Stroop Effects in Persons with Traumatic Brain Injury: Selective Attention, Speed of Processing, or Color-Naming? A Meta-analysis
Bibliographic record
Abstract
The color word Stroop test is the most common tool used to assess selective attention in persons with traumatic brain injury (TBI). A larger Stroop effect for TBI patients, as compared to controls, is generally interpreted as reflecting a decrease in selective attention. Alternatively, it has been suggested that this increase in Stroop effects is influenced by group differences in generalized speed of processing (SOP). The current study describes an overview and meta-analysis of 10 studies, where persons with TBI (N = 324) were compared to matched controls (N = 501) on the Stroop task. The findings confirmed that Stroop interference was significantly larger for TBI groups (p = .008). However, these differences may be strongly biased by TBI-related slowdown in generalized SOP (r² = .81 in a Brinley analysis). We also found that TBI-related changes in sensory processing may affect group differences. Mainly, a TBI-related increase in the latency difference between reading and naming the font color of a color-neutral word (r² = .96) was linked to Stroop effects. Our results suggest that, in using Stroop, it seems prudent to control for both sensory factors and SOP to differentiate potential changes in selective attention from other changes following TBI.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".