Skip to contents

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

Source: R/predictratingdist_DDConf.R
predictDDConf.Rd

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

Usage

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

predictDDConf_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 DDConf 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 predictDDConf_Conf (integration)), 9 (for predictDDConf_RT).

subdivisions

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

Value

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

Details

The function predictDDConf_Conf consists merely of an integration of the response time density, dDDConf, over the response time in a reasonable interval (0 to maxrt). The function predictDDConf_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 DDConf model may be used.

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.

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.

Author

Sebastian Hellmann.

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 <- predictDDConf_Conf(paramDf,  maxrt = 15)
head(preds_Conf)
#>   condition stimulus response correct rating          p info          err
#> 1         1       -1       -1       1      1 0.32260885   OK 9.980641e-06
#> 2         2       -1       -1       1      1 0.32597348   OK 2.002264e-05
#> 3         1        1       -1       0      1 0.11438204   OK 3.269940e-06
#> 4         2        1       -1       0      1 0.04054175   OK 2.148754e-06
#> 5         1       -1        1       1      1 0.12691602   OK 1.552460e-06
#> 6         2       -1        1       1      1 0.04961359   OK 6.704916e-07

# 3. Compute RT density
preds_RT <- predictDDConf_RT(paramDf, maxrt=4, subdivisions=200) #(scaled=FALSE)
# same output with scaled density column:
preds_RT <- predictDDConf_RT(paramDf, maxrt=4, subdivisions=200,
                              scaled=TRUE, DistConf = preds_Conf)
head(preds_RT)
#>   condition stimulus response correct rating        rt dens densscaled
#> 1         1       -1       -1       1      1 0.1000000    0          0
#> 2         1       -1       -1       1      1 0.1195980    0          0
#> 3         1       -1       -1       1      1 0.1391960    0          0
#> 4         1       -1       -1       1      1 0.1587940    0          0
#> 5         1       -1       -1       1      1 0.1783920    0          0
#> 6         1       -1       -1       1      1 0.1979899    0          0

# \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))
#> # A tibble: 6 × 7
#>   condition stimulus response correct rating     p     q
#>       <int>    <dbl>    <dbl>   <dbl> <fct>  <dbl> <dbl>
#> 1         1       -1       -1       1 unsure   0.1 0.962
#> 2         1       -1       -1       1 unsure   0.3 1.16 
#> 3         1       -1       -1       1 unsure   0.5 1.39 
#> 4         1       -1       -1       1 unsure   0.7 1.75 
#> 5         1       -1       -1       1 unsure   0.9 2.49 
#> 6         1       -1       -1       1 sure     0.1 0.335