fitMetaDprime {statConfR}

Fits meta-d' and meta-d'/d' ratios for data from one or several subjects

Description

This function computes meta-d' and meta-d'/d' for each participant in the data, respectively.

Usage

fitMetaDprime(data, model = "ML", nInits = 5, nRestart = 3,
  .parallel = FALSE, n.cores = NULL)

Arguments

data

a data.frame where each row is one trial, containing following variables:

• rating (discrete confidence judgments, should be given as factor; otherwise will be transformed to factor with a warning),

• stimulus (stimulus category in a binary choice task, should be a factor with two levels, otherwise it will be transformed to a factor with a warning),

• correct (encoding whether the response was correct; should be 0 for incorrect responses and 1 for correct responses)

• participant (giving the subject ID; the models given in the second argument are fitted for each subject individually.

model

character of length 1. Either "ML" to use the original model specification by Maniscalco and Lau (2012, 2014) or "F" to use the model specification by Fleming (2017)'s HmetaD method. Defaults to "ML"

nInits

integer. Number of initial values used for maximum likelihood optimization. Defaults to 5.

nRestart

integer. Number of times the optimization is restarted. Defaults to 3.

.parallel

logical. Whether to parallelize the fitting over models and participant (default: FALSE)

n.cores

integer. Number of cores used for parallelization. If NULL (default), the available number of cores -1 will be used.

Details

The function computes meta-d' and meta-d'/d' either using the hypothetical signal detection model assumed by Maniscalco and Lau (2012, 2014) or the one assumed by Fleming (2014). The fitting routine first performs a coarse grid search to find promising starting values for the maximum likelihood optimization procedure. Then the best nInits parameter sets found by the grid search are used as the initial values for separate runs of the Nelder-Mead algorithm implemented in optim. Each run is restarted nRestart times. Warning: meta-d'/d' is only guaranteed to be unbiased from discrimination sensitivity, discrimination bias, and confidence criteria if the data is generated according to the independent truncated Gaussian model (see Rausch et al., 2023).

Value

Gives data frame with rows for each participant and columns dprime, c, metaD, and Ratio

• dprime is the discrimination sensitivity index d, calculated using a standard SDT formula

• c is the discrimination bias c, calculated using a standard SDT formula

• metaD is meta-d', discrimination sensitivity estimated from confidence judgments conditioned on the response

• Ratio is meta-d'/d', a quantity usually referred to as metacognitive efficiency.

Author(s)

Manuel Rausch, [e-mail]

References

Fleming, S. M. (2017). HMeta-d: Hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings. Neuroscience of Consciousness, 1, 1–14. doi: 10.1093/nc/nix007

Maniscalco, B., & Lau, H. (2012). A signal detection theoretic method for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422–430.

Maniscalco, B., & Lau, H. C. (2014). Signal Detection Theory Analysis of Type 1 and Type 2 Data: Meta-d’, Response- Specific Meta-d’, and the Unequal Variance SDT Model. In S. M. Fleming & C. D. Frith (Eds.), The Cognitive Neuroscience of Metacognition (pp. 25–66). Springer. doi: 10.1007/978-3-642-45190-4_3

Rausch, M., Hellmann, S., & Zehetleitner, M. (2023). Measures of metacognitive efficiency across cognitive models of decision confidence. Psychological Methods. doi: 10.31234/osf.io/kdz34

Examples

# 1. Select two subject from the masked orientation discrimination experiment
data <- subset(MaskOri, participant %in% c(1:2))
head(data)

# 2. Fit meta-d/d for each subject in data
MetaDs <- fitMetaDprime(data, model="F", .parallel = FALSE)