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Multiscale interactions in psychological systems

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For many years now, researchers have documented evidence of fractal scaling in psychological time series. Explanations of fractal scaling have come from many sources but those that have gained the most traction in the literature are theories that suggest fractal scaling originates from the interactions among the multiple scales…

For many years now, researchers have documented evidence of fractal scaling in psychological time series. Explanations of fractal scaling have come from many sources but those that have gained the most traction in the literature are theories that suggest fractal scaling originates from the interactions among the multiple scales that make up behavior. Those theories, originating in the study of dynamical systems, suffer from the limitation that fractal analysis reveals only indirect evidence of multiscale interactions. Multiscale interactions must be demonstrated directly because there are many means to generate fractal properties. In two experiments, participants performed a pursuit tracking task while I recorded multiple behavioral and physiological time series. A new analytical technique, multiscale lagged regression, was introduced to capture how those many psychological time series coordinate across multiple scales and time. The results were surprising in that coordination among psychological time series tends to be oscillatory in nature, even when the series are not oscillatory themselves. Those and other results demonstrate the existence of multiscale interactions in psychological systems.
ContributorsLikens, Aaron D (Author) / Amazeen, Polemnia G (Thesis advisor) / Amazeen, Eric L (Committee member) / Cooke, Nancy L (Committee member) / Glenberg, Arthur M. (Committee member) / Arizona State University (Publisher)
Created2016
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Leveraging metadata for extracting robust multi-variate temporal features

Description
In recent years, there are increasing numbers of applications that use multi-variate time series data where multiple uni-variate time series coexist. However, there is a lack of systematic of multi-variate time series. This thesis focuses on (a) defining a simplified inter-related multi-variate time series (IMTS) model and (b) developing robust…

In recent years, there are increasing numbers of applications that use multi-variate time series data where multiple uni-variate time series coexist. However, there is a lack of systematic of multi-variate time series. This thesis focuses on (a) defining a simplified inter-related multi-variate time series (IMTS) model and (b) developing robust multi-variate temporal (RMT) feature extraction algorithm that can be used for locating, filtering, and describing salient features in multi-variate time series data sets. The proposed RMT feature can also be used for supporting multiple analysis tasks, such as visualization, segmentation, and searching / retrieving based on multi-variate time series similarities. Experiments confirm that the proposed feature extraction algorithm is highly efficient and effective in identifying robust multi-scale temporal features of multi-variate time series.
ContributorsWang, Xiaolan (Author) / Candan, Kasim Selcuk (Thesis advisor) / Sapino, Maria Luisa (Committee member) / Fainekos, Georgios (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013