pysdt A module for computing signal detection theory measures.

`pysdt` – Signal Detection Theory Measures¶

A module for computing signal detection theory measures. Some of the functions in this module have been ported to python from the ‘psyphy’ R package of Kenneth Knoblauch http://cran.r-project.org/web/packages/psyphy/index.html

pysdt.compute_proportions(nCA, nTA, nIB, nTB, corr)[source]¶

Compute proportions with optional corrections for extreme proportions.

Parameters:	nCA : float Number of correct ‘A’ trials nTA : int Number of total ‘A’ trials nIB : float Number of incorrect ‘B’ trials nTB : int Number of total ‘B’ trials corr : string The correction to apply, none for no correction, ‘loglinear` for the log-linear correction, and 2N for the ‘2N’ correction.
Returns:	HR : float Hit rate FA : float False alarm rate

References

[1]	Hautus, M. J. (1995). Corrections for extreme proportions and their biasing effects on estimated values of d’. Behavior Research Methods, Instruments, & Computers, 27(I), 46–51. http://doi.org/10.3758/BF03203619

[2]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

Examples

>>> H,F = compute_proportions(8, 10, 2, 10, "loglinear")
>>> H,F = compute_proportions(10, 10, 2, 10, "loglinear")
>>> H,F = compute_proportions(10, 10, 2, 10, "2N")

pysdt.dprime_ABX(H, FA, meth)[source]¶

Compute d’ for ABX task from ‘hit’ and ‘false alarm’ rates.

Parameters:	H : float Hit rate. FA : float False alarms rate. meth : string ‘diff’ for differencing strategy or ‘IO’ for independent observations strategy.
Returns:	dprime : float d’ value

References

[1]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

Examples

>>> dp = dprime_ABX(0.7, 0.2, 'IO')
>>> dp = dprime_ABX(0.7, 0.2, 'diff')

pysdt.dprime_ABX_from_counts(nCA, nTA, nCB, nTB, meth, corr)[source]¶

Compute d’ for ABX task from counts of correct and total responses.

Parameters:

nCA : int: Number of correct responses in ‘same’ trials.
nTA : int: Total number of ‘same’ trials.
nCB : int: Number of correct responses in ‘different’ trials.
nTB : int: Total number of ‘different’ trials.
meth : string: ‘diff’ for differencing strategy or ‘IO’ for independent observations strategy.
corr : logical: if True, apply the correction to avoid hit and false alarm rates of 0 or one.

Returns:

dprime : float: d’ value

References

[1]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

Examples

>>> dp = dprime_ABX(0.7, 0.2, 'IO')

pysdt.dprime_SD(H, FA, meth)[source]¶

Compute d’ for one interval same/different task from ‘hit’ and ‘false alarm’ rates.

Parameters:	H : float Hit rate. FA : float False alarms rate. meth : string ‘diff’ for differencing strategy or ‘IO’ for independent observations strategy.
Returns:	dprime : float d’ value

References

[1]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

[2]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

Examples

>>> dp = dprime_SD(0.7, 0.2, 'IO')

pysdt.dprime_SD_from_counts(nCA, nTA, nCB, nTB, meth, corr)[source]¶

Compute d’ for one interval same/different task from counts of correct and total responses.

Parameters:

nCA : int: Number of correct responses in ‘same’ trials.
nTA : int: Total number of ‘same’ trials.
nCB : int: Number of correct responses in ‘different’ trials.
nTB : int: Total number of ‘different’ trials.
meth : string: ‘diff’ for differencing strategy or ‘IO’ for independent observations strategy.
corr : logical: if True, apply the correction to avoid hit and false alarm rates of 0 or one.

Returns:

dprime : float: d’ value

References

[1]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

[2]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

Examples

>>> dp = dprime_SD(0.7, 0.2, 'IO')

pysdt.dprime_mAFC(Pc, m)[source]¶

Compute d’ corresponding to a certain proportion of correct responses in m-AFC tasks.

Parameters:	Pc : float Proportion of correct responses. m : int Number of alternatives.
Returns:	dprime : float d’ value

References

[1]	Green, D. M., & Swets, J. A. (1988). Signal Detection Theory and Psychophysics. Los Altos, California: Peninsula Publishing.

[2]	Green, D. M., & Dai, H. P. (1991). Probability of being correct with 1 of M orthogonal signals. Perception & Psychophysics, 49(1), 100–101.

Examples

>>> dp = dprime_mAFC(0.7, 3)

pysdt.dprime_oddity(prCorr, meth=u'diff')[source]¶

Compute d’ for oddity task from proportion of correct responses. Only valid for the case in which there are three presentation intervals.

