Package 'hmetad' reference manual

Title:	Fit the Meta-D' Model of Confidence Ratings Using 'brms'
Description:	Implementation of Bayesian regressions over the meta-d' model of psychological data from two alternative forced choice tasks with ordinal confidence ratings. For more information, see Maniscalco & Lau (2012) <doi:10.1016/j.concog.2011.09.021>. The package is a front-end to the 'brms' package, which facilitates a wide range of regression designs, as well as tools for efficiently extracting posterior estimates, plotting, and significance testing.
Authors:	Kevin O'Neill [aut, cre, cph] (ORCID: <https://orcid.org/0000-0001-7401-9802>), Stephen Fleming [aut, cph] (ORCID: <https://orcid.org/0000-0003-0233-4891>)
Maintainer:	Kevin O'Neill <kevin.o'[email protected]>
License:	GPL (>= 3)
Version:	0.1.2
Built:	2026-05-15 16:24:24 UTC
Source:	https://github.com/metacoglab/hmetad

Aggregate `data` by `response`, `confidence`, and other columns

Description

Counts number of rows in data with unique combinations values in the columns response, confidence, and any other columns in ....

Usage

aggregate_metad(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  .name = "N",
  K = NULL
)
aggregate_metad(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  .name = "N",
  K = NULL
)

Arguments

data

The data frame to aggregate

...

Grouping columns in data. These columns will be converted to factors.

.stimulus

The name of "stimulus" column

.response

The name of "response" column

.confidence

The name of "confidence" column

.joint_response

The name of "joint_response" column

.name

The name of the resulting column containing trial counts

K

The number of confidence levels in data. If NULL, this is estimated from data using the maximum value of either the confidence column or joint response column.

Details

The data frame data must have one column with the name given by .stimulus. Additionally, it must have either:

Two columns with names given by .response and .confidence
One column with the name given by .joint_response

Finally, it must also have columns for any additional variables in ....

Value

A tibble with one row per combination of the variables in ..., and another column named by the value of .response containing trial counts. For $K$ confidence levels, this will be an $N \times K*4$ matrix, such that the columns represent (for stimulus $S$ , type 1 response $R$ , and type 2 response $C$ ):

$[N_{S=0, R=0, C=K}, \ldots, N_{S=0, R=0, C=1}, \\ N_{S=0, R=1, C=1}, \ldots, N_{S=0, R=1, C=K}, \\ N_{S=1, R=0, C=K}, \ldots, N_{S=1, R=0, C=1}, \\ N_{S=1, R=1, C=1}, \ldots, N_{S=1, R=1, C=K}] \\$

Examples

# aggregate a dataset without grouping factors
d <- sim_metad()
aggregate_metad(d)

# aggregate a dataset with grouping factors
d2 <- sim_metad_condition()
aggregate_metad(d2, condition)

# can also aggregate ignoring grouping factors
aggregate_metad(d2)

# aggregate data with only `joint_response` column
library(dplyr)
d |>
  ungroup() |>
  mutate(joint_response = joint_response(
    response, confidence,
    max(as.integer(confidence))
  )) |>
  select(-response, -confidence) |>
  aggregate_metad()

# aggregate a dataset without grouping factors
d <- sim_metad()
aggregate_metad(d)

# aggregate a dataset with grouping factors
d2 <- sim_metad_condition()
aggregate_metad(d2, condition)

# can also aggregate ignoring grouping factors
aggregate_metad(d2)

# aggregate data with only `joint_response` column
library(dplyr)
d |>
  ungroup() |>
  mutate(joint_response = joint_response(
    response, confidence,
    max(as.integer(confidence))
  )) |>
  select(-response, -confidence) |>
  aggregate_metad()

Generate a correlation matrix with all off-diagonal values equal to `r`

Description

Generate a correlation matrix with all off-diagonal values equal to r

Usage

cor_matrix(r, nrow = 2)
cor_matrix(r, nrow = 2)

Arguments

r

The correlation to fill in the matrix off-diagonals

nrow

The number of rows (and columns) of the resulting matrix

Value

An ⁠[nrow x nrow]⁠ matrix with values along the diagonal equal to 1 and values off of the diagonal equal to r

Examples

cor_matrix(0, nrow = 3)

cor_matrix(-.5, nrow = 4)
cor_matrix(0, nrow = 3)

cor_matrix(-.5, nrow = 4)

Generate a covariance matrix.

Description

Generate a covariance matrix.

Usage

cov_matrix(S, OMEGA)
cov_matrix(S, OMEGA)

Arguments

S

A vector of standard deviations

OMEGA

A correlation matrix

Value

an $N \times N$ covariance matrix, where N = length(S).

Examples

sds <- c(1, 2)
corrs <- matrix(c(1, .5, .5, 1), nrow = 2)
cov_matrix(sds, corrs)
sds <- c(1, 2)
corrs <- matrix(c(1, .5, .5, 1), nrow = 2)
cov_matrix(sds, corrs)

Obtain posterior draws of joint response probabilities

Description

Given a data frame and a meta-d' model, adds estimates of joint type 1 and type 2 response probabilities. For epred_draws_metad and add_epred_draws_metad, estimates are returned in a tidy tibble with one row per posterior draw. For epred_rvars_metad and add_epred_rvars_metad, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

epred_draws_metad(object, newdata, ...)

add_epred_draws_metad(newdata, object, ...)

epred_rvars_metad(object, newdata, ...)

add_epred_rvars_metad(newdata, object, ...)
epred_draws_metad(object, newdata, ...)

add_epred_draws_metad(newdata, object, ...)

epred_rvars_metad(object, newdata, ...)

add_epred_rvars_metad(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional arguments passed to tidybayes::add_epred_draws or tidybayes::add_epred_rvars

Value

a tibble containing posterior draws of model parameters with the following columns:

.row: the row of newdata
.chain, .iteration, .draw: for epred_draws_metad, identifiers for the posterior sample
stimulus, joint_response, response, confidence: identifiers for the response type
.epred: probability of the type 1 and type 2 response given the stimulus, $P(R, C \;\vert\; S)$

Examples


newdata <- tidyr::tibble(.row = 1)

# obtain model predictions
# equivalent to `add_epred_draws_metad(newdata, example_model())`
epred_draws_metad(example_model(), newdata)

# obtain model predictions (`posterior::rvar`)
# equivalent to `add_epred_rvars_metad(newdata, example_model())`
epred_rvars_metad(example_model(), newdata)

newdata <- tidyr::tibble(.row = 1)

# obtain model predictions
# equivalent to `add_epred_draws_metad(newdata, example_model())`
epred_draws_metad(example_model(), newdata)

# obtain model predictions (`posterior::rvar`)
# equivalent to `add_epred_rvars_metad(newdata, example_model())`
epred_rvars_metad(example_model(), newdata)

Simulated data for example model fitting

Description

A simulated data set of 1000 trials from a two-alternative forced choice task with 4 levels of confidence.

Usage

example_data()
example_data()

Value

A tibble of 1000 observations containing the following columns:

trial: the trial number
stimulus: the stimulus presence (0 or 1)
response: the simulated type 1 response
confidence: the simulated type 2 response
correct: the accuracy of the simulated type 1 response
dprime, c, meta_dprime, M, meta_c2_0, meta_c2_1: the parameters of the model used for simulation
theta, theta_1, theta_2: the joint, type 1, and type 2 response probabilities of the model used for simulation

Examples

# Fit an empty model on the example data
# (remove empty=TRUE to actually fit the model)
fit_metad(N ~ 1, example_data(), empty = TRUE)
# Fit an empty model on the example data
# (remove empty=TRUE to actually fit the model)
fit_metad(N ~ 1, example_data(), empty = TRUE)

Example meta-d' model for model post-processing

Description

A model fit to the simulated data example_data. This model includes one constant set of parameters, with no multilevel structure.

