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Description
Previous research indicates that difficulties in emotion regulation and greater dissociation from one's emotions are often observed among trauma survivors. Further, trauma survivors often show greater negative emotions such as anger, and diminished positive emotions such as happiness. Relatively less is known about the relationship between posttraumatic stress symptoms, dissociation, emotion regulation difficulties, and non-trauma related emotional experiences in daily life. This study examined whether greater reports of posttraumatic stress symptoms, difficulties in emotion regulation, and dissociative tendencies were associated with greater intensity of anger and lower intensity of happiness during a relived emotions task (i.e., recalling and describing autobiographical memories evoking specific emotions). Participants were 50 individuals who had experienced a traumatic event and reported a range of posttraumatic stress symptoms. Participants rated how they felt while recalling specific emotional memories, as well as how they remembered feeling at the time of the event. Results showed that dissociative tendencies was the best predictor of greater intensity of anger and, contrary to the hypothesis, dissociative tendencies was predictive of greater happiness intensity as well. These findings are consistent with previous research indicating a paradoxical effect of heightened anger reactivity among individuals with dissociative tendencies. In addition, researchers have argued that individuals with a history of traumatization do not report lower positive emotional experiences. The present findings may suggest the use of dissociation as a mechanism to avoid certain trauma related emotions (e.g, fear and anxiety), in turn creating heightened experiences of other emotions such as anger and happiness.
ContributorsTorres, Dhannia L (Author) / Robinson Kurpius, Sharon (Thesis advisor) / Roberts, Nicole A. (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
Numerous studies have examined the attachments individuals have to the places they visit, and that those attachments are formed through experiencing a place in person. This study is unique in that it examines pre-trip place attachment formation via the use of mobile technology and social media. It proposes that media experienced through the use of a participant's smartphone can foster the development of positive emotions, which in turn, facilitates greater mental imagery processing that ultimately influences pre-trip place attachment formation. An experimental design was constructed to examine how text and video on a destination's Facebook page influences an individual's emotions, mental imagery, and subsequently attachment to that destination. Specifically, a 2 (narrative text vs. descriptive text) x 2 (short, fast-paced video vs. long, slow-paced video) between-subjects design was used. A total of 343 usable participant responses were included in the analysis. The data was then analyzed through a two-step process using structural equation modeling. Results revealed no significant influence of textual or video media on emotions although the choice in text has a greater influence on emotions than choice in video. Additionally, emotions had a significant impact on mental imagery. Finally, mental imagery processing had a significant impact on only the social bonding dimension of place attachment. In conclusion, while media had no significant impact on emotions, the effect of previous traveler's retelling of personal accounts on the emotions of potential travelers researching a destination should be examined more closely. Further, the study participants had no prior experience with the destination, yet emotions influenced mental imagery, which also influenced social bonding. Thus further research should be conducted to better understand how potential traveler's image of a destination can be affected by the stories or others.
ContributorsPlunkett, Daniel (Author) / Budruk, Megha (Thesis advisor) / Lee, Woojin (Thesis advisor) / Wetmore, Jameson (Committee member) / Wise, Greg (Committee member) / Arizona State University (Publisher)
Created2013
Description
Previous studies have established a link between parenting style (e.g. authoritarian, authoritative, permissive) and depression in children and adolescents. Parenting factors are also implicated in the development of emotion regulation. There is a gap in the literature, however, concerning perceptions of parenting in relation to adult depression. The current study examined the effect of parenting on reported adult depressive symptoms. Of interest was the role of emotion regulation strategies in this relationship. Participants were recruited through Amazon Mechanical Turk, and the sample consisted of 302 adults (125 males, 177 females) ranging in age from 18 to 65. Measures of how participants were parented by their mothers and fathers, emotion regulation strategies most frequently utilized, and current depressive symptoms were collected using an online survey. The emotion regulation strategy, positive reappraisal, was found to moderate the relation between maternal authoritative parenting and depression. Permissive parenting was also significantly predictive of depression, but catastrophizing fully mediated only the relation between maternal permissive parenting and depressive symptoms. Authoritarian parenting was unrelated to depression and emotion regulation in this study. The findings of this study indicate that the effects of how an individual was parented may persist into adulthood. Implications of these findings and future directions for further research are discussed.
ContributorsHuisstede, Lauren van (Author) / Miller, Paul A. (Thesis advisor) / Tinsley, Barbara (Committee member) / Roberts, Nicole A. (Committee member) / Arizona State University (Publisher)
Created2013
Description
When discussing human factors and performance, researchers recognize stress as a factor, but overlook mood as contributing factor. To explore the relationship between mood, stress and cognitive performance, a field study was conducted involving fire fighters engaged in a fire response simulation. Firefighter participants completed a stress questionnaire, an emotional state questionnaire, and a cognitive task. Stress and cognitive task performance scores were examined before and after the firefighting simulation for individual cognitive performance depreciation caused by stress or mood. They study revealed that existing stress was a reliable predictor of the pre-simulation cognitive task score, that, as mood becomes more positive, perceived stress scores decrease, and that negative mood and pre-simulation stress are also positively and significantly correlated.
