hBayesDM

Compute Highest-Density Interval

Rescorla-Wagner (Delta) Model

Rescorla-Wagner (Gamma) Model

Rescorla-Wagner (Delta) Model

Kalman Filter

3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)

4 Parameter Model, without C (choice perseveration)

5 Parameter Model, without C (choice perseveration) but with xi (noise)

5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).

4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.

Exponential-Weight Mean-Variance Model

Re-parameterized version of BART model with 4 parameters

Cumulative Model

Drift Diffusion Model

Drift Diffusion Model

Choice Reaction Time task, linear ballistic accumulator modeling

Choice Reaction Time task, linear ballistic accumulator modeling

Exponential Subjective Value Model

Linear Subjective Value Model

Probability Weight Function

Constant-Sensitivity (CS) Model

Constant-Sensitivity (CS) Model

Exponential Model

Hyperbolic Model

Hyperbolic Model

Function to estimate mode of MCMC samples

Extract Model Comparison Estimates

RW + noise

RW + noise + bias

RW + noise + bias + pi

RW (rew/pun) + noise + bias + pi

Hierarchical Bayesian Modeling of Decision-Making Tasks

hBayesDM Model Base Function

Outcome-Representation Learning Model

Prospect Valence Learning (PVL) Decay-RI

Prospect Valence Learning (PVL) Delta

Value-Plus-Perseverance

Function to plot multiple figures

Other-Conferred Utility (OCU) Model

General Purpose Plotting for hBayesDM. This function plots hyper parameters.

Plots the histogram of MCMC samples.

Plots highest density interval (HDI) from (MCMC) samples and prints HDI in the R console. HDI is indicated by a red line. Based on John Kruschke's codes.

Plots individual posterior distributions, using the stan_plot function of the rstan package

Print model-fits (mean LOOIC or WAIC values in addition to Akaike weights) of hBayesDM Models

Experience-Weighted Attraction Model

Fictitious Update Model

Fictitious Update Model

Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE)

Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE), without alpha (indecision point)

Fictitious Update Model, without alpha (indecision point)

Reward-Punishment Model

Reward-Punishment Model

Q Learning Model

Gain-Loss Q Learning Model

Prospect Theory, without loss aversion (LA) parameter

Prospect Theory, without risk aversion (RA) parameter

Prospect Theory

Happiness Computational Model

Function for extracting Rhat values from an hBayesDM object

Signal detection theory model

Hybrid Model, with 4 parameters

Hybrid Model, with 6 parameters

Hybrid Model, with 7 parameters (original model)

Ideal Observer Model

Rescorla-Wagner (Delta) Model

Sequential Learning Model