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name: cogpsych-theory-and-hypotheses description: Use when stating the theory, formalizing the model, and deriving predictions for a Cognitive Psychology (Elsevier) manuscript. The journal rewards a formal/computational account whose parameters mean something and whose predictions discriminate it from rivals. Structures the theory and the model that the experiments test; it does not fit the model or run analyses.

Theory, Models & Hypotheses (cogpsych-theory-and-hypotheses)

Cognitive Psychology rewards a formal account of a cognitive process — a computational or mathematical model whose parameters have interpretable meaning and whose predictions can be fit to data and compared against rival models. The cardinal move here is to turn a verbal theory into a model that makes the experiments discriminating, and to separate predicted (confirmatory) from discovered (exploratory) results.

When to trigger

• Specifying the theory and the formal/computational model that the experiments will test
• Deriving the predictions that separate your account from rival models
• Co-designing the model with the experiments (iterate with cogpsych-study-design)
• A reviewer said the work is "atheoretical," "the model is just a curve fit," or "your data don't distinguish the accounts"

Build the theory-and-model

1. State the cognitive theory. What mechanism or representation explains the phenomenon, and why — in words, before equations. Name the rival accounts you intend to adjudicate.
2. Formalize it. Write the model: its representations, processes, free parameters, and what each parameter means psychologically. A model whose parameters lack interpretation is a red flag here.
3. Name the rival model(s). Specify the competing account(s) in the same formal language so the comparison is fair (nested or matched-flexibility where possible).
4. Derive discriminating predictions. Identify the data pattern that the models predict differently — that qualitative or quantitative signature is what your experiments must produce.
5. Mark prediction status. Separate confirmatory (pre-committed/preregistered) predictions from exploratory model exploration done after seeing data; do not present a post hoc fit as predicted.
6. State what would disconfirm the model. Which data pattern, or which parameter estimate, would count against your account — this is what makes the model a theory, not a fitting exercise.

Avoiding the "just a curve fit" objection

• A model that fits anything explains nothing. Show the model is falsifiable (some data it cannot produce) and identifiable (its parameters can be recovered — handoff to cogpsych-data-analysis).
• Prefer qualitative signatures that one model predicts and the other forbids over a small numerical edge in fit; reviewers trust a crossed prediction more than a smaller AIC.

Worked micro-example — theory to discriminating prediction (illustrative)

A recognition-memory program adjudicating two models, written so prediction status is legible.

Theory:  Recognition reflects a single continuous memory-strength signal;
         the unequal-variance signal-detection (UVSD) model formalizes it.
Rival:   A dual-process account adds a threshold recollection process (DPSD).
Formalization:
         UVSD parameters: d', sigma(old). DPSD parameters: R (recollection),
         d' (familiarity). Both fit the same confidence-ROC data.
Discriminating prediction (confirmatory, preregistered, Exps 1-3):
         The z-ROC slope is < 1 and *linear* under UVSD; DPSD predicts a
         characteristic U-shaped/curved z-ROC. The shape, not the fit index,
         separates them.
Exploratory: any post hoc parameter that improves DPSD fit is reported as
         exploratory, not as a prediction.
Disconfirming: a reliably curved z-ROC across experiments counts against UVSD,
         stated up front.

Theory-stage reviewer pushback and the venue fix

Reviewer pushback	Cognitive Psychology fix
"Atheoretical / mechanism unclear"	state the mechanism in words, then give the formal model before the experiments
"The model is just a curve fit"	show a falsifiable, identifiable model with a crossed qualitative prediction, not only a fit edge
"Your data can't distinguish the accounts"	design the discriminating signature into the experiments; formalize both rivals in the same language
"Parameters are uninterpretable"	give each free parameter a psychological meaning and a recovery check
"This looks post hoc"	mark confirmatory vs. exploratory; pre-commit the model comparison where feasible

Theory calibration anchors

• The contribution is the model-as-theory, not the experiments alone; experiments earn their place by discriminating models, and the model earns its place by being falsifiable and identifiable.
• A crossed qualitative prediction (one model predicts a pattern the other forbids) is worth more than a marginal fit advantage; lead with it.
• Pre-commit the model space and the comparison criteria before fitting where you can; deciding the winning model after seeing the fits is the modeling form of HARKing.
• Match model flexibility when comparing — a more flexible model that fits better may simply be overfitting; this is why parameter recovery and model recovery matter (cogpsych-data-analysis).

Anti-patterns

• A verbal theory with no formal model where the phenomenon is plainly formalizable
• A model with uninterpretable parameters or that cannot fail to fit
• Comparing models of unequal flexibility without acknowledging it
• Presenting a post hoc model selection as a predicted result
• No statement of which data or parameter estimate would disconfirm the account

Output format

【Theory】the mechanism/representation, briefly
【Model】formalization: parameters + their psychological meaning
【Rival(s)】competing account(s) in matched formal language
【Discriminating prediction】the signature that separates the models
【Status】confirmatory (pre-committed) vs exploratory
【Disconfirming evidence】what would count against the model
【Next】cogpsych-literature-positioning

Supplementary resources

• ../../resources/external_tools.md — modeling frameworks, model-recovery and preregistration tools
• ../../resources/official-source-map.md — scope and modeling emphasis