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) (see Hellmann et al. 2023) for details; Preprint available here). In addition, the package includes functions for parameter fitting, prediction and simulation of data.
Installation
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")Usage
Density functions
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)## [1] 0.02387629
dWEV(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)## [1] 0.0143774
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)## [1] 0.07616855
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)## [1] 0.08346152Workflow for data analysis
When using the package for model comparison, the suggested workflow is:
Data should be of the form
head(data)## 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 1where 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).
Fitting
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.
head(fitted_pars)## 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 NAPrediction
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 ofpredictConfis required.
Usage example:
fitted_pars %>%
group_by(model, participant) %>%
summarise(predictConf(pick(everything()), model=cur_group()$model[1]))Further functions
Implementation of a simulation of observations in the Leaky Competing Accumulator model (see rLCA).
References
Hellmann S, Zehetleitner M, Rausch M. Simultaneous modeling of choice, confidence, and response time in visual perception. Psychol Rev. 2023 Mar 13. doi: 10.1037/rev0000411. Epub ahead of print. PMID: 36913292.
Contact
For comments, remarks, and questions please contact me: sebastian.hellmann@ku.de or submit an issue.