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statConfR: Models of Decision Confidence and Metacognition

The statConfR package provides functions to fit static models of decision-making and confidence derived from signal detection theory for binary discrimination tasks, as well as meta-d′/d′ (Rausch & Hellmann, 2024). The package can be used to test the assumptions underlying meta-d′/d′. Several models provide a metacognition parameter that may serve as an alternative when the assumptions of meta-d′/d′ assuming the corresponding model provides a better fit to the data. At this point in time, the following models are included:

Installation

The latest released version of the package is available on CRAN via

install.packages("statConfR")

The easiest way to install the development version is using devtools and install from GitHub:

devtools::install_github("ManuelRausch/StatConfR")

Usage

Data structure

The package includes a demo data set from a masked orientation discrimination task with confidence judgments (Hellmann et al., 2023, Exp. 1.

library(statConfR)
data("MaskOri")
head(MaskOri)
##   participant stimulus correct rating diffCond trialNo
## 1           1        0       1      0      8.3       1
## 2           1       90       0      4    133.3       2
## 3           1        0       1      0     33.3       3
## 4           1       90       0      0     16.7       4
## 5           1        0       1      3    133.3       5
## 6           1        0       1      0     16.7       6

Data should be in the form of a data.frame object columns for following variables:

Fitting

It is strongly recommended that if metacognitive efficiency is to be measured using the meta-d′/d′ method that researchers fist determine whether the Independent Truncated Gaussian Model, the confidence model implied by the meta-d′/d′ method, is an adequate description of the data. Using the function fitConfModel, we can fit several confidence models to the data of each participant. The argument .parallel=TRUEallows for parallelization over all but one available core.

fitted_pars <- fitConfModels(MaskOri, models=c("SDT", "WEV"), .parallel = TRUE) 

This parallelizes the fitting process over participant-model combinations. The output is then a data frame with one row for each participant-model combination and columns for parameters and measures for model performance (negative log-likelihood, BIC, AIC and AICc). These may be used for quantitative model comparison.

head(fitted_pars)
##   model participant negLogLik    N  k      BIC     AICc      AIC    d_1    d_2
## 1   SDT           1  2721.256 1620 14 5545.975 5470.739 5470.513 0.0428 0.4593
## 2   WEV           1  2621.110 1620 16 5360.464 5274.520 5274.221 0.2027 0.6142
## 3   SDT           2  1946.258 1620 14 3995.979 3920.743 3920.517 0.0000 0.0950
## 4   WEV           2  1827.221 1620 16 3772.684 3686.741 3686.441 0.0512 0.1920
## 5   SDT           3  1706.178 1620 14 3515.818 3440.582 3440.356 0.2708 0.4673
## 6   WEV           3  1661.617 1620 16 3441.476 3355.533 3355.233 0.4146 0.8561
##      d_3    d_4    d_5       c theta_minus.4 theta_minus.3 theta_minus.2
## 1 1.0526 3.6806 4.7779 -0.2723       -1.5467       -1.0333       -0.6336
## 2 1.0797 3.4746 4.0799 -0.2957       -2.0665       -1.2485       -0.4152
## 3 0.8601 6.1410 8.0556 -0.1394       -2.0092       -1.9193       -1.4097
## 4 1.0412 4.1423 5.2886 -0.1475       -2.0441       -1.9500       -1.3982
## 5 1.9117 6.4257 7.5755 -1.1510       -1.9938       -1.6372       -1.2600
## 6 2.7115 6.9164 7.9863 -1.3743       -2.7625       -1.9192       -0.3724
##   theta_minus.1 theta_plus.1 theta_plus.2 theta_plus.3 theta_plus.4  sigma
## 1       -0.4543      -0.0944       0.2152       0.9850       1.5735     NA
## 2        0.1296      -0.6196       0.1544       1.3976       2.1879 1.0105
## 3       -0.9580       0.7857       1.3781       2.0879       2.2369     NA
## 4       -0.9030       0.8201       1.4484       2.2447       2.4030 0.6391
## 5       -1.1668      -1.1143      -0.7344       0.2961       0.9314     NA
## 6        0.9328      -2.7695      -1.1313       0.7714       1.7520 1.3289
##        w wAIC wAICc wBIC
## 1     NA    0     0    0
## 2 0.5361    1     1    1
## 3     NA    0     0    0
## 4 0.5020    1     1    1
## 5     NA    0     0    0
## 6 0.3818    1     1    1

If the Independent Truncated Gaussian model provides a decent account of the data (which is not the case though in the demo dataset), it is legitimate to quantify metacognitive efficiency with meta-d′/d′:

MetaDs <- fitMetaDprime(subset(MaskOri, diffCond == "33.3"), 
                        model="ML", .parallel = TRUE)

Contact

For comments, remarks, and questions please contact either manuel.rausch@hochschule-rhein-waal.de or sebastian.hellmann@ku.de or submit an issue.

References

Hellmann, S., Zehetleitner, M., & Rausch, M. (2023). Simultaneous modeling of choice, confidence, and response time in visual perception. Psychological Review. 130(6), 1521–1543. doi:10.1037/rev0000411

Rausch, M., Hellmann, S. & Zehetleitner, M. (2023). Measures of metacognitive efficiency across cognitive models of decision confidence. Psychological Methods. doi:10.1037/met0000634

Rausch, M., & Hellmann, S. (2024). statConfR: An R Package for Static Models of Decision Confidence and Metacognition. PsyArXiv. doi:10.31234/osf.io/dk6mr

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.