Revision Rebuttal (cogpsych-rebuttal)
SkillAI & modelsUse when writing the response to a Cognitive Psychology (Elsevier) major/minor revision. Reviews here often demand added experiments, more model comparisons, recovery analyses, or fuller reproducibility, so the response must address every point and strengthen the model-driven inference. Structures the response letter; it does not fabricate new results or model fits.
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Then ask your AI: use the Revision Rebuttal (cogpsych-rebuttal) 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-rebuttal/SKILL.md and read by ahel’s review.
A Cognitive Psychology revision typically asks for more modeling rigor — an additional experiment, a further model comparison, parameter/model recovery, alternative priors, or reproducible code — because the contribution is a model-driven theoretical claim. The response letter must convert every reviewer and reassure the editor that the model adjudication is now airtight, while keeping the integrative argument coherent.
When to trigger
- A major/minor revision arrived and you are planning the revision + response letter
- Reviewers requested added experiments, model comparisons, recovery, or open-code changes
- A requested analysis or model would change the conclusion
- Writing the cover note to the handling editor
Strategy
- Read the editor's letter as the rubric. Solve the decisive points first; the editor adjudicates among reviewers and decides the next round.
- Point-by-point, every comment. Quote each comment, then respond; never skip one.
- Strengthen the model inference, don't just defend. Many requests (fit a further rival, add recovery, cross-validate, refit hierarchically, share code) make the adjudication stronger — do them and say where. A request that exposes overfitting must be addressed, not waved away.
- Keep the program coherent. A new experiment or model should slot into the argument; update the
General Discussion so the synthesis still holds (see
cogpsych-writing-style). - Concede or rebut with evidence. Did what was asked (cite the location), or push back respectfully with a reason (e.g., why a requested model is not identifiable) — don't add an analysis that quietly undercuts the claim without saying so.
- Keep the modeling reproducible. New analyses must be reflected in the deposited model/analysis
code and regenerate in a fresh session (see
cogpsych-open-science-and-transparency).
Response-letter format
For each reviewer comment:
> [Quoted reviewer comment]
Response: [What we did / why we respectfully disagree].
Change: [Manuscript section, supplement/appendix section, table/figure, or
deposited-code file].
Open with a short summary of the main changes to the editor; group by reviewer; end each entry with the location (note when added analyses or experiments went to the supplement/appendix).
Worked micro-example (illustrative response entries)
For the recognition-memory program, a major revision asked for a further model and recovery.
> R2: You compare UVSD and DPSD, but a mixture model might fit better -
> have you ruled it out?
Response: We agree this rival should be tested. We added a finite-mixture
SDT model, fit under matched flexibility; it does not improve penalized fit
(dBIC = 9 favoring UVSD) and model recovery confirms the comparison is
diagnostic at our design's N/trials.
Change: Results (model comparison, Table 1 expanded); recovery → Appendix B;
fitting code updated (deposit, fit_mixture.R).
> R1: Can you recover the DPSD parameters at your trial counts?
Response: Yes - we now report parameter recovery for all three models
(recovered values within credible intervals). This is why the model
comparison is interpretable rather than an artifact of identifiability.
Change: Appendix B (recovery); deposited code recovery_sim.R; one sentence
in Results pointing to it.
Revision triage — where each request lands
| Reviewer ask | Default home | Note |
|---|---|---|
| Fit a further rival model | Results + model-comparison table | refit all models under matched flexibility |
| Parameter / model recovery | appendix/supplement | summarize the result in one main-text sentence |
| Refit hierarchically / alternative priors | Results + diagnostics | report convergence; sensitivity in supplement |
| New experiment | Methods/Results (it is contribution) | integrate into the General Discussion synthesis |
| "Soften the theoretical claim" | General Discussion | scale wording to what the comparison licenses |
| Reproducibility / code | deposit + Open Practices statement | ensure fits regenerate in a fresh session |
Recurring revision pushback and the venue fix
- "You only ruled out one rival" → fit the additional model(s) under matched flexibility; report the penalized comparison and recovery; never argue from a single fit.
- "Your better fit might be overfitting" → add cross-validation/penalized criteria and model recovery; if the edge does not survive, adjust the claim.
- "I couldn't reproduce your fits" → ship seeded code + a pinned environment + a fresh-session run log; reference it in the response.
- "The new analysis weakens the effect" → disclose it, interpret it, and scale the theoretical claim; concealment is the cardinal sin.
Anti-patterns
- Ignoring or merging away a comment without a visible response
- Defending a single fit instead of adding the requested comparison/recovery
- Adding an experiment or model that breaks the program's coherence without re-synthesizing
- Adding analyses that contradict the original claim without acknowledgment
- Letting deposited model code/data drift out of sync with the revision
Output format
【Editor's decisive points】addressed first? [list]
【Coverage】every reviewer comment answered? [Y/N]
【Model inference strengthened】added comparison/recovery/hierarchy? [Y/N]
【Program coherent】new experiment/model integrated into the synthesis? [Y/N]
【Reproducible】deposited code updated + fits regenerate? [Y/N]
【Next】resubmit via Editorial Manager
Supplementary resources
../../resources/official-source-map.md— review model, revision norms, reproducibility expectations
Signals
- GitHub stars
- 1k
- Forks
- 155
- Last commit
- Sep 2026
Advanced
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- Key
cogpsych-rebuttal- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills
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