Submission Preflight (cogpsych-submission)

SkillAI & models

Use when running the final pre-submission preflight for Cognitive Psychology (Elsevier) via Editorial Manager, scope/length fit, abstract and keywords, APA-style reporting, model-comparison completeness, reproducible model/analysis code, and the Elsevier research-data and competing-interest declarations. Final checks; it does not draft content. Verify volatile specifics on the official guide for authors.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Submission Preflight (cogpsych-submission) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Cognitive-Psychology-Skills/skills/cogpsych-submission/SKILL.md and read by ahel’s review.

The last check before submitting through Elsevier Editorial Manager. Two things sink Cognitive Psychology submissions most often: a wrong-shape contribution (single effect, no model/theory, or a model that is never compared) and non-reproducible modeling (fits that can't be regenerated, no code deposit). Verify volatile specifics (length, abstract limit, review model, current editor, declarations) on the official guide for authors first (检索于 2026-06;以官网为准).

When to trigger

  • "Submitting tomorrow" — last pass before upload
  • Unsure what the scope, format, and declaration requirements demand
  • Confirming the abstract, exhibits, model comparison, code deposit, and declarations are in order

Process facts (verify volatile items on the official page)

  • Owner / publisher: Elsevier. (检索于 2026-06;以官网为准)
  • Portal: Elsevier Editorial Manager (linked from the ScienceDirect guide for authors).
  • Identity: a leading cognitive-science journal favoring longer, integrative, model-driven articles; multi-experiment programs combined with formal/computational modeling.
  • Scope: attention, perception, memory, learning, language, categorization, reasoning, problem-solving, judgment and decision-making; modeling/neuroscientific approaches and substantive reviews welcome.
  • Style: APA-style statistical reporting and references; Elsevier formatting via the guide for authors. Confirm the current reference style and any structured-abstract requirement.
  • Open science: authors are strongly encouraged to share data, model/analysis code, and materials; complete the Elsevier research-data statement and declaration of competing interest.
  • Review model / length / abstract limits: volatile — confirm single- vs. double-anonymized review, any word/length guidance, and the abstract limit on the live page. 待核实.

Preflight checklist

Scope & shape

  • Contribution is a theoretical advance carried by a program (multi-experiment and/or a model)
  • If a model is central, it is fit and compared to a rival under matched flexibility
  • Not a single-effect / atheoretical paper better suited to a short-report venue

Front matter & format

  • Abstract present (confirm length/structure on the official page); keywords included
  • APA-style statistics (effect sizes + intervals; model-comparison criteria) and references
  • Exhibits show observed data + model fit (not bars of means); model-comparison table included
  • Manuscript follows Elsevier formatting/section conventions per the guide for authors

Modeling & disclosure

  • Model comparison reported (AIC/BIC/BF or cross-validation) with free-parameter counts
  • Parameter + model recovery reported
  • Mixed/hierarchical structure where the design demands it, with diagnostics
  • Confirmatory vs. exploratory clearly separated

Open science & declarations

  • Data deposited + DOI + codebook (or justified restriction)
  • Model/analysis code deposited; fits regenerate in a fresh session (run log)
  • Materials deposited + DOI
  • Elsevier research-data statement + competing-interest declaration + funding/contributions
  • Preregistration linked where applicable

Worked micro-example — preflight verdict (illustrative)

Running the recognition-memory program through the gate the night before upload:

Shape:      3 experiments + UVSD vs. DPSD comparison → theoretical advance → PASS
Model:      both models fit (matched flexibility); dBIC + BF reported;
            parameter + model recovery included → PASS
Hierarchy:  hierarchical Bayesian; R-hat ~ 1.00, ESS adequate → PASS
Exhibits:   z-ROC figure overlays observed data + both model curves; model-
            comparison table with free-parameter counts → PASS
Open sci:   data + model code + materials each have a DOI; fits regenerate
            in a fresh session (run log attached) → PASS
Declarations: competing interest (none) + research-data statement + funding
            completed in Editorial Manager → PASS
Verdict:    ready to upload to Editorial Manager.

Last-mile failure modes that trigger a return

FailureCaught byQuick fix
Model fit but never comparedscope/shape checkfit the rival; add the comparison table
Fits don't regenerate from codereproducibility checkdeposit seeded code + a fresh-session run log
Bars of means, no model overlayexhibit checkredraw with observed data + model fit
"Data on request" instead of a DOIopen-science checkdeposit and mint a persistent identifier
Competing-interest/data statement blankdeclaration checkcomplete the Elsevier declarations before upload

Volatile specifics (length guidance, abstract limit, review model, current editor, exact declarations) change. Re-confirm every item above against the journal's current guide for authors before relying on it (检索于 2026-06;以官网为准).

Anti-patterns

  • Submitting a single-effect/atheoretical paper to a model-driven, long-form venue
  • A central model that is fit but never compared to a rival
  • Modeling that cannot be reproduced from the deposited code
  • Exhibits that hide the fit (bars of means) or assert "best fit" with no table
  • Missing Elsevier research-data / competing-interest declarations

Output format

【Shape】theoretical advance via a program; model fit + compared? [Y/N]
【Format】abstract + keywords + APA-style reporting + Elsevier formatting? [Y/N]
【Modeling】comparison + recovery + hierarchy + diagnostics? [Y/N]
【Open science】data + model code (reproducible) + materials + DOIs? [Y/N]
【Declarations】competing interest + research-data + funding? [Y/N]
【Next】await decision → cogpsych-rebuttal on revision

Supplementary resources

Signals

GitHub stars
1k
Forks
155
Last commit
Sep 2026
Advanced
Item type
skill
Key
cogpsych-submission
Source
github.com/brycewang-stanford/awesome-journal-skills