Bag-of-words deception detection systems outperform humans, but are still not always accurate enough to be useful. In interrogation settings, present models do not take into account potential influence of the words in a question on the words in the answer. According to the theory of verbal mimicry, this ought to exist. We show with our research that it does exist: certain words in a question can "prompt" other words in the answer. However, the effect is receiver-state-dependent. Deceptive and truthful subjects in archival data respond to prompting in different ways. We can improve the accuracy of a bag-of-words deception model by training a machine learning algorithm on both question words and answer words, allowing it to pick up on differences in the relationships between these words. This approach should generalize to other bag-of-words models of psychological states in dialogues. / Thesis (Master, Computing) -- Queen's University, 2012-12-17 14:42:19.707

This project is an investigation of visual illusions created through the interaction of light on different surfaces and structures. Illusion implies deception - an optical illusion, in a sense is misunderstood information that creates a 'false' visual reality. This project incorporates macro and non-macro photography to generate illusion through scale and shape. Through deliberate acts of deception the images play on the human desire for mystery. It is through the 'eye of the imagination' that the images reveal themselves. The photographic images are of dioramas created on the top of a light box, using everyday substances and materials such as glass, felt, coloured transparencies, detergents and liquids of varying consistencies. This project reveals the extraordinary in the ordinary. The outcome of this project is a photographic body of work, the product of my experimentation and research, in which the ambiguous content of the composition, compels the viewer to their own interpretation. Translating some of the resulting images into a three-dimensional light based installation of an illusory nature invites people to take on a participatory role, furthering their experience with the artwork. This project makes us aware of our role within the experiential process, ma king us appreciate and question its very nature.

Eileen Chang (1920-1995), a legendary female writer in Chinese literature history, lived in the most turbulent time of contemporary China. Her works are all about love. Her characters are all insignificant people, unrealistically invested with illusory dreams of life, but meanwhile, absorbed by worldly pursuits and pleasures. And her tone is desolate. People worship her, both as a great writer and as a mysterious woman. From 1980s, the fascination with her and her literature overtly announces itself in Chinese language cinema. Several critically acclaimed directors have intended to adapt her aura and charisma through adapting her literature. But, the result of their efforts is not optimistic; the disparity between the films and the novels still has been widely sensed. My thesis focuses on the film adaptations of her novels. I intend to explore this woman?s world through cinema; precisely, through the unexpected gap between her novels and their film versions, in order to explore the reality and moods in her mind that are hard to visualize. The same events, moments and situations presented in both the novels and the films also offer great opportunities for the comparison of two different ways of story-telling.

Thesis (M.S.)--Villanova University, 2008. / Psychology Dept. Includes bibliographical references.

Thesis (Ph. D.)--Byrn Mawr college, 1918. / Vita. Published also without thesis note. Bibliography: p. 193-198.

Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. Joey F. George, Florida State University, College of Business, Dept. of Management Information Systems. Title and description from dissertation home page (viewed Sept. 23, 2004). Includes bibliographical references.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 295-307). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 295-307). Also available on the Internet.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 47-50).

Different ways to measure human affective and deceptive reactions to stimulus have been developed. One method is a multimodal approach using web camera, thermal imaging camera and physiological sensors data to extract different features in the human face (verbal and non-verbal behavior) such as breathing rate, heart rate, face temperature, skin conductance, eye tracking, language analysis and facial expressions among others. Human subjects from different ages and ethnicity were exposed to two different experiments were they watched videos (affection recognition) and others answered an interview session (deception recognition). With the data collected from videos (thermal and visual), different regions of interest (ROI) of the face were selected as well as the whole picture. The ROI were determined based on the most sensitive parts of the face where larger changes of temperature or other physiological features are recorded. It was also analyzed the language (written and spoken) in order to obtain the verbal modalities. The data has been compared among the subjects to determine whether the deceptive and affective reactions of a person can be predicted using multimodal approach. From the multiple data obtained, a characterization of reactions is proposed when subjects are exposed to different stimulus, positive or negative, as well as deceptive behavior and later on recognize if the person is happy, sad, nervous, anxious, telling the truth, lying etc. Using the multimodal approach we were able to predict automatically, with higher accuracy than the baseline, affective and deceptive states of a person. In the affective state recognition, the classifier software differentiated affective state versus neutral state with 92.85% accuracy. Then it differentiated Positive State, Negative State and Neutral State with 57.14% accuracy. Additionally, it differentiated Positive State versus Negative State with 73.21% accuracy. Finally, the classifier was able to predict Deceptive State (people lying) and Non Deceptive State (people telling the truth) with 72.72% accuracy.