Dissertations / Theses on the topic 'Psychophysiological state'

A spacewalk, or extravehicular activity (EVA), is one of the most mission critical and physically and cognitively challenging tasks that crewmembers complete. With next-generation missions to the Moon and Mars, exploration EVA will challenge crewmembers in partial gravity environments with increased frequency, duration, and autonomy of operations. Given the distance from Earth, associated communication delays, and durations of exploration missions, there is a monumental shift in responsibility and authority taking place in spaceflight; moving from Earth-dependent to crew self-reliant. For the safety, efficacy, and efficiency of future surface EVAs, there is a need to better understand crew health and performance. With this knowledge, technology and operations can be designed to better support future crew autonomy. The focus of this dissertation is to develop and evaluate a psychophysiological monitoring tool to classify cognitive workload during an operationally relevant EVA task. This was completed by compiling a sensor suite of commercial wearable devices to record physiological signals in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. The approach employs supervised machine learning to recognize patterns in psychophysiological features across different psychological states. This relies on the ability to simulate, or induce, cognitive workload in order to label data for training the model. A virtual reality (VR) Translation Task was developed to control and quantify cognitive demands during an immersive, ambulatory EVA scenario. Participants walked on a passive treadmill while wearing a VR headset to move along a virtual lunar surface. They walked with constraints on time and resources, while simultaneously identifying and recalling waypoints in the scene. Psychophysiological features were extracted and labeled according to the task demands, i.e. high or low cognitive workload, for the novel Translation Task, as well as for the benchmark Multi-Attribute Task Battery (MATB). Predictive models were created using the K Nearest Neighbor (KNN) algorithm. The contributions of this dissertation span the simulation, characterization, and modeling of cognitive state. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation.
Doctor of Philosophy
A spacewalk is one of the most important and physically and mentally challenging tasks that astronauts complete. With next-generation missions to the Moon and Mars, exploration spacewalks will challenge astronauts in reduced-weight environments (1/6 and 1/3 Earth's gravity) with longer, more frequent spacewalks and with less help from mission control. To keep astronauts safe while exploring there is a need to better understand astronaut health and performance (physical and mental) during spacewalks. With knowledge of how astronauts will respond to high workload and stressful events, we can plan missions and design tools that can best assist them during spacewalks on the Moon and Mars when help from Earth mission control is limited. Traditional tools of quantifying mental state are not suitable for real-time assessment during spacewalks. Current methods, including subjective surveys and performance-based computer tests, require time and attention to complete and cannot assess real-time operations. The focus of this dissertation is to create a psychophysiological monitoring tool to measure mental workload during a virtual reality (VR) spacewalk. Psychophysiological monitoring uses physiological measures, like heart rate and breathing rate, to predict psychological state, like high workload or stress. Physiological signals were recorded using commercial wearable devices in two human research studies, one at Virginia Tech and one at NASA Johnson Space Center. With machine learning, computer models can be trained to recognize patterns in physiological measures for different psychological states. Once a model is trained, it can be tested on new data to predict mental workload. To train and test the models, participants in the studies completed high and low workload versions of the VR task. The VR task was specifically designed for this study to simulate and measure performance during a mentally-challenging spacewalk scenario. The participants walked at their own pace on a treadmill while wearing a VR headset to move along a virtual lunar surface, while balancing their time and resources. They were also responsible for identifying and recalling flags along their virtual path. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more realistic environments using wearable devices, and of generalizing predictive models across participants, times, and tasks. This work paves the way for future field studies and real-time implementation to close the loop between human and automation.

