psychrev-theory-construction

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."

1. 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.

1. **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.

1. 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.

1. 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.)

1. Connect to the explanandum. Map each posited mechanism to the phenomena it is meant to

explain — and flag phenomena it leaves to other processes.

1. 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)