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# Time Bandits ## Table of Contents * [General info](#general-info) * [Data](#experimental-data) * [Behavioral Analyses](#behavioral-analyses) * [RL model](#rl-model) * [Model-based Analyses](#model-based-analyses) * [Other](#other) ## General info This repository contains all the code and (anonymized) data necessary for reproducing [Wu, Schulz, Pleskac, & Speekenbrink (*bioRxiv* 2021)](https://charleywu.github.io/downloads/wu2021time.pdf). The following text provides a summary of each file and it's function, with further comments provided throughout the code. ## Experiment data * `Data/fullData.csv` contains the anonymized participant data ## Behavioral Analyses * `behavioralPlots.R` contains the behavioral plots presented in the paper * `behavioralRegressions.R` contains the mixed effects regressions performed on the behavioral data ## RL model * `models.R` defines the RL models, specifically the Bayesian mean tracker (BMT) * `generatebmtpredictions.R` reads in participant data and generates predictions at each timepoint about the expected reward and uncertainty of each arm, based on the RL model (BMT). The predictions are saved in `modelPreds/`. In addition, `BMTpredictionsPlot.R` examines the pattern of predictions, separated by time pressure and payoff condition ## Model-based Analyses * `hierarchicalSoftmax.R` runs a softmax regression, using the model(s) defined in `STANmodels/*.stan` to predict choices as a function of the posterior means and variances estimated from `generatebmtpredictions.R` * `hierarchicalRT.R` runs a hierarchical regression predicting reaction time based on the posterior means and variances estimated from `generatebmtpredictions.R`. The model posteriors are stored in `brmsModels/`. * `lba.R` estimates a linear ballistic accumulator model using code from [Annis, Miller, \& Palmeri (2017)](https://link.springer.com/article/10.3758/s13428-016-0746-9), which is located in `LBA/lba_single.stan`. The parameter estimates of the LBA model are then analyzed and plottedin `lbaParams.R`. * `analyseLBA.R` fits a Bayesian mixed effects regression using the posterior means and variances estimated from `generatebmtpredictions.R` to predict the estimated drift rates estimated from `lba.R`, The model posteriors are stored in `brmsModels/` * `posteriorPlots.R` puts together the results of all model-based analyses into a single plot (in addition to more detailed SI figures) * `modelComparison.R` is compares the different softmax models * `modelInteractions.R` visualized the different interactions of the RT and LBA regression models * `modelsVsBehavior.R` links the outcomes of the model-based analysis with behavioral patterns ## Other * `statFormat.R` is used to provide LaTeX formatted outputs for the statistical tests