Science Score: 57.0%
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Low similarity (8.2%) to scientific vocabulary
Repository
Qhelp 2023 project
Basic Info
- Host: GitHub
- Owner: lukekorthals
- License: gpl-3.0
- Language: R
- Default Branch: main
- Size: 309 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
qhelp-copula
As part of the mathematical psychology seminar we created this shiny app to investigate different copula and how they relate to the general race model of reaction times.
Shiny App
You can run the app from your browser: https://lukekorthals.shinyapps.io/stop-signal-copula/
You can run the app from R Studiod:
runGitHub( "qhelp-copula", "lukekorthals", subdir = "shiny")
Qhelp 2023 project
The goal of this project is to generate and visualize cumulative probability distributions of reaction times in the general race model using copulas. With the help of a shiny-app, the user can try out different starting configurations by choosing custom copulae and marginals.
Stop signal paradigm:
The stop signal paradigm is an experimental setup within which subjects are instructed to respond to a cue ("go" signal) as fast as possible by for example pressing a button (no stop signal trials). In a small subset of trials, shortly after the "go" signal, an additional "stop" signal is presented which requires participants to inhibit the initiated response. Within these stop-signal trials, participants might or might not manage to inhibit the go-signal.
The general race model describes the random processing time for the go and stop signals duding the stop-signal trials as a bivariate distribution function.
$$ H(s, t) = P(T{go} \le s, T{stop} \le t) $$
for all $s,t \ge 0$
The marginals of this distribution can be described with:
$$ F{go}(s) = P(T{go} \le s, T{stop} < \infty) $$ $$ F{stop}(t) = P(T{go} < \infty, T{stop} \le t) $$
Copulae
According to Sklar's theorem (1959), any bivariate distribution function $F(x1, x2)$ with margins $F1(x1)$ and $F2(x2)$ can be described using a unique copula $C$ such that:
$$ F(x1, x2) = C(F1(x1), F2(x2)) $$
Assuming that $F1(x1)$ and $F2(x2)$ are the marginals of $F(x1, x2)$, copula $C$ can be written as:
$$ C(u1, u2) = F(F1^{-1}(u1), F2^{-1}(u2)) $$
Where $F1^{-1}$ and $F2^{-1}$ are the quantile functions of the margins.
In context of the stop signal paradigm, this relationship allows to model processing times using a predefined copula whose uniform marginals can be transformed into fitting distributions using the quantile functions $F1^{-1}$ and $F2^{-1}$.
Race model inequality thingie ?!?
References:
Cite this project:
Owner
- Name: Luke Korthals
- Login: lukekorthals
- Kind: user
- Repositories: 1
- Profile: https://github.com/lukekorthals
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Korthals" given-names: "Luke" - family-names: "Hoffstadt" given-names: "Madlen" - family-names: "Groot" given-names: "Laura" - family-names: "van der Meer" given-names: "Daniel" title: "qhelp-copula" version: 1.0.0 doi: 10.5281/zenodo.1234 date-released: 2023-03-31 url: "https://github.com/lukekorthals/qhelp-copula"