The evolution of knowledge specialization: Expertise, trust, and the supernatural across traditional cultures

Evolutionary approaches to the acquisition and transmission of cultural knowledge are diverse, and they have motivated much theorizing among anthropologists and other social scientists. However, fewer have focused on cross-cultural and data-driven approaches to investigating specialists who invest heavily in locally useful knowledge, who will be referred to as knowledge specialists. This dissertation therefore analyzes how and why knowledge specialists acquire useful cultural knowledge, and explores how they provide services to others in their communities. Conversely, it also investigates the criteria by which others come to trust the information provided to them by a knowledgeable source, such as a specialist. The key findings in this dissertation suggest that (1) most individuals are skeptical of socially learned information when it might not stand to benefit them personally, and (2) knowledge specialists who socially supply individuals with information either effectively provide a beneficial service in exchange for payment, and/or mentor their acolytes in exchange for status and prestige. The extent to which individuals trust socially learned information is associated with external influences such as acculturation and market integration, and the extent to which knowledge specialists provide effective services vs. mentorship is associated with characteristics of their domain of expertise, such as how rare and uncertain the problems they resolve are, and whether or not their skills involve readily observable behaviors that are easy to copy. When a domain of expertise involves rare, serious, and mysterious phenomena, specialists frequently provide services to clients while invoking the supernatural and keeping their proprietary know-how a secret, whereas their clients judge specialists' services based on their effectiveness. Throughout this dissertation, this idea is developed into a hypothesis to be tested in future research, referred to as a market for specialists. In general, the findings in this dissertation speak to a broader trend in the current cultural evolutionary literature, which favors a continuing convergence of previously disparate schools of thought. Specifically, they demonstrate that the relevance of existing theoretical models of cultural transmission and knowledge specialization might depend strongly and non-randomly upon context

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Knowledge and knowers of the past: A study in the philosophy of evolutionary biology.

This dissertation proposes an exploration of a variety of themes in philosophy of science through the lens of a case study in evolutionary biology. It draws from a careful analysis and comparison of the hypotheses from Bill Martin and Tom Cavalier-Smith. These two scientists produced contrasted and competing accounts for one of the main events in the history of life, the origin of eukaryotic cells. This case study feeds four main philosophical themes around which this dissertation is articulated. (1) Theorizing: What kind of theory are hypotheses about unique events in the past? (2) Representation: How do hypotheses about the past represent their target? (3) Evidential claims: What kind of evidence is employed and how do they constrain these hypotheses? (4) Pluralism: What are the benefits and the risks associated with the coexistence of rival hypotheses? This work both seeks to rearticulate traditional debates in philosophy of science in the light of a lesser-known case of scientific practice and to enrich the catalogue of existing case studies in the philosophy of historical sciences

Predicting and preventing relapse of depression in primary care: a mixed methods study

BackgroundMost people with depression are managed in primary care. Relapse (reemergence of depression symptoms after improvement) is common and contributes to the burden and morbidity associated with depression. There is a lack of evidence-based approaches for risk-stratifying people according to risk of relapse and for preventing relapse in primary care.MethodsIn this mixed methods study, I initially reviewed studies looking to predict relapse of depression across all settings. I then attempted to derive and validate a prognostic model to predict relapse within 6-8 months in a primary care setting, using multilevel logistic regression analysis on individual participant data from seven studies (n=1244). Concurrently, a qualitative workstream, using thematic analysis, explored the perspectives of general practitioners (GPs) and people with lived experience of depression around relapse risk and prevention in practice.ResultsThe systematic review identified eleven models; none could currently be implemented in a primary care setting. The prognostic model developed in this study had inadequate predictive performance on internal validation (Cstatistic 0.60; calibration slope 0.81). I carried out twenty-two semi-structured interviews with GPs and twenty-three with people with lived experience of depression. People with lived experience of depression and GPs reflected that a discussion around relapse would be useful but was not routinely offered. Both participant groups felt there would be benefits to relapse prevention for depression being embedded within primary care.ConclusionsWe are currently unable to accurately predict an individual’s risk ofdepression relapse. The longer-term care of people with depression ingeneral practice could be improved by enabling continuity of care, increased consistency and clarity around follow-up arrangements, and focussed discussions around relapse risk and prevention. Scalable, brief relapse prevention interventions are needed, which would require policy change and additional resource. We need to better understand existing interventions and barriers to implementation in practice