Usage

example_model()
example_model()

Value

A brmsfit object fitted to simulated data

Examples


# inspect summary of posterior distribution
summary(example_model())

# obtain posterior expectations
epred_draws_metad(example_model(), tidyr::tibble(.row = 1))

# inspect summary of posterior distribution
summary(example_model())

# obtain posterior expectations
epred_draws_metad(example_model(), tidyr::tibble(.row = 1))

Fit the meta-d' model using `brms` package

Description

This function is a wrapper around brms::brm() using a custom family for the meta-d' model.

Usage

fit_metad(
  formula,
  data,
  ...,
  aggregate = TRUE,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  K = NULL,
  distribution = "normal",
  metac_absolute = TRUE,
  stanvars = NULL,
  categorical = FALSE,
  logit = TRUE
)
fit_metad(
  formula,
  data,
  ...,
  aggregate = TRUE,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  K = NULL,
  distribution = "normal",
  metac_absolute = TRUE,
  stanvars = NULL,
  categorical = FALSE,
  logit = TRUE
)

Arguments

formula

A model formula for some or all parameters of the metad brms family. To display all parameter names for a model with K confidence levels, use metad(K).

data

A tibble containing the data to fit the model.

If aggregate==TRUE, data should have one row per observation with columns stimulus, response, confidence, and any other variables in formula
If aggregate==FALSE, it should be aggregated to have one row per cell of the design matrix, with joint type 1/type 2 response counts in a matrix column (see aggregate_metad()).

...

Additional parameters passed to the brm function.

aggregate

If TRUE, automatically aggregate data by the variables included in formula using aggregate_metad(). Otherwise, data should already be aggregated.

.stimulus

The name of "stimulus" column

.response

The name of "response" column

.confidence

The name of "confidence" column

.joint_response

The name of "joint_response" column

K

The number of confidence levels in data. If NULL, this is estimated from data using the maximum level of either the confidence column or joint response column.

distribution

The noise distribution to use for the signal detection model. By default, uses a normal distribution with a mean parameterized by dprime.

metac_absolute

If TRUE, fix the type 2 criterion to be equal to the type 1 criterion. Otherwise, equate the criteria relatively such that metac/metadprime = c/dprime.

stanvars

Additional stanvars to pass to the model code, for example to define an alternative distribution or a custom model prior (see brms::stanvar()).

categorical

If FALSE (default), use the multinomial likelihood over aggregated data. If TRUE, use the categorical likelihood over individual trials.

logit

If TRUE (default), use the logit parameterization of the likelihood over the log joint response probabilities. If FALSE, use the standard parameterization of the likelihood over the actual joint response probabilities. In most cases, the logit parameterization should provide more stable numerical computations, but the standard parameterization might be preferable in some settings.

Details

fit_metad(formula, data, ...) is approximately the same as brm(formula, data=aggregate_metad(data, ...), family=metad(...), stanvars=stanvars_metad(...), ...). For some models, it may often be easier to use the more explicit version than using fit_metad.

Value

A brmsfit object containing the fitted model

Examples

# check which parameters the model has
metad(3)

# fit a basic model on simulated data
# (use `empty=true` to bypass fitting, *do not use in real analysis*)
fit_metad(N ~ 1, sim_metad(), empty = TRUE)

# fit a basic model on simulated data
fit_metad(N ~ 1, sim_metad())

# fit a model with condition-level effects
fit_metad(
  bf(
    N ~ condition,
    dprime + c + metac2zero1diff + metac2zero2diff +
      metac2one1diff + metac2one1diff ~ condition
  ),
  data = sim_metad_condition()
)

# check which parameters the model has
metad(3)

# fit a basic model on simulated data
# (use `empty=true` to bypass fitting, *do not use in real analysis*)
fit_metad(N ~ 1, sim_metad(), empty = TRUE)

# fit a basic model on simulated data
fit_metad(N ~ 1, sim_metad())

# fit a model with condition-level effects
fit_metad(
  bf(
    N ~ condition,
    dprime + c + metac2zero1diff + metac2zero2diff +
      metac2one1diff + metac2one1diff ~ condition
  ),
  data = sim_metad_condition()
)

Convert between separate and joint type 1/type 2 responses

Description

Confidence ratings and decisions are collected in one of two ways.

For separate ratings, there will be a type 1 response ( $R \in \{0, 1\}$ ) and a type 2 response ( $C \in [1, K]$ ).
For joint ratings, there is instead a combined type 1/type 2 response ( $J \in [1, 2K]$ ), with values in $[1, K]$ indicating a type 1 response of $0$ and values in $[K+1, 2K]$ indicating a type 1 response of $1$ , with confident responses at the ends of the scale.

joint_response converts separate type 1 and type 2 responses into the joint format

type1_response and type2_response convert the joint response into separate responses.

Usage

joint_response(response, confidence, K)

type1_response(joint_response, K)

type2_response(joint_response, K)
joint_response(response, confidence, K)

type1_response(joint_response, K)

type2_response(joint_response, K)

Arguments

response

A type 1 response (0 or 1)

confidence

A type 2 response/confidence rating (in 1:K)

K

The number of confidence levels

joint_response

A joint type 1/type 2 response

Value

A joint response (for joint_response), type 1 response (for type1_response), or type 2 response (for type2_response)

Examples

# convert joint_response to separate responses
joint <- 1:8
K <- 4
type1_response(joint, K)
type2_response(joint, K)

# convert separate responses to a joint response
t1 <- rep(c(0, 1), each = 4)
t2 <- c(4:1, 1:4)
joint_response(t1, t2, K)

# convert joint_response to separate responses
joint <- 1:8
K <- 4
type1_response(joint, K)
type2_response(joint, K)

# convert separate responses to a joint response
t1 <- rep(c(0, 1), each = 4)
t2 <- c(4:1, 1:4)
joint_response(t1, t2, K)

Obtain posterior draws of meta-d' model parameters

Description

Given a data frame and a meta-d' model, adds estimates of all model parameters. For linpred_draws_metad and add_linpred_draws_metad, parameters are returned in a tidy tibble with one row per posterior draw. For linpred_rvars_metad and add_linpred_rvars_metad, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

linpred_draws_metad(object, newdata, ..., pivot_longer = FALSE)

add_linpred_draws_metad(newdata, object, ..., pivot_longer = FALSE)

linpred_rvars_metad(object, newdata, ..., pivot_longer = FALSE)

add_linpred_rvars_metad(newdata, object, ..., pivot_longer = FALSE)
linpred_draws_metad(object, newdata, ..., pivot_longer = FALSE)

add_linpred_draws_metad(newdata, object, ..., pivot_longer = FALSE)

linpred_rvars_metad(object, newdata, ..., pivot_longer = FALSE)

add_linpred_rvars_metad(newdata, object, ..., pivot_longer = FALSE)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional arguments passed to tidybayes::add_linpred_draws or tidybayes::add_linpred_rvars

pivot_longer

Return the draws in long format?

if TRUE, resulting data frame has one row per posterior draw per model parameter
if FALSE (default), resulting data frame has one row per posterior draw

Value

a tibble containing posterior draws of model parameters with the following columns:

.row: the row of newdata
.chain, .iteration, .draw: for linpred_draws_metad, identifiers for the posterior sample
.variable, .value: if pivot_longer=TRUE, .variable identifies different meta-d' model parameters and .value stores posterior samples
M, dprime, c, meta_dprime, meta_c, ⁠meta_c2_0_<k>⁠, ⁠meta_c2_1_<k>⁠: if pivot_longer=FALSE, posterior samples of all meta-d' model parameters

