The Role of Passage Topic Knowledge in Typical and Poor Comprehenders' Recall

This dissertation examines the role of topic knowledge (TK) in comprehension among typical readers and those with Specifically Poor Comprehension (SPC), i.e., those who demonstrate deficits in understanding what they read despite adequate decoding. Previous studies of poor comprehension have focused on weaknesses in specific skills, such as word decoding and inferencing ability, but this dissertation examined a different factor: whether deficits in availability and use of TK underlie poor comprehension. It is well known that TK tends to facilitate comprehension among typical readers, but its interaction with working memory and word decoding is unclear, particularly among participants with deficits in these skills. Across several passages, we found that SPCs do in fact have less TK to assist their interpretation of a text. However, we found no evidence that deficits in working memory or word decoding ability make it difficult for children to benefit from their TK when they have it. Instead, children across the skill spectrum are able to draw upon TK to assist their interpretation of a passage. Because TK is difficult to assess and studies vary in methodology, another goal of this dissertation was to compare two methods for measuring it. Both approaches score responses to a concept question to assess TK, but in the first, a human rater assigns a score whereas in the second, a computer algorithm, Latent Semantic Analysis (LSA; Landauer & Dumais, 1997) assigns a score. We found similar results across both methods of assessing TK, suggesting that a continuous measure is not appreciably more sensitive to variations in knowledge than discrete human ratings. This study contributes to our understanding of how best to measure TK, the factors that moderate its relationship with recall, and its role in poor comprehension. The findings suggest that teaching practices that focus on expanding TK are likely to improve comprehension across readers with a variety of abilities.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.