A Bayesian framework for simultaneously modeling neural and behavioral data.
about
Topographic factor analysis: a Bayesian model for inferring brain networks from neural dataTemporal Cortex Activation to Audiovisual Speech in Normal-Hearing and Cochlear Implant Users Measured with Functional Near-Infrared Spectroscopy.Individual differences in attention influence perceptual decision making.Bayesian latent variable models for the analysis of experimental psychology data.Towards a mechanistic understanding of the human subcortex.Brain and behavior in decision-makingIntertemporal choice as discounted value accumulation.Will big data yield new mathematics? An evolving synergy with neuroscience.Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach.An overview of Bayesian methods for neural spike train analysis.An exemplar of model-based cognitive neuroscience.Using Decision Models to Enhance Investigations of Individual Differences in Cognitive Neuroscience.The algorithmic level is the bridge between computation and brain.Integrating Theoretical Models with Functional Neuroimaging.Parameter recovery, bias and standard errors in the linear ballistic accumulator model.Misfortune may be a blessing in disguise: Fairness perception and emotion modulate decision making.Modelling individual difference in visual categorization.Models of inhibitory control.A single trial analysis of EEG in recognition memory: Tracking the neural correlates of memory strength.A generative joint model for spike trains and saccades during perceptual decision-making.The neural basis of value accumulation in intertemporal choice.How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters.Bayesian statistical approaches to evaluating cognitive models.Computational neuroscience across the lifespan: Promises and pitfalls.The embodied brain.Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models.Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior.Local search methods for the solution of implicit inverse problems
P2860
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P2860
A Bayesian framework for simultaneously modeling neural and behavioral data.
description
2013 nî lūn-bûn
@nan
2013 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
A Bayesian framework for simultaneously modeling neural and behavioral data.
@ast
A Bayesian framework for simultaneously modeling neural and behavioral data.
@en
A Bayesian framework for simultaneously modeling neural and behavioral data.
@nl
type
label
A Bayesian framework for simultaneously modeling neural and behavioral data.
@ast
A Bayesian framework for simultaneously modeling neural and behavioral data.
@en
A Bayesian framework for simultaneously modeling neural and behavioral data.
@nl
prefLabel
A Bayesian framework for simultaneously modeling neural and behavioral data.
@ast
A Bayesian framework for simultaneously modeling neural and behavioral data.
@en
A Bayesian framework for simultaneously modeling neural and behavioral data.
@nl
P2860
P50
P1433
P1476
A Bayesian framework for simultaneously modeling neural and behavioral data.
@en
P2093
Birte U Forstmann
Brandon M Turner
P2860
P304
P356
10.1016/J.NEUROIMAGE.2013.01.048
P407
P577
2013-01-28T00:00:00Z