Reaction Time Lab
A browser-based reaction time playground for students, demos, and first-year studies. Try things out, compare conditions, and export usable data without turning your project into an argument with bad software.
Explore four classic cognitive tasks in one clean tool: simple reaction time, Go / No-Go, Stroop, and dot comparison. Use Free Play if you want to poke around. Use Project Mode if you need participant instructions, practice trials, and data you can actually work with.
Task Setup
Configure the task for this participant. Keep it simple. Your future self will appreciate it.
Task Instructions
Show these instructions to the participant before the task begins.
A short 3-trial practice round will begin first. These practice trials are not included in the final results.
Task Complete
Participant: | Condition:
What the Reaction Time Suite Does
The Reaction Time Suite gives you four classic cognitive tasks in one clean browser-based tool. It is built for students, demos, and first-year studies where you want something more useful than improvised nonsense but less painful than old lab software. Use Free Play to test things out, or Project Mode if you need participant instructions, practice trials, and exportable data.
The Four Tasks, Simply Put
Simple Reaction Time measures how quickly someone responds to a basic target. It is a good place to start if you want a straightforward speed task.
Go / No-Go adds inhibitory control. Participants respond to one stimulus and withhold response to another, which makes it useful for looking at attention and impulsive responding.
Stroop measures interference and selective attention. Participants respond to the ink colour rather than the word itself, which is where the mild irritation begins.
Dot Comparison is a perceptual decision task. Participants choose which side contains more dots, making it useful for quick left-right decisions and simple comparison work.
How to Use It for a Simple Study
Pick one task and keep the design simple. Choose an independent variable such as caffeine, time of day, background music, or fatigue. Keep the testing conditions as consistent as possible, run each participant through the same task, then export the results and compare reaction times, accuracy, or error rates depending on the task.
How to Interpret Your Results
Reaction Time Lab gives you trial-by-trial CSV data, which is much more useful than a single final score. The sections below explain what the download contains, what to look at first, and how to avoid making a dramatic claim out of one slightly odd row in a spreadsheet.
Your CSV includes one row per trial. That usually means participant ID, task name, condition label, trial number, stimulus type, response, correct response, accuracy, reaction time in milliseconds, error type, and timestamp.
In simple terms, each row tells you what happened on that trial, how the participant responded, and how quickly they did it.
Open the CSV in Excel or Google Sheets. The first sensible step is to sort or filter by condition label so you can compare groups or testing conditions more easily.
After that, focus on the columns that matter for the task you used rather than staring at the whole sheet and hoping meaning appears through force of will.
Simple Reaction Time: Start with mean reaction time, median reaction time, fastest correct responses, and false starts. If one condition is consistently slower, that is usually more useful than one unusually fast or slow trial.
Go / No-Go: Do not just look at speed. Look at commission errors, omission errors, and overall accuracy alongside mean reaction time for correct go trials. Fast but messy responding tells a different story from slower but more controlled responding.
Stroop: The key comparison is between congruent and incongruent trials. The main number to notice is the interference cost, which is the difference between reaction times for incongruent and congruent items.
Dot Comparison: Look at both accuracy and mean correct reaction time. A faster condition is not automatically a better one if accuracy falls apart at the same time.
Keep the design simple. If you ran a caffeine versus no caffeine comparison, or morning versus evening, sort the sheet by condition label and compare the key outcome for the task you used.
Simple Reaction Time: compare mean or median reaction times across conditions.
Go / No-Go: compare commission errors, omission errors, and go trial reaction times across conditions.
Stroop: compare interference cost across conditions rather than relying on one overall average.
Dot Comparison: compare both accuracy and mean correct reaction time, because either one on its own can give you a slightly warped picture.
If one condition looks different across several trials in a consistent way, that is worth noticing. If the difference mostly depends on one strange row in the sheet, it probably is not.
A reaction time difference is not automatically a deep psychological truth. Small differences can come from distraction, fatigue, browser timing variation, or participants not taking the task especially seriously.
Error rates matter as much as speed, and sometimes more. It is also worth remembering that this is a browser-based teaching and introductory research tool. It is very useful for classroom work, exploratory designs, and undergraduate projects, but it is not a high-precision lab setup.
Open the CSV, sort by condition, then compare the main outcome for your chosen task before trying any formal analysis.
In most cases, that means reaction time for Simple Reaction Time, error patterns for Go / No-Go, interference cost for Stroop, and accuracy plus reaction time for Dot Comparison.
Example Study Ideas
A first-year project does not need to be heroic. These are the sort of ideas this tool handles well:
caffeine vs no caffeine on simple reaction time
morning vs evening on Stroop interference
music vs silence on Go / No-Go accuracy
reversed key mapping on dot comparison to think about handedness and response bias
A Quick Note on Precision
This is a browser-based tool designed for teaching, demos, exploratory work, and introductory student research. It is useful, clean, and a lot less irritating than doing everything by hand, but it is not pretending to replace specialist experimental software in high-precision lab settings.
Perfect for undergrads. For PhDs, that is what your budget is for :)
