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Showing content from https://github.com/SeHellmann/dynConfiR below:

SeHellmann/dynConfiR: The R package for dynamical confidence models

This package includes implementation for several sequential sampling models of decision making and confidence judgments. The package includes density functions for decision, confidence and response time outcomes for following models: Dynamic visibility, time, and evidence model (dynaViTE), Dynamic weighted evidence and visibility (dynWEV), two-stage signal detection (2DSD), inpedendent and partially-correlated race models (IRM/PCRM). In addition, the package includes functions for parameter fitting, prediction and simulation of data. See the Preprint (Hellmann et al., 2025) for more information about implemented models and usage.

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

install.packages("dynConfiR")

For the current development version, the easiest way of installation is using devtools and install from GitHub:

devtools::install_github("SeHellmann/dynConfiR")

library(dynConfiR)
d2DSD(rt=0.7, th1=1, th2=2.5, response="lower", 
      a=2, v=0.7, t0=0, z =0.5, sv=0, st0=0.1, tau=1, lambda=0.5)

ddynaViTE(rt=2.7, th1=1, th2=2.5, response="lower", 
      tau=1, a=2, v=0.7, t0=0, z =0.5, sv=0, st0=0.1, lambda=0.2,
     simult_conf = TRUE)

dIRM(1.2, response=2, mu1=0.5, mu2=-0.5, a=0.8, b=0.5, th1=-0.5, th2=2, 
     wx=0.5, wrt=0.2, wint=0.3, t0=0.3, st0=0.2)

dPCRM(1.2, response=2, mu1=0.5, mu2=-0.5, a=0.8, b=0.5, th1=-0.5, th2=2, 
     wx=0.5, wrt=0.2, wint=0.3, t0=0.3, st0=0.2)

When using the package for model comparison, the suggested workflow is:

Data should be of the form

##   participant direction coherence response   rt rating
## 1           1      left       0.3     left 1.21      2
## 2           1      left       0.5     left 1.09      3
## 3           1     right       0.3     left 0.97      2
## 4           1     right       0.5     left 1.45      1
## 5           1      left       0.1    right 1.22      1

where the task may have been to discriminate direction and coherence was manipulated for higher or lower accuracy. Fitting confidence models requires the data to be in a data.frame or tibble object with columns for following variables:

• stimulus: In a binary decision task the stimulus identity gives the correct response
• condition: The experimental manipulation that is expected to affect model parameters should be present
• response: The actual decision in the choice task
• rt: The recorded response time.
• rating: A discrete variable encoding the decision confidence (high: very confident; low: less confident)

Alternatively to stimulus or response it is possible to use a column, correct, representing whether the decision was correct or wrong. Alternative column names may be passed to the fitting functions (see below).

If there are several participants, for which the models should be fitted independently, and the models of interest are dynWEV and 2DSD, then fitting the models is done using the fitRTConfModels function:

fitted_pars <- fitRTConfModels(data, models=c("dynWEV","2DSD"), stimulus="direction", condition="coherence")

By default, 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.

##    participant model    a    z   sz   v1   v2   v3   sv   t0  st0 thetaLower1
## 1            1  2DSD 1.90 0.35 0.35 0.01 0.00 2.14 0.18 0.24 0.45       -1.29
## 21           3  2DSD 2.53 0.39 0.16 0.00 0.02 1.02 0.54 0.33 0.22       -0.95
## 28           4  2DSD 1.70 0.43 0.62 0.00 0.01 2.05 0.00 0.33 0.33       -1.82
## 29           5  2DSD 1.94 0.43 0.00 0.00 0.83 3.34 0.60 0.33 0.09       -1.76
## 30           6  2DSD 1.33 0.51 0.51 0.00 0.01 2.27 0.81 0.38 0.35       -1.70
## 31           7  2DSD 1.67 0.74 0.47 0.03 0.01 3.49 0.26 0.34 0.58       -1.92
##    thetaLower2 thetaUpper1 thetaUpper2 tau negLogLik   N  k     BIC    AICc
## 1        -0.90        2.15        2.19   1   1019.23 527 20 2163.80 2079.96
## 21       -0.73        2.63        2.71   1   1475.68 534 20 3076.97 2992.84
## 28       -1.60        1.58        1.79   1    967.71 533 20 2060.99 1976.90
## 29       -1.41        2.96        3.20   1    741.86 536 20 1609.41 1525.20
## 30       -1.41        1.75        2.13   1    911.06 531 20 1947.62 1863.61
## 31        0.05        3.09        3.09   1    647.34 533 20 1420.25 1336.17
##        AIC  w sig sigmu
## 1  2078.45 NA  NA    NA
## 21 2991.36 NA  NA    NA
## 28 1975.42 NA  NA    NA
## 29 1523.72 NA  NA    NA
## 30 1862.12 NA  NA    NA
## 31 1334.68 NA  NA    NA

