Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› psychrev-theory-construction
psychrev-theory-construction
GitHub用于为心理学综述构建正式理论或计算模型，将问题转化为显式假设、机制和形式化结构。涵盖从原始假设到机制说明、数学建模及行为推导的完整流程，区分核心承诺与实现细节，确保理论严谨性而非仅数据拟合。
Trigger Scenarios
已有问题和竞争对手，需构建具体理论
仅有直觉但缺乏显式假设或机制
模型仅为无动态方程的图表
需将口头主张形式化以推导预测
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill psychrev-theory-construction -g -y
SKILL.md
Frontmatter
{
"name": "psychrev-theory-construction",
"description": "Use when building the actual theory or formal\/computational model for a Psychological Review manuscript — turning a framed problem into explicit assumptions, mechanisms, formal structure, and derivations. Constructs the model; it does NOT derive and test predictions against data (that is psychrev-argument-development) or set scope and identifiability limits (psychrev-boundary-conditions)."
}
Theory & Model Construction (psychrev-theory-construction)
When to trigger
- The problem and the rivals are set; now you must build the theory
- You have intuitions but no explicit assumptions or mechanism
- Your "model" is a diagram with no specified dynamics or equations
- Verbal claims need to be made formal enough to derive predictions
The build order
A Psychological Review theory is assembled in a disciplined sequence. Skipping a step is the most common reason a draft reads as "a story, not a theory."
- State the assumptions (primitives). What entities, representations, and processes does the theory posit, and what is taken as given? Separate core commitments (the theory lives or dies by these) from auxiliary/implementational assumptions (convenient, replaceable). Reviewers attack hidden or load-bearing-but-unstated assumptions hardest.
- Specify the mechanism — the why. The mechanism is the engine: the causal/dynamic story by which the primitives produce the phenomena. A model without a mechanism is a curve-fit. State it in prose before you formalize it.
- Formalize. Render the mechanism as math, a computational process, or a precise conceptual structure. For a formal/computational model: give the equations or algorithm, define every parameter (psychological meaning, range, units), and state the functional forms and why those forms. A free parameter with no interpretation is a liability.
- Derive the model's behavior. Show what the model does: closed-form results where
possible, otherwise simulation. The behavior, not the equations alone, is the theory's
content. (Confront that behavior with data in
psychrev-argument-development.) - Connect to the explanandum. Map each posited mechanism to the phenomena it is meant to explain — and flag phenomena it leaves to other processes.
- Distinguish theory from implementation. Be explicit about which results follow from the core commitments versus from implementational choices, so a reviewer cannot dismiss the theory by attacking a replaceable detail.
Formal-model discipline (for computational/mathematical theories)
- Every parameter has a psychological interpretation, not just a fitted value.
- State functional forms and justify them theoretically, not by fit alone.
- Distinguish structural assumptions (architecture) from parametric ones (settings).
- If the model is fit to data, say so plainly and treat fit as illustration/constraint, not as the empirical contribution — the contribution is the theory.
- Plan for
psychrev-boundary-conditions: note where identifiability or scope may be at risk.
Conceptual-model discipline (for non-formal frameworks)
- Each construct: a precise definition, what it includes and excludes, and how it differs from the nearest existing construct (no relabels).
- Each relationship: named form (causal, recursive, constitutive, inhibitory) and a mechanism.
- The framework must yield derivable predictions, even if stated verbally — generality without testable consequences is not a Review contribution.
Checklist
- Core commitments separated from auxiliary/implementational assumptions
- The mechanism (the why) is stated in prose before formalization
- Every parameter / construct has a psychological interpretation and stated range or domain
- Functional forms / relationship forms are justified theoretically
- The model's behavior is derived (closed-form or simulated), not just its equations listed
- Each mechanism is mapped to the phenomena it explains
- Theory-level results are distinguished from implementation-level choices
Anti-patterns
- A model that is a redescription of the data with enough free parameters to fit anything
- Parameters introduced with no psychological meaning ("a scaling constant" doing real work)
- Listing equations without ever showing what the model does
- Hidden core assumptions exposed later by a reviewer
- A "framework" of boxes and arrows with no mechanism and no derivable prediction
- Smuggling in a new experiment as the contribution — data only motivate or constrain here
Output format
【Assumptions】core commitments | auxiliary/implementational
【Mechanism】[the why, in prose]
【Formal structure】equations / algorithm / conceptual structure; parameters with meaning + range
【Model behavior】[derived results: closed-form or simulation summary]
【Explanandum map】mechanism → phenomena explained (and phenomena left to others)
【Next step】psychrev-argument-development (derive predictions, confront data + rivals)
Version History
- 1839142 Current 2026-07-05 14:15
