From cognitive-psychology-skills
Guides data analysis and model fitting for Cognitive Psychology manuscripts, emphasizing principled model comparison, recovery, mixed models, and reproducible code.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cognitive-psychology-skills:cogpsych-data-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Cognitive Psychology holds analyses to a **model-based** standard: fit the formal model, **compare it
Cognitive Psychology holds analyses to a model-based standard: fit the formal model, compare it to rivals with principled criteria, demonstrate that parameters and models are recoverable, use mixed models or hierarchical Bayesian estimation where the design demands it, and report effect sizes with uncertainty for behavioral results — all regenerable from deposited code. This is the experiment-to-model-fit loop that defines the venue.
cogpsych-open-science-and-transparency).A preregistered three-experiment recognition-memory program fitting UVSD vs. DPSD to confidence-ROC data.
Model comparison (preregistered) — pooled across Exps 1-3
Fit (hierarchical Bayesian, matched flexibility):
UVSD favored: dBIC = 14 vs. DPSD; Bayes factor ~ 30 in favor of UVSD
Recovery (required): parameter recovery good (recovered d', sigma within
credible intervals); model recovery ~ 92% correct at the design's N/trials
Diagnostic signature: z-ROC slope 0.78, 95% CrI [0.72, 0.84], and linear
(no reliable curvature) — the qualitative pattern UVSD predicts and DPSD
forbids, consistent across all three experiments
Behavioral effect (mixed model)
List-strength manipulation on d': b = 0.31, 95% CI [0.18, 0.44]
Exploratory (labeled)
A small response-bias drift surfaced post hoc; reported as exploratory
Why this passes Cognitive Psychology scrutiny: the model is compared (not just fit), recovery makes the comparison interpretable, the qualitative signature corroborates the fit index, hierarchy respects subject/item variance, and the exploratory drift is honestly demoted.
| Reviewer pushback | What it signals here | Cognitive Psychology fix |
|---|---|---|
| "You only fit your model" | one-model storytelling | fit the rival under matched flexibility; report AIC/BIC/BF and what it licenses |
| "Better fit may be overfitting" | flexibility imbalance | add model recovery + cross-validation; penalize complexity |
| "Can you recover these parameters?" | identifiability doubt | run and report parameter + model recovery simulations |
| "Aggregated means hide variance" | wrong error structure | refit with crossed-random-effects mixed model / hierarchical Bayesian |
| "Is this the model you predicted?" | post hoc selection | pre-commit the comparison; relabel post hoc fits exploratory |
| "I can't rerun your fits" | reproducibility gate | ship seeded model code + a fresh-session run log |
【Model comparison】rivals fit under matched flexibility + criterion (AIC/BIC/BF)? [Y/N]
【Recovery】parameter + model recovery reported? [Y/N]
【Hierarchy】mixed model / hierarchical Bayesian where apt + diagnostics? [Y/N]
【Behavioral effects】effect sizes + intervals? [Y/N]
【Confirmatory vs exploratory】separated? [Y/N]
【Reproducible】seeded code + data dictionary + fresh-session check? [Y/N]
【Next】cogpsych-tables-figures
../../resources/external_tools.md — modeling, model-comparison, lme4/brms/Stan, JAGS, recovery simulation../../resources/official-source-map.md — statistical and modeling expectationsnpx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin cognitive-psychology-skillsExplains Cognitive Psychology's review process: editorial triage for theoretical impact, expert scrutiny of model rigor, recovery, design, and reproducibility. Helps stress-test papers before submission and interpret decision letters.
Guides analysis and reporting for Journal of Applied Psychology manuscripts, covering SEM, multilevel models, mediation/moderation, and meta-analysis with proper effect sizes and confidence intervals.
Guides data analysis and reporting for Psychological Science manuscripts, emphasizing effect sizes with confidence intervals, full disclosure, and confirmatory/exploratory splits.