Cogsci Visualization

Cognitive Science Visualization

Purpose

This skill encodes domain-specific visualization knowledge for cognitive science and neuroscience. It covers which plot types to use for different data types, field conventions for brain data visualization, color accessibility standards, and publication formatting requirements. A general-purpose data scientist would produce suboptimal or misleading figures without this knowledge.

When to Use This Skill

• Creating figures for a cognitive science or neuroscience manuscript
• Visualizing RT distributions, ERP waveforms, fMRI results, or behavioral data
• Choosing colors, scales, and formatting for publication
• Reviewing whether a figure follows field conventions and accessibility standards

Research Planning Protocol

Before creating visualizations, you MUST:

1. State the purpose — What message should this figure communicate? What comparison or pattern should be visible?
2. Justify the plot choice — Why this plot type? What alternatives were considered?
3. Declare the target audience — Journal submission, conference poster, internal review?
4. Note potential misrepresentations — Could this visualization mislead? Are axes, scales, or colors appropriate?
5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Plot Type Selection by Data Type

Behavioral Data: RT Distributions

Use raincloud plots, NOT bar charts.

Bar charts with error bars conceal the distribution shape, hide bimodality, and can obscure important effects (Weissgerber et al., 2015). Cognitive science RT data are characteristically right-skewed with potential multimodality.

Recommended: Raincloud plots combine a half-violin (density), individual data points (jitter), and a boxplot summary (Allen et al., 2019).

Behavioral Data: Accuracy and Proportions

• Dot plots with within-subject CI: Show condition means with dots and individual subject data points connected by lines (for within-subjects designs)
• Within-subject confidence intervals: Use the Morey (2008) correction for repeated-measures CI -- standard CIs are inappropriate for within-subjects designs because they include between-subject variance
• Calculation: Remove each subject's mean, add back grand mean, then compute standard CI (Cousineau, 2005; Morey, 2008 correction factor: multiply SE by sqrt(k / (k-1)) where k = number of conditions)

Interaction Plots

• Use line plots (condition x time or condition x group) with individual trajectories shown as thin semi-transparent lines behind the group means
• For 2x2 designs: plot the continuous variable on x-axis, DV on y-axis, and use color/linetype for the second factor
• Always show error bars (within-subject CI for repeated measures; Morey, 2008)

ERP Visualization Conventions

Waveform Plots

Polarity convention: There is a longstanding debate about whether to plot negative up or negative down.

Recommendation: Follow the target journal's convention. If in doubt, negative up is the traditional ERP convention (Luck, 2014, Ch. 3). Always label the y-axis clearly with polarity.

Waveform Plotting Standards

• Line width: 1.0-1.5 pt for condition waveforms; 0.5 pt for axis lines (Luck, 2014)
• Color: Use colorblind-safe palette; distinguish conditions by both color AND linetype (solid, dashed)
• Time axis: Mark stimulus onset (0 ms) with a vertical dashed line
• Amplitude axis: Label in microvolts (uV); include zero line
• Baseline period: Shade or mark the pre-stimulus baseline period (typically -200 to 0 ms)
• Component windows: Shade or bracket the time window of interest (e.g., N400: 300-500 ms; Kutas & Federmeier, 2011)
• Grand average: Plot grand average waveforms; optionally show individual subject waveforms as thin semi-transparent lines

Difference Waves

• Plot the difference wave (condition A minus condition B) as a separate panel or overlaid in a distinct color
• Include the 95% CI or standard error band around the difference wave
• Difference waves are essential for verifying that an effect is present before interpreting the grand average (Luck, 2014, Ch. 2)

Topographic Maps

• Plot at specific time points or averaged within the component time window
• Use a diverging colormap (blue-white-red or blue-zero-red) centered on zero (Crameri et al., 2020)
• Include a color bar with labeled range (in uV)
• Show electrode positions as dots on the map
• Use consistent scale across conditions for fair comparison
• Common time windows: N1 (80-120 ms), P2 (150-250 ms), N400 (300-500 ms), P600 (500-800 ms) (Luck, 2014)

fMRI Visualization Standards

Statistical Map Overlays

• Never use jet/rainbow colormap (Borland & Taylor, 2007; Crameri et al., 2020). These colormaps introduce perceptual artifacts: perceived boundaries where none exist, and unequal perceptual steps.
• Recommended colormaps:

fMRI Display Types

fMRI Visualization Requirements

• Always show a color bar with the statistic scale (z-score or t-value)
• Report the threshold used (e.g., "z > 3.1, cluster-level p < 0.05 FWE")
• Report coordinate space (MNI-152 or Talairach) and template
• Show both hemispheres unless the hypothesis is lateralized
• For ROI analyses, overlay the ROI mask on an anatomical image
• Use 1 mm isotropic anatomical underlay (MNI152 T1 template) for sufficient anatomical detail

What NOT to Do in fMRI Figures

• Do not use 3D rendered brains with inconsistent lighting and angles (obscures data)
• Do not show only a single slice cherry-picked to show the largest effect
• Do not use an overly liberal threshold to make results "look better" (threshold must match the reported statistics)
• Do not use jet/rainbow (Borland & Taylor, 2007)

Brain Connectivity Visualization

Matrix Plots (Connectivity Matrices)

• Order regions by network membership (e.g., DMN, FPN, visual, motor) to reveal modular structure
• Use hierarchical clustering to determine optimal ordering if no a priori network assignment
• Use a sequential colormap for positive-only connectivity (e.g., correlation: viridis)
• Use a diverging colormap for signed connectivity (e.g., partial correlation: RdBu_r centered on zero)
• Annotate network boundaries with grid lines or color bars along axes

Chord Diagrams / Connectome Plots

• Use for showing specific significant connections
• Line width proportional to connection strength
• Color by network membership of source or target
• Use mne-connectivity or nilearn plot_connectome for 3D brain-space visualization

Color Accessibility

Mandatory: Colorblind-Safe Palettes

Approximately 8% of males and 0.5% of females have color vision deficiency (Birch, 2012). All figures must be interpretable by colorblind readers.

Recommended palettes:

Color Usage Guidelines

• Never rely on color alone to convey information; use shape, linetype, or labels as redundant cues (WCAG 2.1 guideline 1.4.1)
• For categorical comparisons, limit to 6-8 colors maximum (Miller, 1956 -- chunking limit; also practical perceptual limit)
• Test figures with a CVD simulator (e.g., Coblis, Color Oracle) before submission
• Avoid red-green contrasts (most common CVD is deuteranopia/protanopia)

Publication Formatting Standards

APA 7th Edition Figure Requirements