ContributorsGomez-Herbert, Maria Elena (Author) / Cooke, Nancy J. (Thesis advisor) / Becker, Vaughn (Committee member) / Branaghan, Russell (Committee member) / Hyunjin, Song (Committee member) / Arizona State University (Publisher)
Created2014
Description
Research in design, emotion, and product experience has focused on establishing a connection between the aesthetic qualities of products and emotions. Studies in product expression have demonstrated relevant patterns between aesthetics and spatial reasoning. In design research, fully understanding latent qualities of consumers assists in developing an immersive product experience which in turn can engender a lasting product relationship. This study evaluates how people interpret the emotionality of form in order to establish a veritable method for interpreting emotional variables in 3D objects.
This research assesses the emotional perception of aesthetic values in 2D and 3D teapots. A teapot image collection and taxonomy was constructed with 101 images of teapots across four centuries. Eighty-four participants completed a card sorting task of twenty randomly distributed teapot images (taken from the total 101 image collection) into Plutchik's eight emotion categories. Individual pieces of the teapots were coded according to the base, handle, lid, or spout that was presented in the image. The coded pieces from the card-sorting task were arranged per frequency in the overall set. Through the use of response data from the card sorting task, a network of the images was developed in Pathfinder. The content of these results were compared to images of models gathered during an interview with an interactive co-creation method referred to as Magnetic Modeling. Magnetic Modeling is a methodological tool that allowed participants to manipulate individualized pieces of 3D printed teapots into proposed emotional labels.
The findings of this research establish prototypical associations in aesthetic traits and teapot piece combinations for each emotion category. Participant responses were categorized into 4 personas representing the types of perceptual bias in the studies' participants. A discussion and comparison of the methods for academic and theoretical practice is provided.
This research assesses the emotional perception of aesthetic values in 2D and 3D teapots. A teapot image collection and taxonomy was constructed with 101 images of teapots across four centuries. Eighty-four participants completed a card sorting task of twenty randomly distributed teapot images (taken from the total 101 image collection) into Plutchik's eight emotion categories. Individual pieces of the teapots were coded according to the base, handle, lid, or spout that was presented in the image. The coded pieces from the card-sorting task were arranged per frequency in the overall set. Through the use of response data from the card sorting task, a network of the images was developed in Pathfinder. The content of these results were compared to images of models gathered during an interview with an interactive co-creation method referred to as Magnetic Modeling. Magnetic Modeling is a methodological tool that allowed participants to manipulate individualized pieces of 3D printed teapots into proposed emotional labels.
The findings of this research establish prototypical associations in aesthetic traits and teapot piece combinations for each emotion category. Participant responses were categorized into 4 personas representing the types of perceptual bias in the studies' participants. A discussion and comparison of the methods for academic and theoretical practice is provided.
ContributorsHorner, Candace (Author) / Takamura, John (Thesis advisor) / McDermott, Lauren (Committee member) / Branaghan, Russel; (Committee member) / Arizona State University (Publisher)
Created2014
Description
Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
ContributorsShah, Mohit (Author) / Spanias, Andreas (Thesis advisor) / Chakrabarti, Chaitali (Thesis advisor) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
Description
While discrete emotions like joy, anger, disgust etc. are quite popular, continuous
emotion dimensions like arousal and valence are gaining popularity within the research
community due to an increase in the availability of datasets annotated with these
emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle
and complex affect dimensions but are difficult to predict.
Dimension reduction techniques form the core of emotion recognition systems and
help create a new feature space that is more helpful in predicting emotions. But these
techniques do not necessarily guarantee a better predictive capability as most of them
are unsupervised, especially in regression learning. In emotion recognition literature,
supervised dimension reduction techniques have not been explored much and in this
work a solution is provided through probabilistic topic models. Topic models provide
a strong probabilistic framework to embed new learning paradigms and modalities.
In this thesis, the graphical structure of Latent Dirichlet Allocation has been explored
and new models tuned to emotion recognition and change detection have been built.
