simulateWEV: Simulation of confidence ratings and RTs in dynWEV and 2DSD...

In dynConfiR: Dynamic Models for Confidence and Response Time Distributions

Simulation of confidence ratings and RTs in dynWEV and 2DSD confidence models

Description

Simulates the decision responses and reaction times together with a discrete confidence judgment in the dynaViTE model, 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. Also computes the Gamma rank correlation between the confidence ratings and condition (task difficulty), reaction times and accuracy in the simulated output. Basically, this function is a wrapper for rWEV and r2DSD for application in confidence experiments with manipulation of specific parameters.

Usage

simulateWEV(paramDf, n = 10000, model = "dynWEV", simult_conf = FALSE,
  gamma = FALSE, agg_simus = FALSE, stimulus = c(-1, 1), delta = 0.01,
  maxrt = 15, seed = NULL, process_results = FALSE)

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,....
n	integer. The number of samples (per condition and stimulus direction) generated. Total number of samples is n*nConditions*length(stimulus).
model	character scalar. One of "dynaViTE", "dynWEV", or "2DSD".
simult_conf	logical. TRUE, if 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 tau is added to the simulated decision time. If FALSE returned response time will only be decision time plus non-judgment time component.
gamma	logical. If TRUE, the gamma correlation between confidence ratings, rt and accuracy is computed.
agg_simus	logical. Simulation is done on a trial basis with RTs outcome. If TRUE, the simulations will be aggregated over RTs to return only the distribution of response and confidence ratings. Default: FALSE.
stimulus	numeric vector. Either 1, -1 or c(-1, 1) (default). Together with condition represents the experimental situation. In a binary decision task the presented stimulus belongs to one of two categories. In the default setting trials with both categories presented are simulated but one can choose to simulate only trials with the stimulus coming from one category (1 for the category that is associated with positive drift in the decision process where "upper"/1 responses are considered correct and -1 correspondingly for negative drifts and "lower"/-1 correct decisions).
delta	numeric. Discretization steps for simulations with the stochastic process.
maxrt	numeric. Maximum reaction time returned. If the simulation of the stochastic process exceeds a rt of maxrt, the response will be set to 0 and maxrt will be returned as rt.
seed	numerical. Seeding for non-random data generation.
process_results	logical. Whether the output simulations should contain the final state of the decision (and visibility) process as additional column. Default is FALSE, meaning that no additional columns for the final process states are returned.

Details

Simulation of response and decision times is done by simulating normal variables in discretized steps until the lower or upper boundary is met (or the maximal rt is reached). Afterwards, a confidence measure is simulated according to the respective model.

The confidence outputs are then binned according to the given thresholds. The output of the fitting function fitRTConf with the respective model fits the argument paramDf for simulation. The Gamma coefficients are computed separately for correct/incorrect responses for the correlation of confidence ratings with condition and rt and separately for conditions for the correlation of accuracy and confidence. The resulting data frames in the output thus have two columns. One for the grouping variable and one for the Gamma coefficient.

Value

Depending on gamma and agg_simus.

If gamma is FALSE, returns a data.frame with columns: condition, stimulus, response, correct, rt, conf (the continuous confidence measure) and rating (the discrete confidence rating), and dec and vis (only if process_results=TRUE) for the final states of accumulators in the simulation or (if agg_simus=TRUE): condition, stimulus,response, correct, rating and p (for the probability of a response and rating, given the condition and stimulus).

If gamma is TRUE, returns a list with elements: simus (the simulated data frame) and gamma, which is again a list with elements condition, rt and correct, each a tibble with two columns (see details for more information).

Note

Different parameters for different conditions are only allowed for drift rate, v, drift rate variability, sv and diffusion constant s. 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

# 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=0.1, v2=1, t0=0.1,z=0.55,
                      sz=0.3,sv=0.8, st0=0,  tau=3, w=0.1,
                      theta1=0.8, svis=0.5, sigvis=0.8)

# 2. Simulate trials for both stimulus categories and all conditions (2)
simus <- simulateWEV(paramDf, model="dynWEV")
head(simus)

  library(ggplot2)
  simus <- simus[simus$response!=0,]
  simus$rating <- factor(simus$rating, labels=c("unsure", "sure"))
  ggplot(simus, aes(x=rt, group=interaction(correct, rating),
                    color=as.factor(correct), linetype=rating))+
    geom_density(size=1.2)+xlim(c(0,5))+
    facet_grid(rows=vars(condition), labeller = "label_both")


# automatically aggregate simulation distribution
# to get only accuracy x confidence rating distribution for
# all conditions
agg_simus <- simulateWEV(paramDf, model="dynWEV", agg_simus = TRUE)
head(agg_simus)

  agg_simus$rating <- factor(agg_simus$rating, labels=c("unsure", "sure"))
  library(ggplot2)
  ggplot(agg_simus, aes(x=rating, group=correct, fill=as.factor(correct), y=p))+
    geom_bar(stat="identity", position="dodge")+
    facet_grid(cols=vars(condition), labeller = "label_both")


  # Compute Gamma correlation coefficients between
  # confidence and other behavioral measures
  # output will be a list
  simu_list <- simulateWEV(paramDf,n = 400, model="dynWEV", gamma=TRUE)
  simu_list

dynConfiR documentation built on May 29, 2024, 5:10 a.m.