# dprime.ABX: d' for ABX Paradigm In psyphy: Functions for Analyzing Psychophysical Data in R

## Description

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

## Usage

 `1` ```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

`dprime.mAFC`, `dprime.SD`, `dprime.oddity`

## Examples

 ```1 2 3 4``` ```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.