Simulation as a tool for formalising null hypotheses in cognitive science research
Bibliographic record
Abstract
The default null hypothesis in typical statistical modelling software is that a parameter's value is equal to zero. However, this may not always correspond to the actual conditions that would hold if the effect of interest did not exist. In two case studies based on recent research in cognitive science and linguistics, we illustrate how data simulation can shed light on unspoken, sometimes even incorrect, assumptions about what the null hypothesis is. In particular, we consider information-theoretic measures of how learners regularise linguistic variability, where the null condition is not always equal to zero change, and an investigation of a cognitive bias for skewed distributions based on the assumption that, without such a bias, distributions would always remain uniform. All in all, simulating null conditions not only improves each researcher's understanding of their own analysis and results, but also contributes to the practice of "open theory". Formalising one's assumptions is, in itself, an important contribution to the scientific community.
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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 | 0.062 | 0.183 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".