Examples


newdata <- tidyr::tibble(.row = 1)

# obtain model parameters (wide format)
# equivalent to `add_linpred_draws_metad(newdata, example_model())`
linpred_draws_metad(example_model(), newdata)

# obtain model parameters (long format)
# equivalent to `add_linpred_draws_metad(newdata, example_model(), pivot_longer = TRUE)`
linpred_draws_metad(example_model(), newdata, pivot_longer = TRUE)

# obtain model parameters (wide format, posterior::rvar)
# equivalent to `add_linpred_rvars_metad(newdata, example_model())`
linpred_rvars_metad(example_model(), newdata)

# obtain model parameters (long format, posterior::rvar)
# equivalent to `add_linpred_rvars_metad(newdata, example_model(), pivot_longer = TRUE)`
linpred_rvars_metad(example_model(), newdata, pivot_longer = TRUE)

newdata <- tidyr::tibble(.row = 1)

# obtain model parameters (wide format)
# equivalent to `add_linpred_draws_metad(newdata, example_model())`
linpred_draws_metad(example_model(), newdata)

# obtain model parameters (long format)
# equivalent to `add_linpred_draws_metad(newdata, example_model(), pivot_longer = TRUE)`
linpred_draws_metad(example_model(), newdata, pivot_longer = TRUE)

# obtain model parameters (wide format, posterior::rvar)
# equivalent to `add_linpred_rvars_metad(newdata, example_model())`
linpred_rvars_metad(example_model(), newdata)

# obtain model parameters (long format, posterior::rvar)
# equivalent to `add_linpred_rvars_metad(newdata, example_model(), pivot_longer = TRUE)`
linpred_rvars_metad(example_model(), newdata, pivot_longer = TRUE)

Obtain posterior draws of mean confidence

Description

Computes posterior mean confidence conditional on stimulus and response ( $\mathbb{E}[C \;\vert\; S=s,R=r]$ ), stimulus (averaging over responses, $\mathbb{E}[C \;\vert\; S=s]$ ), response (averaging over stimuli, $\mathbb{E}[C \;\vert\; R=r]$ ), neither (averaging over stimuli and responses, $\mathbb{E}[C]$ ), or accuracy ( $\mathbb{E}[C \;\vert\; A=(r=s)]$ ). For mean_confidence_draws and add_mean_confidence_draws, estimates are returned in a tidy tibble with one row per posterior draw, stimulus, and response. For mean_confidence_rvars and add_mean_confidence_rvars, estimates are returned as posterior::rvars, with one row per row in newdata.

add_mean_confidence_draws is an alias of mean_confidence_draws with argument order swapped.

Usage

mean_confidence_draws(
  object,
  newdata,
  ...,
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = FALSE
)

add_mean_confidence_draws(newdata, object, ...)

mean_confidence_rvars(
  object,
  newdata,
  ...,
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = FALSE
)

add_mean_confidence_rvars(newdata, object, ...)
mean_confidence_draws(
  object,
  newdata,
  ...,
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = FALSE
)

add_mean_confidence_draws(newdata, object, ...)

mean_confidence_rvars(
  object,
  newdata,
  ...,
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = FALSE
)

add_mean_confidence_rvars(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional arguments to tidybayes::epred_draws or tidybayes::epred_rvars

by_stimulus

If TRUE, predict mean confidence separately by stimulus. Otherwise, predict mean confidence averaging over stimuli. Ignored if by_correct==TRUE.

by_response

If TRUE, predict mean confidence separately by response Otherwise, predict mean confidence averaging over responses. Ignored if by_correct==TRUE.

by_correct

If TRUE, predict mean confidence separately for correct and incorrect responses.

Value

a tibble containing posterior draws of mean confidence with the following columns:

.row: the row of newdata
.chain, .iteration, .draw: for mean_confidence_draws and add_mean_confidence_draws, identifiers for the posterior sample
stimulus: indicator for stimulus presence (if by_stimulus==TRUE & by_correct==FALSE)
response: indicator for type 1 response (if by_response==TRUE & by_correct==FALSE)
correct: indicator for the accuracy of the type 1 response (if by_correct==TRUE)
.epred: the predicted mean confidence

Examples


newdata <- tidyr::tibble(.row = 1)

# compute mean confidence by stimulus and response
# equivalent to `add_mean_confidence_draws(newdata, example_model())`
mean_confidence_draws(example_model(), newdata)

# compute mean confidence by stimulus
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_response = FALSE)`
mean_confidence_draws(example_model(), newdata, by_response = FALSE)

# compute mean confidence by response
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_stimulus = FALSE)`
mean_confidence_draws(example_model(), newdata, by_stimulus = FALSE)

# compute mean confidence by accuracy
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_correct = TRUE)`
mean_confidence_draws(example_model(), newdata, by_correct = TRUE)

# compute mean confidence averaging over stimuli and responses
# equivalent to `add_mean_confidence_draws(newdata, example_model(), ...)`
mean_confidence_draws(example_model(), newdata, by_stimulus = FALSE, by_response = FALSE)

# use `posterior::rvar` for increased efficiency
# equivalent to `add_mean_confidence_rvars(newdata, example_model())`
mean_confidence_rvars(example_model(), newdata)

newdata <- tidyr::tibble(.row = 1)

# compute mean confidence by stimulus and response
# equivalent to `add_mean_confidence_draws(newdata, example_model())`
mean_confidence_draws(example_model(), newdata)

# compute mean confidence by stimulus
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_response = FALSE)`
mean_confidence_draws(example_model(), newdata, by_response = FALSE)

# compute mean confidence by response
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_stimulus = FALSE)`
mean_confidence_draws(example_model(), newdata, by_stimulus = FALSE)

# compute mean confidence by accuracy
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_correct = TRUE)`
mean_confidence_draws(example_model(), newdata, by_correct = TRUE)

# compute mean confidence averaging over stimuli and responses
# equivalent to `add_mean_confidence_draws(newdata, example_model(), ...)`
mean_confidence_draws(example_model(), newdata, by_stimulus = FALSE, by_response = FALSE)

# use `posterior::rvar` for increased efficiency
# equivalent to `add_mean_confidence_rvars(newdata, example_model())`
mean_confidence_rvars(example_model(), newdata)

Obtain a vector of the names of the `K-1` parameters representing the differences between successive confidence criteria for the meta-d' model with `K` levels of confidence.

Description

Obtain a vector of the names of the K-1 parameters representing the differences between successive confidence criteria for the meta-d' model with K levels of confidence.

Usage

metac2_parameters(K, response = "both")
metac2_parameters(K, response = "both")

Arguments

K

The number of confidence levels

response

If "both", list confidence criteria parameters for both "0" and "1" responses. If "zero" or "0", list only confidence criteria for the "0" response. If "one" or "1", list only confidence criteria for the "1" response.

Examples

# list confidence criteria parameters for K=3 confidence levels
metac2_parameters(K = 3)

# list parameters for "0" responses
metac2_parameters(K = 3, response = "zero")

# useful for setting model priors
set_prior("normal(0, 1)", class = "b", dpar = metac2_parameters(K = 4))

# list confidence criteria parameters for K=3 confidence levels
metac2_parameters(K = 3)

# list parameters for "0" responses
metac2_parameters(K = 3, response = "zero")

# useful for setting model priors
set_prior("normal(0, 1)", class = "b", dpar = metac2_parameters(K = 4))

Obtain posterior draws of an index of metacognitive bias

Description

Computes $\textrm{meta-}\Delta$ , an index of metacognitive bias. $\textrm{meta-}\Delta$ is the distance between meta_c and the average of the the confidence criteria meta_c2_0 and meta_c2_1. For metacognitive_bias_draws and add_metacognitive_bias_draws, parameters are returned in a tidy tibble with one row per posterior draw and per response. For metacognitive_bias_rvars and add_metacognitive_bias_rvars, parameters are returned as posterior::rvars, with one row per row in newdata and per response.

