Browsing by Subject "Implicit learning"
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Item Cerebello-striatal connectivity and implicit learning in autism spectrum disorders(2012-05) Morley, Richard Henry; Allen, Greg, doctor of clinical psychology; Schellart, Diane; Robinson, Daniel; Svinicki, Marillia; Stafford, MarkPrevious studies have indicated that persons with autism spectrum disorder have distinct cerebella, striatum, and an impaired ability to anticipate implicit learning sequences; also, previous research indicates anatomic connections among these regions. Investigating distinctions in connectivity and impairments in the ability to anticipate implicit sequences linked to ASD would help clarify some of the core deficits associated with the disorder. This dissertation sought to explore differences in functional connectivity among the cerebellum, thalamus, and striatum. This dissertation also sought to determine if an impaired ability to anticipate implicit sequences is associated with ASD. Twelve ASD participants and 11 control participants were scanned using an MRI while engaged in a modified serial reaction task. The findings indicate that the cerebellum and the striatum are functionally connected and the thalamus mediates this connection. The results indicate that ASD participants have stronger connections than the control, and ASD participants demonstrated some impairments in learning. However, there was not enough evidence to link ASD to an impaired ability to anticipate implicit sequences. This dissertation recommends that future studies consider the roles that these distinct connections play in symptoms of ASD.Item Development of a novel stopping technique for optimization(Texas Tech University, 1997-12) Iyer, Mahesh SubramaniamNeural networks are being used widely in areas of process control, pattern recognition, etc. The possibility of improving the efficiency of data utilization in neural network training and automating the decision to stop training, using a novel Steady-state Identifier (SSID) algorithm, have been investigated. One conclusion is that complete automation of the decision criterion to stop training is probably beyond the realm of possibility and human judgment seems unavoidable. However, as a beneficial outcome of this study, a technique has been developed to determine the number of neural network training repetitions to guarantee the convergence of the training algorithm within a certain vicinity ofthe global optimum of the objective function, with a desired level of confidence. The concept used is the weakest-link-in-the chain analysis. As another outcome, a novel approach of stopping neural network training has been developed. In this technique, a random fraction ofthe training set data is sampled at each epoch. The error on the random fraction is tested for its attainment of steady-state or otherwise using either a novel Steady-State Identifier or equivalently by visual observation by a human operator. Training is stopped when the error on the random fraction attains Steady-State. This technique, in general, is more cost effective than cross-validation. The overall developments are perfectly general and can also be applied to optimization problems other than neural network training.Item Mechanisms and constraints underlying implicit sequence learning(2005) Gureckis, Todd Matthew; Love, Bradley C.Our ability to learn about sequences of events allows us to perceive melody in music, to coordinate the movement of our bodies, and to understand spoken language. Much of this sequential behavior proceeds outside of our conscious awareness. In this dissertation, I consider two questions: 1. What is the nature of the processes underlying our implicit sequence learning behavior? 2. To what degree does sequential behavior in different domains and tasks rely on similar underlying mechanisms? A cross section of current theories of sequential learning are evaluated in their ability to account for human learning studies drawn from both serial reaction time (SRT) and statistical word learning tasks. Five novel experiments are reported which differentiate between competing theories concerning the mechanisms underlying implicit sequence learning. A view emerges which describes implicit sequence learning as a relatively simple and limited process with a memory substrate utilizing distinct spatial codes for events in time as opposed to aggregate context.