bmm (version 1.2.0)

imm: Interference measurement model by Oberauer and Lin (2017).

Description

Three versions of the Interference measurement model by Oberauer and Lin (2017). - the full, bsc, and abc. IMMfull(), IMMbsc(), and IMMabc() are deprecated and will be removed in the future. Please use imm(version = 'full'), imm(version = 'bsc'), or imm(version = 'abc') instead.

Usage

imm(
  resp_error,
  nt_features,
  nt_distances,
  set_size,
  regex = FALSE,
  version = "full",
  ...
)
IMMfull(resp_error, nt_features, nt_distances, set_size, regex = FALSE, ...)
IMMbsc(resp_error, nt_features, nt_distances, set_size, regex = FALSE, ...)
IMMabc(resp_error, nt_features, set_size, regex = FALSE, ...)

Value

An object of class bmmodel

Arguments

resp_error

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 bsc and full versions.

set_size

Name of the column containing the set size variable (if set_size varies) or a numeric value for the set_size, if the set_size 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.

version

Character. The version of the IMM model to use. Can be one of full, bsc, or abc. The default is full.

...

used internally for testing, ignore it

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: full

• 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

  • a: General activation of memory items

  • c: Context activation

  • s: Spatial similarity gradient

• Fixed parameters:

  • mu1 = 0

  • mu2 = 0

  • kappa2 = -100

• Default parameter links:

  • mu1 = tan_half; kappa = log; a = log; c = log; s = log

• Default priors:

  • mu1:

    • main: student_t(1, 0, 1)

  • kappa:

    • main: normal(2, 1)

    • effects: normal(0, 1)

  • a:

    • main: normal(0, 1)

    • effects: normal(0, 1)

  • c:

    • main: normal(0, 1)

    • effects: normal(0, 1)

  • s:

    • main: normal(0, 1)

    • effects: normal(0, 1)

Version: bsc

• 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

  • c: Context activation

  • s: Spatial similarity gradient

• Fixed parameters:

  • mu1 = 0

  • mu2 = 0

  • kappa2 = -100

• Default parameter links:

  • mu1 = tan_half; kappa = log; c = log; s = log

• Default priors:

  • mu1:

    • main: student_t(1, 0, 1)

  • kappa:

    • main: normal(2, 1)

    • effects: normal(0, 1)

  • c:

    • main: normal(0, 1)

    • effects: normal(0, 1)

  • s:

    • main: normal(0, 1)

    • effects: normal(0, 1)

Version: abc

• 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

  • a: General activation of memory items

  • c: Context activation

• Fixed parameters:

  • mu1 = 0

  • mu2 = 0

  • kappa2 = -100

• Default parameter links:

  • mu1 = tan_half; kappa = log; a = log; c = log

• Default priors:

  • mu1:

    • main: student_t(1, 0, 1)

  • kappa:

    • main: normal(2, 1)

    • effects: normal(0, 1)

  • a:

    • main: normal(0, 1)

    • effects: normal(0, 1)

  • c:

    • main: normal(0, 1)

    • effects: normal(0, 1)

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) { # isTRUE(Sys.getenv("BMM_EXAMPLES"))
# load data
data <- oberauer_lin_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
# by default this fits the full version of the model
model1 <- imm(
  resp_error = "dev_rad",
  nt_features = paste0("col_nt", 1:7),
  nt_distances = paste0("dist_nt", 1:7),
  set_size = "set_size"
)

# fit the model
fit <- bmm(
  formula = ff,
  data = data,
  model = model1,
  cores = 4,
  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 <- imm(
  resp_error = "dev_rad",
  nt_features = "col_nt",
  nt_distances = "dist_nt",
  set_size = "set_size",
  regex = TRUE
)

# fit the model
fit <- bmm(
  formula = ff,
  data = data,
  model = model2,
  cores = 4,
  backend = "cmdstanr"
)

# you can also specify the `bsc` or `abc` versions of the model to fit a reduced version
model3 <- imm(
  resp_error = "dev_rad",
  nt_features = "col_nt",
  set_size = "set_size",
  regex = TRUE,
  version = "abc"
)
fit <- bmm(
  formula = ff,
  data = data,
  model = model3,
  cores = 4,
  backend = "cmdstanr"
)
}