Tasks & Models

alt_delta()

Rescorla-Wagner (Delta) Model

alt_gamma()

Rescorla-Wagner (Gamma) Model

bandit2arm_delta()

Rescorla-Wagner (Delta) Model

bandit4arm2_kalman_filter()

Kalman Filter

bandit4arm_2par_lapse()

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

bandit4arm_4par()

4 Parameter Model, without C (choice perseveration)

bandit4arm_lapse()

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

bandit4arm_lapse_decay()

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

bandit4arm_singleA_lapse()

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

bart_ewmv()

Exponential-Weight Mean-Variance Model

bart_par4()

Re-parameterized version of BART model with 4 parameters

choiceRT_ddm()

Drift Diffusion Model

choiceRT_ddm_single()

Drift Diffusion Model

cgt_cm()

Cumulative Model

cra_exp()

Exponential Subjective Value Model

cra_linear()

Linear Subjective Value Model

dbdm_prob_weight()

Probability Weight Function

dd_cs()

Constant-Sensitivity (CS) Model

dd_cs_single()

Constant-Sensitivity (CS) Model

dd_exp()

Exponential Model

dd_hyperbolic()

Hyperbolic Model

dd_hyperbolic_single()

Hyperbolic Model

gng_m1()

RW + noise

gng_m2()

RW + noise + bias

gng_m3()

RW + noise + bias + pi

gng_m4()

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

igt_orl()

Outcome-Representation Learning Model

igt_pvl_decay()

Prospect Valence Learning (PVL) Decay-RI

igt_pvl_delta()

Prospect Valence Learning (PVL) Delta

igt_vpp()

Value-Plus-Perseverance

peer_ocu()

Other-Conferred Utility (OCU) Model

prl_ewa()

Experience-Weighted Attraction Model

prl_fictitious()

Fictitious Update Model

prl_fictitious_multipleB()

Fictitious Update Model

prl_fictitious_rp()

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

prl_fictitious_rp_woa()

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

prl_fictitious_woa()

Fictitious Update Model, without alpha (indecision point)

prl_rp()

Reward-Punishment Model

prl_rp_multipleB()

Reward-Punishment Model

pst_Q()

Q Learning Model

pst_gainloss_Q()

Gain-Loss Q Learning Model

ra_noLA()

Prospect Theory, without loss aversion (LA) parameter

ra_noRA()

Prospect Theory, without risk aversion (RA) parameter

ra_prospect()

Prospect Theory

rdt_happiness()

Happiness Computational Model

task2AFC_sdt()

Signal detection theory model

ts_par4()

Hybrid Model, with 4 parameters

ts_par6()

Hybrid Model, with 6 parameters

ts_par7()

Hybrid Model, with 7 parameters (original model)

ug_bayes()

Ideal Observer Model

ug_delta()

Rescorla-Wagner (Delta) Model

wcs_sql()

Sequential Learning Model

Diagnostics

estimate_mode()

Function to estimate mode of MCMC samples

extract_ic()

Extract Model Comparison Estimates

HDIofMCMC()

Compute Highest-Density Interval

multiplot()

Function to plot multiple figures

plotDist()

Plots the histogram of MCMC samples.

plotHDI()

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.

plotInd()

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

printFit()

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

rhat()

Function for extracting Rhat values from an hBayesDM object