21 research outputs found
Meta-KANSEI modeling with Valence-Arousal fMRI dataset of brain
Background: Traditional KANSEI methodology is an important tool in the field of psychology to comprehend the concepts and meanings; it mainly focusses on semantic differential methods. Valence-Arousal is regarded as a reflection of the KANSEI adjectives, which is the core concept in the theory of effective dimensions for brain recognition. From previous studies, it has been found that brain fMRI datasets can contain significant information related to Valence and Arousal. Methods: In this current work, a Valence-Arousal based meta-KANSEI modeling method is proposed to improve the traditional KANSEI presentation. Functional Magnetic Resonance Imaging (fMRI) was used to acquire the response dataset of Valence-Arousal of the brain in the amygdala and orbital frontal cortex respectively. In order to validate the feasibility of the proposed modeling method, the dataset was processed under dimension reduction by using Kernel Density Estimation (KDE) based segmentation and Mean Shift (MS) clustering. Furthermore, Affective Norm English Words (ANEW) by IAPS (International Affective Picture System) were used for comparison and analysis. The data sets from fMRI and ANEW under four KANSEI adjectives of angry, happy, sad and pleasant were processed by the Fuzzy C-Means (FCM) algorithm. Finally, a defined distance based on similarity computing was adopted for these two data sets. Results: The results illustrate that the proposed model is feasible and has better stability per the normal distribution plotting of the distance. The effectiveness of the experimental methods proposed in the current work was higher than in the literature. Conclusions: mean shift can be used to cluster and central points based meta-KANSEI model combining with the advantages of a variety of existing intelligent processing methods are expected to shift the KANSEI Engineering (KE) research into the medical imaging field
KEER2022
AvanttÃtol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202
Digitizing arquetypal human expereience through physiological signals
The problem of capturing human experience is relevant in many application domains. In fact, the process of describing and sharing individual experience lies at the heart of human culture. This advancement came at a price of losing some of the multidimensional aspects of primary, bodily experience during its projection into the symbolic formThroughout the courses of our lives we learn a great deal of information about the world from other people's experience. Besides the ability to share utilitarian experience such as whether a particular plant is poisonous, humans have developed a sophisticated competency of social signaling that enables us to express and decode emotional experience. The natural way of sharing emotional experiences requires those who share to be co-present during this event. However, people have overcome the limitation of physical presence by creating a symbolic system of representations.Recent research in the field of affective computing has addressed the question of digitization and transmission of emotional experience through monitoring and interpretation of physiological signals. Although the outcomes of this research represent a great step forward in developing a technology that supports sharing of emotional experiences, they do not seem to help in preserving the original phenomenological experience during the aforementioned projection. This circumstance is explained by the fact that in affective computing the focus of investigation has been aimed at emotional experiences which can be consciously evaluated and described by individuals themselves. Therefore, generally speaking, applying an affective computing technique for capturing emotions of an individual is not a deeper or more precise way to project her experience into the symbolic form than asking this person to write down a description of her emotions on a piece of paper. One can say that so far the research in affective computing has aimed at delivering technology that could automate the projection but it has not considered the problem of improving the projection in order to preserve more of the multidimensional aspects of human experience.This dissertation examines whether human experience, which individuals are not able to consciously transpose into the symbolic representation, can still be captured using the techniques of affective computing.First, a theoretical framework for description of human experience which is not accessible for conscious awareness was formulated. This framework was based on the work of Carl Jung who introduced a model of a psyche that includes three levels: consciousness, the personal unconscious and the collective unconscious. Consciousness is the external layer of the psyche that consists of those thoughts and emotions which are available for one¿s conscious recollection. The personal unconscious represents a repository for all of an individual¿s feelings, memories, knowledge and thoughts that are not conscious at a given moment of time.The collective unconscious is a repository of universal modes and behaviors that are similar in all individuals. According to Jung, the collective unconscious is populated with archetypes. Archetypes