# Cardioception
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---
```{important}
The Cardioception Python Toolbox is a fork of the [original cardioception repository](https://github.com/embodied-computation-group/Cardioception) that I ([Nicolas Legrand](https://github.com/LegrandNico/)) created while working in [the ECG lab](https://www.the-ecg.org/) from 2019 to 2022. My previous lab has taken full control of the repository since then, meaning that I am unfortunately unable to maintain it as it should be. This repository allows me to pursue the maintenance of the package, aiming to provide reliable and robust tasks to measure cardiac interoception, together with computational modelling tools to analyse data gathered with these tasks.
```
The repository implements two measures of cardiac interoception (cardioception):
1. The **Heartbeat counting task (HBC)**, also known as the **Heartbeat tracking task**, developed by Rainer Schandry {cite:p}`1978:dale,1981:schandry`. This task cardiac measures interoception by asking participants to count their heartbeats for a given period of time. An accuracy score is then derived by comparing the reported heartbeats and the true number of heartbeats.
2. The **Heart Rate Discrimination task** {cite:p}`2022:legrand` implements an adaptive psychophysical measure of cardiac interoception where participants have to estimate the frequency of their heart rate by comparing it to tones that can be faster or slower. By manipulating the difference between the true heart rate and the presented tone using different staircase procedures, the bias (threshold) and precision (slope) of the psychometric function can be estimated either online or offline (see *Analyses* below), together with metacognitive efficiency.
```{note}
While having slightly similar names, the **Heartbeat counting task (HBC)** and the **Heart Rate Discrimination task** are different in terms of implementation and the measures they provided and should not be conflated. We developed the cardioception package first to provide an open-sourced version of the *HBC*, which was lacking, with easy support to record heart rate via cheap pulse oximetry via [Systole](https://github.com/LegrandNico/systole). In addition to that, we developed the **HRD** task as a new measure of cardiac interoception {cite:p}`2022:legrand`, grounding on different reasoning and trying to control for the confounds other interoception tasks might have.
```
These tasks can run using minimal experimental settings: a computer and a recording device to monitor the heart rate of the participant. The default version of the task uses the [Nonin 3012LP Xpod USB pulse oximeter](https://www.nonin.com/products/xpod/) together with [Nonin 8000SM 'soft-clip' fingertip sensors](https://www.nonin.com/products/8000s/). This sensor can be plugged directly into the stim PC via USB and will work with Cardioception without additional coding. The tasks can also integrate easily with other recording devices and experimental settings (ECG, M/EEG, fMRI...).
## Looking for help?
If you have questions regarding the tasks, want to report a bug or discuss data analysis, please ask on the public discussion page in this repository.
If you want to report a bug, you can open an issue on the [GitHub page](https://github.com/LegrandNico/cardioception).
## Development
This package is a fork of the original [Cardioception](https://github.com/embodied-computation-group/Cardioception) repository and is maintained by [Nicolas Legrand](https://github.com/LegrandNico).
```{toctree}
---
hidden:
---
Theory
Guide
API
Statistical analysis
Cite
References
```