Understanding information use in multiattribute decision making

An inference task in one in which some known set of information is used to produce an estimate about an unknown quantity. Existing theories of how humans make inferences include specialized heuristics that allow people to make these inferences in familiar environments quickly and without unnecessarily complex computation. Specialized heuristic processing may be unnecessary, however; other research suggests that the same patterns in judgment can be explained by existing patterns in encoding and retrieving memories. This dissertation compares and attempts to reconcile three alternate explanations of human inference. After justifying three hierarchical Bayesian version of existing inference models, the three models are com- pared on simulated, observed, and experimental data. The results suggest that the three models capture different patterns in human behavior but, based on posterior prediction using laboratory data, potentially ignore important determinants of the decision process.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.

Cortisol Reactivity and Observed Parenting among Mothers of Children with and without ADHD

Parenting is a robust predictor of developmental outcomes among children with ADHD. Early parenting predicts the persistence and course of ADHD and comorbid problems above and beyond risk associated with shared genetic effects. Yet, on average, mothers of children with ADHD are less positive and more negative in their parent-child interactions compared to mothers of non-disordered children. Little is known about psychobiological markers which may be associated with individual variations in maternal parenting in families of children with ADHD. Neurobiological models of parenting suggest that maternal cortisol levels following a stressor may be positively associated with hostile and intrusive parenting; however, to date no studies have examined maternal cortisol reactivity and parenting in school-age, or clinical samples of, children. Mothers’ regulation of physiological stress responses may be particularly important for families of children with ADHD, as parenting a child with chronically challenging behaviors represents a persistent environmental stressor. The current study sought to extend the existing literature by providing an empirical examination of the relationship between maternal cortisol reactivity following two laboratory stressors and parenting among mothers of children with and without ADHD. It was hypothesized that child ADHD group would moderate the relationship between cortisol reactivity and self-reported and observed parenting. Greater total cortisol output and greater increase in cortisol during the TSST were associated with decreased positive parenting and increased negative and directive parenting, with the exception of parental involvement, which was associated with increased cortisol output during the TSST. Conversely, cortisol output during the PCI was associated with increased positive parenting, increased parental involvement, and decreased negative parenting. In contrast to the TSST, a greater decrease in cortisol during the PCI indicated more positive parenting and parental involvement. These associations were specific to mothers of children with ADHD, with the exception of maternal directiveness, which was specific to comparison mothers. Findings add to our understanding of physiological processes associated with maternal parenting and contribute to an integrative biological, psychological, and cognitive process model of parenting in families of children with ADHD.