Observing the observer (I): meta-bayesian models of learning and decision-making.
about
Optimizing experimental design for comparing models of brain functionInferring on the intentions of others by hierarchical Bayesian learningVBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural dataModelling trial-by-trial changes in the mismatch negativitySpatial attention, precision, and Bayesian inference: a study of saccadic response speedCortical Coupling Reflects Bayesian Belief Updating in the Deployment of Spatial AttentionHierarchical prediction errors in midbrain and septum during social learning.Observing the observer (II): deciding when to decide.Toward a new application of real-time electrophysiology: online optimization of cognitive neurosciences hypothesis testing.A model-based analysis of impulsivity using a slot-machine gambling paradigmCholinergic stimulation enhances Bayesian belief updating in the deployment of spatial attentionThe social Bayesian brain: does mentalizing make a difference when we learn?Theory of mind: did evolution fool us?Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism.Pharmacological Fingerprints of Contextual Uncertainty.Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment.Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG.Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design.Dissecting psychiatric spectrum disorders by generative embedding.Psychopathy-related traits and the use of reward and social information: a computational approach.Modeling Trait Anxiety: From Computational Processes to Personality.How can a Bayesian approach inform neuroscience?The role of prediction in social neuroscience.Moving beyond qualitative evaluations of Bayesian models of cognition.Inference in the Brain: Statistics Flowing in Redundant Population Codes.Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression.Active inference and epistemic value.Computational Phenotyping in Psychiatry: A Worked Example.Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?Comparative Analysis of Behavioral Models for Adaptive Learning in Changing Environments.Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.Uncertainty in perception and the Hierarchical Gaussian Filter.Optimal inference with suboptimal models: addiction and active Bayesian inference.Brain-computer interfaces: a neuroscience paradigm of social interaction? A matter of perspective.A bayesian foundation for individual learning under uncertainty.A neurocomputational model of the mismatch negativity.A normative inference approach for optimal sample sizes in decisions from experience.Reading wild minds: A computational assay of Theory of Mind sophistication across seven primate species.The Computational Anatomy of Visual Neglect.
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P2860
Observing the observer (I): meta-bayesian models of learning and decision-making.
description
2010 nî lūn-bûn
@nan
2010 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
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2010年学术文章
@wuu
2010年学术文章
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2010年学术文章
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2010年学术文章
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2010年学术文章
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2010年學術文章
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name
Observing the observer (I): meta-bayesian models of learning and decision-making.
@ast
Observing the observer (I): meta-bayesian models of learning and decision-making.
@en
type
label
Observing the observer (I): meta-bayesian models of learning and decision-making.
@ast
Observing the observer (I): meta-bayesian models of learning and decision-making.
@en
prefLabel
Observing the observer (I): meta-bayesian models of learning and decision-making.
@ast
Observing the observer (I): meta-bayesian models of learning and decision-making.
@en
P2860
P50
P1433
P1476
Observing the observer (I): meta-bayesian models of learning and decision-making
@en
P2093
Matthias Pessiglione
P2860
P304
P356
10.1371/JOURNAL.PONE.0015554
P407
P577
2010-12-14T00:00:00Z