Current practices in nutrition management and disease incidence of common marmosets (Callithrix jacchus).

BACKGROUND: A survey was developed to characterize disease incidence, common pathology lesions, environmental characteristics, and nutrition programs within captive research marmoset colonies. METHODS: Seventeen research facilities completed the electronic survey. RESULTS: Nutritional management programs varied amongst research institutions housing marmosets; eight primary base diets were reported. The most common clinical syndromes reported were gastrointestinal disease (i.e. inflammatory bowel disease like disease, chronic lymphocytic enteritis, chronic malabsorption, chronic diarrhea), metabolic bone disease or fracture, infectious diarrhea, and oral disease (tooth root abscesses, gingivitis, tooth root resorption). The five most common pathology morphologic diagnoses were colitis, nephropathy/nephritis, enteritis, chronic lymphoplasmacytic enteritis, and cholecystitis. Obesity was more common (average 20% of a reporting institution's population) than thin body condition (average 5%). CONCLUSIONS: Through review of current practices, we aim to inspire development of evidence-based practices to standardize husbandry and nutrition practices for marmoset research colonies.

Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization.

Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.