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Department of Psychology

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https://hdl.handle.net/10217/100503
These digital collections include theses, dissertations, faculty publications, and datasets from the Department of Psychology.

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Browsing Department of Psychology by Author "Anderson, Charles W., committee member"

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    Deep transfer learning for prediction of health risk behaviors in adolescent psychiatric patients
    (Colorado State University. Libraries, 2021) Kentopp, Shane, author; Conner, Bradley T., advisor; Prince, Mark A., committee member; Henry, Kimberly L., committee member; Anderson, Charles W., committee member
    Binge drinking and non-suicidal self-injury are significant health-risk behaviors that are often initiated during adolescence and contribute to a host of negative outcomes later in life. Selective prevention strategies are targeted toward individuals most at-risk for developing these behaviors. Traditionally, selective interventions are tailored based on risk factors identified by human experts. Machine learning algorithms, such as deep neural networks, may improve the effectiveness of selective interventions by accounting for complex interactions between large numbers of predictor variables. However, their use in psychological research is limited due to the tendency to overfit and the need for large volumes of training data. Deep transfer learning can overcome this limitation by leveraging samples of convenience to facilitate training deep neural networks in small, clinically relevant samples. The author trained deep neural networks on data from a sample of adolescent psychiatric inpatients to retrospectively classify individuals according to their history of alcohol misuse and nonsuicidal self-injury. Next, the performance of these models was compared to deep neural networks that were pretrained in a convenience sample of college undergraduates and fine-tuned in the sample of psychiatric patients. Deep transfer learning did not improve classification accuracy but buffered against overfitting. The deep neural networks that were not pretrained maintained maximum classification accuracy for a very small number of training epochs before performance deteriorated due to overfitting the training data. Conversely, the pretrained networks maintained their maximum classification accuracy across many training epochs and performance was not hindered by overfitting. This suggests that convenience samples can be utilized to reduce the risk of overfitting when training complex deep neural networks on small clinical samples. In the future, this process may be employed to facilitate powerful predictive models that inform selective prevention programs and contribute to the reduction of health risk behavior prevalence amongst vulnerable adolescent populations.
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    Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex
    (Colorado State University. Libraries, 2012) Peterson, Erik J., author; Seger, Carol A., advisor; Troup, Lucy J., committee member; Anderson, Charles W., committee member
    Lesion, drug, single-cell recording, as well as human fMRI studies, suggest dopaminergic projections from VTA/SNc (ventral tagmental area/substantia nigra pars compacta) and cortically driven striatal activity plays a key role in associating sensory events with rewarding actions both by facilitating reward processing and prediction (i.e. reinforcement learning) and biasing and later updating action selection. We, for the first time, isolated BOLD signal changes for stimulus, pre-response, response and feedback delivery at three reward levels. This design allowed us to estimate the degree of involvement of individual striatal regions across these trial components, the reward sensitivity of each component and allowed for a novel comparison of potential (and potentially competing) reinforcement learning computations. Striatal and lateral premotor cortex regions of interest (ROIs) significant activations were universally observed (excepting the ventral striatum) during stimulus presentation, pre-response, response and feedback delivery, confirming these areas importance in all aspects of visuomotor learning. The head of the caudate showed a precipitous drop in activity pre-response, while in the body of the caudate showed no significant changes in activity. The putamen peaked in activity during response. Activation in the lateral premotor cortex was strongest during stimulus presentation, but the drop off was followed by a trend of increasing activity as feedback approached. Both the head and body of the caudate as well as the putamen displayed reward-level sensitivity only during stimulus, while the ventral striatum showed reward sensitivity at both stimulus and feedback. The lack of reward sensitivity surrounding response is inconsistent with theories that the head and ventral striatum encode the value of actions. Which of the three examined reinforcement learning models correlated best with BOLD signal changes varied as a function of trial component and ROI suggesting these regions computations vary depending on task demand.
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    Three types of sensory gating: exploring interrelationships, individual differences, and implications
    (Colorado State University. Libraries, 2010) Yadon, Carly Ann, author; Davies, Patricia L., advisor; Nerger, Janice L., advisor; Anderson, Charles W., committee member; Cleary, Anne M., committee member
    The primary purpose of this dissertation was to determine how information is selectively processed in the brain through sensory gating mechanisms. Filtering, habituation, and orienting are three types of sensory gating that have never been investigated together in the same study. Although it has been well established that sensory gating is abnormal in many clinical groups, there remains a fundamental lack of understanding regarding the mechanisms of gating. For example, the functional significance of sensory gating, as well as how different types of sensory gating are related to basic brain processes and to each other, is poorly understood. Using an event-related potential (ERP) paradigm, I measured P50, N100, and P200 filtering, habituation, and orienting and administered a sequence of neuropsychological measures of attention to forty-two healthy adults. I found that filtering, orienting, and habituation and the three ERP components had different patterns of results, suggesting that the three paradigms measured distinct types of sensory gating and that gating is a multistage process. For all three types of sensory gating, higher-level attention tasks tended to predict gating responses better than lower-level attention tasks. This dissertation demonstrated that sensory gating has functional importance and these three gating paradigms seem to reflect different types of gating that should be explored in their own right.