| 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
- 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.
- 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.
- 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).
- Derive discriminating predictions. Identify the data pattern that the models predict
differently — that qualitative or quantitative signature is what your experiments must produce.
- 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.
- 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