Reversal theory (Apter, 1982) is a psychological theory of motivation, emotion, and personality that purports to explain human behaviour as well as experience. In 1999 Svebak proposed a triangular relationship between metamotivational dominance, sport preference and biological composition among elite athletes using a reversal theory framework. Svebak’s proposition would suggest that when three of these components match (e.g., telic dominant individual with slow-twitch muscle fibre predominance who participates in endurance activities), they have a higher probability of success, and of optimizing the joy of participating. However, this proposition has only been examined in elite athletes (Braathen & Svebak, 1990). The purpose of the present research was to examine whether components of Svebak’s triangular relationship could be demonstrated in the general population and to examine the relationships between dominance, state and physical performance. A series of four studies were conducted with subjects purposely sampled and allocated to telic and paratelic groups from a pool of individuals who initially completed the Paratelic Dominance Scale (PDS; Cook & Gerkovich, 1993). Subjects who scored higher than one standard deviation above the mean were classified as the paratelic dominant (PD), while those who scored lower than one standard deviation below the mean were classified as the telic dominant (TD) (Gerkovich, Cook, Hoffman, & O’Connell, 1998). Age, sex, preferred sports/exercise activity and frequency of exercise per week were recorded for each subject. In each study, state was manipulated via video stimuli into telic and paratelic in a crossover design before completing an exercise task, the subjects continued to watch the video stimuli for the duration of the exercise. Manipulation checks indicated that the video stimulus was successful in changing state. The Telic State Measure (TSM; Svebak & Murgatroyd, 1985) and Tension and Effort Stress Inventory (TESI; Svebak, 1993) were completed during each of the studies. The exercise performed in each study varied with 5 s isometric leg extension used in Study 1; 100 repetition isokinetic leg extensions in Study 2; 30 s Wingate test in Study 3 and ramped peak test in Study 4. Exercise preference in all four studies indicated that telic dominant individuals preferred endurance exercise activities, while paratelic dominant individuals preferred explosive exercise activities, supporting previous research (Cogan & Brown, 1999; Kerr, 1991; Kerr & Svebak, 1989; Svebak & Kerr, 1989). Physiological responses (EMG and HR for Studies 1 and 2; HRV and HR for Studies 3 and 4) showed mixed results. For the Study 1, EMG was employed to examine if either state or dominance influenced neural activation during isometric leg contraction, however no significant differences were found. Study 2, muscle tension was examined using EMG gradients during isokinetic leg exercise with results indicating dominance determining EMG gradients rather than state (gradients present in telic dominant individuals). HRV has never been investigated previously in relation to reversal theory. In both Studies 3 and 4 the majority of HRV components decreased after exercise as a result of vagal withdrawal. When examining metamotivational dominance/state and HRV variables, changes showed no statistical significance in both studies, but responses in HRV variables were observed in relation to differences between metamotivational dominance/state suggesting a need for further studies. Heart rate was measured in all four studies and results were consistent with telic dominant individuals having a lower resting heart rate. No significant differences were found between metamotivational dominance in relation to performance. However, across all four studies there was a tendency for paratelic dominant individuals to perform better during explosive/power activities while telic dominant individuals performed better during endurance activities, both groups tended to perform better in their preferred state. There was a strong tendency for telic dominant individuals to report higher levels of stress than the paratelic dominant individuals regardless of the state condition. State effects were observed for some of somatic emotions (i.e., excitement, relaxation, boredom and anxiety), while other emotions showed time effects due to the results of exercise in all four studies. Generally, both telic and paratelic dominant individuals were more excited and less bored at post-exercise; and telic dominant individuals were more anxious throughout the course of experiment. The present research has made contributions to reversal theory research by supporting previous findings in exercise preference and resting heart rates, and presenting new data on changes in stress and emotions in telic/paratelic state conditions during exercise. Furthermore, muscle tension, as indicated by EMG gradients, has been observed relative to state and HRV has been measured for the first time in relation to reversal theory, indicating possible links between HRV and metamotivational dominance/state. Methodological issues such as state manipulation and dominant group selection have contributed to the reversal theory research. The data provide some support for Svebak’s proposed triangular relationships among general populations.

