fit_meta_d_MLE: Fit Type 2 SDT using Maximum Likelihood Estimation.

In craddm/metaSDT: Calculate Type 1 and Type 2 Signal Detection Measures

Fit Type 2 SDT using Maximum Likelihood Estimation.

Description

Provides a type-2 SDT analysis of data from a typical experiment in which observers discriminate between two response alternatives and provide ratings of confidence in their judgements.

Usage

fit_meta_d_MLE(nr_s1, nr_s2, s = 1, add_constant = TRUE)

Arguments

nr_s1	Responses to S1 stimulus. See below for advice.
nr_s2	Responses to S2 stimulus. See below for advice.
s	Ratio of standard deviations for the S1 and S2 stimulus. Defaults to 1.
add_constant	Adds a small constant to the data (1/number of possible responses) to account for 0 or 1 values. Defaults to TRUE for ease of use across multiple datasets.

Details

The expected input is two vectors, one for responses to each stimulus, encoding the observers response and confidence. For example, for two stimului labelled A and B, with three confidence ratings, participants could respond to stimulus A as follows:

Response: A, rating: 3, count: 60

Response: A, rating: 2, count: 30

Response: A, rating: 1, count: 10

Response: B, rating: 1, count: 7

Response: B, rating: 2, count: 4

Response: B, rating: 3, count: 1

The appropriate vector would be nr_s1 <- c(60,30,10,7,4,1)

For stimulus B, we would have the respective vector for responses to stimulus B, eg:

Response: A, rating: 3, count: 4

Response: A, rating: 2, count: 6

Response: A, rating: 1, count: 11

Response: B, rating: 1, count: 13

Response: B, rating: 2, count: 23

Response: B, rating: 3, count: 61

nr_s2 <- c(4,6,11,13,23,61)

The helper function sdt_counts can be used to get the data into the right format.

The output is a data frame with various metacognitive measures, including m-ratio and meta-d, estimated using Maximum Likelihood Estimation. The output will have n-1 rows, where n is the number of confidence ratings. Most measures are duplicated across rows, but some vary, as they reflect cutpoints between the confidence levels.

For more details, see Maniscalco & Lau's webpage http://www.columbia.edu/~bsm2105/type2sdt/ Please cite that page and their articles if using this command.

Author(s)

Maniscalco & Lau. Ported to R by Matt Craddock, [e-mail]

References

Maniscalco, B., & Lau, H. (2012). A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition. http://dx.doi.org/10.1016/j.concog.2011.09.021

Examples

nr_s1 <- c(60,30,10,7,4,1)
nr_s2 <- c(4,6,11,13,23,61)
fit_meta_d_MLE(nr_s1, nr_s2)

craddm/metaSDT documentation built on Feb. 10, 2024, 6:57 a.m.