Usage

metacognitive_bias_draws(object, newdata, ..., by_response = TRUE)

add_metacognitive_bias_draws(newdata, object, ...)

metacognitive_bias_rvars(object, newdata, ..., by_response = TRUE)

add_metacognitive_bias_rvars(newdata, object, ...)
metacognitive_bias_draws(object, newdata, ..., by_response = TRUE)

add_metacognitive_bias_draws(newdata, object, ...)

metacognitive_bias_rvars(object, newdata, ..., by_response = TRUE)

add_metacognitive_bias_rvars(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional parameters passed to tidybayes::epred_draws or tidybayes::epred_rvars

by_response

If TRUE, compute metacognitive bias separately for the two type 1 responses. If FALSE, compute an un-weighted average of the two measures.

Value

a tibble containing posterior draws of $\textrm{meta-}\Delta$ with the following columns:

.row: the row of newdata
.chain, .iteration, .draw: for metacognitive_bias_draws and add_metacognitive_bias_draws, identifiers for the posterior sample
response: the type 1 response for perceived stimulus presence
metacognitive_bias: the distance between meta_c and the average of the confidence criteria ⁠meta_c2_{response}⁠.

Examples


newdata <- tidyr::tibble(.row = 1)

# compute metacognitive bias
# equivalent to `add_metacognitive_bias_draws(newdata, example_model())`
metacognitive_bias_draws(example_model(), newdata)

# use `posterior::rvar` for increased efficiency
# equivalent to `add_metacognitive_bias_rvars(newdata, example_model())`
metacognitive_bias_rvars(example_model(), newdata)

# average over the two type 1 responses
metacognitive_bias_rvars(example_model(), newdata, by_response = FALSE)

newdata <- tidyr::tibble(.row = 1)

# compute metacognitive bias
# equivalent to `add_metacognitive_bias_draws(newdata, example_model())`
metacognitive_bias_draws(example_model(), newdata)

# use `posterior::rvar` for increased efficiency
# equivalent to `add_metacognitive_bias_rvars(newdata, example_model())`
metacognitive_bias_rvars(example_model(), newdata)

# average over the two type 1 responses
metacognitive_bias_rvars(example_model(), newdata, by_response = FALSE)

`brms` family for the metad' model

Description

brms family for the metad' model

Usage

metad(K, distribution = "normal", metac_absolute = TRUE, categorical = FALSE)
metad(K, distribution = "normal", metac_absolute = TRUE, categorical = FALSE)

Arguments

K

The number of confidence levels

distribution

The noise distribution to use for the signal detection model

metac_absolute

If TRUE, fix the type 2 criterion to be equal to the type 1 criterion. Otherwise, equate the criteria relatively such that

$\frac{\textrm{meta-}c}{\textrm{meta-}d'} = \frac{c}{d'}$

categorical

If FALSE (default), use the multinomial likelihood over aggregated data. If TRUE, use the categorical likelihood over individual trials.

Value

A brms family for the metad' model with $K$ confidence levels

Examples

# create a family using the normal distribution and 3 levels of confidence
metad(3)

# create a family with meta_c = M * c
metad(3, metac_absolute = FALSE)

# create a family with an alternative distribution
# note: cumulative distribution functions must be defined
# in R and in Stan using [brms::stanvar()]
metad(4, distribution = "gumbel_min")
# create a family using the normal distribution and 3 levels of confidence
metad(3)

# create a family with meta_c = M * c
metad(3, metac_absolute = FALSE)

# create a family with an alternative distribution
# note: cumulative distribution functions must be defined
# in R and in Stan using [brms::stanvar()]
metad(4, distribution = "gumbel_min")

Generate (log) probability simplex over the joint type 1/type 2 responses

Description

Generate (log) probability simplex over the joint type 1/type 2 responses

Usage

metad_pmf(
  stimulus,
  dprime,
  c,
  meta_dprime,
  meta_c,
  meta_c2_0,
  meta_c2_1,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf,
  log = FALSE
)
metad_pmf(
  stimulus,
  dprime,
  c,
  meta_dprime,
  meta_c,
  meta_c2_0,
  meta_c2_1,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf,
  log = FALSE
)

Arguments

stimulus

the stimulus (0 or 1)

dprime

the type 1 sensitivity

c

the type 1 response criterion

meta_dprime

the type 2 sensitivity

meta_c

the type 1 criterion for generating confidence ratings

meta_c2_0

the type 2 response criteria for "0" responses, indexed by increasing confidence levels

meta_c2_1

the type 2 response criteria for "1" responses, indexed by increasing confidence levels

lcdf

The log cumulative distribution function for the underlying distribution in the metad' model. By default, uses the normal distribution with a standard deviation of 1.

lccdf

The log complement cumulative distribution function for the underlying distribution in the metad' model. By default, uses the normal distribution with a standard deviation of 1.

log

if TRUE, return log probabilities instead of probabilities

Value

A probability simplex

$\begin{bmatrix} P(R=0, C=K \vert S=0), \ldots, P(R=0, C=1 \vert S=0), P(R=0, C=1 \vert S=1), \ldots, P(R=1, C=1 \vert S=1)\end{bmatrix}$

for response $R$ and confidence $C$ given stimulus $S$ , as defined by the meta-d' model.

Examples

metad_pmf(
  stimulus = 0, dprime = 2, c = .5, meta_dprime = 1, meta_c = .5,
  meta_c2_0 = c(0, -.5), meta_c2_1 = c(1, 1.5)
)
metad_pmf(
  stimulus = 0, dprime = 2, c = .5, meta_dprime = 1, meta_c = .5,
  meta_c2_0 = c(0, -.5), meta_c2_1 = c(1, 1.5)
)

Normal cumulative distribution functions

Description

Normal cumulative distribution functions

Usage

normal_lcdf(x, mu)

normal_lccdf(x, mu)
normal_lcdf(x, mu)

normal_lccdf(x, mu)

Arguments

x

The quantile to evaluate the l(c)cdf at

mu

The mean of the normal distribution

Value

$log(P(X < x))$ (for normal_lcdf) or $log(P(X > x))$ (for normal_lccdf) where $X$ is sampled from a normal distribution with mean mu and standard deviation of $1$

Examples

normal_lcdf(0, mu = 1)
normal_lccdf(0, mu = 1)

normal_lcdf(0, mu = 1)
normal_lccdf(0, mu = 1)

Obtain posterior predictions of joint responses

Description

Given a data frame and a meta-d' model, adds predictions of joint type 1 and type 2 responses For predicted_draws_metad and add_predicted_draws_metad, predictions are returned in a tidy tibble with one row per posterior draw. For predicted_rvars_metad and add_predicted_rvars_metad, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

predicted_draws_metad(object, newdata, ...)

add_predicted_draws_metad(newdata, object, ...)

predicted_rvars_metad(object, newdata, ...)

add_predicted_rvars_metad(newdata, object, ...)
predicted_draws_metad(object, newdata, ...)

add_predicted_draws_metad(newdata, object, ...)

predicted_rvars_metad(object, newdata, ...)

add_predicted_rvars_metad(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional arguments passed to tidybayes::add_predicted_draws or tidybayes::add_predicted_rvars

Value

a tibble containing posterior draws of model parameters with the following columns:

