Experimental and theoretical models of cultural evolution

This thesis contributes to the field of cultural evolution by presenting two experimental and two theoretical models of cultural evolution. Prior to presenting these I survey existing experimental and theoretical models of cultural evolution. In the first experiment, I test the hypothesis that increasing group size speeds up cultural accumulation, using a novel puzzle-solving task and within a transmission chain design. I find support for this hypothesis, in contrast with previous experiments. In the second experiment, also using a transmission chain design, I examine perceptual errors in recreating Acheulean handaxes and ask whether such errors can account for the variability of Acheulean technology over time. Using the accumulated copying error model to compare the experimental data to archaeological records, I conclude that perceptual errors alone were likely not the driving force behind Acheulean evolution. In the first theoretical chapter, I present models of cultural differences between populations and of cumulative culture, which build on existing models and accord with empirical data. I then show that the models, when combined, have two qualitative regimes which may correspond to human and nonhuman culture. In the second theoretical chapter, I present a ‘fundamental theorem of cultural selection’, an equivalent of Fisher’s Fundamental Theorem of Natural Selection for cultural evolution. I discuss how this theorem formalizes and sheds light on cultural evolutionary theory. Finally I conclude and discuss future research directions

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Detailed occupancy prediction, occupancy-sensing control and advanced behavioural modelling within whole-building energy simulation

Cette étude a pour but de combler l'écart entre l'état actuel de la simulation énergétique dans le domaine du bâtiment (i.e. hypothèses et modèles) et la connaissance empirique sur le comportement des usagers en matière de contrôle environnemental. L'application principale issue de cette thèse est un module de simulation autonome qui vise la modélisation à haute résolution et à haute fréquence des interactions personne-milieu: de l'occupation des locaux (i.e. l'affectation individuelle d'un environnement modélisé), du contrôle basé uniquement sur la présence ou l'absence des occupants (e.g. détecteurs de mouvement), jusqu'aux modèles comportementaux plus avancés (e.g. commutation manuelle des appareils d'éclairage, l'utilisation des fenêtres ouvrantes). L'intégration du module au sein du logiciel libre ESP-r, un programme qui permet de simuler l'ensemble des interactions bâtiment-systèmes-environnement, permet d'étudier à quel point les modèles d'interactions personne-milieu, issus des études en milieu réel, peuvent influencer les besoins énergétiques d'un bâtiment donné. Certains traits comportementaux, couramment associés aux modèles de contrôle manuel des systèmes d'éclairage, caractérisent également le comportement individuel au niveau des fenêtres ouvrantes; une conclusion issue d'une étude pilote en milieu réel sur le campus de l'Université Laval (Québec). Cette constatation suggère certains traits communs pouvant décrire le comportement des usagers en matière de contrôle environnemental. Le module développé permet également d'étudier le potentiel écoénergétique de stratégies innovatrices: l'application de stratégies de contrôle reposant sur l'adaptation thermique dans un contexte de climatisation hybride, et basées sur l'opération de fenêtres ouvrantes en tant que commutateurs entre climat naturel et climat artificiel. Les résultats préliminaires suggèrent que pour les climats nordiques ou méridionaux, ces approches permettent effectivement de réduire les besoins en climatisation, mais qu'en contre partie les besoins en chauffage augmentent considérablement en raison de l'utilisation des fenêtres en périodes plus tempérées. L'intérêt de la méthode est ici mis en évidence dans sa capacité à simuler globalement l'ensemble des conséquences énergétiques de l'interaction sociale avec l'environnement bâti.This study sets out to bridge the gap between building energy simulation and empirical evidence on occupant behaviour. The major output is a self-contained simulation module that aims to control all occupant-related phenomena which can affect energy use in buildings. It provides high resolution and high frequency occupancy prediction (i.e. when occupants as individual agents occupy a modelled environment), occupant-sensing control (i.e. as driven by the mere presence of one or more occupants, such as occupancy-sensing lighting controls), as well as advanced behavioural models (i.e. active personal control, such as manual switching of lights, manual adjustments to window blinds, operable windows, personalized air-conditioning units). The module is integrated within the ESP-r free software, a whole-building energy simulation program. Simulation results clearly show that occupants-based phenomena exert a strong influence on simulated energy use, revealing a number of limitations in key assumptions in current energy simulation practice. Key behavioural traits, commonly associated to lighting behavioural patterns, also appear to be associated to personal control of operable windows, as demonstrated in a pilot field study in a Université Laval pavilion in Québec. This may suggest an abstract quality to certain behavioural concepts regarding different environmental controls. The study then focuses on the use of the developed work to investigate the energy saving potential of novel yet untried strategies: adaptive comfort control algorithms in hybrid environments, based on the use of operable windows as switching mechanisms between natural and artificial modes of environmental control. Results suggest that for both heating- and cooling-dominant climates, adaptive comfort control effectively reduces cooling requirements, yet operable window use during cooler conditions appear to increase heating requirements. The usefulness of the original method is here illustrated by providing a more complete view on energy use attributed to occupant behaviour