Modern systems produce a great amount of information and cues from which human operators must take action. On one hand, these complex systems can place a high demand on an operator's cognitive load, potentially overwhelming them and causing poor performance. On the other hand, some systems utilize extensive automation to accommodate their complexity; this can cause an operator to become complacent and inattentive, which again leads to deteriorated performance (Wilson, Russell, 2003a; Wilson, Russell, 2003b). An ideal human-machine interface would be one that optimizes the functional state of the operator, preventing overload while not permitting complacency, thus resulting in improved system performance. An operator's functional state (OFS) is the momentary ability of an operator to meet task demands with their cognitive resources. A high OFS indicates that an operator is vigilant and aware, with ample cognitive resources to achieve satisfactory performance. A low OFS, however, indicates a non-optimal cognitive load, either too much or too little, resulting in sub-par system performance (Wilson, Russell, 1999). With the ability to measure and detect changes in OFS in real-time, a closed-loop system between the operator and machine could optimize OFS through the dynamic allocation of tasks. For instance, if the system detects the operator is in cognitive overload, it can automate certain tasks allowing them to better focus on salient information. Conversely, if the system detects under-vigilance, it can allocate tasks back to the manual control of the operator. In essence, this system operates to "dynamically match task demands to [an] operator's momentary cognitive state", thereby achieving optimal OFS (Wilson, Russell, 2007). This concept is termed adaptive aiding and has been the subject of much research, with recent emphasis on accurately assessing OFS in real-time. OFS is commonly measured indirectly, like using overt performance metrics on tasks; if performance is declining, a low OFS is assumed. Another indirect measure is the subjective estimate of mental workload, where an operator narrates his/her perceived functional state while performing tasks (Wilson, Russell, 2007). Unfortunately, indirect measures of OFS are often infeasible in operational settings; performance metrics are difficult to construct for highly-automated complex systems, and subjective workload estimates are often inaccurate and intrusive (Wilson, Russell, 2007; Prinzel et al., 2000; Smith et al., 2001). OFS can be more directly measured via psychophysiological signals such as electroencephalogram (EEG) and electrooculography (EOG). Current research has demonstrated these signals' ability to respond to changing cognitive load and to measure OFS (Wilson, Fisher, 1991; Wilson, Fisher, 1995; Gevins et al., 1997; Gevins et al., 1998; Byrne, Parasuraman, 1996). Moreover, psychophysiological signals are continuously available and can be obtained in a non-intrusive manner, pre-requisite for their use in operational environments. The objective of this study is to advance schemes which detect change in OFS by monitoring psychophysiological signals in real-time. Reviews on similar methods can be found in, e.g., Wilson and Russell (2003a) and Wilson and Russell (2007). Many of these methods employ pattern recognition to classify mental workload into one of several discrete categories. For instance, given an experiment with easy, medium and hard tasks, and assuming the tasks induce varying degrees of mental workload on a subject, these methods classify which task is being performed for each epoch of psychophysiological data. The most common classifiers are artificial neural networks (ANN) and multivariate statistical techniques such as stepwise discriminant analysis (SWDA). ANNs have proved especially effective at classifying OFS as they account for the non-linear and higher order relationships often present in EEG/EOG data; they routinely achieve classification accuracy greater than 80%. However, the discrete output of these classification schemes is not conducive to real-time change detection. They accurately classify OFS, but they do not indicate when OFS has changed; the change points remain ambiguous and left to subjective interpretation. Thus, the present study introduces several online algorithms which objectively determine change in OFS via real-time psychophysiological signals. The following chapters describe the dataset evaluated, discuss the statistical properties of psychophysiological signals, and detail various algorithms which utilize these signals to detect real-time changes in OFS. The results of the algorithms are presented along with a discussion. Finally, the study is concluded with a comparison of each method and recommendations for future application.

The purpose of this thesis was to (1) examine the psychological morbidity associated with atrial fibrillation (AF), and (2) determine the effects of acute mental and postural stress, and hydration status on indices haemorhelogy, endothelial function, and platelet reactivity. Symptoms of depression (BDI scores > 10) persisted in 38% of patients with AF, with elevated state and trait anxiety (STAI score >40) being reported in 28% and 38% of patients, respectively. No significant differences in depression, state anxiety, and QoL were observed between AF and hypertensive patients; however, AF patients displayed higher levels of trait anxiety. Multiple regression analysis demonstrated baseline depression scores provided the best independent prediction of future QoL. Acute mental and postural stress yielded significant changes in haemodynamics and haemorhelogy, in addition to increasing biomarkers of endothelial dysfunction and platelet reactivity. Increased hydration status reduced blood pressure and markers of endothelial perturbation and platelet morphology both at rest and in response to the stress tasks. In conclusion, AF patients display comparable levels of depression and anxiety to patients following a myocardial infarction. In addition, behavioural activities such as mental and postural stress may implicated in the pathogenesis of acute coronary syndromes through the development of a prothrombotic state.

