Essays on Sequential Sampling in Value-Based Choice

This dissertation comprises three chapters related to the fields of psychology, computational neuroscience, and experimental economics. Chapters 1 and 2 use experimental and computational methods to study the role of attention in simple, value-based choices. Chapter 3 examines risky choices from experience and tests some of the underlying assumptions of sequential sampling models. A growing body of research has shown that simple choices involve the construction and comparison of values at the time of decision. These processes are modulated by attention in a way that leaves decision makers susceptible to attentional biases. In Chapter 1, co-authored with Stephanie Dolbier and Antonio Rangel, we studied the role of peripheral visual information on the choice process and on attentional choice biases. We used an eye-tracking experiment in which participants (N = 50 adults) made binary choices between food items that were displayed in marked screen ``shelves'' in two conditions: (a) where both items were displayed, and (b) where items were displayed only when participants fixated within their shelves. We found that removing the nonfixated option approximately doubled the size of the attentional biases. The results show that peripheral visual information is crucial in facilitating good decisions and suggest that individuals might be influenceable by settings in which only one item is shown at a time, such as e-commerce. In Chapter 2, co-authored with Stephen Gonzalez and Antonio Rangel, we studied the role of attention in aversive risky choices where all outcomes were unpleasant. We used two eye-tracking experiments in which participants made binary choices between two lotteries in two conditions: (a) a gain condition where outcomes for lotteries were weakly positive, and (b) a loss condition where outcomes were weakly negative. Contrary to the predictions of the standard aDDM, we found that attentional choice biases in the loss condition were identical to those found in the gain condition, suggesting that attention nudges choices towards the attended option even in losses. To explain these results, we propose a variation of the Attentional Drift-Diffusion-Model (called the Hybrid aDDM) that incorporates (a) both a value-dependent and a value-independent effect of attention on the choice process and (b) reference-dependent value signals. We show that the observed attentional choice biases and other behavioral signatures in the loss condition can only be explained by the Hybrid aDDM with a reference-point rule that sets the reference-point at or below the minimum possible outcome in a given context. In Chapter 3, co-authored with Antonio Rangel, we establish that sequential sampling models apply to risky decisions from experience and test some of the underlying assumptions of these models. We ran an online study in which participants chose to Play or Skip a slot machine, based on a stream of samples drawn from its outcome distribution. We found evidence for leakage, collapsing decision boundaries, and a delay in sample integration. We also found evidence of non-linear sample weighting depending on when the sample occurred during the trial. As a bonus, we established a link between the fixed decision boundaries in a Drift-Diffusion-Model and a Modified Probit model, allowing for estimation of decision boundaries in cumulative sample space without the need to fit a computational model.</p

Planning for an Uncertain Future: Tree-Based Methods for Real-Time Fault Estimation, Collision Avoidance, and Multi-Agent Reconfiguration

Autonomous spacecraft making independent high-level decisions present the promise of dramatically increased productivity in space for both exploration and economic activity. While autonomy has seen limited use in space to date owing to a lack of flight heritage, limited computational resources, and a traditionally risk adverse industry, the growing numbers of spacecraft and increasingly ambitious missions will soon render the current ground-intensive mode of space operation untenable. In this thesis, we develop two critical capabilities for an autonomous future in space. The first is proactive fault estimation, which seeks to rapidly and safely identify the root causes of onboard anomalies by planning sequences of test actions to gather information while probabilistically ensuring safety. The second is real-time reconfiguration to enable formations of spacecraft to respond quickly and effectively to changing environments or mission objectives. We achieve both goals using various forms of Monte-Carlo Tree Search planning. By formalizing each capability as sequential decision-making problems, and developing algorithms well suited to information gathering, we show that our algorithms provably converge to optimal solutions while maintaining the ability to run in real-time on robotic spacecraft simulators. We present several algorithmic innovations, including marginalized filtering, sampling-based chance constraint evaluation, and an array-based implementation of Monte-Carlo Tree Search. Through and numerical simulations and hardware experiments, we demonstrate that these modifications enable our algorithms to outperform existing tree search methods and achieve better scaling across system complexity, noise, and simulation depth.</p

