Adaptive decision making critically depends on agents’ ability to reduce uncertainty. To reduce uncertainty, agents need to perceive relevant environmental signals, extract useful information, and incorporate it into their knowledge and behavior. This is not a trivial task for two reasons. First, the environment is full of sensory stimuli, but most of them are irrelevant. Agents need to evaluate relevance of signals so that they can prioritize perception and processing of the most relevant ones. Second, perceived signals can be analyzed in countless ways, but most of them do not provide useful information. Agents need to be selective in how to process perceived signals before trying to make use of them in behavior.

This thesis reports three attempts to understand neurocognitive mechanisms of uncertainty reduction in value-based decision making. It is relatively well understood how human brain produces decision-making behavior under uncertainty. It is also well understood how human brain perceives and processes sensory stimuli. However, little is known how these systems cooperate to enable adaptive uncertainty reduction. Three chapters in this thesis try to fill this gap by behavioral experiments, functional magnetic resonance imaging (fMRI) experiments, and computational modeling.

Chapter 1 examines the way human brain processes perceived signals under conditions of reducible and irreducible uncertainty. Because not all uncertainty can be reduced in the same way, agents should take into account the nature of uncertainty they are facing. This challenges a widespread idea that uncertainty reduction is driven by the extent to which signals violate prior expectancy. It is behaviorally shown that subjects are sensitive to reducibility of uncertainty, and it can be quantitative characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Furthermore, fMRI results reveal that neural processes underlying belief and value updating are separable from responses to expectancy violation, and that reducibility of uncertainty in value modulates connection from belief- to value-updating regions. These results illustrate how human brain uses knowledge on uncertainty to process signals adaptively.

Chapters 2 and 3 examine the way human brain evaluates incoming signals’ relevance to economic choices. Human often seeks for information that is irrelevant to the task at hand. Although this challenges a normative account in which agents assign positive value only to instrumental information, it has been unclear how general and important such preference is in decision-making settings. Chapter 2 reveals that, behaviorally, subjective value of information exceeds the normative prediction not only when information is non-instrumental but also when it is instrumental. Overvaluation depends on outcomes at stake, rejecting a popular theory of internal drive for entropy reduction. Observed valuation bias can be explained by introducing recursive utility, which penalizes choices under the lack of information, to the normative account. Using this novel model and fMRI, Chapter 3 reports that activation in striatum represents value of information, unifying information’s relevance to the task and penalty for uninformed choice. These results propose a new neurocognitive account on how human brain acquires information with various degrees of uncertainty reduction.

The ability to initiate goal-directed behaviors related to every day outcomes is a crucial component of motivation. Across clinical and subclinical domains, variability in motivated decision-making accounts for failures to both begin and complete actions towards improved health. When faced with decisions that involve expending our own energetic resources in order to attain rewards, whether for ourselves or others, individuals vary broadly in the behaviors they take, or fail to take, to reach said goals. In order to classify, and potentially provide a mechanistic account of, differences in these cost-benefit processes, an objective investigation into the computations underlying motivated behavior is tantamount. This is particularly relevant to the clinical domain, wherein the broad goal of computational psychiatry is a methodologically quantitative approach to taxonomic classification. The following essays offer an overview of three studies with the aim of providing formal accounts of integrating information from individual differences (chapters 1 and 2) and stereotype content (chapter 3) into mathematical models of motivated behavior. I will begin by discussing a computational approach towards predictive modeling of effort-based decision-making utilizing dimensions of clinical and subclinical approach and avoidant traits. In the first chapter, I will focus on modeling behavior in a community-recruited sample of adults by integrating a number of demographic and individual difference measures into behavioral economic models of effort discounting. In the second, I will focus my analyses on a separate but demographically similar cohort of adults who completed the same experimental paradigm during functional neuroimaging. Here, I will investigate changes in neural activity (inferred by blood-oxygen level dependent (BOLD) signal) responding to different levels of effort-reward tradeoffs. I will utilize the same behavioral modeling processes as the first chapter in order to extract individual difference measures by estimating free parameters specific to individual subjects, and relating them to changes in the neural responses to cost-benefit evaluations. Finally, I will discuss the manner in which evaluations of effort expenditure for other-rewarding outcomes are distinct from those with self-rewarding outcomes, and how social information – or stereotypes – about the recipient of said rewards robustly contributes to effort-related choice processes

Countries around the world are putting substantial effort into the development of wind energy technologies. The urgent need of renewable energy puts pressure on the wind energy industry research and development to enhance the current wind generation capabilities and decrease the associated costs. Currently most wind turbine aerodynamics and aeroelasticity simulations are performed using low-fidelity methods. These methods are simple to implement and fast to execute; however, the cases involving important features, such as unsteady flow, turbulence, and details of the wind turbine geometry, are beyond their range of applicability. In this dissertation, we introduce a paradigm shift in wind turbine analysis by developing 3D, complex geometry, time-dependent, multi-physics modeling procedures for wind turbine fluid-structure interaction (FSI).

The proposed framework consists of a collection of numerical methods combined into a single framework for FSI modeling and simulation of wind turbines at full scale. The use of the Navier-Stokes equations of incompressible flows for wind turbine aerodynamics is validated against experimental data. The structural modeling of the composite blades is based on the Kirchhoff-Love thin shell theory discretized using isogeometric analysis. The coupled FSI formulation is derived using the augmented Lagrangian approach and accommodates non-matching fluid-structure interface discretizations. The challenges of fluid-structural coupling and the handling of computational domains in relative motion are discussed, and the FSI computations of a 5 MW offshore baseline wind turbine are shown.

Marketing theory and practice have become increasingly customer-centered in recent decades. To that end, marketers, consumer researchers, and the lay public alike have begun to take special interest in how understanding the human brain can help them better understand consumers. Despite advances in knowledge of how the brain represents simple goods such as those involving primary rewards, however, there is great difficulty in extending this understanding to more complex goods typical of modern human society, and in particular how the brain represents the set of intangible characteristics resulting from social and cultural influences, for example, the intangible characteristics captured by a good’s brand. Here we combine newly available machine learning techniques with functional neuroimaging data to characterize the set of processes that give rise to the intangible associations people have with brands. Our findings represent an important advance in the application of neuroscientific methods to consumer research, moving from work focused on cataloguing brain regions associated with marketing stimuli to testing and refining mental constructs central to theories of consumer behavior.