Slow and steady: Training induced improvements to response time consistency are due to overall slowing and minimized extremely slow responses.
Bibliographic record
Abstract
Previous studies on response time intraindividual variability (RT IIV) have focused on differences between groups, ignoring the potential for modification. The current study provides a detailed analysis of RT IIV training effects across three age groups. Healthy adults (40 young [aged 18-30], 40 young-old [aged 65-74], and 41 old-old [aged 75-85]) were assigned to feedback or no feedback (standard) conditions during a touch-screen feature integration task. In the feedback condition, participants were shown their performance on the previous block of trials and encouraged to improve going forward. Transfer was assessed by comparing pre- and posttraining performance on a 4-choice RT task. Data were analyzed with respect to RT IIV, ex-Gaussian distribution fitting, and the diffusion model of RT decision making. Significant feedback-related reductions were observed in Target RT IIV and the ex-Gaussian parameter τ, accompanied by an increase in μ. There was no significant change in σ, and no evidence of transfer to the 4-choice RT task. The diffusion model analysis indicated that feedback training promoted a reduction in response threshold for the young and young-old groups, as well as a modulation of drift rate throughout training in the young group. The findings indicate that training to improve consistency induces overall slowing, but also reduces the frequency of extremely slow responses that have been linked to brief attention lapses. The results provide evidence that RT consistency is malleable, but improvements are not necessarily transferable. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".