Are Children Performing Better on the Digit Span Backward Task Than the Digit Span Forward Task? An Exploratory Analysis
Bibliographic record
Abstract
PURPOSE: This research note was motivated by community speech-language pathologists (SLPs) who have expressed concerns about how to interpret the unexpected observation of higher performance on the Digit Span Backward task than the Digit Span Forward task on the Test of Integrated Language and Literacy Skills (TILLS). We therefore conducted an exploratory analysis to examine the pattern of digit span performance in children to help clinicians interpret their findings. METHOD: = 1,262). Raw scores, standard scores, and percentile ranks were systematically compared. In addition, we were able to estimate span length (longest number of items that can be accurately recalled) for 69 participants and evaluated the relation between performance and span length. RESULTS: Our exploratory analyses revealed that "better" performance on the backward than forward task was rare. Instead, the pattern that SLPs are reporting can be explained by three factors: (a) a statistical phenomenon exacerbating small differences, (b) comparing standard scores or percentile ranks instead of raw scores or span length, and (c) better backward scores can still mean longer forward span length. CONCLUSIONS: It is likely that community SLPs tended to report cases of better backward than forward recall because it is the more remarkable and interesting finding, leading to the perception that the pattern is common, when it is not. Overall, we caution clinicians against overinterpreting their own client's performance on the digit span tests.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".