Perception & Design

PSY 410: Data Science for Psychology

Dr. Sara Weston

2026-04-22

How we see data

This first block (~25 min) covers perception, color theory, and decluttering. The opening “what’s wrong” slide is meant to be interactive — pause and let students call out problems before revealing the answer. Assignment 3 is due this week and Assignment 4 (Visualization Deep Dive) is assigned today.

What’s wrong with this?

Red-green palette (colorblind-unfriendly). No informative title. Tiny text. Gridline noise. Bars hiding the data.

Today we learn why your brain rejects bad figures — and how to design ones that work.

Let the reveal build naturally — use the pause (. . .) to give students 30 seconds to identify problems on their own first. Cold-call a few students or ask pairs to share. Most will catch the red-green issue since we mentioned it last session. Fewer will notice the bar chart hiding data or the uninformative title. This sets up the whole session: design isn’t decoration, it’s communication.

Your brain processes some visual features before you even think

Some visual features are processed almost instantly — before conscious attention kicks in:

Attribute	Example
Color	A red dot among blue dots
Size	A large circle among small ones
Position	A point far from the others
Shape	A triangle among circles
Orientation	A tilted bar among vertical bars

These are your tools for drawing the viewer’s eye.

This connects directly to psych content students may have seen in intro psych or perception courses. Preattentive processing happens in ~250ms — before conscious attention. The practical implication for data viz: if you want something to stand out, use one of these attributes. If you use too many at once, nothing stands out. Spend ~3 minutes here. Students often ask about animation — yes, motion is preattentive too, but we won’t use it in static figures.

Preattentive in action

Your eye goes to the red points, the big points, and the outlier — instantly.

Ask students: “Where did your eye go first in each panel?” The answer should be obvious and immediate — that’s the whole point. The practical takeaway: use color to highlight your key finding, not to decorate. If everything is colorful, nothing pops.

Gestalt principles (brief)

The brain groups things automatically:

• Proximity — things close together feel like a group
• Similarity — things that look alike feel like a group
• Enclosure — things inside a border feel like a group

Keep Gestalt brief — 1-2 minutes. Students don’t need to memorize the terms, just the intuition: your brain groups things that are close together, look similar, or are enclosed together. This matters for figure design because spacing and visual similarity communicate grouping whether you intend it or not. Facets use proximity; color-coding uses similarity.

Color theory

Three types of color palettes

Type	When to use	Example
Sequential	One continuous variable (low → high)	Blues, viridis
Diverging	Values relative to a midpoint	Red–white–blue
Qualitative	Categorical groups (no order)	Set1, tab10

Using the wrong type is one of the most common visualization mistakes.

This is a high-value slide — spend ~3 minutes here. The most common mistake: using a qualitative palette for ordered data (like a rainbow for low-to-high), or using a sequential palette for unrelated categories. Ask students: “If you had data on anxiety levels from low to high, which palette type?” (Sequential.) “If you had data on four different therapy types?” (Qualitative.) Test their intuition before moving to examples.

Sequential palettes

Qualitative palettes

Colorblind-friendly choices

Emphasize that 8% of men is roughly 1 in 12 — in a class of 50, there are likely 2-3 students with some color vision deficiency. This is not an edge case. The practical advice is simple: use viridis or a tested palette, and never rely on color alone (also use shape or labels). Students often ask “how do I check?” — suggest the colorblindness simulator at davidmathlogic.com/colorblind or the colorblindr R package.

About 8% of men have some form of color vision deficiency. Red-green is the most common.

# viridis is colorblind-safe AND sequential
scale_fill_viridis_d()   # discrete
scale_fill_viridis_c()   # continuous

# ColorBrewer palettes designed for colorblindness
scale_fill_brewer(palette = "Set2")

# Or set colors manually with safe choices
scale_fill_manual(values = c("#0072B2", "#E69F00", "#009E73"))
# (blue, orange, green — distinguishable for most color vision types)

A bad color choice vs a good one

Let the side-by-side comparison speak for itself. The blue-orange version on the right is distinguishable to virtually all viewers. You can mention that some journals now require colorblind-accessible figures. The hex codes shown earlier (#0072B2, #E69F00, #009E73) are from the Okabe-Ito palette — a safe default students can copy into any project.

Decluttering

The default is cluttered

Transition: “Now that we know what to show, let’s talk about what to remove.” This section draws heavily on Knaflic’s “declutter” philosophy. The before/after comparison here is dramatic — the gray background, heavy gridlines, and redundant legend all add zero information. Students sometimes resist removing gridlines because they think figures look “empty” without them. Remind them: if the reader needs a gridline to read the value, add a direct label instead.

ggplot2’s default theme adds a lot of visual noise. Compare:

The declutter checklist

This is a reference slide students should internalize. Walk through the “Remove” column and ask students to justify each removal — e.g., “Why remove the legend when we have two conditions?” (Because the x-axis already labels them.) The “Keep” column matters too: titles should state a finding, not label the chart type. “Boxplot of RT” is a label; “Treatment reduces reaction time” is a finding.

Ask: does this element help the reader understand the data?

