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Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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A Two-interval Forced-choice Task for Multisensory Comparisons
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Fixed vs. variable noise in 2AFC contrast discrimination: lessons from psychometric functions.

Miguel A García-Pérez1, Rocío Alcalá-Quintana

  • 1Departamento de Metodología, Facultad de Psicología, Universidad Complutense, Campus de Somosaguas, 28223 Madrid, Spain. miguel@psi.ucm.es

Spatial Vision
|July 23, 2009
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Summary

The nature of noise limiting 2AFC discrimination performance cannot be determined by fitting models to data. Both fixed and variable noise models can equally fit performance data, regardless of experimental methods.

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Area of Science:

  • Psychophysics
  • Computational Neuroscience
  • Sensory Perception

Background:

  • The nature of internal noise is crucial for understanding sensory discrimination limits.
  • Distinguishing between fixed and variable noise has been a focus of recent psychophysical research.

Purpose of the Study:

  • To demonstrate that goodness-of-fit statistics cannot resolve whether sensory noise is fixed or variable.
  • To analyze the limitations of model fitting in psychophysical research.

Main Methods:

  • Theoretical analysis of model fitting in psychophysics.
  • Examination of how transducer functions interact with noise models.
  • Critique of experimental methods aimed at dissociating response mean and variance.

Main Results:

  • Data from detection and discrimination tasks can be fitted equally well by models assuming either fixed or variable noise.
  • Model fitting success is dependent on the chosen transducer function, not the noise type.
  • Goodness-of-fit does not reveal the underlying nature of sensory noise.

Conclusions:

  • The question of fixed versus variable noise cannot be solved by fitting models to performance data.
  • Further theoretical and experimental advancements are needed to characterize sensory noise.