.row: the row of newdata
.chain, .iteration, .draw: for predicted_draws_metad, identifiers for the posterior sample
stimulus, joint_response, response, confidence: identifiers for the response type
.prediction: predicted type 1 and type 2 responses given the stimulus

Examples


newdata <- aggregate_metad(example_data())

# obtain model predictions
# equivalent to `add_predicted_draws_metad(newdata, example_model())`
predicted_draws_metad(example_model(), newdata)

# obtain model predictions (posterior::rvar)
# equivalent to `add_predicted_rvars_metad(newdata, example_model())`
predicted_rvars_metad(example_model(), newdata)

newdata <- aggregate_metad(example_data())

# obtain model predictions
# equivalent to `add_predicted_draws_metad(newdata, example_model())`
predicted_draws_metad(example_model(), newdata)

# obtain model predictions (posterior::rvar)
# equivalent to `add_predicted_rvars_metad(newdata, example_model())`
predicted_rvars_metad(example_model(), newdata)

Compute joint response probabilities from aggregated counts

Description

Compute joint response probabilities from aggregated counts

Usage

response_probabilities(counts)
response_probabilities(counts)

Arguments

counts

A vector (or matrix) of counts of joint type 1/type 2 responses as provided by aggregate_metad

Details

For response $R$ , confidence $C$ , stimulus $S$ , and number of confidence levels $K$ , counts should be a vector (or matrix with rows) of the form:

$[N_{S=0, R=0, C=K}, \ldots, N_{S=0, R=0, C=1}, \\ N_{S=0, R=1, C=1}, \ldots, N_{S=0, R=1, C=K}, \\ N_{S=1, R=0, C=K}, \ldots, N_{S=1, R=0, C=1}, \\ N_{S=1, R=1, C=1}, \ldots, N_{S=1, R=1, C=K}] \\$

Returns a vector (or matrix with rows) of the form:

$[P(R=0, C=K \;\vert\; S=0), ..., P(R=0, C=1 \;\vert\; S=0), \\ P(R=1, C=1 \;\vert\; S=0), ..., P(R=1, C=K \;\vert\; S=0), \\ P(R=0, C=K \;\vert\; S=1), ..., P(R=0, C=1 \;\vert\; S=1), \\ P(R=1, C=1 \;\vert\; S=1), ..., P(R=1, C=K \;\vert\; S=1)]$

Value

A vector (or matrix) of response probabilities $P(R, C \;\vert\; S)$

Examples

# Aggregate responses from simulated data
d <- sim_metad() |> aggregate_metad()

# Compute conditional response probabilities
response_probabilities(d$N)

# Also works on matrices
matrix(rep(1, 16), nrow = 2) |> response_probabilities()
# Aggregate responses from simulated data
d <- sim_metad() |> aggregate_metad()

# Compute conditional response probabilities
response_probabilities(d$N)

# Also works on matrices
matrix(rep(1, 16), nrow = 2) |> response_probabilities()

Sample from a matrix-normal distribution

Description

Sample from a matrix-normal distribution

Usage

rmatrixnorm(mu, L_sigma_rows, L_sigma_cols)
rmatrixnorm(mu, L_sigma_rows, L_sigma_cols)

Arguments

mu

a matrix of means

L_sigma_rows

the Cholesky-decomposed covariance matrix for the rows

L_sigma_cols

the Cholesky-decomposed covariance matrix for the columns

Value

A single sample from a matrix-normal distribution with mean mu (a matrix), row-wise covariances sigma_rows, and column-wise covariances sigma_cols, where L_sigma_rows and L_sigma_cols are the Cholesky-decomposed covariance matrices

Examples

mu <- matrix(rep(0, 8), nrow = 4)
sd_rows <- rep(1, 4)
sd_cols <- rep(1, 2)
r_rows <- cor_matrix(.25, 4)
r_cols <- cor_matrix(.75, 2)
L_sigma_rows <- chol(cov_matrix(sd_rows, r_rows))
L_sigma_cols <- chol(cov_matrix(sd_cols, r_cols))
rmatrixnorm(mu, L_sigma_rows, L_sigma_cols)
mu <- matrix(rep(0, 8), nrow = 4)
sd_rows <- rep(1, 4)
sd_cols <- rep(1, 2)
r_rows <- cor_matrix(.25, 4)
r_cols <- cor_matrix(.75, 2)
L_sigma_rows <- chol(cov_matrix(sd_rows, r_rows))
L_sigma_cols <- chol(cov_matrix(sd_cols, r_cols))
rmatrixnorm(mu, L_sigma_rows, L_sigma_cols)

Obtain posterior draws of the pseudo type 1 receiver operating characteristic (ROC) curve.

Description

Given a data frame and a meta-d' model, adds estimates of the cumulative probability over joint_responses. For roc1_draws and add_roc1_draws, estimates are returned in a tidy tibble with one row per posterior draw and per joint response. For roc1_rvars and add_roc1_rvars, parameters are returned as posterior::rvars, with one row per row in newdata and per joint response.

Usage

roc1_draws(object, newdata, ..., bounds = FALSE)

add_roc1_draws(newdata, object, ...)

roc1_rvars(object, newdata, ..., bounds = FALSE)

add_roc1_rvars(newdata, object, ...)
roc1_draws(object, newdata, ..., bounds = FALSE)

add_roc1_draws(newdata, object, ...)

roc1_rvars(object, newdata, ..., bounds = FALSE)

add_roc1_rvars(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional parameters passed to tidybayes::epred_draws or tidybayes::epred_rvars

bounds

If TRUE, include the endpoints of the ROC at $(0, 0)$ and $(1, 1)$ . Otherwise, the endpoints are excluded.

Value

a tibble containing posterior draws of the pseudo type 1 ROC with the following columns:

.row: the row of newdata
.chain, .iteration, .draw: for roc1_draws and add_roc1_draws, identifiers for the posterior sample
joint_response: the combined type 1 / type 2 response ( $J \in [1, 2K]$ ) for $K$ confidence levels)
response: the type 1 response for perceived stimulus presence ( $R \in \{0, 1\}$ )
confidence: the type 2 confidence response ( $C \in [1, K]$ )
p_fa: the cumulative probability of a 'present'/'old' response for stimulus==0 ( $P(J \ge j \;\vert\; S=0)$ )
p_hit: the cumulative probability of a 'present'/'old' response for stimulus==1 ( $P(J \ge j \;\vert\; S=1)$ )

Examples


newdata <- tidyr::tibble(.row = 1)

# compute pseudo-type 1 ROC curve
# equivalent to ``
roc1_draws(example_model(), newdata)
add_roc1_draws(newdata, example_model())

# use posterior::rvar for additional efficiency
# equivalent to `add_roc1_draws(newdata, example_model())`
roc1_rvars(example_model(), newdata)

# include the ROC bounds
# equivalent to `add_roc1_draws(newdata, example_model(), bounds = TRUE)`
roc1_draws(example_model(), newdata, bounds = TRUE)

newdata <- tidyr::tibble(.row = 1)

# compute pseudo-type 1 ROC curve
# equivalent to ``
roc1_draws(example_model(), newdata)
add_roc1_draws(newdata, example_model())

# use posterior::rvar for additional efficiency
# equivalent to `add_roc1_draws(newdata, example_model())`
roc1_rvars(example_model(), newdata)

# include the ROC bounds
# equivalent to `add_roc1_draws(newdata, example_model(), bounds = TRUE)`
roc1_draws(example_model(), newdata, bounds = TRUE)

Obtain posterior draws of the response-specific type 2 receiver operating characteristic (ROC) curves.

