A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
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Episodic Tags Enhance Striatal Valuation Signals during Temporal Discounting in pathological Gamblers.Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users.The Good, the Bad, and the Irrelevant: Neural Mechanisms of Learning Real and Hypothetical Rewards and Effort.Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders: Impulsivity Dimensions as Predictors of Current Cocaine DependenceDecomposing the roles of perseveration and expected value representation in models of the Iowa gambling task.ACC Neuro-over-Connectivity Is Associated with Mathematically Modeled Additional Encoding Operations of Schizophrenia Stroop-Task PerformanceDopamine, depressive symptoms, and decision-making: the relationship between spontaneous eye blink rate and depressive symptoms predicts Iowa Gambling Task performanceValidating the PVL-Delta model for the Iowa gambling task.Challenges and promises for translating computational tools into clinical practiceThe drift diffusion model as the choice rule in reinforcement learning.Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision-makers.Association between habenula dysfunction and motivational symptoms in unmedicated major depressive disorder.Heterogeneity of strategy use in the Iowa gambling task: a comparison of win-stay/lose-shift and reinforcement learning models.Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise.A causal account of the brain network computations underlying strategic social behavior.A tutorial on bridge sampling.Performance on the Iowa Gambling Task: From 5 to 89 years of age.Identity prediction errors in the human midbrain update reward-identity expectations in the orbitofrontal cortex.Age of onset of cannabis use and decision making under uncertainty.Selective Effects of the Loss of NMDA or mGluR5 Receptors in the Reward System on Adaptive Decision-Making
P2860
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P2860
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
@en
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
@nl
type
label
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
@en
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
@nl
prefLabel
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
@en
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
@nl
P2093
P2860
P356
P1476
A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation.
@en
P2093
Adam Krawitz
Jerome R Busmeyer
Joshua W Brown
Woo-Young Ahn
Woojae Kim
P2860
P304
P356
10.1037/A0020684
P577
2011-05-01T00:00:00Z