Asymmetries Between Gains and Losses in Mood and Decision Making

The thesis begins by exploring a large-scale data set from the smartphone application The Great Brain Experiment. I leverage this sample size to show that gambling for prospective losses (but not gains) increases throughout the day. I introduce the question of how exploring asymmetries between attitudes and responses to gains and losses may provide useful insights in the field of Computational Psychiatry. The next section of the thesis concerns mood and affective states, and their connections to decision-making. I introduce a novel paradigm: the Future Prospects Task, which allows for a comparison between how people feel about choosing between prospective gains and prospective losses, and how they feel about such prospects in the future. Computational modelling reveals that affective responses to losses are greater than responses to gains, demonstrating an affective negativity bias. It also demonstrates that the valence of future prospects has an impact on affective state, and that risky decision-making increases with proximity to positive futures, and conversely decreases in proximity to negative futures. This novel paradigm was adapted for a new smartphone application The Happiness Project and for fMRI. Some of the early pilot results for the smartphone application are presented, and their feasibility for future longitudinal testing discussed. The fMRI paradigm and hypotheses are described in the discussion chapter, as data collection was disrupted due to COVID-19. I also endeavour in the thesis to further extend our understanding of models of affective dynamics, which have become popular in the last decade. I include analyses of robustness, and highlight the statistical issues that should be taken into account with their usag

