Bayesian Models of Cognition
What's Built in After All
Datos Bibliográficos
| ID | 4079484 |
|---|---|
| Autores | Amy Perfors (0000-0002-6976-0732, The University of Adelaide, autor de correspondencia) |
| Año | 2012 |
| Volumen | 7 |
| Número | 2 |
| Páginas | 127-138 |
| Fecha de publicación | 2012-02-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Philosophy Compass (JOURNAL) |
| Identificadores de la revista | ISSN: 1747-9991 • E-ISSN: 1747-9991 |
| Editorial | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/j.1747-9991.2011.00467.x |
| OpenAlex | W1871659323 |
| Idioma | EN |
| Citas recibidas | 9 |
| Referencias citadas | 27 |
This article explores some of the philosophical implications of the Bayesian modeling paradigm. In particular, it focuses on the ramifications of the fact that Bayesian models pre-specify an inbuilt hypothesis space. To what extent does this pre-specification correspond to simply ''building the solution in''? I argue that any learner (whether computer or human) must have a built-in hypothesis space in precisely the same sense that Bayesian models have one. This has implications for the nature of learning, Fodor's puzzle of concept acquisition, and the role of modeling in cognitive science
Bayesian inference · Bayesian probability · Cognition · Cognitive science · Epistemology · Bayesian Modeling and Causal Inference · Child and Animal Learning Development · Computer Science · Philosophy · Philosophy and History of Science · Psychology · Artificial Intelligence
Fusion is great, and interpretable fusion could be exciting for theory generation
Fusion is great, and interpretable fusion could be exciting for theory generation
Logical word learning
The acquisition of linking theories
Empiricism, syntax, and ontogeny
Bayesian cognitive science, predictive brains, and the nativism debate
Bayes meets Hegel
Delusional Predictions and Explanations
Deep learning
Vision
Probability Theory
The adaptive nature of human categorization.
Topics in semantic representation.
Probabilistic models of cognition
Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition
Variability, negative evidence, and the acquisition of verb argument constructions
Language Evolution by Iterated Learning With Bayesian Agents
Word learning as Bayesian inference
| Obras citantes distintas | 9 |
|---|---|
| Citas por año | 1,13 |
| Intervalo de citas | 2018 - 2025 (8) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 7 |