In this work, it has been shown that the double mixture structure of topic models
helps 1) to visualize feature patterns, and 2) to project features onto a topic simplex
that is more predictive of human emotions, when compared to popular techniques
like PCA and KernelPCA. Traditionally, topic models have been used on quantized
features but in this work, a continuous topic model called the Dirichlet Gaussian
Mixture model has been proposed. Evaluation of DGMM has shown that while modeling
videos, performance of LDA models can be replicated even without quantizing
the features. Until now, topic models have not been explored in a supervised context
of video analysis and thus a Regularized supervised topic model (RSLDA) that
models video and audio features is introduced. RSLDA learning algorithm performs
both dimension reduction and regularized linear regression simultaneously, and has outperformed supervised dimension reduction techniques like SPCA and Correlation
based feature selection algorithms. In a first of its kind, two new topic models, Adaptive
temporal topic model (ATTM) and SLDA for change detection (SLDACD) have
been developed for predicting concept drift in time series data. These models do not
assume independence of consecutive frames and outperform traditional topic models
in detecting local and global changes respectively.
emotion dimensions like arousal and valence are gaining popularity within the research
community due to an increase in the availability of datasets annotated with these
emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle
and complex affect dimensions but are difficult to predict.
Dimension reduction techniques form the core of emotion recognition systems and
help create a new feature space that is more helpful in predicting emotions. But these
techniques do not necessarily guarantee a better predictive capability as most of them
are unsupervised, especially in regression learning. In emotion recognition literature,
supervised dimension reduction techniques have not been explored much and in this
work a solution is provided through probabilistic topic models. Topic models provide
a strong probabilistic framework to embed new learning paradigms and modalities.
In this thesis, the graphical structure of Latent Dirichlet Allocation has been explored
and new models tuned to emotion recognition and change detection have been built.
In this work, it has been shown that the double mixture structure of topic models
helps 1) to visualize feature patterns, and 2) to project features onto a topic simplex
that is more predictive of human emotions, when compared to popular techniques
like PCA and KernelPCA. Traditionally, topic models have been used on quantized
features but in this work, a continuous topic model called the Dirichlet Gaussian
Mixture model has been proposed. Evaluation of DGMM has shown that while modeling
videos, performance of LDA models can be replicated even without quantizing
the features. Until now, topic models have not been explored in a supervised context
of video analysis and thus a Regularized supervised topic model (RSLDA) that
models video and audio features is introduced. RSLDA learning algorithm performs
both dimension reduction and regularized linear regression simultaneously, and has outperformed supervised dimension reduction techniques like SPCA and Correlation
based feature selection algorithms. In a first of its kind, two new topic models, Adaptive
temporal topic model (ATTM) and SLDA for change detection (SLDACD) have
been developed for predicting concept drift in time series data. These models do not
assume independence of consecutive frames and outperform traditional topic models
in detecting local and global changes respectively.
ContributorsLade, Prasanth (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Balasubramanian, Vineeth N (Committee member) / Arizona State University (Publisher)
Created2015
Description
ABSTRACT
What does it mean to feel an emotion? The nature of emotional
experience has often been described in terms overall conscious experience, termed affect. However, even within affective research there are multiple contradicting theories about the nature and structure of affect. I propose that these contradictions are due to methodological issues in the empirical research examining these underlying dimensions. Furthermore, I propose that subjective emotional experience should be examined separately from overall affect. The current study attempts to address past methodological issues by focusing solely on emotional experiences, developing a comprehensive list of emotion items, and including a broad range of emotional experiences. In Study 1, participants were asked to recall an emotional experience and then report their experience of 76 different emotions during that experience. A factor analysis of the emotion ratings revealed a 5-factor categorical structure with categories of Joy, Anger, Sadness, Fear, and Shame/Jealousy. In Study 2, the 76 emotion words from Study 1 were compared in a semantic space derived from a large collection of text samples in an attempt to compare to the results of Study 1. A semantic space derived from a broad range of texts would reflect relationships of emotional concepts. Study 2 revealed a 1-factor structure, drastically different from the structure in Study 1. The implications from Study 2, however, are limited because of the limited range of literature that was used to create the semantic space in which the words were compared. Overall, the results from these studies suggest that subjective emotional experience should be treated as categorical.
What does it mean to feel an emotion? The nature of emotional
experience has often been described in terms overall conscious experience, termed affect. However, even within affective research there are multiple contradicting theories about the nature and structure of affect. I propose that these contradictions are due to methodological issues in the empirical research examining these underlying dimensions. Furthermore, I propose that subjective emotional experience should be examined separately from overall affect. The current study attempts to address past methodological issues by focusing solely on emotional experiences, developing a comprehensive list of emotion items, and including a broad range of emotional experiences. In Study 1, participants were asked to recall an emotional experience and then report their experience of 76 different emotions during that experience. A factor analysis of the emotion ratings revealed a 5-factor categorical structure with categories of Joy, Anger, Sadness, Fear, and Shame/Jealousy. In Study 2, the 76 emotion words from Study 1 were compared in a semantic space derived from a large collection of text samples in an attempt to compare to the results of Study 1. A semantic space derived from a broad range of texts would reflect relationships of emotional concepts. Study 2 revealed a 1-factor structure, drastically different from the structure in Study 1. The implications from Study 2, however, are limited because of the limited range of literature that was used to create the semantic space in which the words were compared. Overall, the results from these studies suggest that subjective emotional experience should be treated as categorical.
