Modeling sensory-motor decisions in natural behavior.
PLoS Comput Biol
; 14(10): e1006518, 2018 10.
Article
em En
| MEDLINE
| ID: mdl-30359364
ABSTRACT
Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Desempenho Psicomotor
/
Reforço Psicológico
/
Tomada de Decisões
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Estados Unidos