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cogpsych-review-process
GitHub解析Cognitive Psychology期刊的审稿流程，涵盖编辑初审、同行评审及可重复性检查。帮助用户在投稿前压力测试稿件、解读决定信并规避拒稿风险，重点强调理论影响、模型严谨性及实验区分度。
Trigger Scenarios
投稿前对论文进行压力测试
解读编辑的决定信并管理预期
评估模型驱动型论文如何适应严格的审稿要求
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-review-process -g -y
SKILL.md
Frontmatter
{
"name": "cogpsych-review-process",
"description": "Use when you need to understand how Cognitive Psychology (Elsevier) evaluates a manuscript — editorial triage for theoretical impact and fit, expert review weighing model rigor, recovery, design, and reproducibility, and the long revision cycles typical of a model-driven journal. Use when stress-testing a paper before submission or interpreting a decision letter. Sets expectations and shapes the paper to survive review; it does not contact editors."
}
Review Process (cogpsych-review-process)
Cognitive Psychology combines selectivity for theoretical impact with deep methodological and modeling scrutiny. Reviewers and editors weigh not only whether the finding is interesting, but whether the model is well-specified, identifiable, and properly compared, whether the experiments discriminate the accounts, and whether the work is reproducible. Knowing this lets you pre-empt the common rejection reasons. Confirm the current process on the official page (检索于 2026-06；以官网为准).
When to trigger
- Before submitting, to stress-test the manuscript
- Interpreting a decision letter and setting expectations
- Deciding how to fit a long, model-driven program to a demanding review
How review works (typical Elsevier journal pattern)
- Editorial triage. A handling editor assesses theoretical impact, scope, and fit; thin, single-effect, or atheoretical submissions may be rejected without external review at this long-form, model-driven venue.
- Expert peer review. Typically multiple referees with cognitive-modeling and experimental expertise. Expect detailed scrutiny of model specification, identifiability/recovery, model comparison, experimental confounds, and the strength of the inference.
- Reproducibility is checked. Reviewers may attempt to run model/analysis code; fits that don't
regenerate, or undocumented model choices, weaken the paper (see
cogpsych-open-science-and-transparency). - Decisions and cycles. Reject, major/minor revision, or accept; integrative model-driven papers often go through substantial, sometimes multiple, revision rounds — added experiments, recovery analyses, or model comparisons are common requests.
Verify the review model (single- vs. double-anonymized), referee count, and timelines on the journal's current guide for authors — these are volatile (检索于 2026-06；以官网为准).
Shape the paper to pass
- Make the theoretical advance explicit and early; show the experiments discriminate the models.
- Fit and compare models under matched flexibility; include parameter and model recovery.
- Respect the data hierarchy (mixed/hierarchical models) and report effect sizes with intervals.
- Make the modeling reproducible from deposited code; complete Elsevier declarations.
- Separate confirmatory from exploratory model work honestly.
Desk-reject and decline-without-review patterns
The long-form, model-driven identity means many submissions never reach external review. Recognize these shapes and pre-empt them:
| Pattern an editor sees | Likely outcome | Pre-empt it by |
|---|---|---|
| One experiment, one effect, no model/theory | desk reject (wrong shape) | grow into a model-driven program or place in a short-report venue |
| Model fit but never compared to a rival | major revision or reject | fit rivals under matched flexibility; report criteria |
| Experiments don't discriminate the accounts | reject (non-diagnostic) | redesign for the discriminating signature |
| Aggregated analyses, ignored subject/item variance | methods flag | refit with mixed/hierarchical models |
| Fits not reproducible; no code | reproducibility flag | deposit seeded model code with a run log |
| Better-fitting but more flexible model claimed as winner | overfitting flag | add recovery + penalized comparison/cross-validation |
Worked micro-example (illustrative triage)
Manuscript: three preregistered recognition-memory experiments; UVSD vs.
DPSD fit and compared (hierarchical Bayesian), recovery reported,
open data + model code with DOIs, diagnostic z-ROC signature.
Editor read: theoretical impact (adjudicates a long-running debate), modeling
rigor (comparison + recovery), reproducibility (code regenerates).
Likely route: external review, probable major revision for added robustness
(alternative priors, a further model, more recovery).
Counter-case: same effect, one experiment, one model fit, request-only data,
no recovery → likely declined without full review.
How reviewers weigh the evidence (calibration anchors)
- The strongest signal is a diagnostic experiment + a recovered, compared model that together pick one account over a real rival — this converts "interesting fit" into "credible adjudication."
- Reviewers distrust a fit advantage without recovery and matched flexibility; a crossed qualitative prediction is more persuasive than a smaller AIC.
- Reproducibility is part of the evidence, not a formality; a fit that doesn't regenerate reads as a result that might not exist.
Anti-patterns
- A single-effect, atheoretical submission expecting full review at a model-driven venue
- A model fit with no rival, no comparison, and no recovery
- Aggregated analyses that ignore crossed subject/item variance
- Expecting acceptance without a substantial, modeling-heavy revision round
- Irreproducible fits or undocumented model choices
Output format
【Theoretical advance】clear early? [Y/N]
【Discrimination】do experiments separate the models? [Y/N]
【Modeling rigor】comparison + recovery + matched flexibility? [Y/N]
【Hierarchy + reporting】mixed/hierarchical + effect sizes/intervals? [Y/N]
【Reproducible】model code regenerates fits? [Y/N]
【Realistic outcome】reject / major revision / minor revision / accept
【Next】cogpsych-submission (or cogpsych-rebuttal if decided)
Supplementary resources
../../resources/official-source-map.md— review model, scope, and reproducibility expectations
Version History
- 1839142 Current 2026-07-05 12:37
