predictWEV: Prediction of Confidence Rating and Response Time Distribution in dynaViTE, dynWEV, and 2DSD confidence models

Description

predictWEV_Conf predicts the categorical response distribution of decision and confidence ratings, predictWEV_RT computes the predicted RT distribution (density) in the 2DSD Model (Pleskac & Busemeyer, 2010) and the dynWEV model (Hellmann et al., 2023), given specific parameter constellations. See dWEV and d2DSD for more information about parameters.

Usage

predictWEV_Conf(paramDf, model = "dynaViTE", maxrt = 15,
  subdivisions = 100L, simult_conf = FALSE, stop.on.error = FALSE,
  precision = 1e-05, .progress = TRUE)
predictWEV_RT(paramDf, model = NULL, maxrt = 9, subdivisions = 100L,
  minrt = NULL, simult_conf = FALSE, scaled = FALSE, DistConf = NULL,
  precision = 1e-05, .progress = TRUE)

Value

predictWEV_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. predictWEV_RT returns a data.frame/tibble with columns: condition, stimulus, response, rating, correct, rt and dens (and densscaled, if scaled=TRUE).

Arguments

paramDf: a list or dataframe with one row. Column names should match the names of dynaViTE and 2DSD model specific 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,....
model: character scalar. One of "dynaViTE", "dynWEV", or "2DSD".
maxrt: numeric. The maximum RT for the integration/density computation. Default: 15 (for predictWEV_Conf (integration)), 9 (for predictWEV_RT).
subdivisions: integer (default: 100). For predictWEV_Conf it is used as argument for the inner integral routine. For predictWEV_RT it is the number of points for which the density is computed.
simult_conf: logical. Whether in the experiment confidence was reported simultaneously with the decision, as then decision and confidence judgment are assumed to have happened subsequent before response and computations are different, when there is an observable interjudgment time (then simult_conf should be FALSE).
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.
precision: numerical scalar value. Precision of calculation. Corresponds to the step size of integration w.r.t. z and t0. Default is 1e-5.
.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 predictWEV_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 predictWEV_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 predictWEV_Conf, which takes some time and the function will throw a message. Default: NULL

Author

Sebastian Hellmann.

Details

The function predictWEV_Conf consists merely of an integration of the response time density, dWEV and d2DSD, over the response time in a reasonable interval (t0 to maxrt). The function predictWEV_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 respective model may be used.

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.

Pleskac, T. J., & Busemeyer, J. R. (2010). Two-Stage Dynamic Signal Detection: A Theory of Choice, Decision Time, and Confidence, Psychological Review, 117(3), 864-901. doi:10.1037/a0019737

Examples

Run this code

# Examples for "dynWEV" model (equivalent applicable for "2DSD" model (with less parameters))
# 1. Define some parameter set in a data.frame
paramDf <- data.frame(a=2.5,v1=0.5, v2=1, t0=0.1,z=0.55,
                      sz=0,sv=0.2, st0=0,  tau=3, w=0.3,
                      theta1=0.8, svis=0.5, sigvis=0.8)

# 2. Predict discrete Choice x Confidence distribution:
preds_Conf <- predictWEV_Conf(paramDf, "dynWEV", maxrt = 15)
head(preds_Conf)
# \donttest{
  # To set simult_conf=TRUE makes a minor difference in the discrete distribution,
  # because we integrate over response times (we just adapt maxrt for comparison)
  preds_Conf2 <- predictWEV_Conf(paramDf, "dynWEV", simult_conf = TRUE, maxrt = 15+paramDf$tau)
  summary(preds_Conf$p-preds_Conf2$p) # difference in predicted probabilities
# }

# 3. Compute RT density
preds_RT <- predictWEV_RT(paramDf, "dynWEV", maxrt=4, subdivisions=200) #(scaled=FALSE)
# same output with scaled density column:
preds_RT <- predictWEV_RT(paramDf, "dynWEV", maxrt=4, subdivisions=200,
                         scaled=TRUE, DistConf = preds_Conf)
head(preds_RT)
# \donttest{
  # produces a warning, if scaled=TRUE and DistConf missing
  preds_RT <- predictWEV_RT(paramDf, "dynWEV", maxrt=4, subdivisions=200,
                           scaled=TRUE)
# }

# \donttest{
  # 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))

Run the code above in your browser using DataLab