Learning and Decision Making in Social Contexts: Neural and Computational Models

Social interaction is one of humanity's defining features. Through it, we develop ideas, express emotions, and form relationships. In this thesis, we explore the topic of social cognition by building biologically-plausible computational models of learning and decision making. Our goal is to develop mechanistic explanations for how the brain performs a variety of social tasks, to test those theories by simulating neural networks, and to validate our models by comparing to human and animal data. We begin by introducing social cognition from functional and anatomical perspectives, then present the Neural Engineering Framework, which we use throughout the thesis to specify functional brain models. Over the course of four chapters, we investigate many aspects of social cognition using these models. We begin by studying fear conditioning using an anatomically accurate model of the amygdala. We validate this model by comparing the response properties of our simulated neurons with real amygdala neurons, showing that simulated behavior is consistent with animal data, and exploring how simulated fear generalization relates to normal and anxious humans. Next, we show that biologically-detailed networks may realize cognitive operations that are essential for social cognition. We validate this approach by constructing a working memory network from multi-compartment cells and conductance-based synapses, then show that its mnemonic performance is comparable to animals performing a delayed match-to-sample task. In the next chapter, we study decision making and the tradeoffs between speed and accuracy: our network gathers information from the environment and tracks the value of choice alternatives, making a decision once certain criteria are met. We apply this model to a two-choice decision task, fit model parameters to recreate the behavior of individual humans, and reproduce the speed-accuracy tradeoff evident in the human population. Finally, we combine our networks for learning, working memory, and decision making into a cognitive agent that uses reinforcement learning to play a simple social game. We compare this model with two other cognitive architectures and with human data from an experiment we ran, and show that our three cognitive agents recreate important patterns in the human data, especially those related to social value orientation and cooperative behavior. Our concluding chapter summarizes our contributions to the field of social cognition and proposes directions for further research. The main contribution of this thesis is the demonstration that a diverse set of social cognitive abilities may be explained, simulated, and validated using a functionally-descriptive, biologically-plausible theoretical framework. Our models lay a foundation for studying increasingly-sophisticated forms of social cognition in future work

Incorporating Biologically Realistic Neuron Models into the NEF

Theoretical neuroscience is fundamentally concerned with the relationship between biological mechanisms, information processing, and cognitive abilities, yet current models often lack either biophysical realism or cognitive functionality. This thesis aims to partially fill this gap by incorporating geometrically and electrophisologically accurate models of individual neurons into the Neural Engineering Framework (NEF). After discussing the relationship between biologically complex neurons and the core principles/assumptions of the NEF, a neural model of working memory is introduced to demonstrate the NEF's existing capacity to capture biological and cognitive features. This model successfully performs the delayed response task and provides a medium for simulating mental disorders (ADHD) and its pharmacological treatments. Two methods of integrating more biologically sophisticated NEURON models into the NEF are subsequently explored and their ability to implement networks of varying complexity are assessed: the trained synaptic weights do realize the core NEF principles, though several errors remain unresolved. Returning to the working memory model, it is shown that bioneurons can perform the requisite computations in context, and that simulating the biophysical effects of pharmacological compounds produces results consistent with electrophysiological and behavioral data from monkeys

Autoimmune Neuromuscular Disorders in Childhood

Autoimmune neuromuscular disorders in childhood include Guillain-Barré syndrome and its variants, chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), juvenile myasthenia gravis (JMG), and juvenile dermatomyositis (JDM), along with other disorders rarely seen in childhood. In general, these diseases have not been studied as extensively as they have been in adults. Thus, treatment protocols for these diseases in pediatrics are often based on adult practice, but despite the similarities in disease processes, the most widely used treatments have different effects in children. For example, some of the side effects of chronic steroid use, including linear growth deceleration, bone demineralization, and chronic weight issues, are more consequential in children than in adults. Although steroids remain a cornerstone of therapy in JDM and are useful in many cases of CIDP and JMG, other immunomodulatory therapies with similar efficacy may be used more frequently in some children to avoid these long-term sequelae. Steroids are less expensive than most other therapies, but chronic steroid therapy in childhood may lead to significant and costly medical complications. Another example is plasma exchange. This treatment modality presents challenges in pediatrics, as younger children require central venous access for this therapy. However, in older children and adolescents, plasma exchange is often feasible via peripheral venous access, making this treatment more accessible than might be expected in this age group. Intravenous immunoglobulin also is beneficial in several of these disorders, but its high cost may present barriers to its use in the future. Newer steroid-sparing immunomodulatory agents, such as azathioprine, tacrolimus, mycophenolate mofetil, and rituximab, have not been studied extensively in children. They show promising results from case reports and retrospective cohort studies, but there is a need for comparative studies looking at their relative efficacy, tolerability, and long-term adverse effects (including secondary malignancy) in children

Physics of Fusion Power

Nuclear fusion reactions produce energy that can be harvested in fusion power plants and used to generate electricity for human consumption. The enormous energy released by fusion reactions, the abundance of fusion fuels, and the safety of fusion reactors indicate that fusion power could be a superior alternative to conventional and renewable power sources. Yet after more than sixty years of fusion research, sustaining a controlled fusion reaction remains an elusive goal for physicists. This paper will review the nuclear and plasma physics necessary to understand fusion reactions, then examine historical and contemporary designs for nuclear reactors, comparing the advantages and disadvantages of each device. I will address the physical and technological hurdles that must be overcome to successfully achieve ignition and finally assess the prospects for peaceful fusion power in the future

Reinforcement Learning, Social Value Orientation, and Decision Making: Computational Models and Empirical Validation

Social environments often impose tradeoffs between pursuing personal goals and maintaining a favorable reputation. We studied how individuals navigate these tradeoffs using Reinforcement Learning (RL), paying particular attention to the role of social value orientation (SVO). We had human participants play an interated Trust Game against various software opponents and analyzed the behaviors. We then incorporated RL into two cognitive models, trained these RL agents against the same software opponents, and performed similar analyses. Our results show that the RL agents reproduce many interesting features in the human data, such as the dynamics of convergence during learning and the tendency to defect once reciprocation becomes impossible. We also endowed some of our agents with SVO by incorporating terms for altruism and inequality aversion into their reward functions. These prosocial agents differed from proself agents in ways that resembled the differences between prosocial and proself participants. This suggests that RL is a useful framework for understanding how people use feedback to make social decisions

A spiking neuron model of inferential decision making : urgency, uncertainty, and the speed-accuracy tradeoff

Decision making (DM) requires the coordination of anatomically and functionally distinct cortical and subcortical areas. While previous computational models have studied these subsystems in isolation, few models explore how DM holistically arises from their interaction. We propose a spiking neuron model that unifies various components of DM, then show that the model performs an inferential decision task in a human-like manner. The model (a) includes populations corresponding to dorsolateral prefrontal cortex, orbitofrontal cortex, right inferior frontal cortex, pre-supplementary motor area, and basal ganglia; (b) is constructed using 8000 leaky-integrate-and-fire neurons with 7 million connections; and (c) realizes dedicated cognitive operations such as weighted valuation of inputs, accumulation of evidence for multiple choice alternatives, competition between potential actions, dynamic thresholding of behavior, and urgency-mediated modulation. We show that the model reproduces reaction time distributions and speed-accuracy tradeoffs from humans performing the task. These results provide behavioral validation for tasks that involve slow dynamics and perceptual uncertainty; we conclude by discussing how additional tasks, constraints, and metrics may be incorporated into this initial framework