Current Search: Emotion Recognition (x)
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(1 - 3 of 3)
- Title
- The Perceptual and Decisional Basis of Emotion Identification in Creative Writing.
- Creator
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Williams, Sarah, Bohil, Corey, Hancock, Peter, Smither, Janan, Johnson, Dan, University of Central Florida
- Abstract / Description
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The goal of this research was to assess the ability of readers to determine the emotion of a passage of text, be it fictional or non-fictional. The research includes examining how genre (fiction and non-fiction) and emotion (positive emotion, such as happiness, and negative emotion, such as anger) interact to form a reading experience. Reading is an activity that many, if not most, humans undertake in either a professional or leisure capacity. Researchers are thus interested in the effect...
Show moreThe goal of this research was to assess the ability of readers to determine the emotion of a passage of text, be it fictional or non-fictional. The research includes examining how genre (fiction and non-fiction) and emotion (positive emotion, such as happiness, and negative emotion, such as anger) interact to form a reading experience. Reading is an activity that many, if not most, humans undertake in either a professional or leisure capacity. Researchers are thus interested in the effect reading has on the individual, particularly with regards to empathy. Some researchers believe reading fosters empathy; others think empathy might already be present in those who enjoy reading. A greater understanding of this dispute could be provided by general recognition theory (GRT). GRT allows researchers to investigate how stimulus dimensions interact in an observer's mind: on a perceptual or decisional level. In the context of reading, this allows researchers to look at how emotion is tied in with (or inseparable from) genre, or if the ability to determine the emotion of a passage is independent from the genre of the passage. In the reported studies, participants read passages and responded to questions on the passages and their content. Empathy scores significantly predicted discriminability of passage categories, as did reported hours spent reading per week. Non-fiction passages were easier to identify than fiction, and positive emotion classification was affiliated with non-fiction classification.
Show less - Date Issued
- 2019
- Identifier
- CFE0007877, ucf:52760
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007877
- Title
- ADAPTIVE INTELLIGENT USER INTERFACES WITH EMOTION RECOGNITION.
- Creator
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NASOZ, FATMA, Christine Lisetti, Dr L., University of Central Florida
- Abstract / Description
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The focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users' affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model created for each user. Controlled experiments were designed and conducted in a laboratory environment and in a Virtual Reality environment to collect...
Show moreThe focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users' affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model created for each user. Controlled experiments were designed and conducted in a laboratory environment and in a Virtual Reality environment to collect physiological data signals from participants experiencing specific emotions. Algorithms (k-Nearest Neighbor [KNN], Discriminant Function Analysis [DFA], Marquardt-Backpropagation [MBP], and Resilient Backpropagation [RBP]) were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. Emotion Elicitation with Movie Clips Experiment was conducted to elicit Sadness, Anger, Surprise, Fear, Frustration, and Amusement from participants. Overall, the three algorithms: KNN, DFA, and MBP, could recognize emotions with 72.3%, 75.0%, and 84.1% accuracy, respectively. Driving Simulator experiment was conducted to elicit driving-related emotions and states (panic/fear, frustration/anger, and boredom/sleepiness). The KNN, MBP and RBP Algorithms were used to classify the physiological signals by corresponding emotions. Overall, KNN could classify these three emotions with 66.3%, MBP could classify them with 76.7% and RBP could classify them with 91.9% accuracy. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users' negative emotional states in order to decrease the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the User Model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as: personality traits and preferences.
Show less - Date Issued
- 2004
- Identifier
- CFE0000126, ucf:46201
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000126
- Title
- Visual Scanpath Training for Facial Affect Recognition in a Psychiatric Sample.
- Creator
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Chan, Chi, Bedwell, Jeffrey, Cassisi, Jeffrey, Sims, Valerie, University of Central Florida
- Abstract / Description
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Social cognition is essential for functional outcome and quality of life in psychiatric patients. Facial affect recognition (FAR), a domain of social cognition, is impaired in many patients with schizophrenia and bipolar disorder. There is evidence that abnormal visual scanpath patterns may underlie FAR deficits, and metacognitive factors may impact task performance. The present study aimed to develop a brief, individually-administered, computerized training program to normalize scanpath...
Show moreSocial cognition is essential for functional outcome and quality of life in psychiatric patients. Facial affect recognition (FAR), a domain of social cognition, is impaired in many patients with schizophrenia and bipolar disorder. There is evidence that abnormal visual scanpath patterns may underlie FAR deficits, and metacognitive factors may impact task performance. The present study aimed to develop a brief, individually-administered, computerized training program to normalize scanpath patterns in order to improve FAR in patient with a psychosis history or bipolar I disorder. The program was developed using scanpath data from 19 nonpsychiatric controls (NC) while they completed a FAR tasks that involved identification of mild or extreme intensity happy, sad, angry, and fearful faces, and a neutral expression. Patients were randomized to a waitlist (WG; n = 16) or training group (TG; n = 18). Both patient groups completed a baseline FAR task (T0), the training (or a repeated FAR task as a control for WG; T1), and a post-training FAR task (T2). Patients evaluated their own performance and eyetracking data were recorded. Results indicated that the patient groups did not differ from NC on FAR performance, metacognitive accuracy, or scanpath patterns at T0. TG was compliant with the training program and showed changes in scanpath patterns during T1, but returned to baseline scanpath patterns at T2. WG and TG did not differ at T2 on FAR performance, metacognitive accuracy, or scanpath patterns. Across both patient groups, FAR performance for mild intensity emotions were more sensitive to the effect of time than for extreme intensity emotions. Exploratory analysis showed that at baseline, greater severity of negative symptoms was associated with poorer metacognitive accuracy (i.e., accuracy in their evaluation of their performance). Limitations to the study and future directions are discussed.
Show less - Date Issued
- 2016
- Identifier
- CFE0006280, ucf:51613
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006280
