Thesis (M.S.)--Brigham Young University. Dept. of Audiology and Speech-Language Pathology, 2006. / Includes bibliographical references (p. 51-56).

Thesis (Ph. D.)--State University of New York at Binghamton, Psychology Department, 2005. / Includes bibliographical references.

Baddeley and Hitch (Baddeley & Hitch, 1974) have described a model of working memory which explains how information can be temporarily held `on-line' in order to carry out everyday cognitive tasks such as learning, reasoning, and comprehension. One component of this model, the visuospatial sketchpad (VSSP), has not been as well described as other components of the model and there is some debate over its structure. Furthermore, the everyday cognitive importance of the VSSP has not been well researched. A battery of visual and spatial measures was developed to investigate the structure of the VSSP and its potential role in reading. A principal component analysis on a group of normal, undergraduate participants did not reveal the expected dissociation of visual from spatial processing. However, a dissociation was found in a group of dyslexic individuals. A series of multiple regressions revealed that while neither visual nor spatial processing reliably contributed to reading ability in normals, spatial processing contributed to reading fluency in the dyslexic sample. These findings suggest that while shared variance techniques fail to reveal the visual vs. spatial dissociation in working memory in normals, the dissociation can be revealed by clinical samples. In general, it appears that the ability to maintain visual vs. spatial information in working memory requires distinct cognitive processes. Furthermore, there is a relationship between VSSP processing and reading. This study has opened many doors for future research on the structure of the model and its importance for reading.

Our senses, while limited, shape our perception of the world and contribute to the functional architecture of the brain. This dissertation investigates the role of sensory modality and task demands in the cortical organization of healthy human adults using functional magnetic resonance imaging (fMRI). This research provides evidence for sensory modality bias in frontal cortical regions by directly contrasting auditory and visual sustained attention. This contrast revealed two distinct visual-biased regions in lateral frontal cortex - superior and inferior precentral sulcus (sPCS, iPCS) - anatomically interleaved with two auditory-biased regions - transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic (resting-state) functional connectivity analysis demonstrated that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Unisensory (auditory or visual) short-term memory (STM) tasks assessed the flexible recruitment of these sensory-biased cortical regions by varying information domain demands (e.g., spatial, temporal). While both modalities provide spatial and temporal information, vision has greater spatial resolution than audition, and audition has excellent temporal precision relative to vision. A visual temporal, but not a spatial, STM task flexibly recruited frontal auditory-biased regions; conversely, an auditory spatial task more strongly recruited frontal visual-biased regions compared to an auditory temporal task. This flexible recruitment extended to an auditory-biased superior temporal lobe region and to a subset of visual-biased parietal regions. A demanding auditory spatial STM task recruited anterior/superior visuotopic maps (IPS2-4, SPL1) along the intraparietal sulcus, but neither spatial nor temporal auditory tasks recruited posterior/interior maps. Finally, a comparison of visual spatial attention and STM under varied cognitive load demands attempted to further elucidate the organization of posterior parietal cortex. Parietal visuotopic maps were recruited for both visual spatial attention and working memory but demonstrated a graded response to task demands. Posterior/inferior maps (IPS0-1) demonstrated a linear relationship with the number of items attended to or remembered in the visual spatial tasks. Anterior/superior maps (IPS2-4, SPL1) demonstrated a general recruitment in visual spatial cognitive tasks, with a stronger response for visual spatial attention compared to STM.

Detta arbete handlar om ifall perceptionen av ljud i stadsmiljöer kan påverka uppfattningen av instruktioner och minne. I den litterära bakgrunden presenteras vad ett ljud är, vad skillnaden är mellan Hi-fi och Lo-fi ljudlandskap samt en generell överblick om vad noise är och hur människans minne fungerar. Frågeställningen som undersöktes var om perceptionen av olika ljud i en stadsmiljö kan påverka uppfattningen av instruktioner. För att kunna svara på frågeställningen skapades tre olika ljudsekvenser, där två av sekvenserna innehåller ljuden som kan påverka instruktionerna och en är kontrollsekvensen för att se om instruktionerna är lätta att följa. Den urvalsgrupp som deltog fick lyssna och utföra olika instruktioner samt svara på kvalitativa frågor. Detta användes sedan för nå en slutsats. Vid ett framtida arbete och fördjupning av undersökningen kan det leda till att information och ljud i spel inte går förlorade i mängden av andra ljud, perspektivet av dem finns kvar.

The purpose of this study is to examine the effects of bilingualism in short-term memory (STM) compared to monolingualism with individuals who have Down syndrome. Five tasks were used for STM skills comparison between monolingual and bilingual participants. Sixteen participants between the ages of 13 to 37 were included in this study. Participants were divided based on their language groups. The experimental tasks consisted of non-verbal activities to examine visual (RVDLT) and spatial (Corsi) STM; as well as three verbal STM tasks (RAVLT, WMS, and Digits). The results showed that bilinguals acquired higher overall correct responses, with a significant difference found in visual STM performance and a trend towards significance in verbal logical STM. Thus, the findings of this study support the bilingual advantage theory. This study will enhance understanding in memory capacity of bilingual individuals with Down syndrome for potential implications to put into practice in clinical intervention strategies.

