Argument Development: Deriving & Confronting Predictions (psychrev-argument-development)

When to trigger

• The model is built but you have not shown what it predicts
• You assert the theory "explains" phenomena without deriving them
• You have not compared your predictions to rival models on diagnostic cases
• A reviewer will ask "could this theory have been wrong?"

What replaces a results section here

Psychological Review has no experiment of its own as the contribution. The work that an empirical paper does with data, a Review paper does with derivation and confrontation: you derive predictions from the model's assumptions, then confront them with already- existing evidence and with what rival models predict. Logical and quantitative soundness is the rigor standard, exactly as statistical inference is at empirical journals.

The derivation discipline

1. Derive, do not assert. For each phenomenon in the explanandum, show how it follows from the assumptions — analytically, or by simulation that traces assumptions → behavior. "The model can explain X" is worthless without the derivation that it does.
2. Separate signature from accommodation. A strong prediction is a signature — a pattern the theory entails and rivals do not, ideally a parameter-free qualitative ordering or a novel pattern not used to build the model. Accommodating known data with fitted parameters is weaker; label it honestly as accommodation, not prediction.
3. Make at least one risky, novel prediction. Falsifiability is the journal's currency: name a pattern that, if observed, would disconfirm the theory, and ideally one not yet tested so future work can adjudicate.

The confrontation discipline

• Confront existing data. Use published datasets (yours or others') to show the model reproduces the diagnostic phenomena. Report fit honestly: degrees of freedom, number of free parameters, and whether parameters were estimated or set a priori.
• Confront rival models head-to-head. On each diagnostic phenomenon, show what your model and the rival each predict, and why the data favor yours. A nested or formal model comparison (e.g., information criteria, parameter recovery) beats a verbal contrast.
• Address alternative explanations. For every prediction your model gets right, ask whether a simpler rival gets it right too; if so, the case is not diagnostic — find one that is.
• Probe robustness. Show the key results do not depend on a fragile parameter setting or an arbitrary functional form (sensitivity over a plausible range).

Quantitative honesty (for formal models)

• State the number of free parameters and what each was fit to.
• Distinguish fit (reproducing data used to build the model) from prediction (data the model was not tuned on).
• Prefer generalization tests (fit on one set, predict another) over in-sample fit.
• Beware flexibility: a model that can fit any pattern predicts nothing — show what it cannot do.

Checklist

• Each explanandum phenomenon is derived, not merely asserted, from the assumptions
• At least one risky, novel, falsifiable prediction is stated
• Signatures (rival-distinguishing) are separated from accommodations (fitted)
• Existing data are used to confront the model; free-parameter count is disclosed
• Head-to-head comparison with rival models on diagnostic phenomena is shown
• Alternative simpler explanations are ruled out on each diagnostic case
• Robustness to parameter/functional-form choices is demonstrated

Anti-patterns

• "The model can explain X" with no derivation that it does
• Fitting known data and calling accommodation a prediction
• A model so flexible it could fit any result (and therefore predicts nothing)
• Verbal hand-waving where a rival has a formal, quantitative account
• Hiding the number of free parameters or which data were used to fit them
• Picking only phenomena where all theories agree (non-diagnostic)
• Introducing a brand-new experiment as the deciding evidence (data only constrain here)

Output format

【Derivations】[phenomenon → how it follows from assumptions] for each
【Signatures vs. accommodations】[risky/novel predictions] | [fitted accommodations]
【Confrontation】existing data used; free-parameter count; fit vs. generalization
【Head-to-head】[diagnostic phenomenon → your prediction vs. rival's vs. data]
【Robustness】key results stable over parameter/form range: yes / fix
【Next step】psychrev-boundary-conditions (scope, identifiability, what it does NOT explain)