Meal Macros and Post-Lunch Reaction Time Science Project

ISEF Category: Behavioral and Social Sciences

Subcategory: Behavioral Neuroscience  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

Some people feel the afternoon slump before they finish lunch. That drop can show up in both reaction time and pupil size, which makes it measurable instead of just a hunch. You can test whether different meal mixes change that dip. If you track the same people before and after lunch, you get a clean within-person comparison.

What Is It?

Your body does not treat every meal the same. A meal's macronutrient ratio, the mix of carbs, protein, and fat, can change how fast glucose enters the blood, how full you feel, and how alert you feel after eating. The post-lunch dip is the slow, sleepy stretch many people notice in the early afternoon.

In this project, you measure two simple signals. Reaction time shows how fast you answer a prompt, like pressing a key when a light appears. Pupil size can act like a window into alertness, because pupils tend to change with fatigue, effort, and arousal. Webcam pupillometry means you use a camera, not a lab eye tracker, to estimate that change from video.

Why This Is a Good Topic

This topic works because you can measure it with tools you can access, and the variable you change, meal ratio, is easy to define. It connects to sleepiness, school performance, and how food affects attention. You also learn how to run a crossover study, hold controls steady, and turn messy human data into something you can compare. That makes it strong practice for real behavioral research.

Research Questions

• How does a higher-carbohydrate lunch change reaction time compared with a higher-protein lunch in the same student?
• What is the effect of meal macronutrient ratio on average pupil diameter after lunch?
• Does the size of the post-lunch reaction-time dip change as more time passes after eating?
• To what extent do equal-calorie meals with different fat, protein, and carb ratios change pupil-based fatigue scores?
• Which meal ratio produces the smallest slowdown on repeated reaction-time trials?
• How does pupil change track reaction-time change across participants?

Basic Materials

• Laptop or desktop with a built-in webcam.
• Free Python install with OpenCV, pandas, and PsychoPy.
• Digital kitchen scale with gram-level accuracy.
• Quiet room with steady front lighting.
• Tripod or laptop stand to hold the camera at a fixed height.
• Standardized meal plan with measured portions for each test condition.
• Consent form and session log for each participant.

Advanced Materials

• Infrared eye tracker with exportable pupil metrics.
• External response box or keyboard timing device.
• Computer with Python, R, and JASP.
• Standardized test meals prepared with precise portion control.
• Continuous glucose monitor, if you add a metabolic comparison.
• Controlled lighting rig or pupillometry calibration setup.

Software & Tools

• Python: Organizes the workflow, from video capture to reaction-time analysis.
• OpenCV: Detects faces and estimates pupil features from webcam frames.
• PsychoPy: Presents timed reaction tasks and logs trial-level responses.
• pandas: Cleans the data and compares each person's change across meal conditions.
• JASP: Runs repeated-measures tests and makes within-subject plots.

Experiment Steps

1. Choose one primary signal, reaction time or pupil diameter, so your analysis stays focused.
2. Design 2 or 3 meal conditions with matched calories and one main macro difference.
3. Set up a within-subject crossover schedule so each person repeats every condition.
4. Standardize lighting, screen distance, sleep, and caffeine so the webcam signal stays comparable.
5. Plan your baseline, post-meal, and repeated-trial windows before you start collecting data.
6. Decide how you will summarize each participant's change score and compare conditions.

Common Pitfalls

• Letting room light change between sessions, which shifts pupil size even when fatigue stays the same.
• Comparing meals with different calories or portion sizes, which hides the macro effect you wanted to test.
• Running the reaction task on a laggy device, which adds device delay to the real response time.
• Skipping caffeine and sleep controls, which can overpower the post-lunch dip signal.
• Using a loose webcam setup, which makes the pupil detector lose the eye when the head moves.

What Makes This Competitive

A stronger project separates food effects from sleep, light, and device lag, then uses the same student across several meal conditions. If you add enough repeated trials and use a repeated-measures model, your results will read more like real research and less like a one-off demo. A smart twist is to test whether pupil change predicts reaction slowdown better than meal label alone. Careful controls and a clear analysis plan matter more here than flashy equipment.

Project Variations

• Compare high-carb, high-protein, and mixed lunches to see which one produces the smallest slowdown.
• Test whether the same meal has a different effect on students who ate breakfast versus those who skipped it.
• Replace reaction time with a short attention task and see whether pupil change still tracks performance.

Learn More

• PubMed: Search review articles on postprandial cognition, pupil dilation, and reaction time.
• PubMed Central: Read free full-text papers on pupillometry methods and fatigue.
• NIH MedlinePlus: Find plain-language background on nutrition, sleepiness, and attention.
• OpenCV documentation: Look up camera capture, face tracking, and image processing basics.
• PsychoPy documentation: Build a timed reaction task and export trial-level data.
• JASP documentation: Run repeated-measures tests and graph within-subject results.