NUMERICAL AND ANALYTICAL VERIFICATION OF A MULTISCALE COMPUTATIONAL MODEL FOR IMPACT PROBLEMS IN HETEROGENEOUS VISCOELASTIC MATERIALS WITH EVOLVING DAMAGE

Composites are engineered materials that take advantage of the particular properties of each of its two or more constituents. They are designed to be stronger, lighter and to last longer which can lead to the creation of safer protection gear, more fuel efficient transportation methods and more affordable materials, among other examples. This thesis proposes a numerical and analytical verification of an in-house developed multiscale model for predicting the mechanical behavior of composite materials with various configurations subjected to impact loading. This verification is done by comparing the results obtained with analytical and numerical solutions with the results found when using the model. The model takes into account the heterogeneity of the materials that can only be noticed at smaller length scales, based on the fundamental structural properties of each of the composite’s constituents. This model can potentially reduce or eliminate the need of costly and time consuming experiments that are necessary for material characterization since it relies strictly upon the fundamental structural properties of each of the composite’s constituents. The results from simulations using the multiscale model were compared against results from direct simulations using over-killed meshes, which considered all heterogeneities explicitly in the global scale, indicating that the model is an accurate and fast tool to model composites under impact loads. Advisor: David H. Allen

Social Cognition and Interaction Training (SCIT) for Individuals with Schizophrenia Spectrum Disorders in Outpatient Treatment Settings

The role of social cognition in severe mental illness (SMI) has gained much attention, especially over the last decade. The impact of deficits in socio-cognitive functioning has been found to have detrimental effects on key areas of day-to-day functioning in individuals with SMI, such as gaining and maintaining employment and overall experienced quality of life. Treatment of individuals with SMI is challenging, as the presentation of individual signs and symptoms is rather heterogeneous. There are several treatment approaches addressing deficits ranging from broader social and interpersonal functioning to neurocognitive and more intrapersonal functioning. As research in the domain of social cognition continues to identify specific deficits and its functional detriments, treatment options need to evolve to better target identified functional deficits. Social Cognition and Interaction Training (SCIT) was recently developed to address specific socio-cognitive deficits in an inpatient population of individuals with schizophrenia-spectrum disorders. This study applied SCIT in an outpatient SMI population as many deficits remain after individuals’ symptoms are less severe and overall functioning is more stable than during the acute inpatient phase of their rehabilitation. Specifically, this study has two objectives. First, to demonstrate that deficits in social cognition persist after the acute phase of illness has abated. Second, to demonstrate that these deficits can be ameliorated via targeted treatment such as SCIT. Data was gathered in local outpatient treatment settings serving a heterogeneous SMI population. Adviser: William D. Spaulding

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks—the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity—generating novel, testable predictions—and generality—spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.