Atomically Thin Spatial Light Modulators with Excitonic Nanomaterials

Achieving active control of light at the ultimate thickness limit—a single atomic layer—offers unprecedented opportunities for next-generation optoelectronic devices. The quest for ultrathin spatial light modulators has long relied on integrating tunable materials with plasmonic or high-index nanoantennas that serve as small, but three-dimensional optical resonators. As structures for controlling light become increasingly complex and compact, the geometrical constraints of these three-dimensional resonators will ultimately limit their scalability and versatility. A new avenue for device miniaturization emerges when harnessing electrically tunable resonances that are intrinsic to atomically thin materials. This thesis explores how exciton resonances, specifically in two-dimensional (2D) van der Waals materials, can serve as the central building blocks for future spatial light modulators that are as thin as atoms. We start by characterizing the gate-tunable optical properties of a monolayer molybdenum diselenide (MoSe₂), a 2D transition metal dichalcogenide. By tuning the exciton resonances with voltage, we demonstrate over 200% modulation in the real and imaginary part of the complex refractive index. We attribute this large tunability to the interplay between radiative and nonradiative decay channels of the excitons. The index modulation gives rise to amplitude and phase modulation of the scattered light, which is then used to engineer an electrically tunable phase gradient across a single monolayer MoSe₂ flake to dynamically steer the reflected beam. Next, we present a theoretical analysis of the complex frequency response of a generalized excitonic heterostructure. We show how the spectral positions of the phase singularities, e.g. zeros and poles, can be dynamically controlled, their impacts on the real frequency phase response, and how they can be used in active metasurface design. Finally, we evaluate excitons in quantum dots as an alternative platform for room temperature optical modulators and show how they present different challenges in designing phase modulators. Overall, our work highlights the novel functionalities enabled by exciton resonances for advanced light manipulation, underscoring their potential for atomically thin light modulators.</p

High-Field Charge Transport and Fluctuation Phenomena in Semiconductors from First Principles

Charge transport and dynamics in semiconductors determine the limits of contemporary high-performance electronic devices. Previously, in order to understand the microscopic mechanisms underlying charge transport, and to efficiently find novel materials for new applications, computational methods were limited to using parameterized scattering rates and simplistic band structure models as inputs. However, with ab-initio methods, only the atomic identities and lattice vectors are needed as inputs. These methods have the capability of providing insights not possible with methods that rely on empirical data, and predicting properties for not-yet-synthesized materials. While ab-initio computation of low-field transport properties have become common in recent years, these methods have not been extensively applied to non-equilibrium phenomena. In addition, the ab-initio simulation of fluctuational properties (such as the diffusion coefficient or power spectral density of current fluctuations) is an area that has been minimally explored. In order to approach quantum-limited noise levels in devices, a better understanding of the mechanisms that govern electronic noise away from equilibrium is needed. Thus, motivated by this, the overarching goal of this work is to develop and use first-principles methods to gain insight into the scattering processes that govern high-field electronic transport and noise in well-known semiconductors, and to use the same approach to make predictions and identify promising device applications for novel materials. The warm electron tensor is a quantity that describes the quadratic change of conductivity with electric field, which provides a quantitative way to examine the heating of the electron gas. However, this has not been examined from first-principles previously. In this work, we report the warm electron tensor of n-Si computed over a large temperature range, and find that the most commonly used order of perturbation theory only captures the qualitative change of the warm electron tensor with angle. However, by including the next-to-leading order two-phonon scattering term in our approach, we find near-quantitative agreement. This finding indicates that two-phonon scattering has a non-negligible role to play in transport in nonpolar semiconductors. We continue our investigation of n-Si by examining the diffusion coefficient and its anisotropy by applying our Boltzmann transport framework to fluctuational variables. We find that the qualitative features of the anisotropy are correct, but its magnitude is greatly underestimated in comparison to experimental data, while the onset of the noise is overestimated. While this suggests an incorrect description of f-type scattering in our work, by computing the frequency dependence of the diffusion coefficient as well as the piezoresistivity (two observables sensitive to the balance of f- and g-type scattering), we find that the qualitative agreement of these two observables with experiment shows that such a discrepancy cannot be due to an incorrect description. Instead, we suggest that the experiment contains charge transport phenomena not accounted for by our electron-phonon scattering framework. Finally, we use the same approach to investigate the high-field transport and noise in the novel ultra-wide-bandgap semiconductor cubic boron nitride (c-BN). While c-BN is known for its excellent mechanical and thermal properties, its high predicted saturation velocity and breakdown field make it a promising candidate in high-power and high-frequency devices. However, very few experimental and theoretical studies have probed its transport properties. Here, we show that c-BN exhibits a negative differential resistance (NDR) region below 140 K, and show that the cause is due to an abrupt valley repopulation effect with applied electric field. We also show that the intervalley time in c-BN is extremely large, on the order of diamond, and that this large intervalley time causes a distinct noise peak, most prominent at low temperatures. We discuss how the NDR region and large intervalley time make c-BN a potential candidate for transferred-electron devices and Gunn oscillators, respectively.</p

