Weber Law Just Noticeable Difference
Evaluate Weber law ΔS = k × S from a supplied Weber fraction and positive baseline magnitude in linear units. Applicability of the fraction to the sensory task belongs to the caller.
Description
A Weber-law increment model multiplies a task-specific fraction by baseline magnitude.
A Weber-law increment model multiplies a task-specific fraction by baseline magnitude. The resulting increment has the same linear units as the baseline.1
Evaluate Weber law ΔS = k × S from a supplied Weber fraction and positive baseline magnitude in linear units. Applicability of the fraction to the sensory task belongs to the caller.
When to use Weber Law Just Noticeable Difference
Use this tool to evaluate Weber law ΔS = k × S from a supplied Weber fraction and positive baseline magnitude in linear units. Applicability of the fraction to the sensory task belongs to the caller.
Prepare the task-specific rates, comparable response-time means, identified spike train, processed ERP or estimated spectra requested. Confirm units, preprocessing, sample pairing and caller-fitted model coefficients.
How to use Weber Law Just Noticeable Difference and understand its results
- Prepare the task-specific rates, comparable response-time means, identified spike train, processed ERP or estimated spectra requested. Confirm units, preprocessing, sample pairing and caller-fitted model coefficients.
- Enter weberFraction, baselineMagnitude using the field table and calculation rules, then run the tool.
- Read justNoticeableIncrement. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| weberFraction | Task-specific Weber fraction. | number; minimum: 0 | Required |
| baselineMagnitude | Positive baseline in linear stimulus units. | number; strictly above: 0 | Required |
Enter nonnegative fraction and strictly positive linear baseline. Return the modeled increment; a zero fraction gives zero.
justNoticeableIncrement=weberFraction×baselineMagnitude Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| justNoticeableIncrement | Predicted increment in the baseline units. | number; minimum: 0 |
{
"input": {
"weberFraction": 0.05,
"baselineMagnitude": 200
},
"output": {
"justNoticeableIncrement": 10
}
}Limitations and assumptions
No fraction is calibrated or sensory threshold measured. Applicability, units and validity of proportional scaling must be established for the task.
Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Weber Fraction: Calculate the dimensionless Weber fraction as measured just-noticeable increment divided by baseline stimulus magnitude. Both magnitudes must use the same linear units; no universal sensory threshold is assumed. For a complementary task, use Hick Choice Reaction Time: Evaluate the equally likely choice form RT = a + b × log2(n), using caller-fitted coefficients in milliseconds. Predictions must be nonnegative; this is a model evaluation without participant calibration.
References
-
Weber Law Just Noticeable Difference — tool.io
Similar or alternative tools
- Weber Fraction
Calculate the dimensionless Weber fraction as measured just-noticeable increment divided by baseline stimulus magnitude. Both magnitudes must use the same linear units; no universal sensory threshold is assumed.
- Hick Choice Reaction Time
Evaluate the equally likely choice form RT = a + b × log2(n), using caller-fitted coefficients in milliseconds. Predictions must be nonnegative; this is a model evaluation without participant calibration.