Cognitive Psychology Workflow Router (cogpsych-workflow)

SkillAI & models

Use when starting or navigating any Cognitive Psychology (Elsevier) manuscript and you are unsure which sub-skill applies. Use when choosing among manuscript types or returning with a decision letter. Routes by lifecycle stage and contribution type (multi-experiment empirical, experiment-plus-model, modeling-led, theoretical/review), and flags the journal's long-form, model-driven house style and code-transparency expectations. It dispatches; it does not draft content.

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 Cognitive Psychology Workflow Router (cogpsych-workflow) 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-workflow/SKILL.md and read by ahel’s review.

The orchestrator for a Cognitive Psychology (Elsevier) submission. The journal's two defining features are that it rewards longer, integrative, model-driven papers — typically a multi-experiment program tied to a formal/computational model — and that it expects data, model code, and analysis scripts to be shareable. The router makes sure the contribution is theory-and-model-shaped from the start, then sends the user to the matching skill.

When to trigger

  • Starting a new Cognitive Psychology paper and unsure where to begin
  • Mid-project and unsure which skill applies next
  • Choosing the contribution type (empirical-only vs. experiment-plus-model vs. modeling-led)
  • Returning with a decision letter (route to cogpsych-rebuttal)

First question: contribution type

SituationTypeRoute note
Several controlled experiments that jointly constrain a theoryMulti-experiment empiricalmain pipeline; make each experiment add inference, not repeat
Experiments plus a fitted formal model (the common shape here)Experiment + modelpull cogpsych-theory-and-hypotheses (formalize) + cogpsych-data-analysis (fit/compare) forward
A new model / analysis tested against existing or new dataModeling-ledcogpsych-theory-and-hypotheses → cogpsych-data-analysis (model recovery + comparison)
Integrative theoretical synthesis or review with a substantive advanceTheoretical / reviewcogpsych-literature-positioning + cogpsych-theory-and-hypotheses

A single short experiment reporting one effect is usually the wrong shape for this venue — that is a Psychological Science / short-report contribution. Cognitive Psychology wants the longer arc.

Routing map (stage → skill)

Idea / fit?                          → cogpsych-topic-selection
Theory + model + predictions?        → cogpsych-theory-and-hypotheses
Where does it sit in the field?      → cogpsych-literature-positioning
Experiments / stimuli / power?       → cogpsych-study-design
Analysis + model fit/comparison?     → cogpsych-data-analysis
Exhibits (data + model fit)?         → cogpsych-tables-figures
Long-form prose clear?               → cogpsych-writing-style
Data + model code + materials open?  → cogpsych-open-science-and-transparency
How will it be judged?               → cogpsych-review-process
Ready to submit (Editorial Manager)? → cogpsych-submission
Got an R&R / decision?               → cogpsych-rebuttal

Default order

topic-selection → theory-and-hypotheses → literature-positioning → study-design → data-analysis → tables-figures → writing-style → open-science-and-transparency → review-process → submission → rebuttal

Theory comes before positioning here on purpose: the formal model is the contribution, so formalize the account first, then position it against rival models. For experiment-plus-model papers, the model is co-designed with the experiments — iterate theory-and-hypotheses ↔ study-design.

Worked micro-example — routing a live project (illustrative)

A team has three preregistered recognition-memory experiments and a competing pair of models (unequal-variance signal detection vs. a dual-process account) they want to fit and compare.

Type:    Experiment + model (multi-experiment program, model comparison).
Entry:   not idea-stage → skip topic-selection; they are at theory/design/analysis.
Route:   theory-and-hypotheses (formalize both models + the predictions that
            separate them)
         → study-design (stimuli, list construction, power for the critical
            contrast across all three experiments)
         → data-analysis (fit both models; compare with AIC/BIC + Bayes
            factors; report parameter recovery)
         → tables-figures (overlay model fit on data, not bars of means)
         → writing-style (integrate three experiments into one argument)
         → open-science-and-transparency (deposit data + model code + scripts)
         → submission (Editorial Manager) ; on R&R → rebuttal.
Flag:    the model comparison must be pre-committed where possible and the
         fits reproducible from the deposited code.

Stage-triage table (symptom → skill)

What the author saysStageRoute to
"Is one experiment enough for this journal?"fitcogpsych-topic-selection
"I have data but no formal model"theorycogpsych-theory-and-hypotheses
"Which model is better, AIC or BIC?"analysiscogpsych-data-analysis
"Reviewer says my experiments don't constrain the theory"designcogpsych-study-design
"How do I show model fit, not just means?"exhibitscogpsych-tables-figures
"Reviewer can't run my model code"transparencycogpsych-open-science-and-transparency
"I have an R&R"revisioncogpsych-rebuttal

Routing pitfalls specific to this venue

  • Sending an experiment-only project straight to cogpsych-submission without a formal model or an integrative theoretical payoff — the journal's identity is model-driven theory development.
  • Treating the model as an afterthought bolted on at the end, instead of co-designing experiments to discriminate models from the start (iterate theory-and-hypotheses ↔ study-design).
  • Deferring cogpsych-open-science-and-transparency: model code and fits should be reproducible from deposit, not promised at acceptance.

Anti-patterns

  • Pitching a single-experiment, single-effect paper (wrong length/ambition for this venue)
  • Reporting experiments with no formal model and no integrative theoretical advance
  • A model that is described in prose but never fit to data or compared to a rival
  • Leaving data/model code/scripts unshareable until acceptance

Output format

【Stage】idea / theory / positioning / design / analysis / exhibits / writing / transparency / review / submit / rebut
【Type】Multi-experiment empirical / Experiment + model / Modeling-led / Theoretical-review
【Route to】cogpsych-<skill>
【Why】one line
【Then】the next skill after that

Supplementary resources

Signals

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Item type
skill
Key
cogpsych-workflow
Source
github.com/brycewang-stanford/awesome-journal-skills