protagonists could recruit spatial metaphors. In Experiments 2 and 3, each vignette was immediately followed by an implicit test of spatial association. In Experiment 2, participants performed a name-recognition task in response to the protagonists’ names presented above or below the central fixation. In this experiment, metaphorical congruency facilitated performance. In Experiment 3, participants were presented with names at central fixation, followed by a visual discrimination target (“X”/”O”) above or below fixation. In this experiment, metaphorical congruency interfered with performance. The diverging patterns of results are explained in terms of the conjunction and separation of the conceptual and perceptual components of the recognition task, respectively, in Experiments 2 and 3. Overall, the findings support the role of spatial metaphors in thinking about other people and, more generally, for the spontaneous use of space in conceptual processes.
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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 | 0.002 | 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".