Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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The Impact of Blocked vs. Distributed Category Learning on Subsequent Generalization

Description
It is a well-established finding in memory research that spacing or distributing information, as opposed to blocking all the information together, results in an enhanced memory of the learned material. Recently, researchers have decided to investigate if this spacing effect is also beneficial in category learning. In a set of…

It is a well-established finding in memory research that spacing or distributing information, as opposed to blocking all the information together, results in an enhanced memory of the learned material. Recently, researchers have decided to investigate if this spacing effect is also beneficial in category learning. In a set of experiments, Carvalho & Goldstone (2013), demonstrated that a blocked presentation showed an advantage during learning, but that ultimately, the distributed presentation yielded better performance during a post-learning transfer test. However, we have identified a major methodological issue in this study that we believe contaminates the results in a way that leads to an inflation and misrepresentation of learning levels. The present study aimed to correct this issue and re-examine whether a blocked or distributed presentation enhances the learning and subsequent generalization of categories. We also introduced two shaping variables, category size and distortion level at transfer, in addition to the mode of presentation (blocked versus distributed). Results showed no significant differences of mode of presentation at either the learning or transfer phases, thus supporting our concern about the previous study. Additional findings showed benefits in learning categories with a greater category size, as well as higher classification accuracy of novel stimuli at lower-distortion levels.
ContributorsJacoby, Victoria Leigh (Author) / Homa, Donald (Thesis director) / Brewer, Gene (Committee member) / Davis, Mary (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
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A Comparison of Hand and Computer Coding of Expressions of Emotion

Description
Hand-coding systems of measuring facial expressions were developed to study and analyze human emotions, but they are time-intensive and thus seldom used. As technology has advanced, new computer software programs, such as Affectiva, were developed to code facial expressions automatically using artificial intelligence and machine learning. Since this technology is…

Hand-coding systems of measuring facial expressions were developed to study and analyze human emotions, but they are time-intensive and thus seldom used. As technology has advanced, new computer software programs, such as Affectiva, were developed to code facial expressions automatically using artificial intelligence and machine learning. Since this technology is still new, Affectiva and its validity remain understudied, and no psychological research has been conducted to compare Affectiva computer coding and hand coding of children’s emotions. The purpose of this study was to compare hand and computer coding of children’s expressions of emotion during a videotaped parent-child interaction. The study answered the following questions: 1) Do hand and computer coding agree?; and 2) Are hand and computer coding in higher agreement for some emotions than others? The sample included 25 pairs of twins from the Arizona Twin Project. Facial expressions were coded from videotape by a trained and reliable human coder and using the software Affectiva. The results showed that hand and computer coded emotion were in agreement for positive, but not negative emotions. Changing the context of the interaction to elicit more negative emotion, and using the same indicators of each emotion in computer and hand coding are suggested to improve the comparison of computer and hand coding.
ContributorsKwok, Connie (Author) / Lemery-Chalfant, Kathryn (Thesis director) / Davis, Mary (Committee member) / Miadich, Samantha (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05