Doutoramento em Psicologia
A insónia primária é a perturbação do comportamento de sono mais prevalente quer na população clínica quer na comunidade. Uma das formas mais comuns é a insónia psicofisiológica (IP). A hiperativação neuropsicofisiológica, afetiva, cognitiva e comportamental assim como o condicionamento mal-adaptativo entre estímulos associados ao sono e à cama com estímulos indutores de ativação são duas das características mais diferenciadoras desta patologia. Tendo por base a importância que esta perturbação assume em termos de saúde pública, levou-se a cabo 4 estudos empíricos com recurso a ressonância magnética funcional: No primeiro estudo comparou-se a ativação neurobiológica entre um grupo de doentes com IP (n=5) e um grupo de indivíduos saudáveis (n=5) emparelhado quanto ao sexo e à idade quando eram confrontados com palavras que remetiam para preocupações do passado/presente, preocupações do futuro e palavras neutras; no segundo estudo, explorou-se as diferenças na ativação referente à default-mode network (DMN) e outras resting-states nos mesmos grupos do estudo 1; no terceiro e quarto estudos, repetiram-se os mesmos procedimentos para um grupo clínico (N=2) após estes terem sido submetidos a terapia cognitivo-comportamental para a insónia (TCC-I). No geral, verificou-se que os doentes com IP exibiram um padrão generalizado de hiperativação em áreas associadas à DMN quer quando confrontados com estímulos ativadores quer em repouso; em termos de ativação nas resting-states, constatou-se que, em repouso, o grupo clínico apresentou disfunções significativas. Após TCC-I, observou-se que os indicadores disfuncionais verificados nos estudos anteriores se esbateram tendendo a aproximar-se do perfil de ativação dos indivíduos saudáveis. Os resultados obtidos reforçam assim a ideia da hiperativação na insónia ao longo das 24 horas do dia assim como do papel fundamental que a ativação cognitiva parece ter na etiopatogenia e no tratamento da insónia. Para além disso, este trabalho contribui para um melhor entendimento da neurobiologia da insónia e sugere que se podem identificar mecanismos neuronais subjacentes às modificações operadas pela TCC-I.
Primary Insomnia is the more prevalent sleep disorder both in clinical and community samples. One of the most frequent subtypes is psychophysiological insomnia (PI). The hyperarousal at different levels – biological, affective, cognitive, and behavioral – and the maladaptive conditioning between sleep-related stimuli and arousal are two major features of PI. Since this is a disorder which assumes an important role in public health, we performed 4 empirical studies recurring to fMRI: In the first study, we compared neurobiological activation between a group of PI patients (n=5) and a sex- and age-matched control group (n=5) when they were exposed to words concerning to past/present worries, future worries and neutral words; in the second study, we explored the activity of default-mode network (DMN) and other brain resting-states in the same groups as study 1; in the third and fourth studies, we repeated both experiments in a clinical group of patients with PI (N=2) after they underwent cognitive-behavioral therapy for insomnia (CBT-I). In general, it was observed that PI patients exhibited a generalized pattern of hyperarousal in several brain areas associated with DMN when they were confronted with affective stimuli and when they were resting in the fMRI scanner. In terms of activation of brain resting networks, we observed that the clinical group presented significant dysfunctions. After CBT-I, it was detected that the dysfunctional indicators observed in previous studies normalize, approaching the activation patterns typical of healthy individuals. The obtained results enhance the idea that the hyperarousal in PI is present during the 24-hours of the day; besides, the key role that cognitive arousal may be in the etiology and therapy of insomnia is also highlighted. In conclusion, this work contributes to a better understanding of neurobiology of insomnia and suggests that it might be possible to identify neural mechanisms underlying modifications accounted by CBT-I.

