The mental imagery scale for art students: Building and validating a short form
Bibliographic record
Abstract
Mental imagery is a vital cognitive skill that significantly influences how reality is perceived while creating art. Its multifaceted nature reveals various dimensions of creative expression, amplifying the inherent complexities of measuring it. This study aimed to shorten the Mental Imagery Scale in Artistic Creativity (MISAC) via the Ant Colony Optimization algorithm (ACO), a metaheuristic methodology for developing psychometrically robust brief scales. Answering 63 items in the original version of MISAC demands a higher cognitive load and, consequently, more time. Therefore, our goal was to shorten it while preserving its psychometric properties. In this study, responses to the MISAC were obtained from 500 undergraduate students enrolled in an art education program. The items on the short form of the MISAC were selected based on pre-specified validity criteria and content representability. The 28-item short form of MISAC demonstrated comparable performance to the original version regarding construct validity, criteria-related validity, and reliability coefficients. Moreover, strict invariance was attained across both gender groups in the validation process of the short form. These results highlight the utility of the shortened version of the MISAC as a valid measure with minimal loss of information of scores compared to the full version.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".