Theory-based Bayesian models of inductive learning and reasoning.
about
Categorial compositionality: a category theory explanation for the systematicity of human cognitionArchitecture of explanatory inference in the human prefrontal cortexCategorial compositionality II: universal constructions and a general theory of (quasi-)systematicity in human cognitionSignificance testing as perverse probabilistic reasoning.Probabilistic brains: knowns and unknownsEmotion and decision-making: affect-driven belief systems in anxiety and depressionCausal reasoning with mental modelsConstructing semantic representations from a gradually-changing representation of temporal contextMeasuring the crowd within again: a pre-registered replication studyModeling cross-situational word-referent learning: prior questionsEvolved priors for ethnolinguistic categorization: A case study from the Quechua-Aymara boundary in the Peruvian AltiplanoCognitive "Category-Based Induction" Research and Social "Persuasion" Research Are Each About What Makes Arguments Believable: A Tale of Two Literatures.From shared contexts to syntactic categories: the role of distributional information in learning linguistic form-classes.Exercising your brain: a review of human brain plasticity and training-induced learningCognitive gains from gist reasoning training in adolescents with chronic-stage traumatic brain injury.A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.Causal inference in multisensory perception.Structure learning in a sensorimotor association taskPeople efficiently explore the solution space of the computationally intractable traveling salesman problem to find near-optimal tours.Structure learning in human sequential decision-makingObserving the observer (I): meta-bayesian models of learning and decision-making.Inferring visuomotor priors for sensorimotor learning.Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neuronsInfants' causal representations of state change events.A review of visual memory capacity: Beyond individual items and toward structured representationsDo capuchin monkeys (Cebus apella) diagnose causal relations in the absence of a direct reward?Bayesian modeling of flexible cognitive controlAcquiring and processing verb argument structure: distributional learning in a miniature languageHow the statistics of sequential presentation influence the learning of structure.Relevance of error: what drives motor adaptation?Bayesian sampling in visual perception.Effects of higher-order cognitive strategy training on gist-reasoning and fact-learning in adolescents.Structure Learning in Bayesian Sensorimotor Integration.Deliberation in the motor system: reflex gains track evolving evidence leading to a decisionProblem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of HanoiTransfer in Rule-Based Category Learning Depends on the Training TaskThe Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit DynamicsA sensorimotor paradigm for Bayesian model selectionNeural signals encoding shifts in beliefs.Prior experiences and perceived efficacy influence 3-year-olds' imitation.
P2860
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P2860
Theory-based Bayesian models of inductive learning and reasoning.
description
2006 nî lūn-bûn
@nan
2006年の論文
@ja
2006年学术文章
@wuu
2006年学术文章
@zh
2006年学术文章
@zh-cn
2006年学术文章
@zh-hans
2006年学术文章
@zh-my
2006年学术文章
@zh-sg
2006年學術文章
@yue
2006年學術文章
@zh-hant
name
Theory-based Bayesian models of inductive learning and reasoning.
@en
Theory-based Bayesian models of inductive learning and reasoning.
@nl
type
label
Theory-based Bayesian models of inductive learning and reasoning.
@en
Theory-based Bayesian models of inductive learning and reasoning.
@nl
prefLabel
Theory-based Bayesian models of inductive learning and reasoning.
@en
Theory-based Bayesian models of inductive learning and reasoning.
@nl
P1476
Theory-based Bayesian models of inductive learning and reasoning.
@en
P2093
Charles Kemp
P304
P356
10.1016/J.TICS.2006.05.009
P577
2006-06-22T00:00:00Z