about
Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiencesConstructing semantic representations from a gradually-changing representation of temporal contextCross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential linksRaising argument strength using negative evidence: a constraint on models of induction.Bayesian learning and the psychology of rule induction.Cognitive "Category-Based Induction" Research and Social "Persuasion" Research Are Each About What Makes Arguments Believable: A Tale of Two Literatures.Structure learning and the Occam's razor principle: a new view of human function acquisition.Structure learning in a sensorimotor association taskMemory, reasoning, and categorization: parallels and common mechanisms.Learning latent structure: carving nature at its joints.Inferring visuomotor priors for sensorimotor learning.Category vs. Object Knowledge in Category-based Induction.Sampling assumptions in inductive generalization.A review of visual memory capacity: Beyond individual items and toward structured representationsHow the statistics of sequential presentation influence the learning of structure.Inferring relevance in a changing world.Structure Learning in Bayesian Sensorimotor Integration.Facilitation of learning induced by both random and gradual visuomotor task variation.A sensorimotor paradigm for Bayesian model selectionStructure learning in actionDefending the concept of "concepts".How to grow a mind: statistics, structure, and abstraction.A taxonomy of inductive problems.The Bayesian boom: good thing or bad?Is there an exemplar theory of concepts?Bayesian models of cognition.Failures of explaining away and screening off in described versus experienced causal learning scenarios.What representations and computations underpin the contribution of the hippocampus to generalization and inference?Leaping to Conclusions: Why Premise Relevance Affects Argument Strength.A Computational Approach towards Visual Object Recognition at Taxonomic Levels of Concepts.Learning words in space and time: probing the mechanisms behind the suspicious-coincidence effect.Letting structure emerge: connectionist and dynamical systems approaches to cognition.Parallel Distributed Processing at 25: further explorations in the microstructure of cognition.Relevant and robust: a response to Marcus and Davis (2013).Inductive reasoning 2.0.The Emergence of Organizing Structure in Conceptual Representation.Judging the Probability of Hypotheses Versus the Impact of Evidence: Which Form of Inductive Inference Is More Accurate and Time-Consistent?How similar are recognition memory and inductive reasoning?Reasoning With Causal Cycles.Occam's Razor in sensorimotor learning.
P2860
Q27339196-23AD0075-8C04-4720-92A9-83457E55246BQ28036001-B27816CD-F9B7-40DD-A4D2-A5784EAE3289Q28789964-BF32B08E-0AF1-4C40-BD3F-FA15B7F6E57AQ30403046-C9C1019E-A46B-4A16-A7AE-E5DEEA5A3765Q30438949-B28EE7E2-139F-4BFC-94C6-64884479D3D6Q30459703-E40D7DFB-835B-45EE-83A0-2218B84AFDF7Q30588586-5B3D331A-E768-4F5F-B5B6-9E8CB65C7EE7Q33528990-BE4D4F04-77A0-4B5D-BC60-F17CFB94770AQ33766520-B2BDE34E-C681-42C0-9986-EC3F234022C5Q33823347-5B16C033-D5E1-47FC-8F72-8D20E624A2B8Q33869016-7A7E1DB3-5DDC-4353-A7B3-B06E8F18A85CQ33887413-DF28E4CB-B1AC-4492-ABCC-1DCC6138002CQ34091658-2C686756-872C-4FEF-A477-28627453C76EQ34187669-19C9C5CE-B8F7-496F-84C7-97F7660B1835Q34701154-9667ACF9-EAA8-4D1B-921A-BA504CE550DCQ35691153-39C69D3E-D81E-4122-A693-32F0C1CED0FFQ35754890-0D2152F2-2DD6-4F72-9B91-8731BD4875E4Q35787552-DFD978D1-5C1A-4FF1-87ED-D6E297B1E1E8Q36364462-01CDE91D-2A42-4112-85BA-DF04B96C93E0Q37589805-8460C52F-338F-4246-8011-92D2605C801EQ37768247-D26A5075-C6B6-4AED-8F63-5D1B1F327E8FQ37851773-2A60B2C7-B97F-4CC6-97A9-B64D771F3F83Q38124000-B98A7FF9-95AC-454B-B0B2-598BD7344669Q38242816-9A4DAD8E-FF13-4438-A58E-4A16030AFF08Q38391411-789B9AF3-7150-4BBE-8B3D-EE050F0D3026Q38566289-5C23D485-2149-4051-B7AC-D5C7096EFCB2Q39202696-5BD70424-3BA9-4CC7-ADD3-6AC58625B01DQ39606611-F80C3D64-F27D-4C64-B50E-E205B8B7A27EQ40426924-BBC7D750-3D84-439B-9792-A603F8A7DD9FQ41425515-DD4143EE-2A12-4FA0-99AB-E66717275C36Q42085477-D03B7636-36D1-405B-95BD-353F658DC6B4Q42684623-17CE2AC4-90F3-4D75-A5F2-1E02B131967AQ45956564-338E88CA-A631-4B2A-92F0-45F75E7B1703Q45958447-7137C6AC-C372-4FC5-AADA-88B6AC507CABQ47547553-0F62A099-B582-4276-941B-3AAB010291F6Q47874758-AE76CC88-CE0B-4D41-9612-62FCF708D6A2Q48114961-159A3436-D594-4924-8C01-B049311035E4Q50761876-7E3E610F-ACB2-406F-A9EF-04225BC49C7BQ51061620-CAD31042-2141-442B-878D-24D3BF416375Q51099435-F38B5870-5D3A-4BA5-8D82-A2C9F8512771
P2860
description
2009 nî lūn-bûn
@nan
2009年の論文
@ja
2009年学术文章
@wuu
2009年学术文章
@zh
2009年学术文章
@zh-cn
2009年学术文章
@zh-hans
2009年学术文章
@zh-my
2009年学术文章
@zh-sg
2009年學術文章
@yue
2009年學術文章
@zh-hant
name
Structured statistical models of inductive reasoning.
@en
Structured statistical models of inductive reasoning.
@nl
type
label
Structured statistical models of inductive reasoning.
@en
Structured statistical models of inductive reasoning.
@nl
prefLabel
Structured statistical models of inductive reasoning.
@en
Structured statistical models of inductive reasoning.
@nl
P356
P1433
P1476
Structured statistical models of inductive reasoning.
@en
P2093
Charles Kemp
P356
10.1037/A0014282
P577
2009-01-01T00:00:00Z