Prediction of Confidence Rating and Reaction Time Distribution in the multiple-threshold log-normal noise race model
Source:R/predictratingdist_MTLNR.R
predictMTLNR.RdpredictMTLNR_Conf predicts the categorical response distribution of
decision and confidence ratings, predictMTLNR_RT computes the
RT distribution (density) in the multiple-threshold log-normal noise
race model (Reynolds et al., 2020), given specific parameter
constellations. See dMTLNR for more information about the models
and parameters.
Usage
predictMTLNR_Conf(paramDf, maxrt = Inf, subdivisions = 100L,
stop.on.error = FALSE, .progress = TRUE)
predictMTLNR_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 dMTLNR parameter names (only
mu_v1andmu_v2are not used in this context but replaced by the parameterv). For different stimulus quality/mean drift rates, names should bev1,v2,v3,.... Additionally, the confidence thresholds should be given by names withthetaUpper1,thetaUpper2,...,thetaLower1,... or, for symmetric thresholds only bytheta1,theta2,.... (see Details for the correspondence to the data)- maxrt
numeric. The maximum RT for the integration/density computation. Default: 15 (for
predictMTLNR_Conf(integration)), 9 (forpredictMTLNR_RT).- subdivisions
integer(default: 100). ForpredictMTLNR_Confit is used as argument for the inner integral routine. ForpredictMTLNR_RTit 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
predictMTLNR_RT. Whether the computed density should be scaled to integrate to one (additional columndensscaled). Otherwise the output contains only the defective density (i.e. its integral is equal to the probability of a response and not 1). IfTRUE, the argumentDistConfshould be given, if available. Default:FALSE.- DistConf
NULLordata.frame. Adata.frameormatrixwith column names, giving the distribution of response and rating choices for different conditions and stimulus categories in the form of the output ofpredictMTLNR_Conf. It is only necessary, ifscaled=TRUE, because these probabilities are used for scaling. Ifscaled=TRUEandDistConf=NULL, it will be computed with the functionpredictMTLNR_Conf, which takes some time and the function will throw a message. Default:NULL
Value
predictMTLNR_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.
predictMTLNR_RT returns a data.frame/tibble with columns: condition, stimulus,
response, rating, correct, rt and dens (and densscaled, if scaled=TRUE).
Details
The function predictMTLNR_Conf consists merely of an integration of
the response time density, dMTLNR, over the
response time in a reasonable interval (0 to maxrt). The function
predictMTLNR_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 names of the accumulation rate parameters differ from those used in
dMTLNR because the accumulation rates for the two options depend
on stimulus and condition. Here, the mean parameter for the accumulation rate
of the correct accumulator is v (v1, v2,... respectively) in paramDf
and the other one has a mean parameter of 0.
Note
Different parameters for different conditions are only allowed for drift rate,
v. All other parameters are used for all
conditions.
References
Reynolds, A., Kvam, P. D., Osth, A. F., & Heathcote, A. (2020). Correlated racing evidence accumulator models. Journal of Mathematical Psychology, 96, 102331. doi: doi: 10.1016/j.jmp.2020.102331
Examples
# 1. Define some parameter set in a data.frame
paramDf <- data.frame(v1=0.5, v2=1.0, t0=0.1, st0=0,
mu_d1=1, mu_d2=1,
s_v1=0.5, s_v2=0.5,
s_d1=0.3, s_d2=0.3,
rho_v=0.2, rho_d=0.1,
theta1=0.8, theta2=1.5)
# 2. Predict discrete Choice x Confidence distribution:
preds_Conf <- predictMTLNR_Conf(paramDf, maxrt=7, subdivisions=50)
head(preds_Conf)
#> condition stimulus response correct rating p info err
#> 1 1 1 1 1 1 0.40261536 OK 8.139278e-07
#> 2 2 1 1 1 1 0.30366063 OK 2.334342e-05
#> 3 1 2 1 0 1 0.21062110 OK 8.047182e-08
#> 4 2 2 1 0 1 0.08291896 OK 3.123715e-06
#> 5 1 1 2 0 1 0.21062110 OK 8.047182e-08
#> 6 2 1 2 0 1 0.08291896 OK 3.123715e-06
# 3. Compute RT density
preds_RT <- predictMTLNR_RT(paramDf, maxrt=7, subdivisions=50)
# same output with scaled density column:
preds_RT <- predictMTLNR_RT(paramDf, maxrt=7, subdivisions=50,
scaled=TRUE, DistConf = preds_Conf)
head(preds_RT)
#> condition stimulus response correct rating rt dens
#> 1 1 1 1 1 1 0.1000000 0.000000e+00
#> 2 1 1 1 1 1 0.2408163 8.369956e-07
#> 3 1 1 1 1 1 0.3816327 5.247208e-04
#> 4 1 1 1 1 1 0.5224490 7.734335e-03
#> 5 1 1 1 1 1 0.6632653 3.234912e-02
#> 6 1 1 1 1 1 0.8040816 7.480043e-02
#> densscaled
#> 1 0.000000e+00
#> 2 2.078896e-06
#> 3 1.303281e-03
#> 4 1.921023e-02
#> 5 8.034747e-02
#> 6 1.857863e-01
# \donttest{
# produces a warning, if scaled=TRUE and DistConf missing
preds_RT <- predictMTLNR_RT(paramDf, maxrt=7, subdivisions=50,
scaled=TRUE)
#> scaled is TRUE and DistConf is NULL. The rating distribution will be computed, which will take additional time.
