A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders.
Ryan SmithRayus KuplickiJustin S. FeinsteinKatherine L. ForthmanJennifer L. StewartMartin P. PaulusTulsa 1000 InvestigatorsSahib S. KhalsaPublished in: PLoS Comput. Biol. (2020)
Keyphrases
- computational model
- computational models
- computational framework
- language acquisition
- cognitive architecture
- maximum likelihood
- statistically significant
- high precision
- computational modeling
- precision and recall
- cognitive modeling
- working memory
- changing environment
- attitudes toward
- bayesian inference
- failure rate
- bayesian networks
- computer vision
- vision system
- computational intelligence
- low level