Main content

Home

Menu

Loading wiki pages...

View
Wiki Version:
# Code descriptions This wiki provides descriptions for the codes used in the manuscript "Signatures proposed to index perceptual effects emerge in a purely cognitive task". ## Preprocessing data "code_preprocess.ipynb" reads raw data files from each of the three experiments ("data_raw_expX.csv" where X is either "1", "2", or "3") and transforms them into data for following model fitting and analyses. Data files for drift-diffusion model fitting and analyses are saved as "data_hddm_expX.csv". Data files for choice, confidence, and RT fitting and analyses are saved as "data_processed_expX.csv". ## Drift-diffusion model fitting "code_fitting_ddm.ipynb" reads data files separately for each of the three experiments ("data_hddm_expX.csv") and fits drift-diffusion models. For each experiment, the script generates posterior samples for each subject. Samples from subject K are saved as "sampleK.csv" where K is the subject index. ## Drift-diffusion model analyses "code_analyses_ddm_expX.ipynb" reads DDM parameters from all subjects in experiment X (sampleK.csv for all K values in experiment X) and performs analyses. ## Choice, confidence, and RT fitting "code_fitting.ipynb" reads data files separately for each of the three experiments ("data_processed_expX.csv") and fits raised Gaussian functions to choice, confidence, and RT. For each experiment, the script saves parameters from both the population-level fitting and the individual-level fitting. Parameters from population-level fitting are saved as "optpar_choice_expX.csv", "optpar_confidence_expX.csv", and "optpar_RT_expX.csv" separately for choice, confidence, and RT fitting. Similarly, parameters from individual-level fitting are saves as "optpar_choice_sbj_expX.csv", "optpar_confidence_sbj_expX.csv", and "optpar_RT_sbj_expX.csv". ## Choice, confidence, and RT analyses "code_analyses_expX.ipynb" reads parameters from both the population-level fitting ("optpar_choice_expX.csv", "optpar_confidence_expX.csv", and "optpar_RT_expX.csv") and the individual-level fitting ("optpar_choice_sbj_expX.csv", "optpar_confidence_sbj_expX.csv", and "optpar_RT_sbj_expX.csv") and performs analyses. Please email sixing.chen@nyu.edu for any question or feedback.
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.