hBayesDM-package: Hierarchical Bayesian Modeling of Decision-Making Tasks

Description Author(s) References See Also


Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of various computational models with a single line of coding. Bolded tasks, followed by their respective models, are itemized below.


2-Armed Bandit (Rescorla-Wagner (delta)) — bandit2arm_delta
4-Armed Bandit with fictive updating + reward/punishment sensitvity (Rescorla-Wagner (delta)) — bandit4arm_4par
4-Armed Bandit with fictive updating + reward/punishment sensitvity + lapse (Rescorla-Wagner (delta)) — bandit4arm_lapse

Delay Discounting

Constant Sensitivity — dd_cs
Constant Sensitivity for single subject — dd_cs_single
Exponential — dd_exp
Hyperbolic — dd_hyperbolic
Hyperbolic for single subject — dd_hyperbolic_single

Orthogonalized Go/Nogo

RW + Noise — gng_m1
RW + Noise + Bias — gng_m2
RW + Noise + Bias + Pavlovian Bias — gng_m3
RW(modified) + Noise + Bias + Pavlovian Bias — gng_m4

Iowa Gambling

Prospect Valence Learning-DecayRI — igt_pvl_decay
Prospect Valence Learning-Delta — igt_pvl_delta
Value-Plus_Perseverance — igt_vpp

Peer influence task

OCU model — peer_ocu

Probabilistic Reversal Learning

Fictitious Update — prl_fictitious
Fictitious Update w/o alpha (indecision point) — prl_fictitious_woa
Fictitious Update and multiple blocks per subject — prl_fictitious_multipleB
Experience-Weighted Attraction — prl_ewa
Reward-Punishment — prl_rp
Reward-Punishment and multiple blocks per subject — prl_rp_multipleB
Fictitious Update with separate learning for Reward-Punishment — prl_fictitious_rp
Fictitious Update with separate learning for Reward-Punishment w/o alpha (indecision point) — prl_fictitious_rp_woa

Risk Aversion

Prospect Theory (PT) — ra_prospect
PT without a loss aversion parameter — ra_noLA
PT without a risk aversion parameter — ra_noRA

Ultimatum Game

Ideal Bayesian Observer — ug_bayes
Rescorla-Wagner (delta) — ug_delta

Choice/Reaction time

Drift Diffusion Model — choiceRT_ddm
Drift Diffusion Model for single subject — choiceRT_ddm_single
Linear Ballistic Accumulator — choiceRT_lba
Linear Ballistic Accumulator for single subject — choiceRT_lba_single

Two-Step task

Full model (7 parameters) — ts_par7
6 parameter model (without eligibility trace, lambda) — ts_par6
4 parameter model — ts_par4


Woo-Young Ahn [email protected]

Nathaniel Haines [email protected]

Lei Zhang [email protected]


Please cite as: Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry. 1, 24-57. https://doi.org/10.1162/CPSY_a_00002

See Also

For tutorials and further readings, visit : http://rpubs.com/CCSL/hBayesDM.

hBayesDM documentation built on Dec. 14, 2018, 1:04 a.m.