The output of the fitting function includes different information criteria, which could be used for a quantitative model comparison (namely: BIC, AIC, and AICc). The package also offers functions to conduct the quantitative comparison either on the level of single individuals or on a group level. For the individual level, the function subject_modelweights computes individual model weights for all fitted models directly from the output of the fitting function. Similarly, for a group-level comparison, the function group_BMS_fits directly uses the output of the fitting function and conducts a group-level Bayesian model selection based on a random effects model of model prevalence across subjects (Rigoux et al., 2014).

For prediction the functions predictConf and predictRT are used, together with parameter sets for the respective models. For multiple participants and models, the output data frame from the function fitRTConfModels may be used in the functions predictConfModels and predictRTModels to simultaneously (and in parallel) predict the distributions.

• predictConf: This function predicts the distribution of decision and rating responses (ignoring response times) for the different stimulus conditions.
• predictRT: This function computes the probability densities for decision, confidence and response time outputs over a range of response times for different stimulus conditions. If required, it also returns a scaled density (i.e. the conditional probability of a certain response time, given the decision and confidence response) - for this the output of predictConf is required.

Usage example:

fitted_pars %>% 
  group_by(model, participant) %>% 
  summarise(predictConf(pick(everything()), model=cur_group()$model[1]))

Implementation of a simulation of observations in the Leaky Competing Accumulator model (see rLCA).

The package is under active development. We are planning to implement new models of decision confidence when they are published. Please feel free to contact us to suggest new models to implement in the package, or to volunteer adding additional models.

Implementing custom models of decision confidence is only recommended for users with experience in cognitive modelling! For readers who want to use our open code to implement models of confidence themselves, the following steps need to be taken:

• Derive the likelihood of a binary response ( $R=-1, 1$ ) at response time ( $T$ ) and a specific level of confidence ( $C=1,...K$ ) according to the custom model and a set of parameters ( $\theta$ ), given the binary stimulus ( $S=-1, 1$ ), i.e. $P(R, T, C | S, \theta)$.
• Write a corresponding density function ‘dyourmodelname’ based on the available densities.
• Use one of the files named ‘likelihood_model.R’ from the package sources and adapt the likelihood function according to your model. According to our convention, name the new file a ‘likelihood_yourmodelname.R’.
• Use one of the files ‘int_fitting_model.R’ from the package sources and adapt the fitting function to reflect the new model.
  • The initial grid used during the grid search should include a plausible range of all parameters of your model.
  • If you want to use a Nelder-Mead algorithm, constraint parameters of the initial grid need be transformed so the parameter vector for optimization is real-valued).
  • If applicable, the parameter vector i obtained during optimization needs to back-transformation for the the output object res.
  • Name the new file according to the convention ‘int_fitting_yourmodelname.R’.
• Add your model and fitting-functions to the high-level functions fitRTConf and fitRTConfModels.
• Add prediction functions for your model based on one of the files ‘predictratingdist_model.R’. The file should include a function for computing the discrete rating distribution ‘predictyourmodel_Conf’ and a function computing the density across a range of response times ‘predictyourmodel_RT’.
• Add your prediction function to the high-level functions predictConf/predictConfModels, and predictRT/predictRTModels.
• Optional: Add simulation functions for your model based on one of the files ‘simulatemodel.R’

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

Hellmann, S., Zehetleitner, M. & Rausch, M. (2024). Confidence Is Influenced by Evidence Accumulation Time in Dynamical Decision Models. Comput Brain Behav 7, 287–313. doi: 10.1007/s42113-024-00205-9

Rigoux, L., Stephan, K. E., Friston, K. J., & Daunizeau, J. (2014). Bayesian model selection for group studies - revisited. NeuroImage, 84, 971–985. doi: 10.1016/j.neuroimage.2013.08.065

For comments, remarks, and questions please contact me: sebastian.hellmann@tum.de or submit an issue.

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