Робота містить 70 сторінок, 25 рисунківта 19 таблиць. Було використано 12 джерел. Мета роботи: підвищити швидкість та ефективність дослідження та коригування психофізіологічного стану людини за рахунок розробки методу підбору та виявлення впливів релаксуючих картин для коригування психофізіологічного стану людини. Проведено огляд основних положень проведення досліджень, збору даних та коригування психофізіологічного стану людини. Описано основні проблеми обробки зібраних даних для аналізу, підбору релаксуючих картин та визначено необхідність у розробці методів для підбору та визначення впливів на психофізіологічний стан релаксуючих картин. Запропоновано метод підбору релаксуючих картин для коригування психофізіологічного стану людини на основі нейронних мереж. Застосування запропонованого методу дозволяє подати на вхід набір параметрів: кольорову преференцію, день народжень, суб’єктивні відчуття та отримати на виході релаксуючу картину для проведення сеансів коригування психофізіологічного стану. Запропоновано метод виявлення впливів на психофізіологічний стан людини, який дозволяє, за рахунок гнучкого механізму конфігурації побудови графіків, будувати різноманітні параметри дослідження психофізіологічного стану людини та виявляти їх залежності та впливи на людину. Розроблено програмне забезпечення запропонованих методів підбору та виявлення впливів релаксуючих картин на стан людини, яке збільшує ефективність роботи психологів з дослідження психофізіологічного стану людини, за рахунок швидких та точних обчислень. Розроблено стартап-проект програмного забезпечення та проведено аналіз ринку, потенційних клієнтів, з якого видно, що ринок поки вільний від аналогів такого продукту, але має у ньому потребу.
The work contains 70 pages, 25 figures, and 19 tables. 12 sources have been used. Goal: to increase the speed and efficiency of research and adjustment of the psychophysiological state of a person by developing a method of matching and detecting influences of relaxing pictures for human psychophysiological state adjustment. A review of the main provisions of research, data collection, and adjustment of the psychophysiological state of humans. The main problems of processing the collected data for the analysis, matching of relaxing pictures are described and the necessity in the development of methods for matching and detecting influences on a psychophysiological condition of relaxing pictures is defined. A method of matching relaxing pictures for correction of the psychophysiological state of a human on the basis of neural networks is offered. The application of the proposed method allows you to submit a set of parameters: color preference, birthday, favorite color, subjective feelings and get a relaxing picture for sessions to correct the psychophysiological state. A method for detecting influences on the psychophysiological state of a person is proposed, which allows, due to a flexible configuration mechanism for plotting, to build various parameters of research of the psychophysiological state of a person and identify their dependencies and influences on a person. The software of the offered methods of matching and detecting influences of relaxing pictures on a condition of the human which increases the efficiency of work of psychologists on the research of a psychophysiological condition of the person is developed. A software startup project has been developed and an analysis of potential customers and the market has been conducted, which shows that the market is still free from analogs of such a product, but needs it.

The peripheral psychophysiological signals (EMG, ECG and GSR) of 13 participants were recorded in the well planned Cognition and Robotics lab at BTH University and 9 participants data were taken for further processing. Thirty(30) pictures of IAPS were shown to each participant individually as stimuli, and each picture was displayed for five-second intervals. Signal preprocessing, feature extraction and selection, models, datasets formation and data analysis and interpretation were done. The correlation between a combination of EMG, ECG and GSR signal and emotional states were investigated. 2- Dimensional valence-arousal model was used to represent emotional states. Finally, accuracy comparisons among selected machine learning classification algorithms have performed. Context: Psychophysiological measurement is one of the recent and popular ways to identify emotions when using computers or robots. It can be done using peripheral signals: Electromyography (EMG), Electrocardiography (ECG) and Galvanic Skin Response (GSR). The signals from these measurements are considered as reliable signals and can produce the required data. It is further carried out by preprocessing of data, feature selection and classification. Classification of EMG, ECG and GSR data can be conducted with appropriate machine learning algorithms for better accuracy results. Objectives: In this study, we investigate and analyzed with psychophysiological (EMG, ECG and GSR) data to find best classifier algorithm. Our main objective is to classify those data with appropriate machine learning techniques. Classifications of psychophysiological data are useful in emotion recognition. Therefore, our ultimate goal is to provide validated classified psychological measures for the automated adoption of human robot performance. Methods: We conducted a literature review in order to answer RQ1. The sources used are Inspec/ Compendex, IEEE, ACM Digital Library, Google Scholar and Springer Link. This helps us to identify suitable features required for the classification after reading the articles and papers that are peer reviewed as well as lie relevant to the area. Similarly, this helps us to select appropriate machine learning algorithms. We conducted an experiment in order to answer RQ2 and RQ3. A pilot experiment, then after main experiment was conducted in the Cognition and Robotics lab at the university. An experiment was conducted to take measures from EMG, ECG and GSR signal. Results: We obtained different accuracy results using different sets of datasets. The classification accuracy result was best given by the Support Vector Machine algorithm, which gives up to 59% classified emotional states correctly. Conclusions: The psychophysiological signals are very inconsistent with individual participant for specific emotion. Hence, the result we got from the experiment was higher with a single participant than all participants were together. Although, having large number of instances are good to train the classifier well.
