Rational approximations to rational models: alternative algorithms for category learning

Cited by (373)

Items 101–200 of 373

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9. Perception, Action, and Utility: The Tangled Skein
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10. Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis
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11. The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science
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12. Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions
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13. The evolutionary origin of Bayesian heuristics and finite memory
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14. Bayesian reverse-engineering considered as a research strategy for cognitive science
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17. A nonparametric Bayesian framework for constructing flexible feature representations.
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19. The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation
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20. Information overload for (bounded) rational agents
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21. Concepts and Techniques
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22. On Learning Natural-Science Categories That Violate the Family-Resemblance Principle
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23. Bayesian encoding and decoding as distinct perspectives on neural coding
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24. A probabilistic account of exemplar and category generation
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25. Re-examining selective adaptation: Fatiguing feature detectors, or distributional learning?
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26. Bayesian modeling of the mind: From norms to neurons
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28. Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning
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31. Complex Probabilistic Inference
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32. What does the mind learn? A comparison of human and machine learning representations
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33. Erasing the engram: The unlearning of procedural skills.
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34. A model of conceptual bootstrapping in human cognition
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35. From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach
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36. Model-guided search for optimal natural-science-category training exemplars: A work in progress
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37. Unifying Principles of Generalization: Past, Present, and Future
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38. Learning bundles of stimuli renders stimulus order as a cue, not a confound
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39. Structure and Flexibility in Bayesian Models of Cognition
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40. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer
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41. Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization
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42. An Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case
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43. Attention, predictive learning, and the inverse base-rate effect: Evidence from event-related potentials
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44. Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence
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45. Bayesian Models of Cognition: What's Built in After All?
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47. Base rate neglect and conservatism in probabilistic reasoning: Insights from eliciting full distributions
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48. Biased evaluations emerge from inferring hidden causes
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49. Linking Across Levels of Computation in Model-Based Cognitive Neuroscience
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50. Online learning of symbolic concepts
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51. Learning How to Generalize
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52. Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation
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53. The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.
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54. Throwing out the Bayesian baby with the optimal bathwater: Response to Endress (2013)
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55. Evaluating the inverse decision-making approach to preference learning
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56. The anchoring bias reflects rational use of cognitive resources
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57. Incremental implicit learning of bundles of statistical patterns
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58. Epistemic Irrationality in the Bayesian Brain
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59. Noise in Cognition: Bug or Feature?
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60. A Bayesian Model of Rule Induction in Raven's Progressive Matrices
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61. The Computational Nature of Memory Modification
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62. Win-Stay, Lose-Sample: A Simple Sequential Algorithm for Approximating Bayesian Inference
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63. The influence of reward magnitude on stimulus memory and stimulus generalization in categorization decisions.
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64. Supervised versus Unsupervised Categorization: Two Sides of the Same Coin?
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65. Risk Representation, Perception, and Propensity in an Integrated Human Lane-Change Decision Model
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66. Two neurocomputational building blocks of social norm compliance
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67. Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners
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68. Impairments in Probabilistic Prediction and Bayesian Learning Can Explain Reduced Neural Semantic Priming in Schizophrenia
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69. Incremental Bayesian Category Learning From Natural Language
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70. An overview of the quantum cognition research program
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71. As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.
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72. Bayesian cognitive science, predictive brains, and the nativism debate
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73. Algorithms of adaptation in inductive inference
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74. Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning
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75. Alternatives to Bayesian Updating
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76. Sculpting Computational‐Level Models
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77. Statistical Learning Model of the Sense of Agency
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78. Time pressure changes how people explore and respond to uncertainty
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79. Human inference in changing environments with temporal structure.
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80. The helpfulness of category labels in semi-supervised learning depends on category structure
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81. Pinning down the theoretical commitments of Bayesian cognitive models
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82. Bayesian Speech Production: Evidence from Latency and Hyperarticulation
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83. (When) are We Dynamically Optimal? A Psychological Field Guide for Marketing Modelers
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84. Naïve and Robust: Class‐Conditional Independence in Human Classification Learning
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85. Context-dependent savings in procedural category learning
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86. How Do People Generalize Causal Relations over Objects? A Non-parametric Bayesian Account
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87. Testing formal cognitive models of classification and old-new recognition in a real-world high-dimensional category domain
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88. Explaining the flaws in human random generation as local sampling with momentum
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89. Contrasting exemplar and prototype models in a natural-science category domain.
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90. Approach-induced biases in human information sampling
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91. Impulsivity and risk-seeking as Bayesian inference under dopaminergic control
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93. Bayesian Inference
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94. Learning time-varying categories
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95. Toward Boundedly Rational Analysis.
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96. Search for the Missing Dimensions: Building a Feature-Space Representation for a Natural-Science Category Domain
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97. Heuristics as Bayesian inference under extreme priors
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98. Infants’ use of contextual cues in the generalization of effective actions from imitation
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99. A Cognitive Model of Semantic Network Learning
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100. Grounding Bayesian accounts of numerosity and variability effects in a similarity-based framework: the case of self-organising maps
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