aat_simulate: Simulate AAT datasets and predict parameters

In AATtools: Reliability and Scoring Routines for the Approach-Avoidance Task

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()

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.

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.

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
## Not run: 
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

## End(Not run)
hist(aat_simulate2(defaults="Lender2018_relevant_raw",slowols=10,fastols=10)$rt)

AATtools documentation built on Aug. 12, 2022, 5:05 p.m.