DPSD: Estimation of recollection and familiarity by fitting to Dual...

In JAQuent/memoryROC: A package to analyse recognition memory data within R.

Description

This function allows to estimate recollection and familiarity by fitting data to the DPSD model. The optimization is attempted by minimizing the total squared difference between observed and predicted hit and false alarm rates. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm from the function. The function first uses standard start values and then random values in order to find the set of parameters, which fit the data best by returning the values with the lowest total squared difference. The function optim is used for optimization. Box constraints limit estimations of recollection and familiarity to be positive.

Usage

DPSD(responseScale, confidenceRatings, oldNew, oldNewLevels = c(0, 1),
  iterations = 200)

Arguments

responseScale	An vector containing possible levels of recognition responses ordered from highest to lowest (e.g. 6:1).
confidenceRatings	An vector containing recognition responses according to levels of the variable responseScale.
oldNew	An vector coding whether an item was new/not-studied or old/studied.
oldNewLevels	An vector containing possible levels of old_newInformation. The first value or level is for new/not-studied
iterations	A numeric value specifying the number of iterations. Default is set to 200.

Value

The function the set of parameters, which showed the lowest total squared difference:

recollection	Estimate of recollection.
familiarity	Estimate of familiarity.

Author(s)

Joern Alexander Quent, [e-mail]

References

Yonelinas, A. P. (1994). Receiver-operating characteristics in recognition memory: evidence for a dual-process model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(6), 1341.

JAQuent/memoryROC documentation built on May 7, 2019, 6:46 a.m.