Reaction time variations in normal aging and elderly MCI patients under various cognitive load conditions
Bibliographic record
Abstract
Objectives To compare reaction time parameters and accuracy rates between cognitively normal older adults and those with mild cognitive impairment (MCI) during the Stroop Color-Word Test, and to investigate how cognitive load modulates performance in MCI. Methods Speech audio samples ( n = 1,920) were collected from 10 cognitively normal older adults and 10 MCI patients during Stroop task execution. Accuracy and reaction time were extracted. Analysis of variance and multiple comparison were used to analyze the differences in reaction time under different task conditions within the group, while the independent sample t-test was used to compare the accuracy and reaction time of the two groups under the same task. Pearson correlation analysis was used to determine the linear relationship between MOCA scores and the accuracy rate and reaction time of MCI patients in the interference suppression task. Results The accuracy rate of the mild cognitive impairment (MCI) group was significantly lower than that of the control group ( p < 0.05). Tasks A-D had different effects on reaction times, with significant main effects observed in both the NC group ( P = 0.000, η 2 = 0.637) and the MCI group ( P = 0.000, η 2 = 0.721). Reaction times in both groups prolonged with increasing cognitive load ( p < 0.05), but the delay was more pronounced in the MCI group (p < 0.05). A positive linear correlation was found between the MoCA score and task accuracy rate ( r = 0.758, P = 0.011). Conclusion Dominant responses require less processing time, whereas tasks demanding interference suppression elicit slower reaction times and higher error rates. MCI patients demonstrate prolonged reaction times and greater susceptibility to proactive interference compared to controls, highlighting impaired interference control mechanisms. These findings suggest that MCI is characterized by early deficits in dominance inhibition, manifesting as reduced ability to suppress automatic responses and increased vulnerability to cognitive conflict.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".