The thesis is focused to classify emotional states from physiological signals. Features extraction and selection of the physiological signal was done, which was used for dataset formation and then classification of those emotional states. IAPS pictures were used to elicit emotional/affective states. Experiment was conducted with 13 participants in cognition and Robotics lab using biosensors EMG, ECG and GSR at BTH University. Nine participants data were taken for further preprocessing. We observed in our thesis the classification of emotions which could be analyzed by a combination of psychophysiological signal as Model A and Model B. Since signals of subjects are different for same emotional state, the accuracy was better for single participant than all participants together. Classification of emotional states is useful for HCI and HRI to manufacture emotional intelligence robot. So, it is essential to provide best classifier algorithms which can be helpful to detect emotions for developing emotional intelligence robots. Our work contribution lies in providing best algorithms for emotion recognition for psychophysiological data and selected features. Most of the results showed that SVM performed best with classification accuracy up to 59 % for single participant and 48.05 % for all participants together. For a single dataset and single participant, we found 60.17 % accuracy from MLP but it consumed more time and memory than other algorithms during classification. The rest of the algorithms like BNT, Naive Bayes, KNN and J48 also gave competitive accuracy to SVM. We conclude that SVM algorithm for emotion recognition from a combination of EMG, ECG and GSR is capable of handling and giving better classification accuracy among others. Tally between IAPS pictures with SAM helped to remove less correlated signals and to obtain better accuracies. Still the obtained results are small in percentage. Therefore, more participants are probably needed to get a better accuracy result over the whole dataset.
amarehenry@gmail.com ; Mobile: 0767042234 amrit.pandey111@gmail.com ; Mobile : 0704763190

In the United States of America (U.S.), institutional marginalization and racial discrimination remains an arguably difficult subject to understand, both conceptually and pragmatically. Regarding governmental sectors, U.S. Armed Forces are institutions where discrimination must be critically explored in an attempt to provide an understanding of the reality faced by those who actually serve. This study involved the examination into racism within a specific elite governmental sector that emphasizes a philosophy of a unified oneness of all its members. Using a phenomenological approach, the study delved into the actual impact of racism within the Marine Corps, on the lives of individual members of a historically marginalized populace, African American/Black. The research explored and analyzed the life stories of three male members of the aforementioned population group, hence seeking to answer the research question: How has Integrated Racial Diversity in the Armed Forces Impacted Experiences of Discrimination Antagonistic to Male African American/Black Marine Corps Officers as Members of a Population Historically Marginalized and Discriminated against in the United States of America? Theories incorporated in the research offered meaning to the experiences of the individual participants. Discoveries illustrated the necessity of adaptation by the individual in coping with the impact of racially charged hostilities in an environment supposedly operating with an objective of oneness of its members. Through the findings, a theory of socio-psycho-bio dissonance was developed by the researcher. This research provides recommendations on practical ways to transformatively address and seek probable resolution in conflict – institutionally.

