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Heart Rate Discrimination (HRD) & Metacognitive Sensitivity Estimation (meta-d')

This document describes an R pipeline for analyzing Heart Rate Discrimination (HRD) task data and estimating metacognitive sensitivity (meta-d'). This approach allows researchers to assess how accurately individuals judge their own performance on an interoceptive discrimination task.

Heart Rate Discrimination Task (HRD)

The HRD task is used to assess interoceptive accuracy—the ability to perceive one’s own heartbeat. Participants judge whether presented auditory tones match their own heartbeats. This method provides a psychophysical estimate of interoceptive precision.

For more details, see:

Legrand, N., Nikolova, N., Correa, C., Brændholt, M., Stuckert, A., Kildahl, N., ... & Allen, M. (2022). The heart rate discrimination task: A psychophysical method to estimate the accuracy and precision of interoceptive beliefs. Biological Psychology, 168, 108239.
DOI: 10.1016/j.biopsycho.2022.108239

Metacognitive Efficiency Estimation (meta-d')

To evaluate metacognitive ability in HRD, we estimate meta-d’, a measure of how well confidence ratings reflect actual task performance. This is adapted in R from Steve Fleming’s MATLAB function fit_meta_d_mcmc.m.

For more details on meta-d’ estimation, see:

Fleming, S. M. (2017). HMeta-d: Hierarchical Bayesian Estimation of Metacognitive Efficiency from Confidence Ratings. Neuroscience of Consciousness.
DOI: 10.1093/nc/nix007.

1. analyze_hrd_data()

Function Overview

This function:

  • Processes HRD task data
  • Fits the hierarchical metacognition model using Bayesian estimation
  • Computes key metrics: meta-d', AUROC2, decision threshold, slope, confidence, and accuracy
  • Saves plots for visualization

Function Signature

analyze_hrd_data(hrd_data, nRatings = 4, plot_results = TRUE, show_traceplot = TRUE, participant_id = "unknown")

Input Arguments

Argument Type Description
hrd_data DataFrame HRD dataset for one subject
nRatings Integer Number of confidence rating bins
plot_results Boolean Whether to display and save plots
show_traceplot Boolean Whether to generate a traceplot
participant_id String Subject identifier for saving figures

Returns

A data frame containing:

  • estimated_threshold: Decision threshold from last trial
  • estimated_slope: Estimated decision slope
  • mean_accuracy: Average accuracy across trials
  • mean_confidence: Average confidence rating
  • auroc: Area Under the ROC Curve (discrimination measure)
  • d: Discrimination index (d')
  • metad: Meta-d' (metacognitive sensitivity)
  • mratio: Meta-d' / d' ratio (metacognitive efficiency)

Example Usage

# Load HRD data
hrd_data <- read_delim("data/SS9909HRD_final.txt", delim = ",")

# Analyze subject data
results <- analyze_hrd_data(hrd_data, nRatings = 4, plot_results = TRUE, show_traceplot = TRUE, participant_id = "SS9909")

# View results
print(results)

Example Figures

Confidence Histogram + Trial Alpha

Confidence Histogram

Posterior Meta-d’ Distribution

Posterior Meta-d

2. process_hrd_data()

Function Overview

Prepares HRD data by:

  • Converting categorical variables into factors
  • Binning confidence ratings into quantiles
  • Removing trials with missing (NA) confidence ratings

Function Signature

process_hrd_data(hrd_data, confbins)

Input Arguments

Argument Type Description
hrd_data DataFrame Raw HRD data
confbins Integer Number of confidence bins

Returns

A processed data frame with:

  • Signal: Binary-coded stimulus type (0/1)
  • Response: Binary-coded subject response (0/1)
  • ConfidenceBinned: Binned confidence levels
  • Other cleaned HRD variables

Example Usage

# Process HRD data before modeling
processed_data <- process_hrd_data(hrd_data, confbins = 4)

# Preview processed data
head(processed_data)

Metacognitive Model Fitting Functions

The metacognitive model uses Bayesian estimation to infer meta-d’. This method is adapted from Steve Fleming’s hierarchical Bayesian framework.

Key Functions in code/

  • fit_metad_indiv.R – Fits individual-level meta-d’ models.
  • trials2counts.R – Converts trial-by-trial responses into summary counts for Bayesian modeling.
  • calc_auroc2.R – Computes AUROC as a measure of discrimination performance.

References

  • Legrand, N., et al. (2022). The heart rate discrimination task: A psychophysical method to estimate the accuracy and precision of interoceptive beliefs. Biological Psychology, 168, 108239.
    DOI: 10.1016/j.biopsycho.2022.108239
  • Fleming, S. M. (2017). HMeta-d: Hierarchical Bayesian Estimation of Metacognitive Efficiency from Confidence Ratings. Neuroscience of Consciousness.
    DOI: 10.1093/nc/nix007