The Neural Computation of Internal Affective States

The study of neural computation has long concentrated on our cognitive abilities, with extensive research dissecting the mechanisms of memory, decision-making, and navigation. In contrast, the realm of social innate behavior and emotion has often been treated as a simpler problem, overlooking the immense complexity and biological significance it entails. This thesis aims to bring neural computation into the domain of emotional or affective states, employing data-driven modeling methods that approximate neural activity as dynamical systems. The application of these methods has uncovered brain representations that encode key qualities of persistence and escalation associated with aggressive states, formalized as line attractors. These emergent features of neural circuits arise from the complex interplay of connectivity and network dynamics, challenging long-held notions of subcortical computation. This discovery led us to rigorously test various key properties of line attractor dynamics. Through closed-loop modeling and holographic neural activation, we demonstrate that the line attractor is intrinsic to the mammalian hypothalamus, providing some of the first causal evidence of this property for any continuous attractor. These experiments also suggest that functional connectivity within the hypothalamus underpins the stability of this attractor. Furthermore, using a new cell-type-specific gene-editing system, we show that the implementation of this line attractor depends on neuropeptides, indicating a non-canonical mechanism that contributes to the robustness of this innate attractor. Finally, we reveal that line attractors encode emotional states beyond aggression, including states of sexual receptivity in the female hypothalamus. Longitudinal recordings of neural data across the estrus cycle show that the line attractor disappears during non-estrus states, suggesting long-timescale modulation of attractor dynamics by hormones. Together, these studies present a new paradigm for understanding subcortical computation underlying internal states and suggest a canonical motif that the brain reuses to encode diverse internal affective states

Explications of a Changing Climate

Climate models encode our collective knowledge about the climate system and are among the best tools available for estimating past and future climate change. However, in response to greenhouse gas forcing, climate models exhibit a large intermodel spread in various aspects of the climate system, adding considerable uncertainty to future climate projections. This dissertation introduces a series of conceptual models and frameworks to understand the behavior of climate models under greenhouse gas forcing and, consequently, Earth's changing climate. A simple statistical model is used to explain and constrain the intermodel spread in Arctic sea ice projections across climate models. The probability of encountering seasonally ice-free conditions in the twenty-first century is also explored by systematically constraining components of the statistical model with observations. A conceptual framework is introduced to understand controls on the strength and structure of the Atlantic meridional overturning circulation (AMOC) in climate models. This framework is used to explain why climate models suggest the present-day and future AMOC strength are related. This framework, in conjunction with observations, implies modest twenty-first-century AMOC weakening. A simple energy budget framework is used to examine precipitation over a wide range of climates simulated by climate models. It is shown that in extremely hot climates, global-mean precipitation decreases despite increasing surface temperatures because of increased atmospheric shortwave absorption from water vapor, which limits energy available for surface evaporation. These results have large implications for understanding weathering rates in past climates as well as Earth's climate during the Hadean and Archaean eons. Finally, a framework is introduced to reconcile two different approaches for quantifying the effect of climate feedbacks on surface temperature change. The framework is used to examine the influence of clouds on Arctic amplification in a climate model and an energy balance model. This work introduces an important non-local mechanism for Arctic amplification and shows that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic warming. This dissertation advances our understanding of various aspects of Earth's changing climate and provides a series of conceptual frameworks that can be used to further constrain the behaviour of climate models in response to external forcing.</p

Development and Characterization of a Table-Top Laser-Produced Plasma Source for In-Situ and Time-Resolved Soft X-Ray Absorption Spectroscopy

