| name | psychrev-boundary-conditions |
| description | Use when setting the scope of a Psychological Review theory — what it explains, what it does NOT, and (for formal models) whether its parameters are identifiable. Bounds the theory; it does NOT derive its predictions (psychrev-argument-development) or frame its advance over rivals (psychrev-contribution-framing). |
Boundary Conditions, Scope & Identifiability (psychrev-boundary-conditions)
When to trigger
- Your theory reads as if it explains everything (a red flag to reviewers)
- You have not said where the theory stops holding
- For a formal model: you have not checked whether distinct parameter settings are distinguishable
- A reviewer will ask "what would falsify this?" or "can you even estimate that parameter?"
Why scope is a contribution, not a confession
At Psychological Review, stating where a theory holds and where it fails is part of the theory
itself, not a limitations paragraph tacked on at the end. A theory that "explains everything"
explains nothing — unbounded scope signals an unfalsifiable model. Editors read explicit
boundaries as a sign of theoretical maturity. Three kinds of limit must be stated.
1. Scope (the explanatory boundary)
- Phenomena in scope vs. phenomena explicitly out of scope (left to other processes).
- Population / domain limits — does the theory claim to hold across development, species,
cultures, tasks, or only within a stated range?
- Level of analysis — computational, algorithmic, or implementational (Marr); be consistent,
and theorize level shifts rather than sliding between them.
- Conditions of breakdown — name the regime where the mechanism should stop producing the
phenomena, and treat that prediction as a test of the theory.
2. Identifiability (for formal/computational models)
This is the modeling-specific boundary reviewers probe hardest:
- Structural identifiability — can the parameters, in principle, be recovered from the kind
of data the theory addresses, or do different settings produce identical predictions (a
mimicry problem)?
- Parameter recovery — demonstrate, by simulation, that fitting the model to data it
generated recovers the true parameters; report where recovery degrades.
- Model mimicry — can your model and a rival mimic each other on the available data? If so,
the comparison is not diagnostic; say what data would separate them.
- Sloppiness / sensitivity — note parameters the predictions barely depend on (and resist
over-interpreting them).
3. What it does NOT explain
A short, explicit list of phenomena the theory deliberately does not address, with one line
each on why (out of scope vs. genuinely open). This pre-empts the "but it can't handle Y"
reviewer objection by conceding Y on your own terms.
Checklist
Anti-patterns
- A theory presented as universal, with no stated breakdown condition
- Boundary conditions written as apologies ("a limitation is...") rather than as theory
- Skipping identifiability for a model with many free parameters
- Claiming parameters are meaningful without ever showing they can be recovered
- Ignoring that a rival model mimics yours on the available data
- Burying scope limits in a final paragraph instead of theorizing them up front
Output format
【In scope】[phenomena explained]
【Out of scope】[phenomena left to other processes, with reasons]
【Domain limits】[development / species / culture / task / Marr level]
【Breakdown regime】[where the mechanism should stop — stated as a test]
【Identifiability】structural: ok/at-risk | recovery: demonstrated/degraded where [...] | mimicry: [rival], separating data: [...]
【Does NOT explain】[short explicit list]
【Next step】psychrev-conceptual-exhibits (diagram + simulation figures) → psychrev-contribution-framing
