fitMetaDprime {statConfR}R Documentation

title Compute measures of metacognitive sensitivity (meta-d') and metacognitive efficiency(meta-d'/d') for data from one or several subjects

Description

This function computes the measures for metacognitive sensitivity, meta-d', and metacognitive efficiency, meta-d'/d' (Maniscalco and Lau, 2012, 2014; Fleming, 2017) to data from binary choice tasks with discrete confidence judgments. Meta-d' and meta-d'/d' are computed using a maximum likelihood method for each subset of the data argument indicated by different values in the column participant, which can represent different subjects as well as experimental conditions.

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 conceptual idea of meta-d' is to quantify metacognition in terms of sensitivity in a hypothetical signal detection rating model describing the primary task, under the assumption that participants had perfect access to the sensory evidence and were perfectly consistent in placing their confidence criteria (Maniscalco & Lau, 2012, 2014). Using a signal detection model describing the primary task to quantify metacognition allows a direct comparison between metacognitive accuracy and discrimination performance because both are measured on the same scale. Meta-d' can be compared against the estimate of the distance between the two stimulus distributions estimated from discrimination responses, which is referred to as d': If meta-d' equals d', it means that metacognitive accuracy is exactly as good as expected from discrimination performance. Ifmeta-d' is lower than d', it means that metacognitive accuracy is suboptimal. It can be shown that the implicit model of confidence underlying the meta-d'/d' method is identical to the independent truncated Gaussian model.

The provided data argument is split into subsets according to the values of the participant column. Then for each subset, the parameters of the hypothetical signal detection model determined by the model argument are fitted to the data subset.

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 one row for each participant and following columns:

Author(s)

Manuel Rausch, manuel.rausch@hochschule-rhein-waal.de

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)

[Package statConfR version 0.2.0 Index]