Probabilistic models for human behavior learning

The problem of human behavior learning is a popular interdisciplinary research topic that has been explored from multiple perspectives, with a principal branch of study in the context of computer vision systems and activity recognition. However, the statistical methods used in these frameworks typically assume short time scales, usually of minutes or even seconds. The emergence of mobile electronic devices, such as smartphones and wearables, has changed this paradigm as long as we are now able to massively collect digital records from users. This collection of smartphone-generated data, whose attributes are obtained in an unobtrusive manner from the devices via multiple sensors and apps, shape the behavioral footprint that is unique for everyone of us. At an individual level, the data projection also di ers from person to person, as not all sensors are equal, neither the apps installed, or the devices used in the real life. This point actually reflects that learning the human behavior from the digital signature of users is an arduous task, that requires to fuse irregular data. For instance, collections of samples that are corrupted, heterogeneous, outliers or have shortterm correlations. The statistical modelling of this sort of objects is one of the principal contributions of this thesis, that we study from the perspective of Gaussian processes (gp). In the particular case of humans, as well as many other life species in our world, we are inherently conditioned to the diurnal and nocturnal cycles that everyday shape our behavior, and hence, our data. We can study these cycles in our behavioral representation to see that there exists a perpetual circadian rhytm in everyone of us. This tempo is the 24h periodic component that shapes the baseline temporal structure of our behavior, not the particular patterns that change for every person. Looking to the trajectories and variabilities that our behavior may take in the data, we can appreciate that there is not a single repetitive behavior. Instead, there are typically several patterns or routines, sampled from our own dictionary, that we choose for every special situation. At the same time, these routines are arbitrary combinations of di erents timescales, correlations, levels of mobility, social interaction, sleep quality or will for working during the same hours on weekdays. Together, the properties of human behavior already indicate to us how we shall proceed to model its structure, not as unique functions, but as a dictionary of latent behavioral profiles. To discover them, we have considered latent variable models. The main application of the statistical methods developed for human behavior learning appears as we look to medicine. Having a personalized model that is accurately fitted to the behavioral patterns of some patient of interest, sudden changes in them could be early indicators of future relapses. From a technical point of view, the traditional question use to be if newer observations conform or not to the expected behavior indicated by the already fitted model. The problem can be analyzed from two perspectives that are interrelated, one more oriented to the characterization of that single object as outlier, typically named as anomaly detection, and another focused in refreshing the learning model if no longer fits to the new sequential data. This last problem, widely known as change-point detection (cpd) is another pillar of this thesis. These methods are oriented to mental health applications, and particularly to the passive detection of crisis events. The final goal is to provide an early detection methodology based on probabilistic modeling for early intervention, e.g. prevent suicide attempts, on psychiatric outpatients with severe a ective disorders of higher prevalence, such as depression or bipolar diseases.El problema de aprendizaje del comportamiento humano es un tema de investigación interdisciplinar que ha sido explorado desde múltiples perspectivas, con una línea de estudio principal en torno a los sistemas de visión por ordenador y el reconocimiento de actividades. Sin embargo, los métodos estadísticos usados en estos casos suelen asumir escalas de tiempo cortas, generalmente de minutos o incluso segundos. La aparición de tecnologías móviles, tales como teléfonos o relojes inteligentes, ha cambiado este paradigma, dado que ahora es posible recolectar ingentes colecciones de datos a partir de los usuarios. Este conjunto de datos generados a partir de nuestro teléfono, cuyos atributos se obtienen de manera no invasiva desde múltiples sensores y apps, conforman la huella de comportamiento que es única para cada uno de nosotros. A nivel individual, la proyección sobre los datos difiere de persona a persona, dado que no todos los sensores son iguales, ni las apps instaladas así como los dispositivos utilizados en la vida real. Esto precisamente refleja que el aprendizaje del comportamiento humano a partir de la huella digital de los usuarios es una ardua tarea, que requiere principalmente fusionar datos irregulares. Por ejemplo, colecciones de muestras corruptas, heterogéneas, con outliers o poseedoras de correlaciones cortas. El modelado estadístico de este tipo de objetos es una de las contribuciones principales de esta tesis, que estudiamos desde la perspectiva de los procesos Gaussianos (gp). En el caso particular de los humanos, así como para muchas otras especies en nuestro planeta, estamos inherentemente condicionados a los ciclos diurnos y nocturnos que cada día dan forma a nuestro comportamiento, y por tanto, a nuestros datos. Podemos estudiar estos ciclos en la representación del comportamiento que obtenemos y ver que realmente existe un ritmo circadiano perpetuo en cada uno de nosotros. Este tempo es en realidad la componente periódica de 24 horas que construye la base sobre la que se asienta nuestro comportamiento, no únicamente los patrones que cambian para cada persona. Mirando a las trayectorias y variabilidades que nuestro comportamiento puede plasmar en los datos, podemos apreciar que no existe un comportamiento único y repetitivo. En su lugar, hay varios patrones o rutinas, obtenidas de nuestro propio diccionario, que elegimos para cada situación especial. Al mismo tiempo, estas rutinas son combinaciones arbitrarias de diferentes escalas de tiempo, correlaciones, niveles de movilidad, interacción social, calidad del sueño o iniciativa para trabajar durante las mismas horas cada día laborable. Juntas, estas propiedades del comportamiento humano nos indican como debemos proceder a modelar su estructura, no como funciones únicas, sino como un diccionario de perfiles ocultos de comportamiento, Para descubrirlos, hemos considerado modelos de variables latentes. La aplicación principal de los modelos estadísticos desarrollados para el aprendizaje de comportamiento humano aparece en cuanto miramos a la medicina. Teniendo un modelo personalizado que está ajustado de una manera precisa a los patrones de comportamiento de un paciente, los cambios espontáneos en ellos pueden ser indicadores de futuras recaídas. Desde un punto de vista técnico, la pregunta clásica suele ser si nuevas observaciones encajan o no con lo indicado por el modelo. Este problema se puede enfocar desde dos perspectivas que están interrelacionadas, una más orientada a la caracterización de aquellos objetos como outliers, que usualmente se conoce como detección de anomalías, y otro enfocado en refrescar el modelo de aprendizaje si este deja de ajustarse debidamente a los nuevos datos secuenciales. Este último problema, ampliamente conocido como detección de puntos de cambio (cpd) es otro de los pilares de esta tesis. Estos métodos se han orientado a aplicaciones de salud mental, y particularmente, a la detección pasiva de eventos críticos. El objetivo final es proveer de una metodología de detección temprana basada en el modelado probabilístico para intervenciones rápidas. Por ejemplo, de cara a prever intentos de suicidio en pacientes fuera de hospitales con trastornos afectivos severos de gran prevalencia, como depresión o síndrome bipolar.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Pablo Martínez Olmos.- Secretario: Daniel Hernández Lobato.- Vocal: Javier González Hernánde

Evaluating Cognitive Screening as a Possible Solution to Reducing Accidents and Improving Workplace Productivity through Early Preventive Detection of Fatigue-Impairment in the Construction Industry

