Interference measurement model by Oberauer and Lin (2017).

Usage

IMMfull(resp_err, nt_features, nt_distances, setsize, regex = FALSE, ...)

IMMbsc(resp_err, nt_features, nt_distances, setsize, regex = FALSE, ...)

IMMabc(resp_err, nt_features, setsize, regex = FALSE, ...)

Arguments

resp_err: The name of the variable in the provided dataset containing the response error. The response Error should code the response relative to the to-be-recalled target in radians. You can transform the response error in degrees to radian using the deg2rad function.
nt_features: A character vector with the names of the non-target variables. The non_target variables should be in radians and be centered relative to the target. Alternatively, if regex=TRUE, a regular expression can be used to match the non-target feature columns in the dataset.
nt_distances: A vector of names of the columns containing the distances of non-target items to the target item. Alternatively, if regex=TRUE, a regular expression can be used to match the non-target distances columns in the dataset. Only necessary for the IMMbsc and IMMfull models.
setsize: Name of the column containing the set size variable (if setsize varies) or a numeric value for the setsize, if the setsize is fixed.
regex: Logical. If TRUE, the nt_features and nt_distances arguments are interpreted as a regular expression to match the non-target feature columns in the dataset.
...: used internally for testing, ignore it

Value

An object of class bmmmodel

Details

Domain: Visual working memory
Task: Continuous reproduction
Name: Interference measurement model by Oberauer and Lin (2017).
Citation:
- Oberauer, K., & Lin, H.Y. (2017). An interference model of visual working memory. Psychological Review, 124(1), 21-59

Version: `IMMfull`

Requirements:
- The response vairable should be in radians and represent the angular error relative to the target
- The non-target features should be in radians and be centered relative to the target
Parameters:
- mu1: Location parameter of the von Mises distribution for memory responses(in radians). Fixed internally to 0 by default.
- kappa: Concentration parameter of the von Mises distribution (log scale)
- a: General activation of memory items
- c: Context activation
- s: Spatial similarity gradient
Fixed parameters:
- mu1 = 0

Version: `IMMbsc`

Requirements:
- The response vairable should be in radians and represent the angular error relative to the target
- The non-target variables should be in radians and be centered relative to the target
Parameters:
- mu1: Location parameter of the von Mises distribution for memory responses(in radians). Fixed internally to 0 by default.
- kappa: Concentration parameter of the von Mises distribution (log scale)
- c: Context activation
- s: Spatial similarity gradient
Fixed parameters:
- mu1 = 0

Version: `IMMabc`

Requirements:
- The response vairable should be in radians and represent the angular error relative to the target
- The non-target features should be in radians and be centered relative to the target
Parameters:
- mu1: Location parameter of the von Mises distribution for memory responses(in radians). Fixed internally to 0 by default.
- kappa: Concentration parameter of the von Mises distribution (log scale)
- a: General activation of memory items
- c: Context activation
Fixed parameters:
- mu1 = 0

Additionally, all IMM models have an internal parameter that is fixed to 0 to allow the model to be identifiable. This parameter is not estimated and is not included in the model formula. The parameter is:

b = "Background activation (internally fixed to 0)"

Examples

if (FALSE) {
# load data
data <- OberauerLin_2017

# define formula
ff <- bmmformula(
  kappa ~ 0 + set_size,
  c ~ 0 + set_size,
  a ~ 0 + set_size,
  s ~ 0 + set_size
)

# specify the full IMM model with explicit column names for non-target features and distances
model1 <- IMMfull(resp_err = "dev_rad",
                  nt_features = paste0('col_nt',1:7),
                  nt_distances = paste0('dist_nt',1:7),
                  setsize = 'set_size')

# fit the model
fit <- fit_model(formula = ff,
                 data = data,
                 model = model1,
                 parallel = T,
                 iter = 500,
                 backend = 'cmdstanr')

# alternatively specify the IMM model with a regular expression to match non-target features
# this is equivalent to the previous call, but more concise
model2 <- IMMfull(resp_err = "dev_rad",
                  nt_features = 'col_nt',
                  nt_distances = 'dist_nt',
                  setsize = 'set_size',
                  regex = TRUE)

# fit the model
fit <- fit_model(formula = ff,
                 data = data,
                 model = model2,
                 parallel=T,
                 iter = 500,
                 backend='cmdstanr')
}