Introducing TAMI: An Objective Test of Ability in Movement Imagery
Bibliographic record
Abstract
Individual ability in mental imagery varies widely across individuals, leading to the development of questionnaires to evaluate mental imagery. Within the domain of movement imagery, questionnaires have previously relied on subjective ratings of vividness, which may be influenced by additional factors such as motor skill confidence, success of imagined actions, and social desirability. These additional factors are of particular importance when making comparisons between samples from different populations, such as athletes versus nonathletes and patients versus healthy individuals. The authors present a novel test of ability in movement imagery (Test of Ability in Movement Imagery [TAMI]) that relies on objective measures and requires participants to make explicit imagined movements from an external perspective. In Study 1, the authors present evidence that young adults perform at a mid-level on the TAMI. In Study 2, they further compare performance on the TAMI with a battery of other measures to better characterize the TAMI by determining its similarities and differences with existing measures. The findings of both studies indicate the TAMI to be a valid and reliable measure of movement imagery ability. The authors additionally discuss future applications of the TAMI to athletic and clinical research.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.005 | 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".