Bioinformatic Investigations Into the Genetic Architecture of Renal Disorders

Modern genomic analysis has a significant bioinformatic component due to the high volume of complex data that is involved. During investigations into the genetic components of two renal diseases, we developed two software tools. // Genome-Wide Association Studies (GWAS) datasets may be genotyped on different microarrays and subject to different annotation, leading to a mosaic case-control cohort that has inherent errors, primarily due to strand mismatching. Our software REMEDY seeks to detect and correct strand designation of input datasets, as well as filtering for common sources of noise such as structural and multi-allelic variants. We performed a GWAS on a large cohort of Steroid-sensitive nephrotic syndrome samples; the mosaic input datasets were pre-processed with REMEDY prior to merging and analysis. Our results show that REMEDY significantly reduced noise in GWAS output results. REMEDY outperforms existing software as it has significantly more features available such as auto-strand designation detection, comprehensive variant filtering and high-speed variant matching to dbSNP. // The second tool supported the analysis of a newly characterised rare renal disorder: Polycystic kidney disease with hyperinsulinemic hypoglycemia (HIPKD). Identification of the underlying genetic cause led to the hypothesis that a change in chromatin looping at a specific locus affected the aetiology of the disease. We developed LOOPER, a software suite capable of predicting chromatin loops from ChIP-Seq data to explore the possible conformations of chromatin architecture in the HIPKD genomic region. LOOPER predicted several interesting functional and structural loops that supported our hypothesis. We then extended LOOPER to visualise ChIA-PET and ChIP-Seq data as a force-directed graph to show experimental structural and functional chromatin interactions. Next, we re-analysed the HIPKD region with LOOPER to show experimentally validated chromatin interactions. We first confirmed our original predicted loops and subsequently discovered that the local genomic region has many more chromatin features than first thought

What I talk about when I talk about integration of single-cell data

Over the past decade, single-cell technologies evolved from profiling hundreds of cells to millions of cells, and emerged from a single modality of data to cover multiple views at single-cell resolution, including genome, epigenome, transcriptome, and so on. With advance of these single-cell technologies, the booming of multimodal single-cell data creates a valuable resource for us to understand cellular heterogeneity and molecular mechanism at a comprehensive level. However, the large-scale multimodal single-cell data also presents a huge computational challenge for insightful integrative analysis. Here, I will lay out problems in data integration that single-cell research community is interested in and introduce computational principles for solving these integration problems. In the following chapters, I will present four computational methods for data integration under different scenarios. Finally, I will discuss some future directions and potential applications of single-cell data integration

Safety Culture, Training, Understanding, Aviation Passion: The Impact on Manual Flight and Operational Performance

The objective of this study was to understand pilots’ proclivity toward automation usage by identifying the relationship among pilot training, aircraft and systems understanding, safety culture, manual flight behavior, and aviation passion. A survey instrument titled Manual Flight Inventory (MFI) was designed to gather and assess self-reported variables of manual flight behavior, aviation passion, safety culture perception, pilot training, and pilot understanding. Demographic data and automation opinion-based questions were also asked to fully understand pilots’ thoughts on automation, safety culture, policies, procedures, training methodologies and assessment measures, levels of understanding, and study techniques. Exploratory Factor Analysis (EFA) was utilized to identify underlying factors from the data, followed by confirmatory factor analysis (CFA) to confirm the factor structure. Structural Equation Modeling (SEM) was utilized to test the relationships between the variables. All hypotheses were significant; however, four of the thirteen hypotheses were not supported due to a negative relationship. The significant predictors of manual flight were identified to be pilot understanding, pilot training, aviation passion, and safety culture. Pilots’ understanding of the aircraft operating systems was determined to have the greatest influence over a pilot’s decision to manually fly. Aviation passion was identified as the second largest influencing factor. Pilot training had the greatest influence over pilot understanding, and safety culture presented the greatest influence over pilot training. Results identified that safety culture was negatively impacting pilot training, and pilot training had a negative influence over pilots’ decision to manually fly. The contributions of this research have identified the significance of safety culture as associated with Safety Management Systems (SMS) as an influencing factor over pilot training and resultant operational performance. Pilot understanding is a direct result of pilot training, and current training practices are negatively influencing the decision for manual flight. Therefore, a solution to the industry problem—operational confusion (understanding), as well as guidance versus control (Abbott, 2015), and the lack of hand flying skills and monitoring ability (OIG, 2016)—can now be addressed by improving training practices. Future research and recommendations were provided

Deep Interpretability Methods for Neuroimaging

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training. We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain. This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial

The Bright and Dark side of motivation in exercise: promoting persistence and adherence

