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Description
Frequency effects favoring high print-frequency words have been observed in frequency judgment memory tasks. Healthy young adults performed frequency judgment tasks; one group performed a single task while another group did the same task while alternating their attention to a secondary task (mathematical equations). Performance was assessed by correct and error responses, reaction times, and accuracy. Accuracy and reaction times were analyzed in terms of memory load (task condition), number of repetitions, effect of high vs. low print-frequency, and correlations with working memory span. Multinomial tree analyses were also completed to investigate source vs. item memory and revealed a mirror effect in episodic memory experiments (source memory), but a frequency advantage in span tasks (item memory). Interestingly enough, we did not observe an advantage for high working memory span individuals in frequency judgments, even when participants split their attention during the dual task (similar to a complex span task). However, we concluded that both the amount of attentional resources allocated and prior experience with an item affect how it is stored in memory.
ContributorsPeterson, Megan Paige (Author) / Azuma, Tamiko (Thesis advisor) / Gray, Shelley (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2013
Description
Military veterans have a significantly higher incidence of mild traumatic brain injury (mTBI), depression, and Post-traumatic stress disorder (PTSD) compared to civilians. Military veterans also represent a rapidly growing subgroup of college students, due in part to the robust and financially incentivizing educational benefits under the Post-9/11 GI Bill. The overlapping cognitively impacting symptoms of service-related conditions combined with the underreporting of mTBI and psychiatric-related conditions, make accurate assessment of cognitive performance in military veterans challenging. Recent research findings provide conflicting information on cognitive performance patterns in military veterans. The purpose of this study was to determine whether service-related conditions and self-assessments predict performance on complex working memory and executive function tasks for military veteran college students. Sixty-one military veteran college students attending classes at Arizona State University campuses completed clinical neuropsychological tasks and experimental working memory and executive function tasks. The results revealed that a history of mTBI significantly predicted poorer performance in the areas of verbal working memory and decision-making. Depression significantly predicted poorer performance in executive function related to serial updating. In contrast, the commonly used clinical neuropsychological tasks were not sensitive service-related conditions including mTBI, PTSD, and depression. The differing performance patterns observed between the clinical tasks and the more complex experimental tasks support that researchers and clinicians should use tests that sufficiently tax verbal working memory and executive function when evaluating the subtle, higher-order cognitive deficits associated with mTBI and depression.
ContributorsGallagher, Karen Louise (Author) / Azuma, Tamiko (Thesis advisor) / Liss, Julie (Committee member) / Lavoie, Michael (Committee member) / Arizona State University (Publisher)
Created2017
Description
Speech analysis for clinical applications has emerged as a burgeoning field, providing valuable insights into an individual's physical and physiological state. Researchers have explored speech features for clinical applications, such as diagnosing, predicting, and monitoring various pathologies. Before presenting the new deep learning frameworks, this thesis introduces a study on conventional acoustic feature changes in subjects with post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI). This work demonstrates the effectiveness of using speech signals to assess the pathological status of individuals. At the same time, it highlights some of the limitations of conventional acoustic and linguistic features, such as low repeatability and generalizability. Two critical characteristics of speech features are (1) good robustness, as speech features need to generalize across different corpora, and (2) high repeatability, as speech features need to be invariant to all confounding factors except the pathological state of targets. This thesis presents two research thrusts in the context of speech signals in clinical applications that focus on improving the robustness and repeatability of speech features, respectively. The first thrust introduces a deep learning framework to generate acoustic feature embeddings sensitive to vocal quality and robust across different corpora. A contrastive loss combined with a classification loss is used to train the model jointly, and data-warping techniques are employed to improve the robustness of embeddings. Empirical results demonstrate that the proposed method achieves high in-corpus and cross-corpus classification accuracy and generates good embeddings sensitive to voice quality and robust across different corpora. The second thrust introduces using the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. A novel regularizer, the ICC regularizer, is proposed to regularize deep neural networks to produce embeddings with higher repeatability. This ICC regularizer is implemented and applied to three speech applications: a clinical application, speaker verification, and voice style conversion. The experimental results reveal that the ICC regularizer improves the repeatability of learned embeddings compared to the contrastive loss, leading to enhanced performance in downstream tasks.
ContributorsZhang, Jianwei (Author) / Jayasuriya, Suren (Thesis advisor) / Berisha, Visar (Thesis advisor) / Liss, Julie (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2023