A Thing Is What We Say It Is: Referential Communication and Indirect Category Learning

This study investigates the interaction of referential communication and the structure of perceptual features on the joint processes of inventing a referential lexicon for novel objects and discovering the functional significance of those objects during an indirect category learning activity. During the learning task, participants worked either individually or as cooperative dyads to learn four combinations of orthogonal functional features--nutritive vs. not nutritive and destructive vs. not destructive--that defined four categories of fictional extra-terrestrial creatures. These categories were not specifically identified or labeled; rather, participants had to infer them indirectly as they predicted the functions. Also, these functionally defined categories exhibited a complex perceptual structure: a unidimensional (simple) rule predicted one function, while a family resemblance (complex) sub-structure predicted the other function. The function-learning task yielded function prediction data. In addition, each learner worked individually to sort the creatures (pre- and post-function learning) and to predict their functions in an individual function prediction posttest that also yielded selective attention data.\ud \ud Together, the prediction data, sort data, and selective attention data supported three a priori hypotheses. Referential communication generates conceptual homogeneity (H3) and enhances indirect category learning (H1), though simple rules are learned earlier and better than complex relationships (H2). In explaining the learning advantages observed among dyadic learners, I argue that referential communication may highlight attention to relationships between features (perceptual and functional) and actions as well as render such relationships more memorable. Moreover, communication may foster greater motivation among collaborators and may allow them to take advantage of the differing expectations and heuristics each collaborator brings to the task. In explaining the simplicity advantages observed among dyadic learners, I argue that referential communication may provide explicit "rules" for otherwise implicit (and perhaps more difficult) judgements. Dyads appear to have established reference to simple rules earlier than they established reference to complex rules; thus, they could explicitly (and perhaps more easily) learn the simple rule earlier than the complex rule. Finally, in explaining the conceptual homogeneity between and within dyads, I consider whether communication pushes "public" conceptualizations and publicly-formed "private" conceptualizations towards a limited range of widely shareable conceptual structures

Examining Partnerships Between Suburban Principals and Superintendents

School districts, and their individual schools, are guided by the leadership of superintendent-principal pairs. While superintendents and principals have a working relationship, not all these working relationships can be described as a partnership. Little is known about how partnerships between superintendents and principals are developed, maintained, and repaired and how established partnerships impact organizational effectiveness. Specific to suburban districts, the recent increased level of accountability for student achievement, under the Every Student Succeeds Act, is amplified due to the decrease in threshold numbers for accountability subgroups. The purpose of the study was to examine principal-superintendent partnerships in suburban districts using the four components of West and Derrington’s (2009) framework for leadership teaming. In addition, the study examined how principal-superintendent partnerships contribute to accountability and organizational effectiveness. The study used a qualitative research design to study the experiences of six principal-superintendent pairs. Data were collected using semi-structured dyadic interviews. Three major findings emerged from the study. First, the principal-superintendent partnership flexed the hierarchical boundaries that exist in K-12 education. Second, the development of principal-superintendent partners is an effective leadership strategy towards increasing organizational effectiveness. Third, as part of a principal-superintendent partnership, principals have the opportunity to become more innovative as leaders. This study provides recommendations for research, superintendents, principals, professional organizations, and higher educational institutions

Computational Bayesian Methods Applied to Complex Problems in Bio and Astro Statistics

In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in a simulation to analyze how estimates of a parameter of interest change across sample sizes

Bayesian Methods for Animal Social Network Analysis

Over the last two decades, animal social network analysis has become central in the study of animal social systems. This methodology has given researchers a powerful set of tools to ask deep questions about the social structures of animals, and how these are linked to many other important biological processes. Animal social networks are often constructed from noisy, uncertain data, which would be well-suited to a Bayesian statistical philosophy. However, despite recent advances in Bayesian methodologies, they remain underutilised in animal social network analysis. In part this is due to unique features of animal network data that have led to the development and use of non-standard statistical procedures in the field. In this thesis I study some of the issues around existing methods, and highlight how a Bayesian methodology could substantially improve animal social network analyses. I introduce, implement, and explore a Bayesian framework for animal social network analysis. The framework makes it possible to conduct new types of analyses while accounting for both uncertainty and sampling biases. In addition to this, I have developed an R software package to allow researchers to use the new Bayesian framework to conduct animal social network analyses. The development of this framework raises new questions and opens up new opportunities in animal social network analysis, which I briefly explore towards the end of this thesis. I hope the developments made in this thesis will help to guide the future of animal social network analyses to make the most of hard-won network data, and to generate more reliable and insightful scientific inferences