Remove

Element	Why
Gray background	Noise, no info
Gridlines (most)	Distraction
Redundant legend	X-axis says it
Generic axis labels	Add units instead

Keep

Element	Why
Title + subtitle	Orients the reader
Caption	Source, N, error bar type
Meaningful color	Highlights comparisons

theme() for fine control

Don’t try to teach every theme() option — there are dozens. The goal is for students to know that theme() exists and to see the pattern: each element gets a function like element_blank() (remove it), element_text() (style text), element_line() (style lines), or element_rect() (style rectangles). They can look up specific options in the ggplot2 documentation. The most common ones they’ll use are legend.position, panel.grid, and plot.title.

theme(
  legend.position = "none",                    # Remove legend
  axis.ticks = element_blank(),                # Remove tick marks
  panel.grid = element_blank(),                # Remove gridlines
  plot.title = element_text(face = "bold"),    # Bold the title
  axis.text = element_text(size = 10),         # Font size
  plot.caption = element_text(hjust = 0)       # Left-align caption
)

Pair coding break

Your turn: 10 minutes

Give 10 minutes. The cluttered graph on the next slide has at least 7 problems: red-green colors, size mapped to accuracy (binary variable — misleading), no informative title, variable names as axis labels, default gray theme, redundant color and fill, and points plotted behind the boxplot. Circulate and ask pairs to name each problem before they start fixing it. Common student approach: they’ll fix the colors first because it’s most obvious. Push them to also address the title and labels.

I’ll put a cluttered graph on screen. With a partner, rewrite it to follow the design principles we just covered.

Remove at least 5 unnecessary elements. Make it tell a clear story.

Tip

Think about: colors, legend, labels, theme, size mapping, and whether every aesthetic is adding information.

Your turn: 10 minutes

# The cluttered version — fix this!
reaction_data |>
  ggplot(aes(x = condition, y = rt, fill = condition, color = condition, size = accuracy)) +
  geom_point() +
  geom_boxplot(alpha = 0.3) +
  scale_fill_manual(values = c("Control" = "red", "Treatment" = "green")) +
  scale_color_manual(values = c("Control" = "red", "Treatment" = "green")) +
  labs(x = "condition", y = "rt") +
  ggtitle("data") +
  theme_gray()

Before we move on

📤 Upload your code to Canvas for participation credit. Paste what you have into today’s in-class submission — it doesn’t need to work perfectly.

Critiquing bad graphs

This section (~10 min) is the second lecture block. It transitions from “how to build good figures” to “how to spot bad ones.” These three examples each illustrate a different category of problem: misleading axes, poor aesthetics, and wrong chart type. Run through them interactively — show the figure, let students diagnose it, then reveal the answer. This builds the critical evaluation skill they’ll need for Assignment 4.

What’s wrong here? (1 of 3)

The problem: The y-axis starts at 470, not 0. The difference looks massive — but it’s only ~40 ms. A truncated axis exaggerates the effect.

Students often ask: “But isn’t it sometimes okay to not start at zero?” Good question. For bar charts, no — the bar height represents the value, so truncating is always misleading. For line charts or dot plots, it can be acceptable because the position (not the area) encodes the value. This nuance matters and comes up in the storytelling session (Session 16) too.

What’s wrong here? (2 of 3)

The problems: Red-green palette (colorblind-unfriendly). Title says “Boxplot” (a label, not a finding). Variable names as labels. Distracting background color and gridlines.

What’s wrong here? (3 of 3)

The problems: Continuous data was binned into arbitrary categories, then displayed as a pie chart. We lost the actual reaction times, the condition comparison, and the ability to see distributions. A histogram or density plot would show far more.

This is the strongest example because it shows a cascade of bad decisions: first they binned continuous data (losing information), then they used a pie chart (making comparison harder), and they dropped the condition variable entirely. Each step destroyed information. The lesson: your choice of chart type determines what story you can tell. A pie chart here makes it impossible to compare conditions or see the distribution.

Get a head start

Assignment 4 preview

Assignment 4 is being assigned today. Briefly outline the three parts so students know what to aim for. Emphasize that the “bad” version should be deliberately bad with specific design violations they can name — not just ugly. They need to articulate what’s wrong with it and why the good version is better. The colorblind-accessible version is a separate deliverable, not just “the good version with viridis.”

Assignment 4 will ask you to:

1. Create a “bad” version of a figure — deliberately violate design principles
2. Create a “good” version following what we covered today
3. Create a colorblind-accessible version

Assignment 4 preview

Start experimenting now:

• Take the reaction_data dataset
• Make the worst possible version of a figure
• Then make it great
• What did you change?

Wrapping up

Design principles checklist

• Is the geom appropriate for the data type?
• Are colors colorblind-friendly?
• Is the legend necessary? (Remove if redundant)
• Are axis labels informative (not just variable names)?
• Is there a title that states the main point?
• Is there visual clutter to remove?
• Do error bars have a caption explaining what they show?

Before next class

📖 Read:

• R4DS Ch 10: Exploratory data analysis (sections 10.1–10.4)

✅ Practice:

• Try the “bad vs good” exercise on your own
• Find a graph online and identify ways to improve it

Key takeaways

1. Preattentive attributes guide the eye — use them intentionally
2. Match your palette type to your data (sequential, diverging, qualitative)
3. Design for colorblindness — always
4. Declutter ruthlessly — every element must earn its place
5. Critical evaluation — always ask whether each element adds information

The one thing to remember

You’re not designing for yourself. You’re designing for a reader who will look at your figure for five seconds.

Next time: Exploratory Data Analysis

End on this line — it reframes everything from today. Students naturally design for themselves (they already know what the data shows). The challenge is designing for someone who doesn’t. Five seconds is roughly how long a reader spends on a figure before deciding whether to engage. Next session shifts from how to present data to how to explore it (EDA). Remind students that Assignment 3 is due this week and Assignment 4 is now available.