Topics on multiple hypotheses testing and generalized linear model

In applications such as studying drug adverse events (AE) in clinical trials and identifying differentially expressed genes in microarray experiments, the data of the experiments usually consists of frequency counts. In the analysis of such data, researchers often face multiple hypotheses testing based on discrete test statistics. Incorporating this discrete property of the data, several stepwise procedures, which allow to use the CDF of p-values to determine the testing threshold, are proposed for controlling familiwise error rate (FWER). It is shown that the proposed procedures strongly control the FWER and are more powerful than the existing ones for discrete data. Through some simulation studies and real data examples, the proposed procedures are shown to outperform the existing procedures in terms of the FWER control and power. An R package “MHTdiscrete” and a web application are developed for implementing the proposed procedures for discrete data. Many complex biomedical studies, such as clinical safety studies and genome-wide association studies, often involve testing multiple families of hypotheses. Most existing multiple testing methods cannot guarantee strong control of appropriate type 1 error rates suitable for such increasingly complex research questions. A novel two-stage procedure based on the recently developed idea of selective inference for clinical safety studies is introduced. In the first stage, some significant families are selected by using some family-level global test, which guarantees control of generalized familywise error rate (k-FWER) among the selected families. In the second stage, individual hypotheses are tested for each selected families by using some multiple testing procedure, which controls conditional false discovery rate (cFDR) based on the fact that the family is selected. By applying the proposed procedure to clinical safety studies, one can not only efficiently flag the significant clinical adverse events (AEs) but also select body systems of interest (BSoI) as extra information for further research. The simulation studies show that the proposed procedure can be more reliable than alternative methods such as Mehrotra and Heyse’s double FDR procedure in the setting of clinical safety. The proposed procedure for multiple families structure is implemented in the R package “MHTmult”. Categorical data arises in biomedical and healthcare experiments naturally. In many of these cases, the outcome variables of interest are the numbers of special events. At least one distinct special event category is observed, when the negative multinomial and extended negative multinomial or generalized inverse sampling scheme-based regression models are used. The new model, based on generalized inverse sampling scheme for several special events, is developed in this dissertation. This research is an adaption to the widely used multinomial logistic regression model. The resulting equations of the proposed model, corresponding to the natural log of the ratio of the expected responses, appears similar to the multinomial logistic regression. Using this expected response ratio of a category to that of the special category, the maximum likelihood estimator of the regression parameters can be computed by creating score equations and the Hessian matrix of the likelihood. The covariance matrix of estimators of the regression parameters for the new model can be estimated by inverting the Hessian matrix to develop the inference. This research also develops model diagnostics such as normality check with deviance and Pearson residuals, and likelihood based computations. The proposed model is implemented in the R package “mvlogit”

Dynamic pain-emotion relations in chronic pain: a theoretical review of moderation studies

Current developments in chronic pain research are changing the focus in the study of pain-emotion relations from the identification of general patterns to the study of dynamic and context-related interactions manifesting both within and between individuals. This shift towards understanding variation at both intra- and interpersonal levels has significant clinical implications for psychological adjustment to chronic pain conditions, and thus represents an important topic for both clinical and health psychology. This article reviews the existing theoretical explanations of these dynamics and their emerging empirical support, and suggests further areas of investigation. A literature search identified research on moderators of pain-emotion relations in chronic pain; existing theories were also examined from this perspective. A theoretical analysis revealed several important contributions, including the concepts of affect differentiation, generalised discrimination ability, resilience, vulnerability, coping, emotion regulation and desynchrony, which are described here together with the relevant empirical research and clinical implications. Important areas for development are the clarification of the common elements and opposing predictions and the empirical examination of mediating mechanisms. Several methodological issues are discussed. This review identifies a rich theoretical basis for research into pain-emotion moderation, and suggests that further examinations of such relationships might hold important clinical consequences

Analysis of repeated measures in clinical trials using summary statistics

This thesis is concerned with statistical methodology for randomized clinical trials with repeated measurements over time, as regards both data analysis and the implications for study design. The inherent within-subject dependencies for repeated measurements necessitate analyses that take account of their covariance structure. There exists a whole battery of methods for analysing repeated measures designs, ranging from very simple (e.g. separate t-tests at each time-point) to very complicated (e.g. multi-level models with arbitrary error structures), but I will focus on "the summary statistic approach" which has recently become increasingly popular. When interest centres around the average response to treatment over time, a logical choice of summary statistic is the mean of each subject's post-randomisation measurements, with appropriate adjustment for pre-treatment measurements. Among the class of "mean summary statistics" analysis of covariance (ANCOVA) is shown to be superior to its competitors. In particular, variance formulae are derived both under a general covariance structure and more specific cases (e.g. compound symmetry) , allowing direct comparisons of efficiency among different summary statistics and repeated measures designs. The importance of precise estimates of the pre-entry levels and the consequences for sample size requirements are emphasized. Some additional topics in relation to mean summary statistics, notably; the bias in estimation if pre-treatment means differ, the choice between additive or multiplicative models, and the summary statistic "area under the curve", are also investigated. For studies with restrictions on the range of baseline measurements the negative consequences incurred by "regression to the mean" are explored, especially regarding the variance for between-group comparisons. For a more general class of true treatment effects over time, the optimal linear summary statistic under any covariance structure is derived. Special interest is devoted to the case of linearly diverging mean treatment curves, where the optimal alternative to the comparison of slopes is defined. Asymptotic relative efficiencies are shown to be a useful tool when contrasting different designs and different summary statistics, both in the planning and reporting of repeated measures clinical trials. Finally, comparisons with other approaches are made, and recommendations given based on the need to balance theoretical considerations with practical matters