Probabilistic programming versus meta-learning as models of cognition.
Behav Brain Sci
; 47: e158, 2024 Sep 23.
Article
em En
| MEDLINE
| ID: mdl-39311521
ABSTRACT
We summarize the recent progress made by probabilistic programming as a unifying formalism for the probabilistic, symbolic, and data-driven aspects of human cognition. We highlight differences with meta-learning in flexibility, statistical assumptions and inferences about cogniton. We suggest that the meta-learning approach could be further strengthened by considering Connectionist and Bayesian approaches, rather than exclusively one or the other.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
/
Cognição
/
Aprendizagem
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article