metaSDT

This is an R implementation of Maniscalco and Lau’s methods of calculating metacognitive SDT measures using maximum likelihood estimation and minimization of the sum of squared errors.

Installation

You can install metaSDT’s development version from GitHub with:

# install.packages("devtools")
devtools::install_github("craddm/metaSDT")

For further details on metacognitive/Type 2 SDT, see their website at http://www.columbia.edu/~bsm2105/type2sdt/ and the following publications:

Maniscalco, B., & Lau, H. (2012). A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422–430. <doi:10.1016/j.concog.2011.09.021>

Maniscalco, B., & Lau, H. (2014). Signal detection theory analysis of type 1 and type 2 data: meta-d’, response-specific meta-d’, and the unequal variance SDT mode. In S. M. Fleming & C. D. Frith (Eds.), The Cognitive Neuroscience of Metacognition (pp.25-66). Springer.

If you use these functions, cite the above papers and scripts on which it is based.

The fit_meta_d_SSE and fit_meta_d_MLE commands require data in the same format outlined on M & L’s webpage, as follows:

Suppose there are two stimuli, A, and B, and three confidence ratings - high (3), medium(2), and low(1). The possible responses are

A3, A2, A1, B1, B2, B3.

Input to the function should be counts for each of these responses separately for each stimulus type.

So for example:

library(metaSDT)
nR_S1 <- c(100, 50, 30, 20, 10, 4)
nR_S2 <- c(4, 20, 21, 35, 60, 90)
fit_MLE <- fit_meta_d_MLE(nR_S1,
                          nR_S2)
fit_MLE
#>         da s meta_da     M_diff  M_ratio    meta_ca   t2ca_rS1  t2ca_rS2
#> 1 1.845043 1 1.87284 0.02779706 1.015066 0.07108153 -1.0297441 0.5524547
#> 2 1.845043 1 1.87284 0.02779706 1.015066 0.07108153 -0.3710477 1.2052824
#>        logL est_HR2_rS1 est_HR2_rS2 est_FAR2_rS1 est_FAR2_rS2 obs_HR2_rS1
#> 1 -444.1742   0.8469277   0.8052557    0.4938669    0.4352026   0.8328717
#> 2 -444.1742   0.5489194   0.4885083    0.1273860    0.1027034   0.5549400
#>   obs_HR2_rS2 obs_FAR2_rS1 obs_FAR2_rS2       t1c1
#> 1   0.8104223   0.53479853    0.4154589 0.07002653
#> 2   0.4860737   0.09157509    0.1207729 0.07002653
fit_SSE <- fit_meta_d_SSE(nR_S1,
                          nR_S2)
fit_SSE
#>         da meta_da     M_diff  M_ratio    meta_ca s t2ca_rS1 t2ca_rS2
#> 1 1.845043    1.89 0.04495688 1.024366 0.07173282 1   -1.057    0.568
#> 2 1.845043    1.89 0.04495688 1.024366 0.07173282 1   -1.057    0.568
#>          SSE est_HR2_rS1 obs_HR2_rS1 est_HR2_rS2 obs_HR2_rS2 est_FAR2_rS1
#> 1 0.00246936   0.5386789   0.5549400   0.7999733   0.8104223     0.118341
#> 2 0.00246936   0.5386789   0.8328717   0.7999733   0.4860737     0.118341
#>   obs_FAR2_rS1 est_FAR2_rS2 obs_FAR2_rS2       t1c1
#> 1   0.09157509    0.4214078    0.4154589 0.07002653
#> 2   0.53479853    0.4214078    0.1207729 0.07002653

Output is a data frame with m-ratio etc.