On using distributional analysis techniques for determining the onset of the influence of experimental variables
Bibliographic record
Abstract
Much of the investigation of eye-movement control in visual cognition has focused on the influence of experimental variables on mean fixation durations. In the present paper we explored the convergence between two distributional analysis techniques that were recently introduced in this domain. First, Staub, White, Drieghe, Hollway and Rayner, (2010) proposed fitting the ex-Gaussian distribution to individual participants' data in order to ascertain whether a variable has a rapid or a slow influence on fixation durations. Second, the Divergence Point Analysis (DPA) procedure was introduced by Reingold, Reichle, Glaholt and Sheridan (2012, Reingold & Sheridan, 2014) in order to determine more precisely the earliest discernible impact of a variable on the distribution of fixation durations by contrasting survival curves across two experimental conditions and determining the point at which the two curves begin to diverge. In the present paper we introduced a new version of the DPA procedure which is based on ex-Gaussian fitting. We evaluated this procedure by re-analysing data obtained in previous empirical investigations as well as by conducting a simulation study. We demonstrated that the new ex-Gaussian DPA technique produced estimates that were consistent with estimates produced by prior versions of DPA procedure, and in the present simulation, the ex-Gaussian DPA procedure produced somewhat more accurate individual participant divergence point estimates. Based on the present findings we also suggest guidelines for best practices in the use of DPA techniques.
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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 | 0.092 | 0.400 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".