Two -way direction of influence in mother-infant interaction as denoted by concurrent reciprocal learning processes

This series of 5 single-subject studies used the operant conditioning paradigm to investigate, within the two-way influence process, how (a) contingent infant attention can reinforce maternal verbal behaviors during a period of mother-infant interaction and under subsequent experimental manipulation. Differential reinforcement was used to determine if it is possible that an infant attending to the mother (denoted by head-turns towards the image of the mother plus eye contact) increases (reinforces) the mother's verbal response (to a cue from the infant) upon which the infant behavior is contingent. There was also (b) an evaluation during the contrived parent-infant interaction for concurrent operant learning of infant vocal behavior via contingent verbal responding (reinforcement) implemented by the mother. Further, it was noted (c) whether or not the mother reported being aware that her responses were influenced by the infant's behavior. Findings showed: the operant conditioning of the maternal verbal behaviors were reinforced by contingent infant attention; and the operant conditioning of infant vocalizations was reinforced by contingent maternal verbal behaviors. No parent reported (1) being aware of the increase in their verbal response reinforced during operant conditioning of parental behavior nor a decrease in those responses during the DRA reversal phase, or (2) noticing a contingency between infant's and mother's response. By binomial 1-tail tests, the verbal-behavior patterns of the 5 mothers were conditioned by infant reinforcement (p < 0.02) and, concurrently, the vocal-response patterns of the 5 infants were conditioned by maternal reinforcement (p < 0.02). A program of systematic empirical research on the determinants of concurrent conditioning within mother-child interaction may provide a way to evaluate the differential effectiveness of interventions aimed at improving parent-child interactions. The work conducted in the present study is one step in this direction. ^

Integrated Performance Assessment of Engineering Projects at the Interface of Emergent Properties and Uncertainty

Investigation of the performance of engineering project organizations is critical for understanding and eliminating inefficiencies in today’s dynamic global markets. The existing theoretical frameworks consider project organizations as monolithic systems and attribute the performance of project organizations to the characteristics of the constituents. However, project organizations consist of complex interdependent networks of agents, information, and resources whose interactions give rise to emergent properties that affect the overall performance of project organizations. Yet, our understanding of the emergent properties in project organizations and their impact on project performance is rather limited. This limitation is one of the major barriers towards creation of integrated theories of performance assessment in project organizations. The objective of this paper is to investigate the emergent properties that affect the ability of project organization to cope with uncertainty. Based on the theories of complex systems, we propose and test a novel framework in which the likelihood of performance variations in project organizations could be investigated based on the environment of uncertainty (i.e., static complexity, dynamic complexity, and external source of disruption) as well as the emergent properties (i.e., absorptive capacity, adaptive capacity, and restorative capacity) of project organizations. The existence and significance of different dimensions of the environment of uncertainty and emergent properties in the proposed framework are tested based on the analysis of the information collected from interviews with senior project managers in the construction industry. The outcomes of this study provide a novel theoretical lens for proactive bottom-up investigation of performance in project organizations at the interface of emergent properties and uncertainty

The impact of migration and intergenerational changes on the Cuban family in the United States

This study examines changes in the Cuban family in the United States produced by time, migration, and the rise of new generations. The thesis will use a data set extracted from the 5% Public Use Microdata Series (PUMS) of the U.S. Decennial Census of Population for the years 1970, 1980 and 1990. Contingency table analysis and comparison of means were used to examine various family-related variables. The analysis points to changes in the traditional Cuban family towards less traditional family arrangements. The multigenerational feature of the Cuban household has diminished as the elderly have become independent and are more likely to be living on their own. Although female labor participation remains high, the occupational patterns of the first generation of Cuban women have diversified and a new trend has emerged for the second generation. The second generation of Cuban women demonstrates a strong inclination for white-collar occupations. Fertility rates remain low.