Description

Given a data frame and a meta-d' model, adds estimates of the cumulative probability over confidence for each type 1 response. For roc2_draws and add_roc2_draws, estimates are returned in a tidy tibble with one row per posterior draw and per joint response. For roc2_rvars and add_roc2_rvars, parameters are returned as posterior::rvars, with one row per row in newdata and per joint response.

Usage

roc2_draws(object, newdata, ..., bounds = FALSE)

add_roc2_draws(newdata, object, ...)

roc2_rvars(object, newdata, ..., bounds = FALSE)

add_roc2_rvars(newdata, object, ...)
roc2_draws(object, newdata, ..., bounds = FALSE)

add_roc2_draws(newdata, object, ...)

roc2_rvars(object, newdata, ..., bounds = FALSE)

add_roc2_rvars(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional parameters passed to tidybayes::epred_draws

bounds

If TRUE, include the endpoints of the ROC at $(0, 0)$ and $(1, 1)$ . Otherwise, the endpoints are excluded.

Value

a tibble containing posterior draws of the pseudo type 1 ROC with the following columns:

.row: the row of newdata
.chain, .iteration, .draw: for roc2_draws and add_roc2_draws, identifiers for the posterior sample
response: the type 1 response for perceived stimulus presence ( $R \in \{0, 1\}$ )
confidence: the type 2 confidence response ( $C \in [1, K]$ )
p_fa2: the cumulative probability of an incorrect response ( $P(C\ge c \;\vert\; R\ne S)$ )
p_hit2: the cumulative probability of a correct response ( $P(C\ge c \;\vert\; R = S)$ )

Examples


newdata <- tidyr::tibble(.row = 1)

# compute type 2 ROC curve
# equivalent to `add_roc2_draws(newdata, example_model())`
roc2_draws(example_model(), newdata)

# use posterior::rvar for additional efficiency
# equivalent to `add_roc2_rvars(newdata, example_model())`
roc2_rvars(example_model(), newdata)

# include the ROC bounds
# equivalent to `roc2_draws(newdata, example_model(), bounds = TRUE)`
roc2_draws(example_model(), newdata, bounds = TRUE)

newdata <- tidyr::tibble(.row = 1)

# compute type 2 ROC curve
# equivalent to `add_roc2_draws(newdata, example_model())`
roc2_draws(example_model(), newdata)

# use posterior::rvar for additional efficiency
# equivalent to `add_roc2_rvars(newdata, example_model())`
roc2_rvars(example_model(), newdata)

# include the ROC bounds
# equivalent to `roc2_draws(newdata, example_model(), bounds = TRUE)`
roc2_draws(example_model(), newdata, bounds = TRUE)

Simulate from the meta-d' model

Description

Generate a simulated dataset from the meta-d' model with sensitivity dprime, response bias c, metacognitive efficiency log_M, and distances between confidence thresholds c2_0_diff and c2_1_diff (for the two responses).

Usage

sim_metad(
  N_trials = 100,
  dprime = 1,
  c = 0,
  log_M = 0,
  c2_0_diff = rep(0.5, 3),
  c2_1_diff = rep(0.5, 3),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)
sim_metad(
  N_trials = 100,
  dprime = 1,
  c = 0,
  log_M = 0,
  c2_0_diff = rep(0.5, 3),
  c2_1_diff = rep(0.5, 3),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)

Arguments

N_trials

Total number of trials to simulate. Half of these trials will have stimulus=0 and half will have stimulus=1.

dprime

The sensitivity of the signal detection agent to simulate

c

The response bias of the signal detection agent to simulate

log_M

The metacognitive efficiency of the agent on the logarithmic scale, where 0 indicates optimal metacognitive sensitivity, negative numbers indicate metacognitive inefficiency, and positive numbers indicate metacognitive hyper-efficiency.

c2_0_diff, c2_1_diff

Distances between confidence thresholds for "0" and "1" responses, such that meta_c2_0 = meta_c - cumsum(c2_0_diff) and meta_c2_1 = meta_c + cumsum(c2_1_diff).

metac_absolute

Determines how to fix the type 1 threshold for modeling confidence ratings. If metac_absolute=TRUE, meta_c = c. Otherwise, meta_c = M * c.

summarize

Aggregate the data?

If FALSE, returns a dataset with one row per observation.
If summarize=TRUE, returns an aggregated dataset where n is the number of observations per response, accuracy, and confidence level.

lcdf, lccdf

The log (complement) cumulative distribution function of the underlying signal distribution. By default, uses a ⁠normal(+/-dprime/2, 1)⁠ distribution.

Value

A simulated dataset of type 1 responses and confidence ratings, with columns:

trial: the simulated trial number
stimulus: the value of the stimulus on each trial (either 0 or 1)
response: the simulated type 1 response (either 0 or 1)
correct: whether stimulus==response (either 0 or 1)
confidence: the simulated type 2 response (from 1 to length(c2_0_diff)+1)
dprime:theta_2: the simulated agent's parameter values

If summarize=TRUE, the trial column is replaced with an n column indicating the number of simulated type 1/type 2 responses for each possible value.

Examples

sim_metad(N_trials = 10)
sim_metad(N_trials = 10000, summarize = TRUE)
sim_metad(N_trials = 10, c2_0_diff = 1, c2_1_diff = 1)
sim_metad(N_trials = 10)
sim_metad(N_trials = 10000, summarize = TRUE)
sim_metad(N_trials = 10, c2_0_diff = 1, c2_1_diff = 1)

Simulate from the meta-d' model across separate conditions

Description

Generate a simulated dataset across separate conditions from the meta-d' model with sensitivity dprime, response bias c, metacognitive efficiency log_M, and distances between confidence thresholds c2_0_diff and c2_1_diff (for the two responses).

Usage

sim_metad_condition(
  N_trials = 100,
  dprime = rep(1, 2),
  c = rep(0, 2),
  log_M = rep(0, 2),
  c2_0_diff = list(rep(0.5, 3), rep(0.5, 3)),
  c2_1_diff = list(rep(0.5, 3), rep(0.5, 3)),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)
sim_metad_condition(
  N_trials = 100,
  dprime = rep(1, 2),
  c = rep(0, 2),
  log_M = rep(0, 2),
  c2_0_diff = list(rep(0.5, 3), rep(0.5, 3)),
  c2_1_diff = list(rep(0.5, 3), rep(0.5, 3)),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)

Arguments

N_trials

Total number of trials to simulate. Half of these trials will have stimulus=0 and half will have stimulus=1.

dprime

The sensitivity of the signal detection agent to simulate

c

The response bias of the signal detection agent to simulate

log_M

c2_0_diff, c2_1_diff

Distances between confidence thresholds for "0" and "1" responses, such that meta_c2_0 = meta_c - cumsum(c2_0_diff) and meta_c2_1 = meta_c + cumsum(c2_1_diff).

metac_absolute

Determines how to fix the type 1 threshold for modeling confidence ratings. If metac_absolute=TRUE, meta_c = c. Otherwise, meta_c = M * c.

summarize

Aggregate the data? If summarize=FALSE, returns a dataset with one row per observation. If summarize=TRUE, returns an aggregated dataset where n is the number of observations per response, accuracy, and confidence level.

lcdf, lccdf

The log (complement) cumulative distribution function of the underlying signal distribution. By default, uses a ⁠normal(+/-dprime/2, 1)⁠ distribution.

Value

A simulated dataset of type 1 responses and confidence ratings, with columns:

trial: the simulated trial number
condition: the simulated condition number
stimulus: the value of the stimulus on each trial (either 0 or 1)
response: the simulated type 1 response (either 0 or 1)
correct: whether stimulus==response (either 0 or 1)
confidence: the simulated type 2 response (from 1 to length(c2_0_diff)+1)
dprime:theta_2: the simulated agent's parameter values

If summarize=TRUE, the trial column is replaced with an n column indicating the number of simulated type 1/type 2 responses for each possible value.

