Build custom reaction time experiments for psychology and neuroscience research. Simple RT, choice RT, and complex paradigms with millisecond-precise timing.
Respond as quickly as possible to any stimulus. Measures basic processing speed without decision-making.
Typical RT: 150-300ms
Discriminate between stimuli and respond accordingly. Measures decision speed and accuracy.
Typical RT: 300-500ms
Multi-step decisions, go/no-go, or conditional responses. Measures executive aspects of RT.
Typical RT: 400-800ms
High-precision timing using requestAnimationFrame and performance.now().
Visual builder for all RT paradigms. Configure timing, stimuli, and responses.
Visual, auditory, or multimodal. Images, shapes, text, or custom media.
Trial-level data with timestamps, RT distributions, and summary statistics.
Sub-millisecond timestamps
Uses performance.now() for high-resolution timing
Frame-synced presentation
requestAnimationFrame for consistent stimulus onset
Keyboard event timing
Native event timestamps for accurate RT capture
Trial-level export
Every trial with stimulus, response, and RT
Automatic outlier flagging
Identifies anticipations and slow responses
Summary statistics
Mean, median, SD, accuracy auto-calculated
Study attention, perception, and decision-making processes.
Measure processing speed in ADHD, concussion, aging, and neurological conditions.
Assess athlete reaction time and decision speed under various conditions.
Evaluate operator performance, fatigue effects, and interface design.
Simple RT involves responding to a single stimulus with a single response (e.g., press spacebar when you see any stimulus). Choice RT requires discriminating between stimuli and selecting the appropriate response (e.g., press left for X, right for O). Choice RT is slower due to decision-making processes.
AssessKit achieves millisecond-level precision using optimized JavaScript timing. While online testing adds some variability compared to lab settings, research shows online RT data is suitable for detecting experimental effects and individual differences.
RT is influenced by: stimulus intensity, foreperiod duration, stimulus-response compatibility, practice effects, fatigue, age, and various clinical conditions. AssessKit lets you control all experimental parameters to isolate effects of interest.
Yes. AssessKit supports visual stimuli (shapes, images, text), auditory stimuli, and multimodal presentations. You can create cross-modal RT experiments or standard visual/auditory RT tasks.
AssessKit calculates: mean RT, median RT, RT standard deviation, accuracy, RT distributions, and trial-by-trial data. You can export raw data for custom analyses including ex-Gaussian fitting or drift diffusion modeling.
Create simple RT, choice RT, and complex paradigms. Free to start, no credit card required.
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