Mother-Infant Reciprocity and the Development of Joint Attention in 12 Month Old Infants

Social contingency is the ability to connect social stimuli, such as those behaviors performed by oneself and those performed by others. Detecting social contingencies occurs by means of reciprocity through shared experiences with others. Reciprocity denotes a circumstance in which two individuals participate in a collaborative exchange, and is distinguished from an event in which two individuals engage in separate, unrelated activities. Specifically, reciprocity incorporates joint attention (JA), which occurs when two individuals simultaneously and visually attend to the same item. JA is facilitated by gazing and pointing, whereby one individual initiates the action and the second individual follows suit by, for example, gaze-following. However, little is known about the role the mother may play in the development of JA. The purpose of our study was to investigate social contingency between mothers and infants engaging in dyadic interactions. Thirty-three 12-month-old typically developing infants (M = 12.2, SD = .19; N = 19 males) were filmed for 10 minutes during free play with their mothers and toys provided by an experimenter. Reciprocity was measured by coding mother-infant interactions when a precise chain of events occurred: (1) mother initiated a bid by introducing a toy/activity or request to the infant, (2) infant accepted the bid/request by engaging in play with the given toy/activity, and (3) mother persisted by continuing to engage in play with said toy/activity. We computed a Pearson Correlation to assess the relation between the mothers’ initiations of JA and their infants’ responses to JA. We found a moderately positive correlation between the two variables (r= 0.37, p<.05). Our findings suggest that reciprocity, an important component of social relationships, during parent-infant dyads may serve as a scaffold for joint attention abilities, which have been linked to social and language development.

Nostalgia do espaço e do tempo: uma leitura da obra memorialística de Câmara Cascudo

This dissertation addresses the work of the memoirs of the potiguar writer Luís da Câmara Cascudo (1898 - 1986) from an integrated reading of four works that comprise: O Tempo e Eu (1968), Pequeno Manual do Doente Aprendiz (1969), Na Ronda do Tempo (1971) and Ontem (1972). Produced under the contingency of the modern movement and urban reform, memories Cascudo evoke the old landscapes of old, populated by those who belonged to the old romantic and provincial Natal that no longer exists, but which still survives in the idealized memory author and that is (re)constructed by him from a written permeated with touches of imagination and a sense of nostalgia. Seeking to analyze how is the process of building memoirist of Cascudo, as well as reflect on the role that memory plays in the (re)construction of a time and a lost space, we used the studies of Maurice Halbwachs (2006) and Ecléa Bosi (1994). Within this theoretical framework, we seek, above all, to understand not only how the lived experiences of Cascudo will work in this matter of his memory, but also as this will guide a writing that touches on the history and social frameworks of the past

Spontaneity and international marketing performance

Purpose: The purpose of this paper is to ascertain how today’s international marketers can perform better on the global scene by harnessing spontaneity. Design/methodology/approach: The authors draw on contingency theory to develop a model of the spontaneity – international marketing performance relationship, and identify three potential moderators, namely, strategic planning, centralization, and market dynamism. The authors test the model via structural equation modeling with survey data from 197 UK exporters. Findings: The results indicate that spontaneity is beneficial to exporters in terms of enhancing profit performance. In addition, greater centralization and strategic planning strengthen the positive effects of spontaneity. However, market dynamism mitigates the positive effect of spontaneity on export performance (when customer needs are volatile, spontaneous decisions do not function as well in terms of ensuring success). Practical implications: Learning to be spontaneous when making export decisions appears to result in favorable outcomes for the export function. To harness spontaneity, export managers should look to develop company heuristics (increase centralization and strategic planning). Finally, if operating in dynamic export market environments, the role of spontaneity is weaker, so more conventional decision-making approaches should be adopted. Originality/value: The international marketing environment typically requires decisions to be flexible and fast. In this context, spontaneity could enable accelerated and responsive decision-making, allowing international marketers to realize superior performance. Yet, there is a lack of research on decision-making spontaneity and its potential for international marketing performance enhancement.

