| 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
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)
