Visual imagery and STEM occupational attainment: Gender matters
Bibliographic record
Abstract
Science, technology, engineering and mathematical (STEM) occupations are widely recognized as important for innovation and economic growth, yet there is a STEM labour shortage, particularly among women. We examined how individual differences in visual imagery relate to characteristics of STEM occupations using a novel coding scheme for the dimensional quantification of occupational attributes. In a discovery cohort of 4545 online participants, we found that spatial thinking positively associated with STEM occupations across genders. Object imagery (mnemonic vividness), however, was negatively associated with STEM occupations that require computational processes, such as software engineers. This negative association was present for males, but not females. We extended and replicated these findings in samples of 1891 individuals with aphantasia (congenitally low imagery) and 186 university undergraduates. Consistent with experimental, observational, and neurobiological evidence of a potentially competitive relationship between imagery and reasoning, these results suggest a role for nonspatial, nonvisual abstract analytic abilities in computational STEM disciplines independent of spatial imagery. These abilities promote computational STEM achievement in males but not females, who may be biased away for social reasons. Visual imagery style could serve as a marker of STEM potential, and selection into computational STEM may draw on skills distinct from other STEM fields. • Spatial vs. object visual imagery differentially relate to STEM achievement. • Spatial imagery is positively related to STEM achievement in males and females. • Object imagery is negatively related to computational STEM achievement, but only for males. • Females with high computational abilities are less likely than males to advance in computational STEM professions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".