Evaluating the Modified-Shortened Token Test as a Working Memory and Language Assessment Tool
Bibliographic record
Abstract
PURPOSE: Working memory and linguistic knowledge are highly intertwined in language tasks. Verbal working memory in particular has been studied as a potential constraint on language performance. This, in turn, highlights the need for a clinical assessment tool that will assist clinicians in understanding individual children's performance in relation to working memory and language. In this study, we investigated whether performance on the Token Test could capture differences in verbal working memory and linguistic knowledge given its manipulation of length and syntactic complexity. METHOD: In Experiment 1, 257 children ages 4-7 years completed our Modified-Shortened Token Test, in which they carried out commands of increasing length and complexity. Experiment 2 was an exploratory study that included a separate group of 24 kindergarten-age children who completed our Modified-Shortened Token Test as well as other memory and language measures. RESULTS: The factor analysis in Experiment 1 revealed a three-factor solution with factors corresponding to verbal working memory, linguistic, and basic attention constructs. In Experiment 2, we conducted exploratory correlations between composite scores formed based on identified factors (from Experiment 1) and related measures. Recalling sentences and formulating sentences correlated with the working memory demands of the Token Test, whereas following directions and word structure correlated with Token Test linguistic factor. CONCLUSIONS: A modified Token Test has the potential to be used clinically to understand language performance. In particular, differential performance across sentences could reveal relative verbal working memory and linguistic knowledge abilities. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19178474.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".