Cette thèse a pour objet l’étude du langage intérieur dans la flexibilité mentale et la mémoire à court terme à l’aide de l’électromyographie de surface, une méthode qui a permis d’enregistrer l’activité électrique des muscles laryngés. Pour étudier le rôle du langage intérieur dans la flexibilité, six expériences ont été réalisées en manipulant la complexité des tâches, ainsi que l’âge des participants. Les résultats montrent une sollicitation plus importante du langage avec la complexité des tâches, que ce soit pour encoder les règles en mémoire ou pour classer les stimuli, et ce d’autant plus que les participants sont âgés. Nous discutons ces résultats en rapport avec les études utilisant la méthode de suppression articulatoire. Concernant le rôle du langage intérieur dans la mémoire à court terme, deux expériences ont été réalisées avec pour objectif principal l’étude de l’effet de similarité phonologique. Les résultats montrent une quantité de langage plus importante pour les lettres dissimilaires, qui ont aussi été mieux restituées par les participants. A l’inverse, nous avons observé un meilleur rappel de l’ensemble des lettres pour les participants sollicitant de façon plus modérée le langage intérieur. Ces résultats sont mis en rapport avec les processus d’interférences motrice et auditive qui gouvernent la mémorisation à court terme. / In this thesis, we examined the role of inner speech in mental flexibility and short-term memory using laryngeal surface electromyography, a method for recording inner speech signals. Six experiments manipulating tasks complexity and age of participants were performed to study mental flexibility. Results showed a greater use of inner speech with tasks complexity, either when encoding the rules in memory or while sorting the stimuli, and especially when the participants were older. We discuss the results with reference to articulatory suppression, an alternative method for studying inner speech. To study inner speech in short-term memory, two other experiments were performed with the main objective of studying the phonological similarity effect. Results showed a greater amount of language for dissimilar letters which were also better recalled by participants. On the contrary, we observed a better recall of letters (being similar or dissimilar) by participants who resorted to language moderately. These results suggest that both motor and auditory types of interferences can affect short-term memorization.

Due to the rapidly increasing prominence of Artificial Intelligence in the last decade and the advancements in technology such as processing power and data storage, there has been increased interest in applying machine learning algorithms to time series prediction problems. There are many machine learning algorithms that can be used for time series prediction problems but selecting an algorithm can be challenging due to algorithms not being suitable to all types of datasets. This research investigates and evaluates machine learning algorithms that can be used for time series prediction. Experiments were carried out using the Artificial Neural Network (ANN), Support Vector Regressor (SVR) and Long Short-Term Memory (LSTM) algorithms on eight datasets. An empirical analysis was carried out by applying each machine learning algorithm to the selected datasets. A critical comparison of the algorithm performance was carried out using the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE) and the Mean Absolute Scaled Error (MASE). The second experiment focused on evaluating the stability and robustness of the optimal models identified in the first experiment. The key dataset characteristics identified; were the dataset size, stationarity, trend and seasonality. It was found that the LSTM performed the best for majority of the datasets, due to the algorithm's ability to deal with sequential dependency. The performance of the ANN and SVR were similar for datasets with trend and seasonality, while the LSTM overall proved superior to the aforementioned algorithms. The LSTM outperformed the ANN and SVR due to its ability to handle temporal dependency. However, due to its stochastic nature, the LSTM and ANN algorithms can have poor stability and robustness. In this regard, the LSTM was found to be a more robust algorithm than the ANN and SVR.

Classifying emails into distinct labels can have a great impact on customer support. By using machine learning to label emails the system can set up queues containing emails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve the manually defined rule based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher F1-score and classification rate. Integrating or migrating from a manually defined rule based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct five experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings and how they work together. In this article a web based interface were implemented which can classify emails into 33 different labels with 0.91 F1-score using a Long Short Term Memory network. The authors conclude that Long Short Term Memory networks outperform other non-sequential models such as Support Vector Machines and ADABoost when predicting labels for emails.

In this paper, we detect the occurrence of epileptic seizures in patients as well as activities namely stand, walk, and exercise in healthy persons, leveraging EEG (electroencephalogram) signals. Using Hilbert vibration decomposition (HVD) on non-linear and non-stationary EEG signal, we obtain multiple monocomponents varying in terms of amplitude and frequency. After decomposition, we extract features from the monocomponent matrix of the EEG signals. The instantaneous amplitude of the HVD monocomponents varies because of the motion artifacts present in EEG signals. Hence, the acquired statistical features from the instantaneous amplitude help in identifying the epileptic seizures and the normal human activities. The features selected by correlation-based Q-score are classified using an LSTM (Long Short Term Memory) based deep learning model in which the feature-based weight update maximizes the classification accuracy. For epilepsy diagnosis using the Bonn dataset and activity recognition leveraging our Sensor Networks Research Lab (SNRL) data, we achieve testing classification accuracies of 96.00% and 83.30% respectively through our proposed method.