A frequentist mixture modeling of stop signal reaction times
Bibliographic record
Abstract
The stop signal reaction time (SSRT), a measure of the latency of the stop signal process, has been theoretically formulated using a horse race model of go and stop signal processes by the American scientist Gordon Logan (1994). The SSRT assumes equal impact of the preceding trial type (go/stop) on its measurement. In the case of a violation of this assumption, we consider estimation of SSRT based on the idea of earlier analysis of cluster type go reaction times (GORT) and linear mixed model (LMM) data analysis results. Two clusters of trials were considered including those trials preceded by a go trial and other trials preceded by a stop trial. Given disparities between cluster type SSRTs, we need to consider some new indexes considering the unused cluster type information in the calculations. We introduce mixture SSRT and weighted SSRT as two new distinct indexes of SSRT that address the violated assumption. Mixture SSRT and weighted SSRT are theoretically asymptotically equivalent under special conditions. An example of stop single task (SST) real data is presented to show equivalency of these two new SSRT indexes and their larger magnitude compared to Logan's single 1994 SSRT.Abbreviations: ADHD: attention deficit hyperactivity disorder; ExG: Ex-Gaussiandistribution; GORT: reaction time in a go trial; GORTA: reaction time in a type A gotrial; GORTB: reaction time in a type B go trial; LMM: linear mixed model; SWAN:strengths and weakness of ADHD symptoms and normal behavior rating scale; SSD: stop signal delay; SR: signal respond; SRRT: reaction time in a failedstop trial; SSRT: stop signal reaction times in a stop trial; SST: stop signaltask.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".