The functional neuroanatomy and connectivity of reward processing in adults are well documented, with relatively less research on adolescents, a notable gap given this developmental period's association with altered reward sensitivity. Here, a large sample (n = 1,510) of adolescents performed the monetary incentive delay (MID) task during functional magnetic resonance imaging. Probabilistic maps identified brain regions that were reliably responsive to reward anticipation and receipt, and to prediction errors derived from a computational model. Psychophysiological interactions analyses were used to examine functional connections throughout reward processing. Bilateral ventral striatum, pallidum, insula, thalamus, hippocampus, cingulate cortex, midbrain, motor area, and occipital areas were reliably activated during reward anticipation. Bilateral ventromedial prefrontal cortex and bilateral thalamus exhibited positive and negative activation, respectively, during reward receipt. Bilateral ventral striatum was reliably active following prediction errors. Previously, individual differences in the personality trait of sensation seeking were shown to be related to individual differences in sensitivity to reward outcome. Here, we found that sensation seeking scores were negatively correlated with right inferior frontal gyrus activity following reward prediction errors estimated using a computational model. Psychophysiological interactions demonstrated widespread cortical and subcortical connectivity during reward processing, including connectivity between reward‐related regions with motor areas and the salience network. Males had more activation in left putamen, right precuneus, and middle temporal gyrus during reward anticipation. In summary, we found that, in adolescents, different reward processing stages during the MID task were robustly associated with distinctive patterns of activation and of connectivity.

Abstract-Symbolic regression (SR) is a well studied method in genetic programming (GP) for discovering free-form mathematical models from observed data. However, it has not been widely accepted as a standard data science tool. The reluctance is in part due to the hard to analyze random nature of GP and scalability issues. On the other hand, most popular deterministic regression algorithms were designed to generate linear models and therefore lack the flexibility of GP based SR (GP-SR). Our hypothesis is that hybridizing these two techniques will create a synergy between the GP-SR and deterministic approaches to machine learning, which might help bring the GP based techniques closer to the realm of big learning. In this paper, we show that a hybrid deterministic/GP-SR algorithm outperforms GP-SR alone and the state-of-the-art deterministic regression technique alone on a set of multivariate polynomial symbolic regression tasks as the system to be modeled becomes more multivariate.

A computational framework is presented for re-engineering the Geographic Information Science and Technology Body of Knowledge (GIS&T BoK). At its core is an ontology that is meant to simplify and extend the original BoK hierarchical structure to better capture relationships existing among concepts. Our approach builds on several key ideas. First is the notion of a knowledge corpus, an aggregate of both the internal cognitive forms of knowledge held by domain actors and the content of external artifacts that are produced and consumed by domain activities. Second is the notion of a reference system within which such artifacts are located and relationships among artifacts can be expressed. Third is the idea that by structuring the GIS&T concepts through the use of semantic web standards for formal ontologies and envisaging it as a reference system for GIS&T artifacts, activities, and actors, a fundamentally different approach to the redesign, content generation, and maintenance of the GIS&T BoK is enabled. This new approach affords replacing the top-down strategies used to generate the original GIS&T BoK, with a bottom-up strategy that combines analytical and participatory components. On the analytical side, computational and visual techniques are used to provide alternative means for accessing BoK content, examining the semantic consistency of current BoK structures, transforming the existing hierarchy into a semantic network, identifying overlaps and gaps in the current BoK, and performing projection of knowledge artifacts onto the BoK to inform its maintenance and update. Participatory approaches to bottom-up restructuring and maintenance of the BoK will support authoring, editing, and validation of concepts using a wiki-like community editing service. The system we describe is deployed as a web service that can be accessed by a range of applications for visualization, analysis, exploration, and contextualization of concepts and their related classes in the new GIS&T Body of Knowledge. The goal is for the new GIS&T BoK2 to evolve into the centerpiece of a cyberinfrastructure ecosystem for the GIS&T domain.