remeta 0.99.1

Reverse engineering of Metacognition toolbox

ReMeta Toolbox

The ReMeta ("Reverse engineering of Metacognition") toolbox allows researchers to estimate latent type 1 and type 2 parameters based on data of cognitive or perceptual decision-making tasks with two response categories.

Guide: https://re-meta.github.io/

Original paper: Guggenmos, M. (2022). Reverse engineering of metacognition. eLife, 11. https://doi.org/10.7554/elife.75420

Citation for Toolbox: Guggenmos, M. (Year of release). ReMeta toolbox (Version X.Y.Z) [Computer software]. GitHub. https://github.com/coconeuro/remeta

Installation

Remeta is a Python toolbox requires a working Python installation. It should run with Python >=3.10.

Install the latest release with pip:

pip install remeta

Or the most recent code base via GitHub:

pip install git+https://github.com/m-guggenmos/remeta.git

(this command requires an installed Git, e.g. gitforwindows)

Minimal example

Three types of data are required to fit a model:

Type	Variable	Description
Stimuli	stimuli	list/array of signed stimulus intensity values, where the sign codes the stimulus category and the absolute value codes the intensity. The stimuli should be roughly in the range [-1; 1], although there is a setting (normalize_stimuli_by_max) to auto-normalize stimuli
Choices	choices	list/array of choices coded as 0 (or alternatively -1) for the negative stimuli category and 1 for the positive stimulus category.
Confidence	confidence	list/array of confidence ratings. Confidence ratings must be normalized to [0; 1]. Discrete confidence ratings must be normalized accordingly (e.g., if confidence ratings are 1-4, subtract 1 and divide by 3).

A minimal example would be the following:

# Minimal example
import remeta
ds = remeta.load_dataset('default')  # load example dataset
rem = remeta.ReMeta()
rem.fit(ds.stimuli, ds.choices, ds.confidence)

Output (for load_dataset):

..Generative model:
    Type 1 noise distribution: normal
    Type 2 noise type: report
    Type 2 noise distribution: beta_mode
..Generative parameters:
    type1_noise: 0.5
    type1_bias: -0.1
    type2_noise: 0.3
    type2_criteria: [0.25 0.5  0.75]
        [extra] Criterion bias: 0.0000
        [extra] Criterion-based confidence bias: 0.0000
..Descriptive statistics:
    No. subjects: 1
    No. samples: 2000
    Accuracy: 85.2% correct
    d': 2.1
    Choice bias: -3.9%
    Confidence: 0.53
    M-Ratio: 0.33
    AUROC2: 0.60

Output (for fit):

+++ Type 1 level +++
  Subject-level estimation (MLE)
    .. finished (0.1 secs).
  Final report
    Parameters estimates (subject-level fit)
        [subject] type1_noise: 0.510 ± 0.018
        [subject] type1_bias: -0.099 ± 0.019
    [subject] Log-likelihood: -717.84 (per sample: -0.3589)
    [subject] Fitting time: 0.13 secs
Type 1 level finished

+++ Type 2 level +++
  Subject-level estimation (MLE)
    .. finished (77.8 secs).
  Final report
    Parameters estimates (subject-level fit)
        [subject] type2_noise: 0.264 ± 0.031
        [subject] type2_criteria: [0.268 ± 0.014, 0.512 ± 0.012, 0.758 ± 0.008]
            [extra] type2_criteria_bias: 0.009 ± 0.008
            [extra] type2_criteria_confidence_bias: -0.009 ± 0.008
    [subject] Log-likelihood: -3425.60 (per sample: -1.713)
    [subject] Fitting time: 32.41 secs
Type 2 level finished

Since the dataset is based on simulation, we know the true parameters of the underlying generative model (see first output), which are quite close to the fitted parameters.

We can access the fitted parameters by invoking the summary() method on the ReMeta instance:

# Access fitted parameters
import numpy as np
result = rem.summary()
for k, v in result.params.items():
    print(f'{k}: {np.array2string(np.array(v), precision=3)}')

Ouput:

type1_noise: 0.51
type1_bias: -0.099
type2_noise: 0.264
type2_criteria: [0.268 0.512 0.758]

Supported parameters

Parameter	Description	Default
type1_noise	Type 1 noise	Enabled
type1_bias	Choice bias	Enabled
type1_thresh	Sensory threshold	Disabled
type1_nonlinear_encoding_gain	Nonlinear encoding	Disabled
type1_nonlinear_encoding_scale	Nonlinear encoding	Disabled
type2_noise	Metacognitive noise	Enabled
type2_evidence_bias	Metacognitive evidence bias	Disabled
type2_confidence_bias	Metacognitive confidence bias	Disabled
type2_criteria	Confidence criteria	Enabled