are prototypical categories of objects, people and situations that existed across evolutionary time and in different cultures.Esta tesis doctoral examina si la experiencia humana, que los individuos no pueden transponer conscientemente a la representación simbólica, aún puede capturarse utilizando las técnicas de computación afectiva. Primero, se formula un marco teórico para la descripción de la experiencia humana que no es accesible para la conciencia consciente. Este marco se basó en el trabajo de Carl Jung, quien introdujo un modelo de psique que incluye tres niveles: la conciencia, el inconsciente personal y el inconsciente colectivo. Habiendo definido nuestro marco teórico, realizamos un experimento en el que se mostraron a los sujetos estÃmulos visuales y auditivos de bases de datos estandarizadas para la obtención de emociones conscientes. Aparte de los estÃmulos para las emociones conscientes, los sujetos fueron expuestos a estÃmulos que representaban el arquetipo del yo. Durante la presentación de los estÃmulos cardiovasculares se registraron las señales de los sujetos. Los resultados experimentales indicaron que las respuestas de la frecuencia cardÃaca de los participantes fueron únicas para cada categorÃa de estÃmulos, incluido el arquetÃpico. Estos hallazgos dieron impulso a realizar otro estudio en el que se examinó un espectro más amplio de experiencias arquetÃpicas. En nuestro segundo estudio, hicimos un cambio de estÃmulos visuales y auditivos a estÃmulos audiovisuales porque se esperaba que los videos fueran más eficientes en la obtención de emociones conscientes y experiencias arquetÃpicas que las imágenes fijas o los sonidos. La cantidad de arquetipos aumentó y los sujetos en general fueron estimulados a sentir ocho experiencias arquetÃpicas diferentes. También preparamos estÃmulos para emociones conscientes. En este experimento, las señales fisiológicas incluyeron actividades cardiovasculares, electrodérmicas, respiratorias y temperatura de la piel. El análisis estadÃstico sugirió que las experiencias arquetÃpicas podrÃan diferenciarse en función de las activaciones fisiológicas. Además, se construyeron varios modelos de predicción basados en los datos fisiológicos recopilados. Estos modelos demostraron la capacidad de clasificar los arquetipos con una precisión que era considerablemente más alta que el nivel de probabilidad. Como los resultados del segundo estudio sugirieron una relación positiva entre las experiencias arquetÃpicas y las activaciones de señales fisiológicas, parecÃa razonable realizar otro estudio para confirmar la generalización de nuestros hallazgos. Sin embargo, antes de comenzar un nuevo experimento, se decidió construir una herramienta que pudiera facilitar la recopilación de datos fisiológicos y el reconocimiento de experiencias arquetÃpicas, asà como de emociones conscientes. Tal herramienta nos ayudarÃa a nosotros y a otros investigadores a realizar experimentos sobre la experiencia humana. Nuestra herramienta funciona en "tablets" y admite la recopilación y el análisis de datos de sensores fisiológicos. El último estudio se realizó utilizando una metodologÃa similar al segundo experimento con varias modificaciones que tenÃan como objetivo obtener resultados más sólidos. El esfuerzo de realizar este estudio se redujo considerablemente al usar la herramienta desarrollada. Durante el experimento, sólo medimos las actividades cardiovasculares y electrodérmicas de los sujetos porque nuestros experimentos anteriores mostraron que estas dos señales contribuyeron significativamente a la clasificación de las emociones conscientes y las experiencias arquetÃpicas. El análisis estadÃstico indicó una relación significativa entre los arquetipos retratados en los videos y las respuestas fisiológicas de los sujetos. Además, utilizando métodos de minerÃa de datos, creamos modelos de predicción que fueron capaces de reconocer las experiencias arquetÃpicas con una precisión menor que en el segundo estudio, pero todavÃa considerablemente..
Emotions in archetypal media content
Emotion is an intriguing and mysterious psychological phenomenon. While everyone
seems to know what it is, researchers have not yet come to consensus on its definition, and
many questions still remain unanswered. While the nature of emotion is yet to discover,
the design community has noticed is importance, and poses the challenge of how emotion
could inform design. We see the necessity to follow the state of the art in psychology and
initiate the undertaking by exploring the emotional qualities in various types of media
content. The first part of this thesis aims at constructing a theoretical framework. Recent
years have seen empirical studies suggest that emotion could be unconscious. While this is
to be further justified, scientists are motivated to reconsider current theories of emotion to
account for this phenomenon. In light of this, we integrate these studies about unconscious
emotion into our literature review. An overview from theory to practice is illustrated to
provide a reference for viewing the current states in application domains, such as affective
computing and emotional design. This review offers a holistic understanding about
emotion from various perspectives, which allow us to look for new directions in future
studies.
