Thesis (Ph. D.) -- California Institute of Technology, 2010. / Title from home page (viewed 02/25/2010). Advisor and committee chair names found in the thesis' metadata record in the digital repository. Includes bibliographical references.

Decision making is pervasive in nature. Organisms from across the phylogenetic spectrum take in information from the external world and pursue courses of action in an attempt to maximize their evolutionary fitness. When faced with several competing alternatives, an individual must decide which option to select or how to distribute their resources amongst the various alternatives. The relatively young field of neuroeconomics has sought to reconcile economics' mathematical tools and formal models of decision processes with physiological measures from the nervous system. How individuals assess the value of competing options and act on internal representations of value is now a major focus of neuroeconomics and systems-level neuroscience. However, experiments in humans and non-human primates face barriers to progress that would be ameliorated in a genetically tractable organism with a compact nervous system. The nematode worm Caenorhabditis elegans has a relatively simple nervous system and a host of genetic tools, making it an advantageous system to elucidate the neural basis of decision making. This dissertation makes several contributions towards establishing C. elegans as a model of value-based decision making. I first develop a behavioral test for C. elegans that parallels paradigms of value-based decision making in human economics. Using microfluidic environments coupled with electrophysiological measures of feeding behavior, I offer worms discrete food choices and monitor how they distribute their 'budget' (i.e., feeding) between the alternatives. By manipulating the relative price (i.e., ease of consumption) of each food, I found that worms alter their spending patterns just as a human consumer does, expanding their consumption of a food as it becomes relatively cheaper. I also found that worms maintain a transitive rank order in their choice preferences, adhering to a classical test of economic rationality. Finally, I show that sensory neuron AWC is necessary for wildtype decision making, and monitor its activity during simulated decision making. AWC is active on the timescale of decision making, but its sensitivity does not fully explain C. elegans food preferences. These results suggest that the representation of value is distributed across a network whose aggregate activity in turn drives value-based decision making in C. elegans. / 10000-01-01

Thesis (Ph. D.)--George Mason University, 2007. / Title from PDF t.p. (viewed Jan. 22, 2008). Thesis director: Kevin A. McCabe. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics. Vita: p. 126. Includes bibliographical references (p. 119-125). Also available in print.

Value based decisions are among the most common types of decisions made by humans. A considerable body of work has investigated how different types of information guide such decisions, as well as how evaluations of their outcomes retroactively inform the parameters that were used to inform them. Several open questions remain regarding the nature of the underlying representations of decision-relevant information. Of particular relevance is whether or not positive and negative information (i.e. rewards/gains vs. punishments/losses/costs) are treated as categorically distinct, or whether they are represented on a common scale. This question was examined across three different studies utilizing a variety of methods (traditional event-related potentials, multivariate pattern classification, and eye tracking) to obtain a more comprehensive picture of how decision-relevant information is represented A common theme among the three studies was that positive and negative types of information seems to be, at least initially, represented as categorically distinct (whether it be information about gains vs. losses, or value vs. effort). Additionally, integration of different types of information appears to take place during the later phases of the decision period, which may also be when distortions in the representation of value information (ex. loss aversion) may occur. Overall, this body of work advances our understanding of the underpinnings of value based decisions by providing additional insight about how decision-relevant information is represented in a dynamic and flexible manner.

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This project attempts to understand the role of expectations in cooperative behavior using the interdisciplinary approach of Decision Neuroscience. While cooperation provides the foundation for a successful society, the underlying bio-psycho-social mechanisms remain surprisingly poorly understood. This investigation deconstructs cooperation into the specific behaviors of trust, reciprocation, and norm enforcement using the Trust and Ultimatum Games from behavioral economics and combines formal modeling and functional magnetic resonance imaging to understand the neurocomputational role of expectations in these behaviors. The results indicate that people appear to use context specific shared expectations when making social decisions. These beliefs are malleable and appear to be dynamically updated after an interaction. Emotions such as guilt and anger can be formally operationalized in terms of others' expectations and appear to be processed by a specific neural system involving the anterior insula, anterior cingulate cortex, and supplemental motor cortex. Importantly, these neural signals appear to motivate people to not only behave consistent with these expectations, but also to help others update their beliefs when these expectations are violated. Further, violations of social expectations appear to promote enhanced memory for norm violators. This work demonstrates the neural and computational basis of moral sentiments.

Decision-making is a complex process. Monetary incentives constitute one of the forces driving it, however the motivational space of decision-makers is much broader. We care about other people, we experience emotional reactions, and sometimes we make mistakes. Such social motivations (Sanfey, 2007) drive our own decisions, as well as affect our beliefs about what motivates others' decisions. Behavioral and brain sciences have started addressing the role of social motivations in economic games (Camerer, 2004; Glimcher et al., 2009), however several aspects of social decisions, such as the process of thinking about others' emotional states - emotional sophistication - have been rarely investigated. The goal of this project is to use automatic measurements of dynamic facial expressions to investigate non-monetary motivations and emotional sophistication. The core of our approach is to use state-of-the-art computer vision techniques to extract facial actions from videos in real-time (based on the Facial Action Coding System of Ekman and Friesen (1978)), while participants are playing economic games. We will use powerful statistical machine learning techniques to make inferences about participants internal emotional states during these interactions. These inferences will be used (a) to predict behavior; (b) to explain why a decision is made in terms of the hidden forces driving it; and (c) to investigate the ways in which people construct their beliefs about other people's future actions. The contributions of this targeted interdisciplinary project are threefold. First, it develops new methodologies to study decision processes. Second, it uses these methods to test hypotheses about the role of first order beliefs about social motivations. Finally, our statistical approach sets the ground for "affectively aware" systems, that can use facial expressions to assess the internal states of their users, thus improving human-machine interactions.