The mere exposure effect, positive affect elicited by exposure to a previously unfamiliar stimulus, is considered one of the most well established findings in the psychological literature. Yet its mechanism remains unknown. In Experiments 1 - 5, memory encoding was examined to determine whether the mere exposure effect was a form of conceptual or perceptual implicit priming, and, if not either, whether cardiovascular psychophysiology could reveal its nature. Experiment 1 examined the effects of study phase level of processing on recognition, the mere exposure effect, and word identification implicit priming. Deep relative to shallow processing improved recognition, but did not influence the mere exposure effect or word identification implicit priming. Experiments 2 and 3 examined the effect of study-test changes in font and orientation, respectively, on the mere exposure effect and word identification implicit priming. Different study-test font and orientation reduced word identification implicit priming, but had no influence on the mere exposure effect. The combined results from Experiments 1-3 suggested that conceptual and perceptual processing do not drive the mere exposure effect. Experiments 4 and 5 developed and used, respectively, an innovative cardiovascular psychophysiological implicit priming paradigm to examine whether stimulus-specific cardiovascular reactivity at study predicted the mere exposure effect at test. At encoding, stimulus-specific peripheral vasodilatation had predictive value for the mere exposure effect, but not for word identification implicit priming. Experiments 6 and 7 examined whether sustained or transitory anxiety (i.e., trait or state, respectively) would influence the mere exposure effect. Greater trait and state anxiety reduced the mere exposure effect. Together, the findings from these experiments (N = 362) identify a novel affective mechanism of implicit priming that is influenced by cardiovascular psychophysiology and variations in trait and state anxiety.

碩士
國立勤益科技大學
景觀設計系
106
Nowadays, traffic noise has become one of the major noise sources in the urban environment which can cause some physical and psychological problems and concerns. In order to improve the quality of the urban environment, this study aims to explore the influence of intervening sensory experience of different natural elements in urban traffic noise environment on space users' tranquility and emotional state. The research is mixing quantitative and qualitative methods. The quantitative data were collected through questionnaires and physiological feedback, while the qualitative data of the emotional reaction process of individual experience were recorded via interviews. Participants were randomly assigned to the green-plant group and the waterscape group. Each participant experienced three sensory stimulations (one for each week), including visual stimulus(green plant/falling water scene), auditory stimulus (bird/water sound), and audio-visual stimulus (green plant and bird sound/falling water and water sound).The results show that experiencing natural elements (green elements and water elements) through three sensory stimulations in the noise environment can not only improve user's tranquility and positive emotions, but also reduce negative emotions significantly. On visual and audio-visual sensory stimulations, compared to water elements, experiencing green elements can enhance user's tranquility, positive emotions, and reduce anxiety. In the physiological benefits, experiencing the natural element through auditory and audio-visual sensory can increase LF% and decrease HF%, which means that intervening natural sounds to the environment can make people feel excited and arousal. Experiencing the green elements through auditory and audio-visual sensory can improve HRV indicating that adding the bird sounds to the noise environment, user would feel emotionally relieved. Overall, experiencing the green elements through audio-visual sensory is able to promote most positive effects. Experiencing the water element through three types of stimulations, users can all significantly improve tranquility, reduce negative emotions, and decrease blood pressure. In addition, experiencing the water element through auditory and audio-visual sensory, users can increase LF% and decrease HF%. The similar trend is found in the qualitative analysis. Most participants state that listening to the bird sounds would have a pleasant feeling, and adding water elements can make them calmer. The results of the emotional reaction process show that participants turn their attention to green plants or waterscape and ignore the effect of noise when they experience visual sensory stimulation. Under auditory sensory stimulation, participants pay attention to listening to the natural sound and feel it can overwhelm the noise to achieve noise reduction. Participants believe that the audio-visual sensory experience of natural elements can achieve the effect of blocking noise and make people more tranquility. Furthermore, when the "green plant and bird sound" element is added to the noise environment, participants can have more pleasant emotions. When the "Waterscape and water sound" element is added to the noise environment, most participants express their calm emotions. Among them, water sound can make participants feel the microclimate change of environment, which makes them feel refreshed. "Waterscape and water sound" can induce richer emotional responses (such as curious, interesting) than "green plant and bird sound". The results can provide the design reference to improve urban noise environment and increase environmental quality.