dprime.ABX: d' for ABX Paradigm

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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

Usage

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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

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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 Nov. 10, 2020, 3:49 p.m.