Tables & Figures (cogpsych-tables-figures)

In Cognitive Psychology the central exhibit usually shows the model fitting the data — observed patterns with the model's predictions overlaid — because the contribution is the model, not the bare effect. Exhibits should reveal distributions and uncertainty, report parameter estimates with intervals, and let a reader judge model comparison at a glance. Bars of means hide exactly what this venue cares about.

When to trigger

• Designing the main model-fit figure or a model-comparison table
• Deciding what goes in the article vs. the supplementary material / appendix
• A reviewer found an exhibit unclear, or said "show the fit, not just the means"
• Visualizing distributions, individual data, model predictions, and uncertainty

Principles

1. Overlay model on data. The headline figure shows observed data (with uncertainty) and the

model's predicted curve/points superimposed, ideally for the rival model too, so the reader sees which account tracks the data. This is the venue's signature exhibit.

1. Show the data and uncertainty. Prefer distributions/individual points with means and

confidence/credible intervals over bar-of-means plots; for model parameters, plot estimates with intervals.

1. Make model comparison legible. A table reports each model's fit (AIC/BIC/BF or cross-validated

score), free-parameter count, and the winning criterion — so the comparison is checkable, not asserted.

1. Self-contained. Titles, notes, axes, Ns, trial counts, units, and "intervals are 95% CIs/CrIs"

make each exhibit intelligible alone, following the journal's (Elsevier/APA-style) conventions.

1. Reproducible + accessible. Generated by the deposited model/analysis code so values match;

colorblind-safe and grayscale-legible.

Worked micro-example — the main model-fit figure (illustrative)

For the recognition-memory program, the primary figure must show the fit, not the means.

Figure 1. Observed and model-predicted z-ROCs, Experiments 1-3.
  Geometry:  observed confidence-ROC points with 95% CIs, UVSD predicted
             curve overlaid (solid) and DPSD predicted curve overlaid
             (dashed) — the reader sees UVSD track the linear z-ROC.
  Panels:    one per experiment; shared axes for comparison.
  Annotation: z-ROC slope 0.78 [0.72, 0.84]; dBIC = 14 favoring UVSD.
  Note:      defines the ROC metric, Ns, trials/bin, exclusion count, and
             that bands are 95% intervals - readable without the main text.
  Source:    rendered by the deposited model-fitting script so values match.
Table 1. Model comparison: free parameters, -2logL, AIC, BIC, BF, by model.

Exhibit triage — article vs. supplementary material

Exhibit-stage reviewer pushback and the venue fix

• "Bar chart hides the spread" → switch to distribution/points + intervals; show individual data where N

allows.

• "Show the fit, not the means" → overlay model predictions (and the rival's) on the observed data.
• "I can't compare the models from this" → add the model-comparison table with criteria and parameter

counts.

• "Figure values don't match Table 1" → regenerate both from the single deposited model script.

Exhibit calibration anchors

• The figure that wins a Cognitive Psychology paper is the one where the reader sees one model track

the data and the rival miss; design for that, not for a decorative bar chart.

• Show parameter estimates with intervals so the model's psychological claims are inspectable, and put

recovery plots in the supplement so the comparison is trustworthy.

• Make the model-comparison table do real work: free-parameter counts and a penalized criterion guard

against the "better fit = overfitting" objection before a reviewer raises it.

• Accessibility is part of credibility: colorblind-safe palettes and grayscale-legible line styles so

the model-vs-data distinction survives printing.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-supplement drift). Full map: execution-with-mcp. Cognitive Psychology is experimental — within-subject designs and mixed models dominate; report the model, the effect size, and multiple-comparison control.

• Tables: etable (multi-model columns) or did_summary_to_latex straight from the

result_id.

• Figures: plot_from_result / enhanced_event_study_plot / event_study_table —

axis units and the SE/clustering note baked in.

• Every note names the estimator + clustering and states the effect size in

interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Anti-patterns

• Bar plots of means that hide distribution, uncertainty, and the fit
• A results figure with no model overlay in a model-driven paper
• Asserting a model "fits best" with no comparison table (criteria + parameter counts)
• Exhibits that need the prose to be intelligible (not self-contained)
• Figure/table values that don't match the deposited model code

Output format

【Main exhibit】observed data + model fit (and rival)? [Y/N]
【Shows distribution + uncertainty + parameter intervals?】[Y/N]
【Model-comparison table】criteria + free-parameter counts? [Y/N]
【Self-contained + accessible?】notes, Ns, trials, grayscale/colorblind-safe? [Y/N]
【Reproducible?】matches deposited model script? [Y/N]
【Next】cogpsych-writing-style

Supplementary resources

• ../../resources/external_tools.md — plotting tools, model-fit visualization, recovery plots
• ../../resources/official-source-map.md — exhibit and house-style expectations