Probabilistic models of cognition: exploring representations and inductive biases

Cited by (569)

Items 201–300 of 569

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22. The Generalized Quantum Episodic Memory Model
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32. Tracking the contribution of inductive bias to individualised internal models
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36. Social Projection and a Quantum Approach for Behavior in Prisoner's Dilemma
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37. Cognitive Rehabilitation in Bilateral Vestibular Patients: A Computational Perspective
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45. Descriptive and Inferential Problems of Induction
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46. A unified account of abstract structure and conceptual change: Probabilistic models and early learning mechanisms
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47. Public service users’ responses to performance information: Bayesian learning or motivated reasoning?
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48. Exploratory mapping of theoretical landscapes through word use in abstracts
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49. Structure Learning in Predictive Processing Needs Revision
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50. The Potential of Quantum Probability for Modeling Cognitive Processes
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51. Cross-Cultural Differences in Informal Argumentation: Norms, Inductive Biases and Evidentiality
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52. Testing one or multiple: How beliefs about sparsity affect causal experimentation.
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53. Representing absence of evidence: why algorithms and representations matter in models of language and cognition
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54. What Is Rational and Irrational in Human Decision Making
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55. Toward a Dynamic Probabilistic Model for Vestibular Cognition
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56. Abstract representations emerge in human hippocampal neurons during inference behavior
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57. What is the “cognitive” in cognitive neuroscience?
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58. Interplay between Bayesian neural networks and deep learning: A survey
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59. Rethinking Rationality
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60. Spiking neuron network Helmholtz machine
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61. Bayesian Analysis in Natural Language Processing
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62. Opening the “black box” of learning to read
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63. Assessing Mathematics Misunderstandings via Bayesian Inverse Planning
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64. A robustness approach to theory building: A case study of language evolution
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65. Approaches to Learning to Control Dynamic Uncertainty
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66. Probabilistic models as theories of children's minds
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68. Introduction to the Special Issue: Parsimony and Redundancy in Models of Language
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70. Bayesian Models of Conceptual Development: Learning as Building Models of the World
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72. The similarity-updating model of probability judgment and belief revision.
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73. Using diverging predictions from classical and quantum models to dissociate between categorization systems
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74. Formalizing Heuristics in Decision-Making: A Quantum Probability Perspective
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75. Neurocognitive models of sense-making
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76. Semantics without categorization
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77. How Limited Systematicity Emerges
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78. Preface
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82. What Are the "True" Statistics of the Environment?
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83. How Young Children Learn From Examples: Descriptive and Inferential Problems
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84. From the Impossible to the Improbable: A Probabilistic Account of Magical Beliefs and Practices Across Development and Cultures
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87. Why We Doubt
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90. Bayes in the Age of Intelligent Machines
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100. The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation
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