ContributorsOsborne, Elizabeth (Author) / Shiota, Michelle N. (Thesis advisor) / Glenberg, Arthur (Committee member) / Neuberg, Steven (Committee member) / Becker, David V. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Do emotions help explain our behaviors? Can they condemn us, excuse us, orr mitigate our moral responsibility orr blameworthiness? Can they explain our rationality and irrationality, orr warrant such attributions? Can they be justified orr warranted? Are they constitutive aspects of our consciousness, identity, characters, virtues, orr epistemic status? The answer to these questions, at least to a significant extent, depends on what emotions are. This illustrates the importance of what emotions are to academics across multiple disciplines, as well as to members of governing bodies, organizations, communities, and groups. Given the great importance of emotions to various aspects of our lives, this dissertation is about the relevance of the topic of emotion as an area of study for the discipline of philosophy. This dissertation is also broadly about the need to bridge the interests, concerns, and collective bodies of knowledge between various distinct disciplines, thereby contributing to the process of unifying knowledge across the various disciplines within the realm of academia.
The primary aim in this dissertation is to initiate the unification of the interests, concerns, and collective bodies of knowledge across disciplines of academia. To do so, however, this dissertation aims to bridge some disciplinary divides between the disciplines of philosophy and psychology. I fulfill this aim by first demonstrating that interdisciplinary research and theorizing is needed within the disciplines of philosophy and psychology. I do this by considering how the problem of skepticism arises within these two disciplines. I also derive, propose, and argue for the acceptance of a new foundation for academic research and theorizing in response to the problem of skepticism. I refer to my proposal, in general, as The Proposal for Unification without Consilience (UC).
The primary aim in this dissertation is to initiate the unification of the interests, concerns, and collective bodies of knowledge across disciplines of academia. To do so, however, this dissertation aims to bridge some disciplinary divides between the disciplines of philosophy and psychology. I fulfill this aim by first demonstrating that interdisciplinary research and theorizing is needed within the disciplines of philosophy and psychology. I do this by considering how the problem of skepticism arises within these two disciplines. I also derive, propose, and argue for the acceptance of a new foundation for academic research and theorizing in response to the problem of skepticism. I refer to my proposal, in general, as The Proposal for Unification without Consilience (UC).
ContributorsMun, Cecilea (Author) / Calhoun, Cheshire (Thesis advisor) / Kobes, Bernard (Committee member) / Shiota, Michelle (Committee member) / Arizona State University (Publisher)
Created2014
Description
This study explores the psychophysical and neural processes associated with the perception of sounds as either pleasant or aversive. The underlying psychophysical theory is based on auditory scene analysis, the process through which listeners parse auditory signals into individual acoustic sources. The first experiment tests and confirms that a self-rated pleasantness continuum reliably exists for 20 various stimuli (r = .48). In addition, the pleasantness continuum correlated with the physical acoustic characteristics of consonance/dissonance (r = .78), which can facilitate auditory parsing processes. The second experiment uses an fMRI block design to test blood oxygen level dependent (BOLD) changes elicited by a subset of 5 exemplar stimuli chosen from Experiment 1 that are evenly distributed over the pleasantness continuum. Specifically, it tests and confirms that the pleasantness continuum produces systematic changes in brain activity for unpleasant acoustic stimuli beyond what occurs with pleasant auditory stimuli. Results revealed that the combination of two positively and two negatively valenced experimental sounds compared to one neutral baseline control elicited BOLD increases in the primary auditory cortex, specifically the bilateral superior temporal gyrus, and left dorsomedial prefrontal cortex; the latter being consistent with a frontal decision-making process common in identification tasks. The negatively-valenced stimuli yielded additional BOLD increases in the left insula, which typically indicates processing of visceral emotions. The positively-valenced stimuli did not yield any significant BOLD activation, consistent with consonant, harmonic stimuli being the prototypical acoustic pattern of auditory objects that is optimal for auditory scene analysis. Both the psychophysical findings of Experiment 1 and the neural processing findings of Experiment 2 support that consonance is an important dimension of sound that is processed in a manner that aids auditory parsing and functional representation of acoustic objects and was found to be a principal feature of pleasing auditory stimuli.
ContributorsPatten, Kristopher Jakob (Author) / Mcbeath, Michael K (Thesis advisor) / Baxter, Leslie C (Committee member) / Amazeen, Eric L (Committee member) / Dorman, Michael F. (Committee member) / Arizona State University (Publisher)
Created2014