Based on our review, we see a promising direction by applying psychoanalysis methods
to analyze the media content as affective stimuli, and these stimuli can be evaluated
by using quantitative measures to investigate the connection between the content and the
corresponding emotions. The analysis on the media content is based on a psychoanalysis
theory¿the theory of archetypes¿proposed by Carl Jung. He argues that there exists a
universal pattern in humans¿ unconscious thoughts, which can be manifested as symbolic
content in various forms of narratives, such as myth and fairy tales. Today, this archetypal
symbolic content can be seen in modern media, particularly in movies. By applying the
Jungian approach, we analyzed the symbolic meaning in movie scenes and edit these feature
scenes into a collection of archetypal media content, which serve as the experimental
materials for later explorations.
In the second part of this thesis, we present three experimental studies that aim at determining
if archetypal media content can be differentiated based on emotional responses.
We adopted the psychoanalytical approach described earlier to collect feature scenes in
movies as archetypal media content. Meanwhile, affective stimuli of explicit emotions are
also included as benchmarks for comparison, such as sadness and joy. Self-reports and
physiological signals are both adopted for measuring emotional responses. These three
studies follow similar experimental design: presenting stimuli and measuring emotion
concurrently. The results of these studies confirm that emotions induced by archetypal
content are different from explicit emotions, and the statistical analysis further indicate
that the predictive model obtained from physiological signals outperforms the model generated
from self-reports while viewing archetypal media content. These results, however,
are opposite to the results gained from affective stimuli of explicit emotions, leading us
to the conclusion that archetypal media content might induce unconscious emotions, and
physiological signals are more effective than self-reports for recognizing emotions induced
by archetypal media content.La emoción es un fenómeno psicológico intrigante y misterioso. Aunque todo el mundo parece saber lo que es, los investigadores aún no han llegado a un consenso sobre su definición, y todavÃa quedan muchas preguntas sin respuesta. Si bien la naturaleza de las emociones está aún por descubrir, la comunidad de profesionales del diseño ha entendido su importancia, y se plantea el desafÃo de interrelacionar ambos mundos, explorando de las cualidades emocionales en diversos tipos de contenido en medios de comunicación. La primera parte de esta tesis tiene como objetivo la construcción de un marco teórico. Recientemente se han realizado estudios empÃricos que sugieren que las emociones puede ser inconscientes. Si bien esto debe justificarse mejor, los cientÃficos están motivados a reconsiderar las teorÃas actuales de la emoción para explicar este fenómeno. En vista de ello, integramos estos estudios sobre las emociones inconscientes en nuestra revisión de referencias bibliográficas incluyendo dominios de aplicación recientes, tales como la Computación Afectiva y el Diseño Emocional. Una dirección prometedora de investigación se basa en la aplicación de métodos del psicoanálisis para analizar contenidos multimedia como estÃmulos afectivos, y estos estÃmulos pueden ser evaluados mediante el uso de medidas cuantitativas para investigar la conexión entre el contenido y las emociones correspondientes. Este análisis se basa en la teorÃa de los arquetipos propuesto por el psicólogo Carl Jung. El autor sostiene que existe una patrón universal en los pensamientos inconscientes de los personas, que puede manifestarse como un sÃmbolo contenido en las diversas formas de narrativas, como en los mitos y los cuentos de hadas. Hoy en dÃa, estos arquetipos de contenido simbólico se puede ver frecuentemente en los contenidos multimedia modernos, sobre todo en las pelÃculas. Mediante la aplicación del enfoque de Jung, analizamos el significado simbólico en escenas de pelÃculas seleccionando las correspondientes a diversos arquetipos, que servirá como material experimental para exploraciones posteriores. En la segunda parte de esta tesis, se presentan tres estudios experimentales que apuntan a determinar si el contenido multimedia arquetÃpico puede diferenciarse en base a respuestas emocionales. Con el enfoque psicoanalÃtico descrito anteriormente para los arquetipos, también se incluye los estÃmulos afectivos de emociones explÃcitas son como puntos de referencia para la comparación, como la tristeza y la alegrÃa. Se realizan auto-informes y se miden señales fisiológicas para la determinación de las respuestas emocionales en todos los experimentos realizados. Los resultados de estos estudios confirman que las emociones inducidas por arquetipos son diferentes de las emociones explÃcitas, y el análisis estadÃstico indica además que el modelo predictivo obtenido a partir de señales fisiológicas supera el modelo generado por los auto-informes durante la visualización de contenidos multimedia arquetÃpicos. Estos resultados, sin embargo, son opuestos a los resultados obtenidos a partir de los estÃmulos afectivos de emociones explÃcitas, llevándonos a la conclusión de que los contenidos de los medios arquetÃpicos podrÃa inducir emociones inconscientes, y que las señales fisiológicas son más eficaces que los auto informes para el reconocimiento de las emociones inducidas por el contenido de medios arquetÃpico. En la tercera parte de esta tesis, exploramos cómo los contenidos arquetÃpicos podrÃan utilizarse para diseñar contenido multimedia mediante "mood boards". Se realizaron dos estudios con diseñadores para responder a la pregunta de investigación de si es posible generar contenido emocionalmente rico a través de la generación automática de contenido arquetÃpico por "mood boards" en comparación con el contenido multimedia no arquetÃpico