X-ray absorption spectroscopy (XAS) has emerged as an indispensable tool in the fields of carbon capture and conversion, providing element-specific insights into electronic structure, oxidation states, and chemical bonding. Of particular interest are soft X-rays (SXRs), which can probe the X-ray water window, enabling detailed studies of carbon, nitrogen, and transition metal L-edges in aqueous environments. Traditionally, access to this technique and this energy range has been limited to large- scale facilities like synchrotrons and XFELs, which can only serve a small population of users in a given year. Furthermore, more complex techniques such as time-resolved and in-situ XAS are practically inaccessible to the majority of users. This thesis explores the development of a table-top laser-produced plasma (LPP) source based on a gaseous target to extend the reach of XAS techniques into laboratory settings. Such sources offer significant advantages in accessibility, flexibility, and cost, while advances in X-ray optics and detection systems have further enhanced their utility. The research presented here focuses on the utilization of gaseous LPP sources for both in-situ and time-resolved XAS, pushing the boundaries of table-top soft X-ray absorption capabilities. Key achievements include exploration of the lower temporal limit of LPP sources for SXR emission, and the first demonstration of liquid-phase XAS measurements using a gaseous LPP source. Gas-phase measurements were also achieved using the system built in this work. Additionally, a novel UV-pump/SXR-probe technique was developed, enabling future time-resolved studies of charge transfer dynamics in transition metal oxides. These advances pave the way for detailed investigations of photodriven processes, interfaces, and catalytic mechanisms critical to carbon capture and conversion. By improving temporal resolution and expanding the scope of in-situ XAS techniques, this work addresses fundamental challenges in the field, bringing the power of synchrotron-like spectroscopy into everyday laboratories. Ultimately, the results presented here aim to democratize XAS, fostering a broader adoption of this technique in catalysis and materials research.</p

Quantitative Nucleic Acid Measurements Inform Strategies to Mitigate Viral Outbreaks

Humans have always been and continue to be at risk of infection by pathogens that surround us. However, recent advancements in quantitative nucleic acid technologies have allowed for more detailed study of these pathogens, how they spread among individuals, and how our immune systems respond to infection. In this thesis, I describe the design and execution of the Caltech COVID-19 Study, which used quantitative nucleic acid measurements to investigate the natural history of SARS-CoV-2 infection and inform strategies for diagnostics and vaccine development to reduce viral transmission. The Caltech COVID-19 Study enrolled participants in the Los Angeles area between September 2020 and April 2022 who were at risk of SARS-CoV-2 infection due to recent exposure to a household contact with acute infection. Participants collected paired upper respiratory specimens (saliva, nasal swabs, and throat swabs) daily or twice daily for approximately two weeks. These specimens underwent SARS-CoV-2 viral load quantification to assess transmission risk and determine whether to extend or terminate study enrollment. For participants who initially tested negative for SARS-CoV-2 RNA but later developed sustained infection, we tracked viral load from the very start of infection. These measurements were then used to evaluate the performance of various COVID-19 diagnostic tests. Our findings revealed a significant advantage of high-analytical-sensitivity tests over those with lower sensitivity, as well as the benefit of testing both the throat and nose rather than just the nose. In addition to viral load quantification, we sequenced human mRNA from these specimens to assess gene expression. Analyzing these changes allowed us to study how the mucosal immune system responds to acute viral infection across multiple anatomical sites over time, providing insights that could improve mucosal vaccine design. Notably, our data showed that, contrary to current models of localized paracrine interferon signaling, distinct compartments of the upper respiratory mucosa exhibited synchronized interferon stimulation during early infection—even in the absence of detectable local viral replication. Mucosal vaccines capable of triggering this coordinated interferon response, maintaining CD8+ T memory cells to rapidly execute effector functions upon viral exposure, may be key to achieving sterilizing immunity. Findings from quantitative nucleic acid measurements in this thesis inform strategies to more effectively mitigate viral outbreaks

Distinct Patterns of Overlapping Neural Representation Of Sensorimotor Variables in Primary and Associative Motor Areas: Insights from Chronic Intracortical Recordings in the Human Brain

