Uncovering the Neural and Behavioral Factors That Underlie Changes in Processing Visual Orientation

From moment to moment, the visual environment appears stable; despite prolonged scrutiny, the edge of a desk is not perceived to change. But this apparent stability emerges from perceptual and decisional systems that undergo continuous modulation. In two chapters, I focus on two different kinds of modulation to the processing of visual orientation (i.e., the tilt of an edge). In both chapters, the form of modulation is latent, obscured by standard analyses. To detect those latent changes in perceptual decisions, I develop in this dissertation new statistical tools, at both behavioral and neural levels. In the first chapter, I consider modulations to behavior in an orientation judgment task. Viewing and responding to an orientation causes systematic errors in subsequent responses (Fischer & Whitney, 2014; Gibson & Radner, 1937): the orientation reported on one trial can appear to be biased either toward (attracted to) or away (repelled) from recent orientations. I performed a meta-analysis of the literature on attractive biases, finding a wide variety of effect sizes, with no experimental variable clearly explaining this variation. I show that this variation likely arises from a mixture of attraction to the last response and repulsion from the last stimulus; both forces affect every response, and for any experiment the relative mixture can result in on-average behavior that is only repulsive, only attractive, or neither. I developed two complementary techniques for disentangling this mixture and demonstrate their effectiveness as applied to both a new experiment and previously published experiments. In the second chapter, I developed a technique for identifying how orientation “tuning” functions change with experimental manipulations (e.g., high/low contrast). These tuning functions and their modulation have been observed with single-cell electrophysiology in animals, but there are no non-invasive methods for identifying them in humans. Using functional magnetic resonance imaging, individual voxels exhibit tuning despite arising from the combined responses of hundreds of thousands of neurons. My technique models the distribution of neurons contributing to each voxel and uses model comparison to identify the most likely form of neuromodulation. I validated this technique with a new neuroimaging experiment

Probabilistic models for contextual agreement in preferences

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ

Modeling the Dynamics of Consumer Behavior from Massive Interaction Data

Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence

Systematic literature review on enhancing recommendation system by eliminating data sparsity

The aim of this project is to develop an approach using machine learning and matrix factorization to improve recommendation system. Nowadays, recommendation system has become an important part of our lives. It has helped us to make our decision-making process easier and faster as it could recommend us products that are similar with our taste. These systems can be seen everywhere such as online shopping or browsing through film catalogues. Unfortunately, the system still has its weakness where it faced difficulty in recommending products if there are insufficient reviews left by the users on products. It is difficult for the system to recommend said products because it is difficult to pinpoint what kind of users would be interested in the products. Research studies have used matrix factorization as the standard to solve this issue but lately, machine learning has come up as a good alternative to solve data sparsity. This project compares results of the recommendation system using RMSE to see how each proposed methods performs using three different datasets from MovieLens. We have selected two models – matrix factorization with SVD and deep learning-based model to evaluate these approaches and understand why they are popular solution to data sparsity. We have found that SVD brought in a lower RMSE as compared to deep learning. The reason behind this was discussed in the latter chapter of this thesis. We have also found possible research in capitalising categorical variables in recommendation system and the experiment achieved a lower RMSE score as compared to SVD and deep learning, showing the many possibilities of the future directions of the research in recommendation system

Spatial ecology and conservation of the critically endangered swift parrot

Conservation of highly mobile resource specialists depends on understanding where and when resources are available and how populations respond to resource configuration. These species are often resource specialists, which can make them vulnerable to resource bottlenecks in time and space. When they also have dynamic distributions, data collection and conservation planning is extremely challenging. Therefore, for species like the swift parrot, which is a highly mobile resource specialist with a dynamic distribution, ecologically relevant and spatiotemporally explicit estimates of distributions are urgently needed to guide conservation planning. Prior to this research little was known of spatiotemporal variation in the distribution of the critically endangered migratory swift parrot in its breeding range. The swift parrot requires co-occurrence of two key functional habitats to breed (nesting and foraging) and relies on the flowering of Eucalyptus globulus and E. ovata for food. The overall aim of this research was to better understand and quantify the spatial ecology of the species to improve conservation planning and outcomes. The main impetus for this research was continuing extensive habitat loss (as a result of industrial-scale logging and land clearance) without an understanding of i) the importance of the loss of key sites or locations and ii) the implications of the discovery of novel predator during the course of the study. Firstly, this thesis quantifies and describes a key functional habitat feature (i.e. nesting trees) to assist accurate identification of nesting habitat (Chapter 2). The research then uses data from a unique multi-year monitoring program to i) extend modelling approaches to account for imperfect detection and spatial autocorrelation, ii) quantify the strong link between changing food availability and the species distribution, and iii)quantify how this varies over time (Chapter 3). Then, using data sampled from each functional habitat the research quantifies annual change in the use, location and availability of functional habitats over the entire breeding range (Chapter 4). Finally, the abundance-occupancy relationship (AOR) is quantified temporally and spatially to better understand the implications of spatiotemporal changes in abundance and resource availability for the interpretation species distribution models (SDMs) (Chapter 5). This research reveals highly aggregated nesting behaviour of the swift parrot at multiple spatial scales, and provides one of the first macroecological examples to quantify a direct link between the spatiotemporal distribution of a highly mobile species and food availability. This spatiotemporal variation in food not only means the availability of functional habitats can vary dramatically between years, but also that an increase or decrease in one functional habitat does necessarily correspond to a relative increase or decrease in the other. This has important ramifications for interpreting SDMs, identifying when and where resource bottlenecks may occur, and the assessment of exposure to other spatially variable threats (e.g. predation). Further, the research shows the AOR for mobile species in dynamic distributions can be highly variable over time and space. Importantly, the results also highlight that locations with high predicted occupancy and/or abundance do not necessarily equate to areas of high quality habitat. This thesis delivers some of the first fundamental and quantitative insights into the spatial ecology of highly mobile species that rely on variable environments, and provides guidance towards informing and developing conservation plans for this difficult to study group of species

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning

In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians\u27 viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic. As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts\u27 eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts\u27 cognitive reasoning processes. The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts\u27 domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions. To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts\u27 sense-making

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study