predictDDMConf {dynConfiR}

Prediction of Confidence Rating and Reaction Time Distribution in the drift diffusion confidence model

Description

predictDDMConf_Conf predicts the categorical response distribution of decision and confidence ratings, predictDDMConf_RT computes the RT distribution (density) in the drift diffusion confidence model (Hellmann et al., 2023), given specific parameter constellations. See dDDMConf for more information about the model and parameters.

Usage

predictDDMConf_Conf(paramDf, maxrt = 15, subdivisions = 100L,
  stop.on.error = FALSE, .progress = TRUE)

predictDDMConf_RT(paramDf, maxrt = 9, subdivisions = 100L, minrt = NULL,
  scaled = FALSE, DistConf = NULL, .progress = TRUE)

Arguments

paramDf

a list or data frame with one row. Column names should match the names of DDMConf model parameter names. For different stimulus quality/mean drift rates, names should be v1, v2, v3,.... Different sv and/or s parameters are possible with sv1, sv2, sv3... (s1, s2, s3,... respectively) with equally many steps as for drift rates. Additionally, the confidence thresholds should be given by names with thetaUpper1, thetaUpper2,..., thetaLower1,... or, for symmetric thresholds only by theta1, theta2,....

maxrt

numeric. The maximum RT for the integration/density computation. Default: 15 (for predictDDMConf_Conf (integration)), 9 (for predictDDMConf_RT).

subdivisions

integer (default: 100). For predictDDMConf_Conf it is used as argument for the inner integral routine. For predictDDMConf_RT it is the number of points for which the density is computed.

stop.on.error

logical. Argument directly passed on to integrate. Default is FALSE, since the densities invoked may lead to slow convergence of the integrals (which are still quite accurate) which causes R to throw an error.

.progress

logical. If TRUE (default) a progress bar is drawn to the console.

minrt

numeric or NULL(default). The minimum rt for the density computation.

scaled

logical. For predictDDMConf_RT. Whether the computed density should be scaled to integrate to one (additional column densscaled). Otherwise the output contains only the defective density (i.e. its integral is equal to the probability of a response and not 1). If TRUE, the argument DistConf should be given, if available. Default: FALSE.

DistConf

NULL or data.frame. A data.frame or matrix with column names, giving the distribution of response and rating choices for different conditions and stimulus categories in the form of the output of predictDDMConf_Conf. It is only necessary, if scaled=TRUE, because these probabilities are used for scaling. If scaled=TRUE and DistConf=NULL, it will be computed with the function predictDDMConf_Conf, which takes some time and the function will throw a message. Default: NULL

Details

The function predictDDMConf_Conf consists merely of an integration of the response time density, dDDMConf, over the response time in a reasonable interval (0 to maxrt). The function predictDDMConf_RT wraps these density functions to a parameter set input and a data.frame output. For the argument paramDf, the output of the fitting function fitRTConf with the DDMConf model may be used.

Value

predictDDMConf_Conf returns a data.frame/tibble with columns: condition, stimulus, response, rating, correct, p, info, err. p is the predicted probability of a response and rating, given the stimulus category and condition. info and err refer to the respective outputs of the integration routine used for the computation. predictDDMConf_RT returns a data.frame/tibble with columns: condition, stimulus, response, rating, correct, rt and dens (and densscaled, if scaled=TRUE).

Note

Different parameters for different conditions are only allowed for drift rate v, drift rate variability sv, and process variability s. Otherwise, s is not required in paramDf but set to 1 by default. All other parameters are used for all conditions.

Author(s)

Sebastian Hellmann.

References

Hellmann, S., Zehetleitner, M., & Rausch, M. (2023). Simultaneous modeling of choice, confidence and response time in visual perception. Psychological Review 2023 Mar 13. doi: 10.1037/rev0000411. Epub ahead of print. PMID: 36913292.

Examples

# 1. Define some parameter set in a data.frame
paramDf <- data.frame(a=2,v1=0.5, v2=1, t0=0.1,z=0.55,
                      sz=0,sv=0.2, st0=0, theta1=0.8)

# 2. Predict discrete Choice x Confidence distribution:
preds_Conf <- predictDDMConf_Conf(paramDf,  maxrt = 15)
head(preds_Conf)

# 3. Compute RT density
preds_RT <- predictDDMConf_RT(paramDf, maxrt=4, subdivisions=200) #(scaled=FALSE)
# same output with scaled density column:
preds_RT <- predictDDMConf_RT(paramDf, maxrt=4, subdivisions=200,
                              scaled=TRUE, DistConf = preds_Conf)
head(preds_RT)


  # Example of visualization
  library(ggplot2)
  preds_Conf$rating <- factor(preds_Conf$rating, labels=c("unsure", "sure"))
  preds_RT$rating <- factor(preds_RT$rating, labels=c("unsure", "sure"))
  ggplot(preds_Conf, aes(x=interaction(rating, response), y=p))+
    geom_bar(stat="identity")+
    facet_grid(cols=vars(stimulus), rows=vars(condition), labeller = "label_both")
  ggplot(preds_RT, aes(x=rt, color=interaction(rating, response), y=dens))+
    geom_line(stat="identity")+
    facet_grid(cols=vars(stimulus), rows=vars(condition), labeller = "label_both")+
    theme(legend.position = "bottom")
  ggplot(aggregate(densscaled~rt+correct+rating+condition, preds_RT, mean),
         aes(x=rt, color=rating, y=densscaled))+
    geom_line(stat="identity")+
    facet_grid(cols=vars(condition), rows=vars(correct), labeller = "label_both")+
    theme(legend.position = "bottom")

# Use PDFtoQuantiles to get predicted RT quantiles
head(PDFtoQuantiles(preds_RT, scaled = FALSE))