Team reflexivity and innovation:the moderating role of team context

We developed and tested a team level contingency model of innovation, integrating theories regarding work demands, team reflexivity - the extent to which teams collectively reflect upon their working methods and functioning -, and team innovation. We argued that highly reflexive teams will be more innovative than teams low in reflexivity when facing a demanding work environment. The relationships between team reflexivity, a demanding work environment (i.e. quality of the physical work environment and work load) and team innovation was examined among 98 primary health care teams (PHCTs) in the UK, comprised of 1137 individuals. Results showed that team reflexivity is positively related to team innovation, and that there is an interaction between team reflexivity, team level workload, and team innovation, such that when team level workload is high, combined with a high level of team reflexivity, team innovation is also higher. The complementary interaction between team reflexivity, quality of physical work environment, and team innovation, showed that when the quality of the work environment is low, combined with a high level of team reflexivity, team innovation was also higher. These results are discussed in the context of the need for team reflexivity and team innovation among teams at work facing high work demands.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

A Longitudinal Investigation of Infant Gesture Use and Parent Speech: Unique and Dynamic Influences on Infant Vocabulary Acquisition

How do infants learn word meanings? Research has established the impact of both parent and child behaviors on vocabulary development, however the processes and mechanisms underlying these relationships are still not fully understood. Much existing literature focuses on direct paths to word learning, demonstrating that parent speech and child gesture use are powerful predictors of later vocabulary. However, an additional body of research indicates that these relationships don’t always replicate, particularly when assessed in different populations, contexts, or developmental periods.

The current study examines the relationships between infant gesture, parent speech, and infant vocabulary over the course of the second year (10-22 months of age). Through the use of detailed coding of dyadic mother-child play interactions and a combination of quantitative and qualitative data analytic methods, the process of communicative development was explored. Findings reveal non-linear patterns of growth in both parent speech content and child gesture use. Analyses of contingency in dyadic interactions reveal that children are active contributors to communicative engagement through their use of gestures, shaping the type of input they receive from parents, which in turn influences child vocabulary acquisition. Recommendations for future studies and the use of nuanced methodologies to assess changes in the dynamic system of dyadic communication are discussed.

The Psychometric Properties of the Developmental Reading Assessment

ABSTRACT The purpose of this study was to examine the technical adequacy of the Developmental Reading Assessment (Beaver & Carter, 2004). Internal consistency analysis, factor analysis, and linear regression analyses were used to test whether the DRA is a statistically reliable measuring of reading comprehension for Grades 7 and 8 students. Correlational analyses, decision consistency analyses, and a focus group of experienced Intermediate (Grades 7 and 8) teachers examined whether there is evidence that the results from the DRA provide valid interpretations regarding students’ reading skills and comprehension. Results indicated that, as currently scored, internal consistency is low and skewness of distribution is high. Factor analyses did not replicate those cited by the DRA developers to prove construct validity. Two-way contingency analyses determined that decision consistency did not vary greatly between the DRA, EQAO, scores and report card marks. Views expressed during the focus group echoed many of the challenges to validity found in the statistical analysis. The teachers found that the DRA was somewhat useful, as there were limited alternative reading assessments available for the classroom, but did not endorse it strongly. The study found little evidence that the DRA provides valid interpretations regarding Intermediate students’ reading skills. Indicated changes to the structure and administration procedures of the DRA may ameliorate some of these issues.

Unsettling Histories: Representation and Indigenous Creative Art Praxis in Official Indian Residential School Redress

This dissertation offers an investigation of the role of visual strategies, art, and representation in reconciling Indian Residential School history in Canada. This research builds upon theories of biopolitics, settler colonialism, and race to examine the project of redress and reconciliation as nation and identity building strategies engaged in the ongoing structural invasion of settler colonialism. It considers the key policy moments and expressions of the federal government—from RCAP to the IRSSA and subsequent apology—as well as the visual discourse of reconciliation as it works through archival photography, institutional branding, and commissioned works. These articulations are read alongside the creative and critical work of Indigenous artists and knowledge producers working within and outside of hegemonic structures on the topics of Indian Residential School history and redress. In particular the works of Jeff Thomas, Adrian Stimson, Krista Belle Stewart, Christi Belcourt, Luke Marston, Peter Morin, and Carey Newman are discussed in this dissertation. These works must be understood in relationship to the normative discourse of reconciliation as a legitimizing mechanism of settler colonial hegemony. Beyond the binary of cooptation and autonomous resistance, these works demonstrate the complexity of representing Indigeneity: as an ongoing site of settler colonial encounter and simultaneously the forum for the willful refusal of contingency or containment.

Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.

How the World Learned to Stop Worrying and Love Failure: Big Data, Resilience and Emergent Causality

In modernity, failure was the discourse of critique, today, it is increasingly the discourse of power: failure has changed its allegiances. Over the last two decades, failure has been enfolded into discourses of power, facilitating the development of new policy approaches. Foremost among governing approaches that seek to include and to govern through failure is that of resilience. This article seeks to reflect upon how the understanding of failure has become transformed in this process, particularly linking this transformation to the radical appreciation of contingency and of the limits to instrumental cause-and-effect approaches to rule. Whereas modernity was shaped by a contestation over failure as an epistemological boundary, under conditions of contingency and complexity there appears to be a new consensus on failure as an ontological necessity. This problematic ‘ontological turn’ is illustrated using examples of changing approaches to risks, especially anthropogenic understandings of environmental threats, formerly seen as ‘natural’.

Administración pública local y construcción de la central hidroeléctrica en la Amazonia

Exponemos los resultados de una observación exploratoria en la que se analizan los impactos funcionales en el sistema administrativo como consecuencia de la construcción de la Central Hidroeléctrica de Belo Monte (CHB) en el municipio de Altamira (Amazonia oriental brasileña). En la perspectiva teórico-conceptual de la teoría de sistemas sociales de Niklas Luhmann, realizamos una observación del sistema administrativo local sobre sus operaciones y funciones, en base a cuatro códigos preestablecidos: toma de decisiones, contingencia, significación simbólica del proyecto y planificación. Las conclusiones permiten entrever en un aplano conceptual la utilidad de los conceptos sistémicos de Luhmann, y en el empírico, una planificación y administración pública municipal reactivas.

Tooth replacement for partially dentate elders: a willingness-to-pay analysis

Objectives: The primary aim of this study was to investigate partially dentate elders' willingness-to-pay (WTP) for two different tooth replacement strategies: Removable Partial Dentures (RPDs) and, functionally orientated treatment according to the principles of the Shortened Dental Arch (SDA). The secondary aim was to measure the same patient groups' WTP for dental implant treatment.Methods: 55 patients who had completed a previous RCT comparing two tooth replacement strategies (RPDs (n=27) and SDA (n=28)) were recruited (Trial Registration no. ISRCTN26302774). Patients were asked to indicate their WTP for treatment to replace missing teeth in a number of hypothetical scenarios using the payment card method of contingency evaluation coupled to different costs. Data were collected on patients' social class, income levels and other social circumstances. A Mann-Whitney U Test was used to compare differences in WTP between the two treatment groups. To investigate predictive factors for WTP, multiple linear regression analyses were conducted.Results: The median age for the patient sample was 72.0 years (IQR: 71-75 years). Patients who had been provided with RPDs indicated that their WTP for this treatment strategy was significantly higher (€550; IQR: 500-650) than those patients who had received SDA treatment (€500; IQR: 450-550) (p=0.003). However patients provided with RPDs indicated that their WTP for SDA treatment (€650; IQR: 600-650) was also significantly higher than those patients who had actually received functionally orientated treatment (€550; IQR: 500-600) (p<0.001). The results indicated that both current income levels and previous treatment allocation were significantly correlated to WTP for both the RPD and the SDA groups. Patients in both treatment groups exhibited little WTP for dental implant treatment with a median value recorded which was half the market value for this treatment (€1000; IQR: 500-1000).Conclusions: Amongst this patient cohort previous treatment experience had a strong influence on WTP as did current income levels. Both treatment groups indicated a very strong WTP for simpler, functionally orientated care using adhesive fixed prostheses (SDA) over conventional RPDs. Clinical significance: Partially dentate older patients expressed a strong preference for functionally orientated tooth replacement as an alternative to conventional RPDs.