Efficacy of perceptual learning in low vision: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Visual perceptual learning (PL) shows promise for enhancing visual functions in individuals with visual impairment. OBJECTIVE: This systematic review aimed to evaluate the effectiveness of PL in improving visual function. STUDY ELIGIBILITY: Eligible studies were those examining the efficacy of PL in individuals with low vision. STUDY APPRAISAL AND SYNTHESIS METHODS: The review protocol was registered with the international Prospective Register of Systematic Reviews (ID CRD42022327545) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Screened studies were synthesized using random-effects meta-analysis and narrative synthesis following Synthesis Without Meta-analysis guidelines. The quality of the evidence was assessed using the Cochrane risk-of-bias tool and the JBI Critical Appraisal Tool for Quasi-Experimental studies. RESULTS: Fifty studies were included, covering various visual impairments and employing different PL interventions. Most studies had low risk of bias. Meta-analysis showed significant improvement in visual search for individuals with cortical blindness (Hedges' g = 0.71; 95% confidence interval, 0.48 to 0.93; p=0.002); all other analyses did not show significant improvements-reading in central vision loss and cortical blindness, and visual field in peripheral vision loss and cortical blindness. However, the narrative synthesis provided evidence showing effectiveness, particularly in individuals with central vision loss and cortical blindness, demonstrating positive effects on reading, contrast sensitivity, visual field, and motion perception. LIMITATIONS: Variations in study design, PL protocols, outcome measures, and measurement methods introduced heterogeneity, limiting the analysis. CONCLUSIONS: The efficacy of PL in vision rehabilitation remains uncertain. Although meta-analysis results were mostly inconclusive, the narrative synthesis indicated improved visual functions following PL, consistent with individual study findings. IMPLICATIONS OF KEY FINDINGS: Future research should optimize intervention parameters, explore long-term effects, and assess generalizability across diverse populations and visual impairment etiologies. Larger randomized controlled trials using standardized outcome measures are needed to advance the field.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".