Computational Core

点击翻译按钮获取中文摘要

PROJECT SUMMARY: COMPUTATIONAL CORE The purpose of the COMPUTATIONAL CORE is to provide services for infrastructure and conceptual support to integrate the four Projects that form our Center. To do this, the Core will ensure that the trial-by-trial behavioral data collected from the four Projects (from the computationally informative DPX and Bandit tasks) are analyzed in a uniform and compatible manner, so that findings across Projects can be compared and modeled. It will also supply overall statistical support to ensure that statistical analyses are done appropriately. The Core consists of a Service Aim and a Modeling Aim. In the Service Aim, we propose to apply causal discovery analyses, a recently developed toolkit of mathematical algorithms that can infer explanatory relationships between co-occurring data parameters. Causal discovery analyses serve as a powerful inferential toolbox that can be applied to all of the PROJECTS independently, facilitating the generation of new hypotheses within and across PROJECTS. In the Modeling Aim, the Core will provide conceptual and modeling support for the theoretical underpinning of state representation dysfunctions relevant to psychosis. As noted in the OVERALL RESEARCH STRATEGY, we take a central computational perspective to translate across species and methodologies, using theoretical constructs to bridge the gap between neural dysfunction and observable manifestations of that dysfunction. To this end, the Core will integrate two existing models: an Algorithmic-Level Model that translates attractor dynamics to behavior, and a Neurophysiology-Level Model that translates neuronal properties to attractor dynamics. Our goal is to examine how neuronal-scale effects, such as those seen in the non-human animal projects (PROJECTS 1 & 2) translate into observable behavioral and neurophysiological effects in healthy and clinical populations, such as those seen in the human projects (PROJECTS 3 & 4), by merging these two models into an integrated model crossing levels, that can provide mechanistic explanatory power for how neurophysiological effects produce attractor dynamics that lead to behavioral outcomes.