The past 40 years have seen an enormous amount of research aimed at investigating human reasoning and decision-making abilities. This research has led to an extended debate about the extent to which humans meet the standards of normative theories of rationality. Recently, it has been proposed that dual-process theories, which posit that there are two distinct types of cognitive systems, offer a way to resolve this debate over human rationality. I will propose that the two systems of dual-process theories are best understood as investigative kinds. I will then examine recent empirical research from the cognitive neuroscience of decision-making that lends empirical support to the theoretical claims of dual-process theorists. I will lastly argue that dual-process theories not only offer an explanation for much of the conflicting data seen in decision-making and reasoning research, but that they ultimately offer reason to be optimistic about the prospects of human rationality.

<p>Humans must integrate information to make decisions. This thesis is concerned with studying neural mechanisms of decision making, and combines tools from economics, psychology, and neuroscience. I employ a neuroeconomic approach to understand the processing of reward information and motivation in the brain, utilizing neural data from functional magnetic resonance imaging (fMRI) to make connections between cognitive neuroscience and economics.</p><p>Chapter 1 lays the groundwork for the thesis and provides background on neuroscience, fMRI, and neuroeconomics. Chapter 2 sketches the central challenges of using neuroscience to address economic questions. The first half of the chapter discusses familiar arguments against the integration of neuroscience and economics: behavioral sufficiency and emergent phenomenon. The second half constructs principles for interdisciplinary research linking mechanistic (neuroscience) data to behavioral (economic) phenomena: mechanistic convergence across experiments and biological plausibility in models.</p><p>Chapters 3 and 4 employ a nonstandard analysis technique, multivariate pattern analysis (MVPA), to identify brain regions that contain information associated with different types of economic valuation. Chapter 3 uses a combinatoric approach to evaluate how brain regions uniquely contribute to the ability to predict different types of valuation (probabilistic or intertemporal). MVPA shows that early valuation phases for these rewards differ in posterior parietal cortex and suggests computational topographies for different rewards. Chapter 4 employs within- and cross-participant MVPA, which rely on potentially different sources of neural variability, to identify brain regions that contain information about monetary rewards (cash) and social rewards (images of faces). Cross-participant analyses reveal systematic changes in predictive power across multiple brain regions, and individual differences in statistical discriminability in ventromedial prefrontal cortex relate to differences in reward preferences. MVPA thus facilitates mapping behavior to both individual-specific functional organization and general organization of the brain across individuals. </p><p>Chapter 5 employs a reward anticipation task to measure variation in relative motivation without observing choices between rewards (money and candy). A reaction-time index captures individual differences in motivation, and heterogeneity in this index maps onto variability in two brain regions: nucleus accumbens and anterior insula. Further, the nucleus accumbens activation mediates the predictive effects of anterior insula. These results show that idiosyncrasies in reward efficacy persist in the absence of a choice environment.</p><p>Chapters 6 and 7 conclude the thesis. Chapter 6 complements discussions of neuroeconomics with text analysis of an exhaustive corpus from top economics journals and references from a large set of review articles. The analysis shows a mismatch between topics of importance to economics and prominent concepts in neuroeconomics. I show how neuroeconomics can grow by employing cognitive neuroscience to identify biologically plausible and generalizable models of a broader class of behaviors.</p> / Dissertation

<p>Cognitive neuroscience is an interdisciplinary enterprise aimed at explaining cognition and behavior. It appears to be succeeding. What accounts for this apparent explanatory success? According to one prominent philosophical thesis, cognitive neuroscience explains by discovering and describing mechanisms. This "mechanist thesis" is open to at least two interpretations: a strong metaphysical thesis that Carl Craver and David Kaplan defend, and a weaker methodological thesis that William Bechtel defends. I argue that the metaphysical thesis is false and that the methodological thesis is too weak to account for the explanatory promise of cognitive neuroscience. My argument draws support from a representative example of research in this field, namely, the neuroscience of decision-making. The example shows that cognitive neuroscience explains in a variety of ways and that the discovery of mechanisms functions primarily as a way of marshaling evidence in support of the models of cognition that are its principle unit of explanatory significance.</p><p> </p><p>The inadequacy of the mechanist program is symptomatic of an implausible but prominent view of scientific understanding. On this view, scientific understanding consists in an accurate and complete description of certain "objective" explanatory relations, that is, relations that hold independently of facts about human psychology. I trace this view to Carl Hempel's logical empiricist reconceptualization of scientific understanding, which then gets extended in Wesley Salmon's causal-mechanistic approach. I argue that the twin objectivist ideals of accuracy and completeness are neither ends we actually value nor ends we ought to value where scientific understanding is concerned. </p><p>The case against objectivism motivates psychologism about understanding, the view that understanding depends on human psychology. I propose and defend a normative psychologistic framework for investigating the nature of understanding in the mind sciences along three empirically-informed dimensions: 1) What are the ends of understanding? 2) What is the nature of the cognitive strategy that we deploy to achieve those ends; and 3) Under what conditions is our deployment of this strategy effective toward achieving those ends? To articulate and defend this view, I build on the work of Elliot Sober to develop a taxonomy of psychologisms about understanding. Epistemological psychologism, a species of naturalism, is the view that justifying claims about understanding requires appealing to what scientists actually do when they seek understanding. Metaphysical psychologism is the view that the truth-makers for claims about understanding include facts about human psychology. I defend both views against objections.</p> / Dissertation