Predicting shifts in generalization gradients with perceptrons

Perceptron models have been used extensively to model perceptual learning and the effects of discrimination training on generalization, as well as to explore natural classification mechanisms. Here, we assess the ability of existing models to account for the time course of generalization shifts that occur when individuals learn to distinguish sounds. A set of simulations demonstrates that commonly used single-layer and multilayer perceptron networks do not predict transitory shifts in generalization over the course of training but that such dynamics can be accounted for when the output functions of these networks are modified to mimic the properties of cortical tuning curves. The simulations further suggest that prudent selection of stimuli and training criteria can allow for more precise predictions of learning-related shifts in generalization gradients in behavioral experiments. In particular, the simulations predict that individuals will show maximal peak shift after different numbers of trials, that easier contrasts will lead to slower development of shifted peaks, and that whether generalization shifts persist or dissipate will depend on which stimulus dimensions individuals use to distinguish stimuli and how those dimensions are neurally encoded.     

BioOne and SPIE: A reflection on the first 3 years of the nonprofit platform partnership

• There is great potential for organizations to form collaborative partnerships, particularly in the nonprofit environment, which can reduce costs and aid in strategic development.
• Regular and transparent communication with internal and external stakeholders is critical to the health of any partnership, especially when doing something new or novel.
• Reciprocal service-level agreements with clear expectations and ongoing evaluation ensure the collaboration keeps pace with commercial alternatives.
• The SPIE-BioOne partnership was built to scale and incorporate future collaborators to further reduce costs and bring new ideas to the user group.

     

Association of Gestational Diabetes Mellitus and Perinatal Maternal Depression with Early Childhood Behavioral Problems: An Environmental Influences on Child Health Outcomes (ECHO) Study

This study examined the association of gestational diabetes mellitus (GDM), prenatal, and postnatal maternal depressive symptoms with externalizing, internalizing, and autism spectrum problems on the Preschool Child Behavior Checklist in 2379 children aged 4.12 ± 0.60 (48% female; 47% White, 32% Black, 15% Mixed Race, 4% Asian, <2% American Indian/Alaskan Native, <2% Native Hawaiian; 23% Hispanic). Data were collected from the NIH Environmental influences on Child Health Outcomes (ECHO) Program from 2009-2021. GDM, prenatal, and postnatal maternal depressive symptoms were each associated with increased child externalizing and internalizing problems. GDM was associated with increased autism behaviors only among children exposed to perinatal maternal depressive symptoms above the median level. Stratified analyses revealed a relation between GDM and child outcomes in males only.