dynConfiR (version 0.0.4)

predictRM: Prediction of Confidence Rating and Reaction Time Distribution in race models of confidence

Description

predictRM_Conf predicts the categorical response distribution of decision and confidence ratings, predictRM_RT computes the RT distribution (density) in the independent and partially anti-correlated race models (Hellmann et al., 2023), given specific parameter constellations. See RaceModels for more information about the models and parameters.

Usage

predictRM_Conf(paramDf, model = "IRM", time_scaled = FALSE, maxrt = 15,
  subdivisions = 100L, stop.on.error = FALSE, .progress = TRUE)
predictRM_RT(paramDf, model = "IRM", time_scaled = FALSE, maxrt = 9,
  subdivisions = 100L, minrt = NULL, scaled = FALSE, DistConf = NULL,
  .progress = TRUE)

Value

predictRM_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. predictRM_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 data frame with one row. Column names should match the names of RaceModels parameter names (only mu1 and mu2 are not used in this context but replaced by the parameter v). For different stimulus quality/mean drift rates, names should be v1, v2, v3,.... Different s parameters are possible with s1, s2, s3,... 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 "IRM" or "PCRM". ("IRMt" and "PCRMt" will also be accepted. In that case, time_scaled is set to TRUE.)

time_scaled

logical. Whether the confidence measure should be scaled by 1/sqrt(rt). Default: FALSE. (It is set to TRUE, if model is "IRMt" or "PCRMt")

maxrt

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

subdivisions

integer (default: 100). For predictRM_Conf it is used as argument for the inner integral routine. For predictRM_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 predictRM_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 predictRM_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 predictRM_Conf, which takes some time and the function will throw a message. Default: NULL

Author

Sebastian Hellmann.

Details

The function predictRM_Conf consists merely of an integration of the response time density, dIRM and dPCRM, over the response time in a reasonable interval (0 to maxrt). The function predictRM_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.

The drift rate parameters differ from those used in dIRM/dPCRM since in many perceptual decision experiments the drift on one accumulator is assumed to be the negative of the other. The drift rate of the correct accumulator is v (v1, v2, ... respectively) in paramDf.

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

# Examples for "PCRM" model (equivalent applicable for "IRM" model)
# 1. Define some parameter set in a data.frame
paramDf <- data.frame(a=2,b=2, v1=0.5, v2=1, t0=0.1,st0=0,
                      wx=0.6, wint=0.2, wrt=0.2,
                      theta1=4)

# 2. Predict discrete Choice x Confidence distribution:
preds_Conf <- predictRM_Conf(paramDf, "PCRM", time_scaled=TRUE)
# equivalent:
preds_Conf <- predictRM_Conf(paramDf, "PCRMt")
head(preds_Conf)

# 3. Compute RT density
preds_RT <- predictRM_RT(paramDf, "PCRMt", maxrt=7, subdivisions=50)
# same output with scaled density column:
preds_RT <- predictRM_RT(paramDf, "PCRMt", maxrt=7, subdivisions=50,
                         scaled=TRUE, DistConf = preds_Conf)
head(preds_RT)
# \donttest{
  # produces a warning, if scaled=TRUE and DistConf missing
  preds_RT <- predictRM_RT(paramDf, "PCRMt", maxrt=7, subdivisions=50,
                           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")
# }
# \donttest{
  # Use PDFtoQuantiles to get predicted RT quantiles
  # (produces warning because of few rt steps (--> inaccurate calculations))
  PDFtoQuantiles(preds_RT, scaled = FALSE)
# }