Parameters:	prCorr : float Proportion of correct responses. meth : string ‘diff’ for differencing strategy or ‘IO’ for independent observations strategy.
Returns:	dprime : float d’ value

References

[1]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

[2]	Versfeld, N. J., Dai, H., & Green, D. M. (1996). The optimum decision rules for the oddity task. Perception & Psychophysics, 58(1), 10–21.

Examples

>>> dp = dprime_oddity(0.7)
>>> dp = dprime_oddity(0.8)

pysdt.dprime_yes_no(H, FA)[source]¶

Compute d’ for one interval ‘yes/no’ type tasks from hits and false alarm rates.

Parameters:	H : float Hit rate. FA : float False alarms rate.
Returns:	dprime : float d’ value

References

[1]	Green, D. M., & Swets, J. A. (1988). Signal Detection Theory and Psychophysics. Los Altos, California: Peninsula Publishing.

[2]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

Examples

>>> dp = dprime_yes_no(0.7, 0.2)

pysdt.dprime_yes_no_from_counts(nCA, nTA, nCB, nTB, corr)[source]¶

Compute d’ for one interval ‘yes/no’ type tasks from counts of correct and total responses.

Parameters:	nCA : int Number of correct responses in ‘signal’ trials. nTA : int Total number of ‘signal’ trials. nCB : int Number of correct responses in ‘noise’ trials. nTB : int Total number of ‘noise’ trials. corr : logical if True, apply the correction to avoid hit and false alarm rates of 0 or one.
Returns:	dprime : float d’ value

References

[1]	Green, D. M., & Swets, J. A. (1988). Signal Detection Theory and Psychophysics. Los Altos, California: Peninsula Publishing.

[2]	Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (2nd ed.). London: Lawrence Erlbraum Associates.

Examples

>>> dp = dprime_yes_no_from_counts(nCA=70, nTA=100, nCB=80, nTB=100, corr=True)

pysdt.gaussianPsy(x, alphax, betax, gammax, lambdax)[source]¶

Compute the gaussian psychometric function.

Parameters:	x : Stimulus level(s). alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function (guess rate). lambdax: The lapse rate.
Returns:	pc : Proportion correct at the stimulus level(s) x.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt.gumbelPsy(x, alphax, betax, gammax, lambdax)[source]¶

Compute the gumbel psychometric function.

Parameters:	x : Stimulus level(s). alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function (guess rate). lambdax: The lapse rate.
Returns:	pc : Proportion correct at the stimulus level(s) x.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt.invGaussianPsy(p, alphax, betax, gammax, lambdax)[source]¶

Compute the inverse gaussian psychometric function.

Parameters:	p : Proportion correct on the psychometric function. alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function. lambdax: The lapse rate.
Returns:	x : Stimulus level at which proportion correct equals p for the listener specified by the function.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt.invGumbelPsy(p, alphax, betax, gammax, lambdax)[source]¶

Compute the inverse gumbel psychometric function.

Parameters:	p : Proportion correct on the psychometric function. alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function. lambdax: The lapse rate.
Returns:	x : Stimulus level at which proportion correct equals p for the listener specified by the function.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt.invLogisticPsy(p, alphax, betax, gammax, lambdax)[source]¶

Compute the inverse logistic psychometric function.

Parameters:	p : Proportion correct on the psychometric function. alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function. lambdax: The lapse rate.
Returns:	x : Stimulus level at which proportion correct equals p for the listener specified by the function.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt.invWeibullPsy(p, alphax, betax, gammax, lambdax)[source]¶

Compute the inverse weibull psychometric function.

Parameters:	p : Proportion correct on the psychometric function. alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function. lambdax: The lapse rate.
Returns:	x : Stimulus level at which proportion correct equals p for the listener specified by the function.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt.logisticPsy(x, alphax, betax, gammax, lambdax)[source]¶

Compute the logistic psychometric function.

Parameters:	x : Stimulus level(s). alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function (guess rate). lambdax: The lapse rate.
Returns:	pc : Proportion correct at the stimulus level(s) x.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt.weibullPsy(x, alphax, betax, gammax, lambdax)[source]¶

Compute the weibull psychometric function.

Parameters:	x : Stimulus level(s). alphax: Mid-point(s) of the psychometric function. betax: The slope of the psychometric function. gammax: Lower limit of the psychometric function (guess rate). lambdax: The lapse rate.
Returns:	pc : Proportion correct at the stimulus level(s) x.

References

[1]	Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A Practical Introduction. Academic Press.

pysdt – Signal Detection Theory Measures¶

`pysdt` – Signal Detection Theory Measures¶