# }
# \donttest{
# Example of visualization
library(ggplot2)
preds_Conf$rating <- factor(preds_Conf$rating, labels=c("unsure", "medium", "sure"))
preds_RT$rating <- factor(preds_RT$rating, labels=c("unsure", "medium", "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)
#> Warning: There are only 50 rows for at least one subgroup of the data set.
#> Consider refining the rt-grid for more accurate computations.
#> # A tibble: 120 × 7
#> condition stimulus response correct rating p q
#> <int> <dbl> <dbl> <dbl> <fct> <dbl> <dbl>
#> 1 1 1 1 1 unsure 0.1 1.09
#> 2 1 1 1 1 unsure 0.3 1.51
#> 3 1 1 1 1 unsure 0.5 1.79
#> 4 1 1 1 1 unsure 0.7 2.35
#> 5 1 1 1 1 unsure 0.9 3.20
#> 6 1 1 1 1 medium 0.1 0.804
#> 7 1 1 1 1 medium 0.3 1.09
#> 8 1 1 1 1 medium 0.5 1.37
#> 9 1 1 1 1 medium 0.7 1.65
#> 10 1 1 1 1 medium 0.9 2.35
#> # ℹ 110 more rows
# }
# Example with asymmetric confidence thresholds
paramDf_asym <- data.frame(v1=0.5, v2=1.0, t0=0.1, st0=0,
mu_d1=1, mu_d2=1,
s_v1=0.5, s_v2=0.5,
s_d1=0.3, s_d2=0.3,
rho_v=0.2, rho_d=0.1,
thetaLower1=0.5, thetaLower2=1.2,
thetaUpper1=0.7, thetaUpper2=1.8)
preds_Conf_asym <- predictMTLNR_Conf(paramDf_asym, maxrt=7, subdivisions=50)
head(preds_Conf_asym)
#> condition stimulus response correct rating p info err
#> 1 1 1 1 1 1 0.24718299 OK 1.669805e-07
#> 2 2 1 1 1 1 0.16124674 OK 6.257019e-06
#> 3 1 2 1 0 1 0.16097535 OK 1.241295e-07
#> 4 2 2 1 0 1 0.06839111 OK 1.582488e-06
#> 5 1 1 2 0 1 0.19735343 OK 4.172561e-09
#> 6 2 1 2 0 1 0.07941819 OK 2.778639e-06
# Example with multiple conditions
paramDf_multi <- data.frame(v1=0.3, v2=0.6, v3=1.2, t0=0.1, st0=0,
mu_d1=1, mu_d2=1,
s_v1=0.5, s_v2=0.5,
s_d1=0.3, s_d2=0.3,
rho_v=0.2, rho_d=0.1,
theta1=0.8, theta2=1.5)
preds_Conf_multi <- predictMTLNR_Conf(paramDf_multi, maxrt=7, subdivisions=50)
head(preds_Conf_multi)
#> condition stimulus response correct rating p info err
#> 1 1 1 1 1 1 0.40207742 OK 1.096812e-07
#> 2 2 1 1 1 1 0.39311988 OK 2.923146e-06
#> 3 3 1 1 1 1 0.24208572 OK 8.452775e-06
#> 4 1 2 1 0 1 0.27256417 OK 1.021176e-07
#> 5 2 2 1 0 1 0.18063605 OK 3.876010e-07
#> 6 3 2 1 0 1 0.05089761 OK 3.140383e-06