Fatigue is emerging as a significant concern in the workplace principally focused on its relationship to accidents and lost productivity. Construction work exposes workers to many hazards and if safety programmes are not effective, accidents will result. Based on the sector’s safety performance, workers are not being adequately protected and improvement is needed. Fatigue-related impairment has been identified as a subject of concern for all workplaces yet it is not yet a focus within construction and few operational studies have been undertaken to develop tools to assist with identification and control of this workplace impairment. This research started with an assessment of the management of impairment within the global construction industry as well as an evaluation of tools that might assist in identification and classification of fatigue levels. In particular, cognitive tests were studied and shown to have sensitivity to natural changes in alertness in an operational setting. A small battery of cognitive tests was compared and showed that cognitive tests based on reaction times were possible candidates to help identify fatigue-related impairment in real time. The top performing tests were then used as possible surrogate measures for fatigue. To finally assess their performance capability their output was compared to estimations from an advanced actigraph-fed fatigue model. 100 volunteer workers each wore an actigraph for a month each to collect information on their personal sleep/wake cycles and activity whilst periodically doing the cognitive tests. The data from the actigraphs was analyzed by proprietary software to determine individual performance effectiveness. It was found that output from these simple, quick, and low cost tests significantly correlated with the most advanced actigraph-fed fatigue model. It is concluded that cognitive tests can be used as screens for fatigue-related impairment in the workplace. All primary parameters used for modelling showed extremely high significance (Pr( >Chisq) < 2.2e-16) in correlation to fatigue-based effectiveness results and could be developed into a screening tool for fatigue-related impairment in the construction industry as part of a fatigue management programme.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Determining Contributing Factors for Passenger Airline Pilot Perceived Fatigue

Fatigue is a recurring concern for pilots and continues to be a common contributing cause of aircraft accidents. The purpose of the dissertation was to determine factors that influence fatigue in commercial airline pilots. The ability to accurately associate fatigue in pilots before a flight begins could have a profound and meaningful impact on aviation safety. Seven factors were identified in the literature review as having possible predictive capabilities of perceived fatigue in pilots working for passenger carriers, including time awake, perceived stress, sleep quality, hours of sleep, age, typically scheduled start time, and hours on duty. An electronic survey instrument was used to gather quantitative data from U.S. passenger-carrying airline pilots. Data collected from 271 responses were randomly assigned to two separate groups. First, a regression equation was created utilizing half of the data collected from a survey instrument. The regression identified that age, hours on duty, and sleep quality (JSS) were significant independent variables (IVs) contributing to fatigue. Next, the regression equation was used to create predicted values of perceived fatigue. Then the second half of the dataset was used to validate if the equation could be utilized to identify contributing factors for passenger airline pilots\u27 perceived fatigue. Data were created with the regression equation and compared to perceived fatigue. The model was a moderate fit for the second data set. The analysis identified age as a negative predictor, indicating that fatigue (FSS) decreases as age increases. Age also had the smallest effect size of the significant IVs. These two items, while counterintuitive, are possibly explained by variances in schedules between pilot seniority. Sleep Quality (JSS) had the most significant effect on fatigue, while hours on duty had a larger effect than age but a smaller effect than sleep quality. Four variables studied were not significant predictors of fatigue and were not used in model creation: time awake, perceived stress, hours of sleep, and typically scheduled start time. Safely operating a flight involves weighing the implications of fatigue and other possible hazards resulting in many possible predictive factors. Heinrich’s domino theory was used to derive the fatigue factors in this dissertation. The significant predictor variables, age, hours on duty, and sleep quality form a potential “domino” for a fatigue- related accident. These fatigue factors may not cause an accident but could be a “domino” in a series of factors. While some fatigue factors have been studied, the factors studied in this dissertation have not previously been studied in the same way by creating a model with this population. Additionally, previous fatigue studies have not typically researched U.S.- based passenger-carrying pilots. Analyzing risks associated with fatigue in passenger- carrying pilots at commercial airlines is particularly complex because many factors can influence fatigue, including scheduling software, union contracts, and norms and practices. Airlines and regulators could use the prediction equation to potentially reduce fatigue-related risks. The equation created can predict fatigue in advance of scheduled flights and serve as a starting point for future fatigue researchers

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202