Although many of the movements we make are produced without much conscious thought, motor control requires the coordination of multiple brain areas and several complex processes to occur as seamlessly as it does, two of which are primary motor cortex (MC) and posterior parietal cortex (PPC). Traditional views of the organization of these areas have mapped separate parts of the body, or effectors, onto separate areas of cortex. However, recent findings that show extensively overlapping representations of different effectors within small populations of neurons in both motor and posterior parietal cortices have reignited a debate over the organization of each area. The studies in this thesis aim to reconcile these conflicting records through a unique opportunity to directly compare between single neuron recordings in both areas in human participants chronically implanted with intracortical electrode arrays. The functional organization of these areas was investigated during movement of different parts of the body in different contexts. In the first study, I found that the entire body is represented within small patches of both MC and PPC, but with a clear emphasis on a single part of the body in MC. In PPC, although single neurons showed specialization for particular effectors, there were an equal number of neurons specialized for every effector resulting in an equal strength in representation of the population across effectors. In the second study, I investigated how spatial information was represented across different effectors. In particular, it has previously been reported that some areas within PPC represent location of an object in space relative to the position of one's eyes, or in an eye-centered coordinate frame, while other areas represent location in space as relative to the position of one's body, for example a hand-centered coordinate frame. We find that the population in PPC flexibly changes the coordinate frame it encodes the location of a visual target in from hand centered during a reach paradigm to eye-centered during a delayed saccade paradigm. In contrast to the multiple coordinate frames coded by the population in PPC, in MC the population predominantly encoded spatial location in hand-centered coordinates during reaches. The flexibility seen in the population results in PPC motivate the study of Chapter 4, where I explore these changing coordinate frames in more detail at the single neuron level. I found that the distinct coordinate frames are encoded by almost entirely separate sets of neurons, with very few neurons engaged in both task. Overall, these results show clearly distinct organization of motor variables within MC and PPC, and offer important insights into the possible functions of each region both within and beyond motor control. In addition, they highlight a need to continue exploring how neurons within a defined region respond beyond their traditionally associated functional roles

Thermal Kinetic Inductance Detectors (TKIDs) for Cosmic Microwave Background (CMB) Polarimetry

The modern era of precision cosmology has been driven by advances in detector technology and observing techniques. Observational cosmology is experiencing a rapid growth in detector numbers. New architectures are emerging for low-loading applications such as far-infrared spectroscopy, ultra-sensitive low-threshold sensors for particle astrophysics, and dark matter investigations. Current millimeter-wave observatories use kilo-pixel arrays of detectors to measure the polarization of the Cosmic Microwave Background (CMB). There is a strong push within the CMB community to deploy new experiments with hundreds of thousands of detectors to achieve novel scientific outcomes. However, for over a decade, CMB detectors have been limited by background noise, where fluctuations in the photon flux incident on the camera overshadow internal detector noise. As a result, improving instrument sensitivity now requires increasing the number of pixels. This focal plane size and detector density increase significantly complicates integration and readout. Thermal Kinetic Inductance Detectors (TKIDs) are an innovative solution for scaling up detector counts, offering high sensitivity and ease of multiplexing. TKIDs are narrow-bandwidth superconducting resonators that can be multiplexed and read out using a single transmission line via microwave frequency division multiplexing. In this thesis, I present the design, development, and laboratory characterization of a TKID polarimeter for CMB studies at 150 GHz with a 25% bandwidth. I provide a detailed physical model of TKID operation and readout, accurately predicting detector noise and responsivity. Three generations of prototype detectors were developed and tested, leading to the final tile design. The first generation demonstrated the feasibility of fabricating TKIDs with internal noise low enough for background-limited performance given the expected optical loading on our telescope. The second generation validated the scalability of the initial design to larger arrays and was crucial for refining fabrication processes, cosmic ray susceptibility testing, and readout development. The third generation integrated the tested detector design with a polarization-sensitive planar phased-array antenna. This required precise fabrication of sub-micron microstrip lines and an in-depth understanding of both the antenna and detector fabrication processes. We show that antenna-coupled TKIDs achieve end-to-end optical efficiency comparable to existing Transition Edge Sensor (TES) detectors and exhibit smooth Gaussian antenna beams matching the design spectral response. Our efforts culminate in the design of a 64-pixel dual-polarization TKID array, intended for CMB observations in a telescope observing from the South Pole. This camera will be the first demonstration of TKIDs in the millimeter-wave regime, advancing the technology for future cosmological and astrophysical applications. I present results from in-lab dark and optical testing of the TKID focal plane, along with design methodologies, electromagnetic simulations, and fabrication procedures for achieving high-yield, uniform TKID arrays