Developing methods and applications for the analysis of cetacean social networks

Cetaceans, the whales, dolphins, and porpoises, represent a taxon of intense interest for researchers studying non-human social structure. Social network analysis has become a central tool for studying these species, however the collection, analysis, and application of cetacean social network data comes with numerous challenges. In this thesis, I address key research gaps in the study of cetacean social networks, using the well-studied southern resident killer whale populations as my study system. In the first chapter, I present a systematic literature review on cetacean social networks, in order to identify open areas for future research and development. In Chapter 2, I address the question of social complexity and its quantification. Using mixture models, I develop and test measure of social complexity based on relationship diversity that can be derived from association networks. In Chapter 3, I demonstrate that a commonly used statistical procedure for regression in association networks does not specify a proper null hypothesis, and results in high type I error rates. In Chapter 4, I use unmanned aerial systems methods to measure association and interaction networks within a group of southern resident killer whales, finding important differences in the structure of these different networks. In Chapter 5, I use long-term photographic data to model the spread of a novel pathogen over the social network of the endangered southern resident killer whale community to assess overall risk and potential management strategies. In Chapter 6, I use a multi-decade dataset of social associations, survival, and fecundity to test the link between aspects of the social environment and fitness in the southern resident killer whale population. In the final chapter, I provide a general discussion and synthesis of my results, and suggest areas for future research, both generally and within the southern resident population specifically.Natural Environment Research Council (NERC

From partners to populations:A hierarchical Bayesian account of coordination and convention

Languages are powerful solutions to coordination problems: they provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads. Yet language use in a variable and non-stationary social environment requires linguistic representations to be flexible: old words acquire new ad hoc or partner-specific meanings on the fly. In this paper, we introduce CHAI (Continual Hierarchical Adaptation through Inference), a hierarchical Bayesian theory of coordination and convention formation that aims to reconcile the long-standing tension between these two basic observations. We argue that the central computational problem of communication is not simply transmission, as in classical formulations, but continual learning and adaptation over multiple timescales. Partner-specific common ground quickly emerges from social inferences within dyadic interactions, while community-wide social conventions are stable priors that have been abstracted away from interactions with multiple partners. We present new empirical data alongside simulations showing how our model provides a computational foundation for several phenomena that have posed a challenge for previous accounts: (1) the convergence to more efficient referring expressions across repeated interaction with the same partner, (2) the gradual transfer of partner-specific common ground to strangers, and (3) the influence of communicative context on which conventions eventually form.Comment: In press at Psychological Revie

Social survival : humpback whales (Megaptera novaeangliae) use social structure to partition ecological niches within proposed critical habitat

Funding: JW received a grant from the following: Save Our Seas Foundation Grant No. 217-2010- 2020 https://saveourseas.com, Willow Grove Foundation Grant No. 001-2010-2020, Fisheries and Oceans Canada Grant CA No.: 2016-2019- HSP-PAC-8287-A, https://www.dfo-mpo.gc.ca/species-especes/sara-lep/hsp-pih/index-eng.html, Donner Canadian Foundation Grant No. E-50-20,E50-19, E-50-18 https://www.donnerfoundation.org, Tides Canada Grant No. GF04712. https://makeway.org.Animal culture and social bonds are relevant to wildlife conservation because they influence patterns of geography, behavior, and strategies of survival. Numerous examples of socially-driven habitat partitioning and ecological-niche specialization can be found among vertebrates, including toothed whales. But such social-ecological dynamics, described here as ‘social niche partitioning’, are not known among baleen whales, whose societies—particularly on foraging grounds—are largely perceived as unstructured and incidental to matters of habitat use and conservation. However, through 16 years of behavioral observations and photo-identifications of humpback whales (Megaptera novaeangliae) feeding within a fjord system in the Canadian Pacific (primarily within Gitga’at First Nation waters), we have documented long-term pair bonds (up to 12 years) as well as a complex societal structure, which corresponds closely to persistent patterns in feeding strategy, long-term site fidelity (extended occupancy and annual rate of return up to 75%), specific geographic preferences within the fjord system, and other forms of habitat use. Randomization tests of network congruency and clustering algorithms were used to test for overlap in patterns of social structure and habitat use, which confirmed the occurrence of social niche partitioning on the feeding grounds of this baleen whale species. In addition, we document the extensive practice of group bubble net feeding in Pacific Canada. This coordinated feeding behavior was found to strongly mediate the social structure and habitat use within this humpback whale society. Additionally, during our 2004–2019 study, we observed a shift in social network structure in 2010–2012, which corresponded with environmental and demographic shifts including a sudden decline in the population’s calving rate. Our findings indicate that the social lives of humpback whales, and perhaps baleen whales generally, are more complex than previously supposed and should be a primary consideration in the assessment of potential impacts to important habitat.Publisher PDFPeer reviewe

Transforms, algorithms and applications

Fourier transforms and other related transforms are an essential tool in applications of science, engineering and technology. In fact, much of the work currently being done in mathematics, physics and engineering has its roots in Fourier's pioneering idea of representing an arbitrary function as the sum of a trigonometric series. The main purpose of these notes is to give a brief overview of some Fourier-related transforms, namely: continuous Fourier transform, Fourier series, discrete Fourier transform, fast Fourier transform (FFT),sine and cosine transforms, Z-transform, Laplace transform, windowed Fourier transform, continuous and discrete wavelet transforms. Our aim is simply to present a summary of these transforms and to describe their main properties and possible applications, and so most of the results are presented with no proof.References containing the proofs and other details about the transforms are always indicated