AATtools (version 0.0.3)

aat_simulate: Simulate AAT datasets and predict parameters

Description

aat_simulate() generates approach-avoidance task datasets.

aat_getstudydata() retrieves the properties of datasets from a number of pre-existing studies

Usage

aat_simulate(
  npps = 36,
  nstims = 16,
  stimreps = 4,
  meanrt = 632,
  meanrt_jitter = 90.1,
  sdrt = 158,
  sdrt_jitter = 49.9,
  pullfx = -39.2,
  pullfx_jitter = 40.5,
  stimfx = -30.9,
  stimfx_jitter = 32.5,
  biasfx = 39,
  biasfx_jitter = 60.1,
  empirical = FALSE,
  ...
)
aat_simulate2(..., defaults = "none", slowols = 0, fastols = 0, olsd = 3)
aat_getstudydata()

Value

aat_simulate() returns a data.frame with the following columns: subj (participant ID), stim (stimulus number), rep (stimulus repetition number), is_pull (0 = avoid, 1 = approach), is_target (0 = control stimulus, 1 = target stimulus), meanrt (participant's mean RT), sdrt (participant's residual standard deviation), pullfx (participant approach-avoidance effect size in ms), stimfx (participant stimulus category effect size in ms), biasfx (participant approach bias effect size in ms), and rt (trial reaction time). Additionally, the data.frame has the attribute population_reliability which represents the expected reliability of the data given the provided parameters.

Arguments

npps

Number of participants

nstims

Number of stimuli

stimreps

Number of repetitions of each stimulus within each group (i.e. within approach target, avoid target, approach control, avoid control)

meanrt

Mean sample reaction time

meanrt_jitter

Extent by which participants' mean RTs deviate from mean sample RT.

sdrt

Standard deviation of samplewide RTs, ignoring effects of movement, stimulus, and approach bias. In essence, this represents the amount of pure noise present in the data.

sdrt_jitter

Extent by which standard deviations of individual participants' RTs are larger or smaller than the samplewide SD.

pullfx

size of the effect of approach-versus-avoidance, in milliseconds

pullfx_jitter

Individual variation in the effect of approach-versus-avoidance

stimfx

size of the effect of stimulus category, in milliseconds

stimfx_jitter

Individual variation in the effect of stimulus category

biasfx

Size of the approach bias effect, in milliseconds

biasfx_jitter

Individual variation in the approach bias effect

empirical

If TRUE, then effect sizes and standard deviations will be exact

...

Any parameters of aat_simulate provided here will override the defaults from the defaults parameter.

defaults

Which set of default values should be used?

slowols

Number of slow outliers to insert per participant

fastols

Number of fats outliers to insert per participant

olsd

Number of standard deviations by which (slow) outliers deviate

Details

Defaults of aat_simulate() are based on Kahveci, Van Alebeek, Berking, & Blechert (2021).

"Lender2018" parameters are taken from the relevant-feature AAT of Lender, Meule, Rinck, Brockmeyer, & Blechert (2018). "Kahveci2021" parameters are taken from Kahveci, Van Alebeek, Berking, & Blechert (in review).

Lender, A., Meule, A., Rinck, M., Brockmeyer, T., & Blechert, J. (2018). Measurement of food-related approach–avoidance biases: Larger biases when food stimuli are task relevant. Appetite, 125, 42-47.

Kahveci, S., Van Alebeek, H., Berking, M., & Blechert, J. (in review). Touchscreen based assessment of food approach biases: investigation of reliability and stimulus-specific effects.

Examples

ts<- aat_simulate(pullfx = 50, stimfx = 10, biasfx = 100)
mod<-lm(rt~is_pull*is_target,data=ts)
coef(mod) #these should be somewhat close to the provided coefficients

# Here's how one might derive the parameters used in this function from a real dataset
if (FALSE) {
mod<-lmer(decisiontime ~ is_pull * is_food + (is_pull * is_food | subjectid),data=dsa)
fixef(mod) # from here, all the fx and mean RTs are derived
ranef(mod)$subjectid %>% apply(2,sd) #from here, all the fx jitters are derived
dsa %>% group_by(subjectid) %>% summarise(sd=sd(resid)) %>%
summarise(m=mean(sd),s=sd(sd)) # from here, sdrt_jitter is derived
}
hist(aat_simulate2(defaults="Lender2018_relevant_raw",slowols=10,fastols=10)$rt)