Fitts Throughput
Measure effective Fitts throughput from observed movement times and endpoint dispersion (We=4.133·sample SD); distinct from predicting MT with fitted Fitts coefficients.
Description
Effective Fitts throughput combines movement speed with endpoint variability.
Effective Fitts throughput combines movement speed with endpoint variability. An effective width based on sample SD adjusts difficulty for observed accuracy.
The tool returns effectiveWidth (effective width); effectiveIndexOfDifficultyBits (effective index of difficulty bits); throughputBitsPerSecond (throughput bits per second). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use Fitts Throughput
Use Fitts Throughput when you already have movementDistances, movementTimesSeconds, endpointErrorsAlongAxis and want effectiveWidth, effectiveIndexOfDifficultyBits, throughputBitsPerSecond. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use Fitts Throughput and understand its results
- Enter movementDistances, movementTimesSeconds, endpointErrorsAlongAxis using the field table below. Match the units, ordering and data boundaries described in the calculation.
- Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
- Read effectiveWidth, effectiveIndexOfDifficultyBits, throughputBitsPerSecond. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
- Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.
| Input field | Meaning | Value type and bounds | Requirement |
|---|---|---|---|
| movementDistances | Movement distances | list of number | Required |
| movementTimesSeconds | Movement times seconds | list of number | Required |
| endpointErrorsAlongAxis | Endpoint errors along axis | list of number | Required |
Use positive paired distances and movement times in seconds with aligned endpoint errors. Require at least two errors with positive sample variance.
We = 4.133×sampleSD(axisErrors); IDe = log2(1+meanDistance/We); throughput = IDe/meanTime The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"movementDistances": [
100,
100,
100
],
"movementTimesSeconds": [
1,
1,
1
],
"endpointErrorsAlongAxis": [
-1,
0,
1
]
},
"output": {
"effectiveWidth": 4.133,
"effectiveIndexOfDifficultyBits": 4.655094160711693,
"throughputBitsPerSecond": 4.655094160711693
}
}Limitations and assumptions
The endpoint axis and trial inclusion must be defined consistently. This is a pooled-condition calculation, without task-design validation or confidence intervals.
Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.
Alternative or Complementary methods
Visual Search Slope: Aggregate correct-trial visual-search RTs by set size and fit equally weighted condition means; signed slopes are retained and no serial/parallel search diagnosis is asserted. Muscle Force Quantification Tool: Fit a linear force-transducer calibration and apply it to recordings, preserving signed estimates outside calibration; no strength norms are embedded.
Methodological background: Fitts throughput calculation reference.1
References
-
Fitts throughput calculation reference — I. Scott MacKenzie, York University
Similar or alternative tools
- Visual Search Slope
Aggregate correct-trial visual-search RTs by set size and fit equally weighted condition means; signed slopes are retained and no serial/parallel search diagnosis is asserted.
- Muscle Force Quantification Tool
Fit a linear force-transducer calibration and apply it to recordings, preserving signed estimates outside calibration; no strength norms are embedded.