Skill UI

This guide outlines the rigorous, model-based standards required for data analysis and statistical reporting in Cognitive Psychology manuscripts. It covers essential techniques such as model comparison (using AIC/BIC or Bayes factors), demonstrating parameter and model recovery, implementing mixed or hierarchical Bayesian models, and ensuring all findings are fully reproducible from shared code.

A comprehensive methodological guide for authors positioning their empirical manuscripts within a theoretical debate, particularly for high-level journals like Cognitive Psychology. It teaches authors to move beyond simple literature reviews by identifying a clear theoretical question, naming specific rival models, and demonstrating precisely why existing evidence is insufficient to resolve the conflict. This structure ensures the contribution is framed as an adjudication or a novel model.

This guide details the rigorous requirements for achieving research reproducibility in academic publishing. It moves beyond simple data sharing, mandating that authors deposit model-fitting code, analysis scripts, and materials with persistent identifiers (DOI). Key steps include ensuring reported results can regenerate in a fresh session, completing declarations of competing interests, and linking pre-registrations, ensuring compliance with strict journal standards.

This guide provides a rigorous strategy for authors responding to major or minor revisions in cognitive psychology journals. It teaches how to systematically address every reviewer comment, strengthen model-driven inferences through techniques like model comparison and recovery analysis, and maintain the coherence and reproducibility of the theoretical argument. Essential reading for scholarly publication.

A comprehensive guide for designing rigorous, multi-experiment cognitive psychology studies. This framework ensures that experiments are engineered to discriminate between competing theoretical models by tightly controlling confounds, optimizing stimulus construction, and providing adequate power for the critical contrast, rather than just the main effect. It guides the entire planning process from hypothesis formulation to pre-data lockdown.

This guide outlines the rigorous standards for presenting tables and figures in Cognitive Psychology manuscripts. It emphasizes moving beyond simple bar charts of means, requiring authors to overlay the model's predicted curve onto observed data, reveal distributions and uncertainty intervals, and accurately report parameter estimates with confidence intervals. The goal is to ensure reproducible, comprehensive, and highly informative scientific communication.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

This comprehensive guide provides expert protocols for writing the 'Related Work' section in Machine Learning papers. It teaches authors how to maintain 'versioned honesty' by meticulously triaging the literature into distinct lanes (e.g., method lineage, model artifacts, evaluation infrastructure). It covers advanced topics like managing concurrent work, citing arXiv status accurately, and avoiding self-citation violations, ensuring the paper is positioned rigorously within the rapidly evolving academic field.

A comprehensive guide detailing best practices for ensuring the reproducibility of computational claims in Large Language Model (LLM) papers. It outlines a layered approach (Exact, Budgeted, Drifting) for verifying results, providing protocols for handling open-weight checkpoints, proprietary API models, and documenting compute costs and failure modes honestly.

This skill helps authors strategically decide the optimal venue for their Language Model research. It provides the critical "object-of-study test" to determine if the LM itself is the subject of the study, or merely a tool for a task. Furthermore, it guides routing against top-tier NLP/AI conferences (ACL, NeurIPS, ICLR, etc.), helping authors target the right audience and maximizing submission chances.

This guide provides best practices for structuring complex academic manuscripts, particularly for conferences requiring a single-PDF submission model. It details how to optimally split content between the limited body (e.g., 12 pages) and the extensive appendix, ensuring that the crucial theorems, proofs, and auxiliary lemmas are presented in a verifiable and highly readable manner for peer reviewers. Key techniques include implementing restatement discipline and writing roadmap paragraphs for clear verification paths.

This comprehensive guide provides detailed instructions for revising academic papers, specifically in the field of learning theory, for the COLT conference. It teaches authors how to structure the argument rigorously, ensuring the model is unambiguous, mastering the informal-to-formal theorem pairing, and maintaining mathematical precision (quantifier and constant hygiene).