diff --git a/README.md b/README.md
index b638ece..c10c2b2 100644
--- a/README.md
+++ b/README.md
@@ -169,8 +169,8 @@ You can also analyze the results in [Google Colab](https://colab.research.google
| Notebook | Colab | nbViewer |
| --- | ---| --- |
-| Heartbeat Counting task report | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/cardioception/notebooks/HeartBeatCounting.ipynb) | [](https://nbviewer.jupyter.org/github/LegrandNico/Cardioception/blob/master/cardioception/notebooks/HeartBeatCounting.ipynb)
-| Heart Rate Discrimination task report | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/cardioception/notebooks/HeartRateDiscrimination.ipynb) | [](https://nbviewer.jupyter.org/github/LegrandNico/Cardioception/blob/master/cardioception/notebooks/HeartRateDiscrimination.ipynb)
+| Heartbeat Counting task report | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/cardioception/notebooks/HeartBeatCounting.ipynb) |
+| Heart Rate Discrimination task report | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/cardioception/notebooks/HeartRateDiscrimination.ipynb) |
## Bayesian modeling
@@ -178,8 +178,8 @@ More advanced subject and group-level Bayesian modelling approaches are describe
| Notebook | Colab | nbViewer |
| --- | ---| --- |
-| Fitting the psychometric function (single subject) | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/docs/source/examples/psychophysics/1-psychophysics_subject_level.ipynb) | [](https://nbviewer.jupyter.org/github/LegrandNico/LegrandNico/Cardioception/blob/master/docs/source/examples/psychophysics/1-psychophysics_subject_level.ipynb)
-| Fitting the psychometric function (group level) | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/docs/source/examples/psychophysics/2-psychophysics_group_level.ipynb) | [](https://nbviewer.jupyter.org/github/LegrandNico/Cardioception/blob/master/docs/source/examples/psychophysics/2-psychophysics_group_level.ipynb)
+| Fitting the psychometric function (single subject) | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/docs/source/examples/psychophysics/1-psychophysics_subject_level.ipynb) |
+| Fitting the psychometric function (group level) | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/docs/source/examples/psychophysics/2-psychophysics_group_level.ipynb) |
# References
@@ -193,7 +193,7 @@ More advanced subject and group-level Bayesian modelling approaches are describe
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).
-
+
# Credit
diff --git a/docs/source/examples/R/Example scripts/Example_analysis_simple.Rmd b/docs/source/examples/R/Example scripts/Example_analysis_simple.Rmd
index fd0cd8c..c31102e 100644
--- a/docs/source/examples/R/Example scripts/Example_analysis_simple.Rmd
+++ b/docs/source/examples/R/Example scripts/Example_analysis_simple.Rmd
@@ -17,7 +17,7 @@ np <- import("numpy")
### **Reading in the data, here the working diectory has be where the exmaple scripts are located**
```{r message=FALSE}
#This line reads in a subject result file:
-df = read_csv("https://raw.githubusercontent.com/LegrandNico/Cardioception/master/docs/source/examples/templates/data/HRD/HRD_final.txt")
+df = read_csv("https://raw.githubusercontent.com/LegrandNico/cardioception-toolbox/master/docs/source/examples/templates/data/HRD/HRD_final.txt")
#These next lines read in the psi posterior distributions for each modality.
#These will be saved with names depending on your conditions, so modify as needed.
diff --git a/docs/source/index.md b/docs/source/index.md
index 1837641..10f4c54 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -4,7 +4,7 @@
---
-
+
```{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.
diff --git a/docs/source/measuring.md b/docs/source/measuring.md
index e19f5f2..3400480 100644
--- a/docs/source/measuring.md
+++ b/docs/source/measuring.md
@@ -6,7 +6,7 @@ Cardiac interoception has been largely investigated using the heartbeat counting
In the classic "heartbeat counting task" {cite:p}`1981:schandry,1978:dale` participants attend to their heartbeats in intervals of various lengths and are asked to count the number of heartbeats they can effectively feel during this period. An accuracy score is then derived by comparing the reported number of heartbeats and the true number of heartbeats. In the original version {cite:p}`1981:schandry`, the task started with a resting period of 60 seconds and consisted of three estimation sessions (25, 35, and 45 seconds) interleaved with resting periods of 30 seconds.
-
+
By default, [Cardioception](https://github.com/LegrandNico/Cardioception) implements the version used in recent publications {cite:p}`2013:hart` in which a training trial of 20 seconds is proposed, after which the 6 experimental trials of different time windows (25, 30, 35,40, 45 and 50s) occurred in a randomized order. The trial length, the condition (`'Rest'`, `'Count'`, `'Training'`), and the randomization can be controlled in the parameters dictionary. This behaviour can be controlled using the `"taskVersion"` parameter.
@@ -32,7 +32,7 @@ After each counting response, the participant is prompted to rate their subjecti
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, together with metacognitive efficiency.
-
+
### Staircases
diff --git a/docs/source/stats.md b/docs/source/stats.md
index aa188d9..5a2b10e 100644
--- a/docs/source/stats.md
+++ b/docs/source/stats.md
@@ -61,8 +61,8 @@ examples/templates/*
| Notebook | Colab |
| --- | ---|
-| {ref}`hbc_template` | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/docs/source/examples/templates/HeartBeatCounting.ipynb)
-| {ref}`hrd_template` | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/docs/source/examples/templates/HeartRateDiscrimination.ipynb)
+| {ref}`hbc_template` | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/docs/source/examples/templates/HeartBeatCounting.ipynb)
+| {ref}`hrd_template` | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/docs/source/examples/templates/HeartRateDiscrimination.ipynb)
## Bayesian modelling of psychophysics
@@ -80,5 +80,5 @@ examples/psychophysics/*
| Notebook | Colab |
| --- | ---|
-| {ref}`psychophysics_subject_level` | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/docs/source/examples/psychophysics/1-psychophysics_subject_level.ipynb)
-| {ref}`psychophysics_group_level` | [](https://colab.research.google.com/github/LegrandNico/Cardioception/blob/master/docs/source/examples/psychophysics/2-psychophysics_group_level.ipynb)
+| {ref}`psychophysics_subject_level` | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/docs/source/examples/psychophysics/1-psychophysics_subject_level.ipynb)
+| {ref}`psychophysics_group_level` | [](https://colab.research.google.com/github/LegrandNico/cardioception-toolbox/blob/master/docs/source/examples/psychophysics/2-psychophysics_group_level.ipynb)
diff --git a/tests/test_reports.py b/tests/test_reports.py
index 6f1991e..94ad4de 100644
--- a/tests/test_reports.py
+++ b/tests/test_reports.py
@@ -13,7 +13,7 @@ def test_preprocessing(self):
"""Test the preprocessing function"""
# load the main result data frame
results = pd.read_csv(
- "https://raw.githubusercontent.com/LegrandNico/Cardioception/master/docs/source/examples/templates/data/HRD/HRD_final.txt"
+ "https://raw.githubusercontent.com/LegrandNico/cardioception-toolbox/master/docs/source/examples/templates/data/HRD/HRD_final.txt"
)
preprocessing(results=results)