Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models.
Top Cogn Sci
; 14(4): 873-888, 2022 10.
Article
em En
| MEDLINE
| ID: mdl-35608284
ABSTRACT
We describe a new approach for developing and validating cognitive process models. In our methodology, graphical models (specifically, hidden Markov models) are developed both from human empirical data on a task and synthetic data traces generated by a cognitive process model of human behavior on the task. Differences between the two graphical models can then be used to drive model refinement. We show that iteratively using this methodology can unveil substantive and nuanced imperfections of cognitive process models that can then be addressed to increase their fidelity to empirical data.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Cognição
Tipo de estudo:
Health_economic_evaluation
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Top Cogn Sci
Ano de publicação:
2022
Tipo de documento:
Article