Main content



Loading wiki pages...

Wiki Version:
# Workflow for "Cognitive flexibility improves memory for delayed intentions" ## Outline - There were two experiments run for this project, one which was behavioral only (N=50), and one that was done also collecting fMRI data (N=28). - For the outline, we are going to first extract data into summary arrays, and then analyze. - I've also included the summaries already, so you can skip step 1 and go straight to the statistics in step 2 if you'd prefer. - The first thing to do is download the zipped folders and put them together in a folder. ## Analysis Steps ### Step 1: Data Summary Creation (in MATLAB) 1. The first step is to extract relevant data for by-probe analyses from the behavioral data sample. - Script: ~/Scripts/byProbe_behSample_preprocessing.m - Output: ~/Data/Summaries/byProbe_BehSample.csv - This is for the most part the raw behavioral data with pmCost on each probe, as well as an indicator whether or not the overall OG accuracy on the trial a probe belongs to was >50% (OG chance). - Labels for the columns need to be added manually from: ~/Summaries/byProbe_beh_colLabels.xlsx 2. The next step is to do the same for the fMRI sample - Script: ~/Scripts/byProbe_fMRIsample_preprocessing.m - Output: ~/Data/Summaries/byProbe_fMRIsample.csv - This is the same basic analysis process as above, but we also collect the difference in classifier evidence for the target-nonTarget categories, referred to as PM intention evidence. We have raw target classifier evidence as well. - Labels for columns need to be manually added from: ~/Summaries/byProbe_fMRI_colLabels.xlsx 3. Next, we are going to use the same inclusion parameters to extract the by-trial measurements for the behavioral (and then fMRI) samples - Script: ~/Scripts/byTrial_behSample_preprocessing.m - Output: ~/Data/Summaries/byTrial_BehSample.csv 4. fMRI sample by-trial data summary extraction - Script: ~/Scripts/byTrial_fMRIsample_preprocessing.m - Output: ~/Data/Summaries/byTrial_fMRIsample.csv ### Step 2: Data Analysis (in R) - Analyses here are shown for both samples combined. Scripts are included that separately analyze the behavioral and fMRI samples to replicate separate analyses. For example: suppl_byProbe_mriOnly would replicate the by-Probe analysis for only the fMRI sample. 1. Analyze the by-Probe data - Script: ~/Scripts/stats\_byProbe_bothhSamples - We use this to get: <br /> **a.** OG RT, OG accuracy, and PM cost by trial type and difficulty <br /> **b.** PM Accuracy by trial type <br /> **c.** PM intention evidence by difficulty <br /> **d.** False Alarm Rate <br /> **e.** General probe count for each participant 2. Analyze the by-Trial data - Script: ~/Scripts/stats\_byTrial_bothSamples - We use this to get: <br /> **a.** PM Cost Slope x Trial Type <br /> **b.** PM Intention Evidence Slope x Trial Type <br /> **c.** Average PM Intention Evidence x Trial Type <br /> **d.** Bootstrap analyses relating PM Accuracy on each trial to PM cost slope and PM intention evidence independently <br /> **e.** Correlation of PM cost slope and PM intention evidence <br /> **f.** Bootstrap including both factors <br /> **g.** Bootstrap for looking at the reliability of the different types of interactions from the full model <br /> **h.** Looking at PM accuracy for trials that have end of trial costs indicative of proactive or reactive control <br /> **i.** Model selection script <br /> **j.** Partial regression analyses 3. By-trial model fitting scripts. - Script: ~/Scripts/stats\_byProbe_trialFitsAIC - These were separately analyzed just to make scripts less confusingly long/clunky - We use this to get: <br /> **a.** AICc and AIC for each trial polynomial fits <br /> **b.** Akaike weights for each trial polynomial fits
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
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.

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.