Turning physical exercise into a habitual behavior is a complex process. Studies have shown that individuals tend to drop-out in the first stages and that the number of withdrawal episodes is highest amongst new participants. Individuals point out the lack of motivation as the one of the main reasons for not engaging in exercise participation. Therefore, understanding the motivational determinants behind exercise commitment seems paramount to reverse the current rates of physical inactivity and sedentary behaviors. Research has assessed several motivational frameworks attempting to deepen the literature on how to increase physical activity rates. However, studies analyzing the entire motivational sequences and/or considering the influence of other cognitive constructs such as intention on exercise commitment are scarce. Thus, the main purpose of the present work was to assess the determinants of the bright and dark side of motivation and their relationship with exercise persistence and adherence. To accomplish this, we reviewed the current literature, translated and validated four scales, and measured the impact of the bright and dark sides of motivational determinants on exercise adherence and persistence. The results of the eight studies included in this thesis showed that: i) our systematic review was the first one to consider the full casual sequence of motivational constructs according to Self-Determination Theory in the exercise context; ii) the four translated and validated scales have adjusted psychometric proprieties and can be reliably used in future research with Portuguese individuals in the exercise context; ii) polynomial regression analysis with response surface methodology is a strong statistical procedure on how two similar but distinct independent variables interact on one dependent variable; iii) enjoyment is a strong predictor of exercise persistence and should be therefore considered by exercise professionals when promoting physical exercise; iv) past exercise adherence is the strongest forecaster of future exercise adherence. Results showed that a regular two times weekly frequency is necessary to promote habitual behavior; v) encompassing several theory constructs into one comprehensive model seems thought-provoking in measuring how they impact directly and indirectly exercise outcomes, and; vi) future interventions should consider interpersonal behaviors as promoters for exercise commitment. Perceived supportive behaviors by exercisers lead to increased intentions to maintain exercise participation, whereas perceived thwarting behaviors are responsible for higher rates of drop-out. Overall, this research provides new insights on how interpersonal behaviors are responsible for exercise outcomes; offers important practical implications for the fitness industry and researchers on how to design adequate interventions aiming at promoting exercise adherence and points out the relevance of the social context and past behavior for exercise outcomes.Tornar o exercício físico num comportamento rotineiro é um processo complexo. Estudos anteriores evidenciam que a desistência da prática de exercício físico ocorre nas primeiras fases do comportamento e que os episódios de desistência são elevados entre novos praticantes. A falta de motivação é apontada por muitos como um dos principais motivos para o não envolvimento na prática regular de exercício físico. Almejando reverter as atuais taxas de inatividade física e comportamentos sedentários, torna-se necessário compreender e analisar o papel das determinantes motivacionais na persistência e na adesão à prática de exercício físico. Diferentes quadros conceptuais e motivacionais têm sido utilizados nos estudos que procuram analisar formas de aumentar as taxas de atividade física. No entanto, a literatura existente não considera as sequências teóricas completas e/ou não considera outros construtos cognitivos como por exemplo a intenção, na análise da persistência da prática de exercício físico. Este trabalho consistiu em analisar as determinantes motivacionais na persistência e adesão à prática de exercício físico. De forma alcançar este objetivo, realizamos uma revisão da literatura, traduzimos e validamos quatro escalas e medimos a sequência teórica e motivacional mais favorável e mais adversa na persistência e na adesão. Os resultados dos oito estudos científicos englobados nesta tese de doutoramento mostram que: i) a nossa revisão sistemática foi a primeira a considerar a sequência teórica e motivacional mais favorável e mais adversa na persistência e na adesão; ii) as regressões polinomiais com gráficos de superfície são um procedimento estatístico robusto, que considera duas variáveis independentes semelhantes, mas distintas, e a sua interação com uma variável dependente; iii) o divertimento é um preditor forte e deve ser tido em conta pelos técnicos profissionais aquando da promoção do exercício físico; iv) a experiência passada é um dos preditores mais fortes na adesão futura; v) abranger vários quadros teóricos num modelo complexo parece revelar em medir o impacto das determinantes de forma direta e indireta no comportamento; e, vi) investigações futuras devem considerar os comportamentos interpessoais como promotores do compromisso com o exercício. A perceção de comportamentos de suporte pelos praticantes de exercícios levam a maiores intenções de manter a participação no exercício no futuro. De forma oposta, a perceção de comportamentos de frustração é responsável por maiores taxas de abandono. Em suma, esta tese contribui para o avanço conceptual sobre como os comportamentos interpessoais se encontrar relacionados com a manutenção da prática de exercício apresenta diversas implicações práticas importantes para a indústria do fitness e para os investigadores sobre a criação de intervenções adequadas na promoção da prática do exercício

Storytelling to Promote Mental Health: A Conceptual Analysis and Application with Acceptance and Commitment Therapy for Depression

Mental health treatments can be delivered in many ways. One approach is to use storytelling to communicate healthy practices. While societies across the world have engaged in storytelling for thousands of years, these practices have been used less in the mental health field. The aim of this project was to study the overlap between the areas of mental health and storytelling. We also tested how a storytelling-based mental health treatment could help solve a particular clinical problem. In this case, the problem of people who receive inadequate help for managing depression through medication alone. We examined one particular mental health intervention, called Acceptance and Commitment Therapy (ACT), and its overlap with basic principles of storytelling. The central goal of ACT is to live more fully according to one’s personal values even in the presence of emotional suffering. We explain technically how reading, hearing, or seeing engaging stories could support this goal. We also describe ways that therapists who use ACT with their patients can draw from these storytelling-based principles. The second part of this project was a specific test of what happens when ACT and storytelling are combined in a mental health treatment. LifeStories is an online mental health program that teaches ACT-based skills for managing depression through the use of personal narrative videos of other patients who have developed effective ways of coping. We tested LifeStories with a group of primary care patients who were prescribed vi antidepressant medication but were not receiving other mental health support. Half of these patients used the LifeStories program for four weeks in addition to taking their medication, while the other half only took medication. We found that patients who used LifeStories had greater increases in quality of life compared to those only taking medication. These patients also became more interested in continuing mental health treatment after the program ended. In both groups of patients, depression severity decreased at the same rate, as did psychological inflexibility. Overall, our study showed that a brief storytelling intervention can improve quality of life and promote interest in seeking further mental health support for primary care patients