Examples

sim_metad_condition(N_trials = 10)
sim_metad_condition(N_trials = 10000, summarize = TRUE)
sim_metad_condition(N_trials = 10, c2_0_diff = list(1, .5), c2_1_diff = list(1, .5))
sim_metad_condition(N_trials = 10)
sim_metad_condition(N_trials = 10000, summarize = TRUE)
sim_metad_condition(N_trials = 10, c2_0_diff = list(1, .5), c2_1_diff = list(1, .5))

Simulate from the hierarchical meta-d' model

Description

Generate a simulated dataset across participants from the meta-d' model with sensitivity dprime, response bias c, metacognitive efficiency log_M, and distances between confidence thresholds c2_0_diff and c2_1_diff (for the two responses).

Usage

sim_metad_participant(
  N_participants = 100,
  N_trials = 100,
  mu_dprime = 1,
  sd_dprime = 0.5,
  mu_c = 0,
  sd_c = 0.5,
  mu_log_M = 0,
  sd_log_M = 0.5,
  mu_z_c2_0 = rep(-1, 3),
  sd_z_c2_0 = rep(0.1, 3),
  r_z_c2_0 = diag(3),
  mu_z_c2_1 = rep(-1, 3),
  sd_z_c2_1 = rep(0.1, 3),
  r_z_c2_1 = diag(3),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)
sim_metad_participant(
  N_participants = 100,
  N_trials = 100,
  mu_dprime = 1,
  sd_dprime = 0.5,
  mu_c = 0,
  sd_c = 0.5,
  mu_log_M = 0,
  sd_log_M = 0.5,
  mu_z_c2_0 = rep(-1, 3),
  sd_z_c2_0 = rep(0.1, 3),
  r_z_c2_0 = diag(3),
  mu_z_c2_1 = rep(-1, 3),
  sd_z_c2_1 = rep(0.1, 3),
  r_z_c2_1 = diag(3),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)

Arguments

N_trials, N_participants

Total number of participants and trials to simulate per participant. Half of these trials will have stimulus=0 and half will have stimulus=1.

mu_dprime, sd_dprime

The mean and standard deviation of sensitivities of the signal detection agents to simulate

mu_c, sd_c

The mean and standard deviation of response bias of the signal detection agents to simulate

mu_log_M, sd_log_M

The mean and standard deviation of metacognitive efficiency of the agents on the logarithmic scale, where 0 indicates optimal metacognitive sensitivity, negative numbers indicate metacognitive inefficiency, and positive numbers indicate metacognitive hyper-efficiency.

mu_z_c2_0, mu_z_c2_1

Mean distance between confidence thresholds for "0" and "1" responses on the log_scale, such that meta_c2_0 = meta_c - cumulative_sum(exp(z_c2_0)) and meta_c2_1 = meta_c + cumulative_sum(exp(z_c2_1)).

sd_z_c2_0, sd_z_c2_1

SD of log distances between confidence thresholds for "0" and "1" responses on the log_scale.

r_z_c2_0, r_z_c2_1

Correlation of log distances between confidence thresholds for "0" and "1" responses on the log_scale.

metac_absolute

Determines how to fix the type 1 threshold for modeling confidence ratings. If metac_absolute=TRUE, meta_c = c. Otherwise, meta_c = M * c.

summarize

Aggregate the data? If summarize=FALSE, returns a dataset with one row per observation. If summarize=TRUE, returns an aggregated dataset where n is the number of observations per response, accuracy, and confidence level.

lcdf

The log cumulative distribution function of the underlying signal distribution. By default, uses a ⁠normal(+/-dprime/2, 1)⁠ distribution.

lccdf

The log complement cumulative distribution function of the underlying signal distribution. By default, uses a ⁠normal(+/-dprime/2, 1)⁠ distribution.

Value

A simulated dataset of type 1 responses and confidence ratings, with columns:

trial: the simulated trial number
participant: the simulated participant number
stimulus: the value of the stimulus on each trial (either 0 or 1)
response: the simulated type 1 response (either 0 or 1)
correct: whether stimulus==response (either 0 or 1)
confidence: the simulated type 2 response (from 1 to length(c2_0_diff)+1)
dprime:theta_2: the simulated agent's parameter values

If summarize=TRUE, the trial column is replaced with an n column indicating the number of simulated type 1/type 2 responses for each possible value.

Examples

sim_metad_participant(N_participants = 10, N_trials = 10)
sim_metad_participant(N_participants = 25, mu_dprime = 2, mu_log_M = -1)
sim_metad_participant(N_participants = 10, N_trials = 10)
sim_metad_participant(N_participants = 25, mu_dprime = 2, mu_log_M = -1)

Simulate from the hierarchical meta-d' model across within-participant conditions

Description

Generate a simulated dataset across participants and conditions from the meta-d' model with sensitivity dprime, response bias c, metacognitive efficiency log_M, and distances between confidence thresholds c2_0_diff and c2_1_diff (for the two responses).

Usage

sim_metad_participant_condition(
  N_participants = 100,
  N_trials = 100,
  mu_dprime = rep(1, 2),
  sd_dprime = rep(0.5, 2),
  r_dprime = diag(2),
  mu_c = rep(0, 2),
  sd_c = rep(0.5, 2),
  r_c = diag(2),
  mu_log_M = rep(0, 2),
  sd_log_M = rep(0.5, 2),
  r_log_M = diag(2),
  mu_z_c2_0 = matrix(rep(-1, 6), nrow = 3, ncol = 2),
  sd_z_c2_0_condition = rep(0.1, 2),
  r_z_c2_0_condition = diag(2),
  sd_z_c2_0_confidence = rep(0.1, 3),
  r_z_c2_0_confidence = diag(3),
  mu_z_c2_1 = matrix(rep(-1, 6), nrow = 3, ncol = 2),
  sd_z_c2_1_condition = rep(0.1, 2),
  r_z_c2_1_condition = diag(2),
  sd_z_c2_1_confidence = rep(0.1, 3),
  r_z_c2_1_confidence = diag(3),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)
sim_metad_participant_condition(
  N_participants = 100,
  N_trials = 100,
  mu_dprime = rep(1, 2),
  sd_dprime = rep(0.5, 2),
  r_dprime = diag(2),
  mu_c = rep(0, 2),
  sd_c = rep(0.5, 2),
  r_c = diag(2),
  mu_log_M = rep(0, 2),
  sd_log_M = rep(0.5, 2),
  r_log_M = diag(2),
  mu_z_c2_0 = matrix(rep(-1, 6), nrow = 3, ncol = 2),
  sd_z_c2_0_condition = rep(0.1, 2),
  r_z_c2_0_condition = diag(2),
  sd_z_c2_0_confidence = rep(0.1, 3),
  r_z_c2_0_confidence = diag(3),
  mu_z_c2_1 = matrix(rep(-1, 6), nrow = 3, ncol = 2),
  sd_z_c2_1_condition = rep(0.1, 2),
  r_z_c2_1_condition = diag(2),
  sd_z_c2_1_confidence = rep(0.1, 3),
  r_z_c2_1_confidence = diag(3),
  metac_absolute = TRUE,
  summarize = FALSE,
  lcdf = normal_lcdf,
  lccdf = normal_lccdf
)

Arguments

N_trials, N_participants

Total number of participants and trials to simulate per participant. Half of these trials will have stimulus=0 and half will have stimulus=1.

