Python

Q-Learning Analysis of Decision Strategies in Participant Data

Apply Q-learning, to analyze decision-making strategies among participants based on their normalized confidence levels and success outcomes, visualizing and comparing Q-values across different strategies.

Apply Q-learning, to analyze decision-making strategies among participants based on their normalized confidence levels and success outcomes, visualizing and comparing Q-values across different strategies.

Mixed-Effects Linear Regression Analysis of Confidence on Strategy Performance

Mixed-effects linear regression to examine how confidence levels ('Confidence') influence performance across different strategies ('Sur', 'Top', 'Rte', 'Rev'), providing insights into their associations.

Mixed-effects linear regression to examine how confidence levels ('Confidence') influence performance across different strategies ('Sur', 'Top', 'Rte', 'Rev'), providing insights into their associations.

Exploratory Analysis of Spatial Strategy Data Using Statistical Checks and Visualizations

This Python script explores spatial strategy data, combining 'Rev' and 'Rte' into a new 'Response' column, visualizing 'Sur' (Place), and conducting statistical checks for linearity, homoscedasticity, normality of residuals, and outlier analysis using scatter plots, histograms, and statistical tests.

This Python script explores spatial strategy data, combining 'Rev' and 'Rte' into a new 'Response' column, visualizing 'Sur' (Place), and conducting statistical checks for linearity, homoscedasticity, normality of residuals, and outlier analysis using scatter plots, histograms, and statistical tests.

R

Bayesian Gender Analysis of Decision-Making Correlations

Investigates whether confidence-related correlations in route and place decisions differ between genders using Bayesian models and hypothesis testing.

Investigates whether confidence-related correlations in route and place decisions differ between genders using Bayesian models and hypothesis testing.

Iowa Gambling Task (IGT) computational modeling

Explore the convergence and model fit of three decision-making models (ORL, PVL, VPP) using Bayesian inference and diagnostic tools on experimental data.

Explore the convergence and model fit of three decision-making models (ORL, PVL, VPP) using Bayesian inference and diagnostic tools on experimental data.

Explore correlations between model parameters and deck E selections using linear regression and visualizes parameter correlations with the Iowa Gambling Task outcomes, assessing normality and outliers in the data.

Explore correlations between model parameters and deck E selections using linear regression and visualizes parameter correlations with the Iowa Gambling Task outcomes, assessing normality and outliers in the data.

Bash Shell

Freesurfer File Transfer and Transformation for Hippocampus and Caudate Masks

Transfer and transform brain segmentation files (hippocampus and caudate) from a previous Freesurfer directory to a subject's setup directory, applying necessary reorientations and transformations to standard space using ANTs tools.

Transfer and transform brain segmentation files (hippocampus and caudate) from a previous Freesurfer directory to a subject's setup directory, applying necessary reorientations and transformations to standard space using ANTs tools.

First-Level fMRI Analysis Pipeline

First-level analysis of fMRI data for multiple subjects using FSL's feat tool, iterating through design files customized for each subject by replacing placeholders, and executing feat for four experimental runs per subject.

First-level analysis of fMRI data for multiple subjects using FSL's feat tool, iterating through design files customized for each subject by replacing placeholders, and executing feat for four experimental runs per subject.

Missing Data Check and File Management

Check for missing data files across specified subjects and runs, creating directories for individual subjects and moving files to temporary folders as necessary, while logging missing files into a designated text file for review and management.

Check for missing data files across specified subjects and runs, creating directories for individual subjects and moving files to temporary folders as necessary, while logging missing files into a designated text file for review and management.