dprime.ABX: d' for ABX Paradigm

In psyphy: Functions for Analyzing Psychophysical Data in R

d' for ABX Paradigm

Description

Calulate d' for ABX paradigm either assuming a differencing strategy or independent observations

Usage

dprime.ABX(Hits, FA, zdiff, Pc.unb, method = "diff")

Arguments

Hits	numeric in [0, 1] corresponding to Hit rate
FA	numeric in [0, 1] corresponding to False alarm rate
zdiff	numeric. Difference of z-scores for Hit and False Alarm rates
Pc.unb	numeric in [0, 1]. Proportion correct for an unbiased observer, pnorm(zdiff)
method	character. Specifies the model to describe the observer's criterion for dividing up the decision space, must be either "diff" for a differencing strategy (the default) or "IO" for independent observations.

Details

Two different strategies have been described for how the observer partitions the decision space in the ABX paradigm, either based on Independent Observations of each stimulus or on a differencing strategy. The differecing strategy is the default. d' can be calculated either from the H and FA rates, from the difference of z-scores or from the probability correct of an unbiased observer.

Value

Returns the value of d'

Author(s)

Kenneth Knoblauch

References

MacMillan, N. A. and Creeman, C. D. (1991) Detection Theory: A User's Guide Cambridge University Press

Green, D. M. and Swets, J. A. (1966) Signal Detection Theory and Psychophysics Robert E. Krieger Publishing Company

See Also

dprime.mAFC, dprime.SD, dprime.oddity

Examples

dprime.ABX(H = 0.75, F = 0.3)
dprime.ABX(H = 0.75, F = 0.3, method = "IO")
dprime.ABX(zdiff = qnorm(0.75) - qnorm(0.3))
dprime.ABX(Pc.unb = pnorm( (qnorm(0.75) - qnorm(0.3))/2 ))

psyphy documentation built on Aug. 19, 2023, 5:07 p.m.