Digitizing arquetypal human expereience through physiological signals
The problem of capturing human experience is relevant in many application domains. In fact, the process of describing and sharing individual experience lies at the heart of human culture. This advancement came at a price of losing some of the multidimensional aspects of primary, bodily experience during its projection into the symbolic formThroughout the courses of our lives we learn a great deal of information about the world from other people's experience. Besides the ability to share utilitarian experience such as whether a particular plant is poisonous, humans have developed a sophisticated competency of social signaling that enables us to express and decode emotional experience. The natural way of sharing emotional experiences requires those who share to be co-present during this event. However, people have overcome the limitation of physical presence by creating a symbolic system of representations.Recent research in the field of affective computing has addressed the question of digitization and transmission of emotional experience through monitoring and interpretation of physiological signals. Although the outcomes of this research represent a great step forward in developing a technology that supports sharing of emotional experiences, they do not seem to help in preserving the original phenomenological experience during the aforementioned projection. This circumstance is explained by the fact that in affective computing the focus of investigation has been aimed at emotional experiences which can be consciously evaluated and described by individuals themselves. Therefore, generally speaking, applying an affective computing technique for capturing emotions of an individual is not a deeper or more precise way to project her experience into the symbolic form than asking this person to write down a description of her emotions on a piece of paper. One can say that so far the research in affective computing has aimed at delivering technology that could automate the projection but it has not considered the problem of improving the projection in order to preserve more of the multidimensional aspects of human experience.This dissertation examines whether human experience, which individuals are not able to consciously transpose into the symbolic representation, can still be captured using the techniques of affective computing.First, a theoretical framework for description of human experience which is not accessible for conscious awareness was formulated. This framework was based on the work of Carl Jung who introduced a model of a psyche that includes three levels: consciousness, the personal unconscious and the collective unconscious. Consciousness is the external layer of the psyche that consists of those thoughts and emotions which are available for one¿s conscious recollection. The personal unconscious represents a repository for all of an individual¿s feelings, memories, knowledge and thoughts that are not conscious at a given moment of time.The collective unconscious is a repository of universal modes and behaviors that are similar in all individuals. According to Jung, the collective unconscious is populated with archetypes. Archetypes are prototypical categories of objects, people and situations that existed across evolutionary time and in different cultures.Esta tesis doctoral examina si la experiencia humana, que los individuos no pueden transponer conscientemente a la representación simbólica, aún puede capturarse utilizando las técnicas de computación afectiva. Primero, se formula un marco teórico para la descripción de la experiencia humana que no es accesible para la conciencia consciente. Este marco se basó en el trabajo de Carl Jung, quien introdujo un modelo de psique que incluye tres niveles: la conciencia, el inconsciente personal y el inconsciente colectivo. Habiendo definido nuestro marco teórico, realizamos un experimento en el que se mostraron a los sujetos estÃmulos visuales y auditivos de bases de datos estandarizadas para la obtención de emociones conscientes. Aparte de los estÃmulos para las emociones conscientes, los sujetos fueron expuestos a estÃmulos que representaban el arquetipo del yo. Durante la presentación de los estÃmulos cardiovasculares se registraron las señales de los sujetos. Los resultados experimentales indicaron que las respuestas de la frecuencia cardÃaca de los participantes fueron únicas para cada categorÃa de estÃmulos, incluido el arquetÃpico. Estos hallazgos dieron impulso a realizar otro estudio en el que se examinó un espectro más amplio de experiencias arquetÃpicas. En nuestro segundo estudio, hicimos un cambio de estÃmulos visuales y auditivos a estÃmulos audiovisuales porque se esperaba que los videos fueran más eficientes en la obtención de emociones conscientes y experiencias arquetÃpicas que las imágenes fijas o los sonidos. La cantidad de arquetipos aumentó y los sujetos en general fueron estimulados a sentir ocho experiencias arquetÃpicas diferentes. También preparamos