hBayesDM-package: Hierarchical Bayesian Modeling of Decision-Making Tasks
Description
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.
- Bandit
- 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
- Probabilistic Reversal Learning
- Fictitious Update --- prl_fictitious
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
- 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
References
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. 1:1. https://doi.org/10.1101/064287