mu_dprime, sd_dprime, r_dprime

The mean, standard deviation, and within-participant correlations of sensitivities of the signal detection agents to simulate

mu_c, sd_c, r_c

The mean, standard deviation, and within-participant correlations of response bias of the signal detection agents to simulate

mu_log_M, sd_log_M, r_log_M

The mean, standard deviation, and within-participant correlations of metacognitive efficiency of the agents on the logarithmic scale, where 0 indicates optimal metacognitive sensitivity, negative numbers indicate metacognitive inefficiency, and positive numbers indicate metacognitive hyper-efficiency.

mu_z_c2_0, mu_z_c2_1

sd_z_c2_0_condition, sd_z_c2_1_condition

SD of log distances across conditions between confidence thresholds for "0" and "1" responses on the log_scale.

r_z_c2_0_condition, r_z_c2_1_condition

Correlation across conditions of log distances between confidence thresholds for "0" and "1" responses on the log_scale.

sd_z_c2_0_confidence, sd_z_c2_1_confidence

SD of log distances across confidence levels between confidence thresholds for "0" and "1" responses on the log_scale.

r_z_c2_0_confidence, r_z_c2_1_confidence

Correlation across confidence levels of log distances between confidence thresholds for "0" and "1" responses on the log_scale.

metac_absolute

Determines how to fix the type 1 threshold for modeling confidence ratings. If metac_absolute=TRUE, meta_c = c. Otherwise, meta_c = M * c.

summarize

lcdf

The log cumulative distribution function of the underlying signal distribution. By default, uses a ⁠normal(+/- dprime/2, 1)⁠ distribution.

lccdf

The log complement cumulative distribution function of the underlying signal distribution. By default, uses a ⁠normal(+/- dprime/2, 1)⁠ distribution.

Value

A simulated dataset of type 1 responses and confidence ratings, with columns:

trial: the simulated trial number
stimulus: the value of the stimulus on each trial (either 0 or 1)
response: the simulated type 1 response (either 0 or 1)
correct: whether stimulus==response (either 0 or 1)
confidence: the simulated type 2 response (from 1 to length(c2_0_diff)+1)
dprime:theta_2: the simulated agent's parameter values If summarize=TRUE, the trial column is replaced with an n column indicating the number of simulated type 1/type 2 responses for each possible value.

Examples

sim_metad_participant_condition(10, 10)

sim_metad_participant_condition(10, 10)

Generate Stan code for the meta-d' model

Description

Generate Stan code for the meta-d' model

Usage

stanvars_metad(
  K,
  distribution = "normal",
  metac_absolute = TRUE,
  categorical = FALSE,
  logit = TRUE
)
stanvars_metad(
  K,
  distribution = "normal",
  metac_absolute = TRUE,
  categorical = FALSE,
  logit = TRUE
)

Arguments

K

The number of confidence levels

distribution

The noise distribution to use. Should be a parameter-free distribution, i.e., one that is mean-centered without additional variance/shape parameters. If the distribution is not already available in stan, you must additionally provide two functions to Stan (one for ⁠<distribution>_lcdf⁠ and one for ⁠<distribution>_lccdf⁠).

metac_absolute

Should the type 2 criterion (metac) be fixed to the absolute type 1 criterion (c)? If TRUE, the model will set metac = c. Otherwise, it will set metac = M * c, such that the type 2 criterion is relatively equal to the type 1 criterion (i.e., meta_c/meta_dprime = c/dprime)

categorical

If FALSE (default), use the multinomial likelihood over aggregated data. If TRUE, use the categorical likelihood over individual trials.

logit

Value

A brms::stanvar object containing Stan code defining the likelihood for the metad' model with K confidence levels, signal distributed according to the distribution distribution, and where metac = c if metac_absolute==TRUE, and metac = M*c otherwise.

Examples

# create stancode for the meta-d' model
# using the normal distribution and 3 levels of confidence
stanvars_metad(3)

# create stancode for the meta-d' model with meta_c = M * c
stanvars_metad(3, metac_absolute = FALSE)

# create stancode for the meta-d' model with
# an alternative distribution
# note: cumulative distribution functions must be defined
# in R and in Stan using [brms::stanvar()]
stanvars_metad(4, distribution = "gumbel_min")

# create stancode for the meta-d' model
# using the normal distribution and 3 levels of confidence
stanvars_metad(3)

# create stancode for the meta-d' model with meta_c = M * c
stanvars_metad(3, metac_absolute = FALSE)

# create stancode for the meta-d' model with
# an alternative distribution
# note: cumulative distribution functions must be defined
# in R and in Stan using [brms::stanvar()]
stanvars_metad(4, distribution = "gumbel_min")

Convert binary variable $x$ between $\{0, 1\}$ and $\{-1, 1\}$

Description

to_signed(x) converts $x \in \{0, 1\}$ to $x' \in \{-1, 1\}$
to_unsigned(x) converts $x \in \{-1, 1\}$ to $x' \in \{0, 1\}$

Usage

to_signed(x)

to_unsigned(x)
to_signed(x)

to_unsigned(x)

Arguments

x

A binary variable

Value

A signed (for to_signed) or unsigned (for to_unsigned) version of x

Examples

# should return `1`
to_signed(0)

# should return `1`
to_signed(1)

# should return `0`
to_unsigned(-1)

# should return `1`
to_unsigned(1)

# `to_signed` also works with objects `R` interprets as `0` or `1`
to_signed(10)

# `to_unsigned` also works with any signed integer
to_unsigned(-10)

# neither function works with factors
try(to_signed(factor(1)))
tryCatch(to_unsigned(factor(1)), warning = function(w) w)
# should return `1`
to_signed(0)

# should return `1`
to_signed(1)

# should return `0`
to_unsigned(-1)

# should return `1`
to_unsigned(1)

# `to_signed` also works with objects `R` interprets as `0` or `1`
to_signed(10)

# `to_unsigned` also works with any signed integer
to_unsigned(-10)

# neither function works with factors
try(to_signed(factor(1)))
tryCatch(to_unsigned(factor(1)), warning = function(w) w)

Package 'hmetad'

Help Index

Aggregate data by response, confidence, and other columns

Description

Usage

Arguments

Details

Value

Examples

Generate a correlation matrix with all off-diagonal values equal to r

Description

Usage

Arguments

Value

Examples

Generate a covariance matrix.

Description

Usage

Arguments

Value

Examples

Obtain posterior draws of joint response probabilities

Description

Usage

Arguments

Value

See Also

Examples

Simulated data for example model fitting

Description

Usage

Value

See Also

Examples

Example meta-d' model for model post-processing

Description

Usage

Value

See Also

Examples

Fit the meta-d' model using brms package

Description

Usage

Arguments

Details

Value

Examples

Convert between separate and joint type 1/type 2 responses

Description

Usage

Arguments

Value

Examples

Obtain posterior draws of meta-d' model parameters

Description

Usage

Arguments

Value

See Also

Examples

Obtain posterior draws of mean confidence

Description

Usage

Arguments

Value

See Also

Examples

Obtain a vector of the names of the K-1 parameters representing the differences between successive confidence criteria for the meta-d' model with K levels of confidence.

Description

Usage

Arguments

Examples

Obtain posterior draws of an index of metacognitive bias

Description

Usage

Arguments

Value

See Also

Examples

brms family for the metad' model

Aggregate `data` by `response`, `confidence`, and other columns

Generate a correlation matrix with all off-diagonal values equal to `r`

Fit the meta-d' model using `brms` package

Obtain a vector of the names of the `K-1` parameters representing the differences between successive confidence criteria for the meta-d' model with `K` levels of confidence.

`brms` family for the metad' model