estÃmulos para emociones conscientes. En este experimento, las señales fisiológicas incluyeron actividades cardiovasculares, electrodérmicas, respiratorias y temperatura de la piel. El análisis estadÃstico sugirió que las experiencias arquetÃpicas podrÃan diferenciarse en función de las activaciones fisiológicas. Además, se construyeron varios modelos de predicción basados en los datos fisiológicos recopilados. Estos modelos demostraron la capacidad de clasificar los arquetipos con una precisión que era considerablemente más alta que el nivel de probabilidad. Como los resultados del segundo estudio sugirieron una relación positiva entre las experiencias arquetÃpicas y las activaciones de señales fisiológicas, parecÃa razonable realizar otro estudio para confirmar la generalización de nuestros hallazgos. Sin embargo, antes de comenzar un nuevo experimento, se decidió construir una herramienta que pudiera facilitar la recopilación de datos fisiológicos y el reconocimiento de experiencias arquetÃpicas, asà como de emociones conscientes. Tal herramienta nos ayudarÃa a nosotros y a otros investigadores a realizar experimentos sobre la experiencia humana. Nuestra herramienta funciona en "tablets" y admite la recopilación y el análisis de datos de sensores fisiológicos. El último estudio se realizó utilizando una metodologÃa similar al segundo experimento con varias modificaciones que tenÃan como objetivo obtener resultados más sólidos. El esfuerzo de realizar este estudio se redujo considerablemente al usar la herramienta desarrollada. Durante el experimento, sólo medimos las actividades cardiovasculares y electrodérmicas de los sujetos porque nuestros experimentos anteriores mostraron que estas dos señales contribuyeron significativamente a la clasificación de las emociones conscientes y las experiencias arquetÃpicas. El análisis estadÃstico indicó una relación significativa entre los arquetipos retratados en los videos y las respuestas fisiológicas de los sujetos. Además, utilizando métodos de minerÃa de datos, creamos modelos de predicción que fueron capaces de reconocer las experiencias arquetÃpicas con una precisión menor que en el segundo estudio, pero todavÃa considerablemente..
Applied Cognitive Sciences
Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline
Internet and Biometric Web Based Business Management Decision Support
Internet and Biometric Web Based Business Management Decision Support
MICROBE
MOOC material prepared under
IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials
Prepared by:
A. Kaklauskas, A. Banaitis, I. Ubarte
Vilnius Gediminas Technical University, Lithuania
Project No: 2020-1-LT01-KA203-07810
Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features
Speech Emotion Recognition (SER) has been widely used in many fields, such as smart home assistants commonly found in the market. Smart home assistants that could detect the user’s emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speech sets were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion
Recommended from our members
Emotional recognition in computing
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University 8/4/2010.Emotions are fundamental to human lives and decision-making. Understanding and expression of emotional feeling between people forms an intricate web. This complex interactional phenomena, is a hot topic for research, as new techniques such as brain imaging give us insights about how emotions are tied to human functions. Communication of emotions is mixed with communication of other types of information (such as factual details) and emotions can be consciously or unconsciously displayed. Affective computer systems, using sensors for emotion recognition and able to make emotive responses are under development. The increased potential for emotional interaction with products and services, in many domains, is generating much interest. Emotionally enhanced systems have potential to improve human computer interaction and so to improve how systems are used and what they can deliver. They may also have adverse implications such as creating systems capable of emotional manipulation of users. Affective systems are in their infancy and lack human complexity and capability. This makes it difficult to assess whether human interaction with such systems will actually prove beneficial or desirable to users. By using experimental design, a Wizard of Oz methodology and a game that appeared to respond to the user’s emotional signals with human-like capability, I tested user experience and reactions to a system that appeared affective. To assess users’ behaviour, I developed a novel affective behaviour coding system called ‘affectemes’. I found significant gains in user satisfaction and performance when using an affective system. Those believing the system responded to emotional signals blinked more frequently. If the machine failed to respond to their emotional signals, they increased their efforts to convey emotion, which might be an attempt to ‘repair’ the interaction. This work highlights how very complex and difficult it is to design and evaluate affective systems. I identify many issues for future work, including the unconscious nature of emotions and how they are recognised and displayed with affective systems; issues about the power of emotionally interactive systems and their evaluation; and critical ethical issues. These are important considerations for future design of systems that use emotion recognition in computing.EPSRC project grant (R81374/01