Browsing by Author "Huettel, Scott A"

The question of what leads an individual to act dishonestly interests researchers, policy-makers, and lay-people alike. While a growing body of research suggests that dishonest behavior is typically limited, and reflects a balance of internal and external incentives, important questions remain unanswered. To what extent is honest behavior guided by stable, internal factors (i.e. moral character), and to what extent is it shaped by situational factors? This question is the subject of continuing and recently revived debate. To what extent do socio-cultural factors impact dishonesty, and to what extent is dishonesty universal? Casual observation suggests significant cross-cultural variation in terms of specific dishonest behaviors (e.g. soliciting bribes), but this source of variation has received little research attention. In five related research chapters encompassing three studies, I explore questions about character and culture using empirical research methods. Using a behavioral die task, I find similar patterns of dishonest behavior across individuals from different countries, though within-country differences are also observed. Using survey data, I find that internal sanctions are the most important deterrent of dishonesty across cultures. In addition, I find that that specific dishonest behaviors vary across cultures, and according to domains. Domain-specific dishonesty and socio-cultural influences are also evident in a study involving socially connected pairs of individuals. I conclude that dishonest tendencies may be best characterized as both universal and culturally sensitive. Furthermore, moral character may be construed as a multidimensional construct, expressed differently across different domains of life.

Humans make decisions in highly complex physical, economic and social environments. In order to adaptively choose, the human brain has to learn about- and attend to- sensory cues that provide information about the potential outcome of different courses of action. Here I present three event-related potential (ERP) studies, in which I evaluated the role of the interactions between attention and reward learning in economic decision-making. I focused my analyses on three ERP components (Chap. 1): (1) the N2pc, an early lateralized ERP response reflecting the lateralized focus of visual; (2) the feedback-related negativity (FRN), which reflects the process by which the brain extracts utility from feedback; and (3) the P300 (P3), which reflects the amount of attention devoted to feedback-processing. I found that learned stimulus-reward associations can influence the rapid allocation of attention (N2pc) towards outcome-predicting cues, and that differences in this attention allocation process are associated with individual differences in economic decision performance (Chap. 2). Such individual differences were also linked to differences in neural responses reflecting the amount of attention devoted to processing monetary outcomes (P3) (Chap. 3). Finally, the relative amount of attention devoted to processing rewards for oneself versus others (as reflected by the P3) predicted both charitable giving and self-reported engagement in real-life altruistic behaviors across individuals (Chap. 4). Overall, these findings indicate that attention and reward processing interact and can influence each other in the brain. Moreover, they indicate that individual differences in economic choice behavior are associated both with biases in the manner in which attention is drawn towards sensory cues that inform subsequent choices, and with biases in the way that attention is allocated to learn from the outcomes of recent choices.

Financial decision-making in a complex and dynamic world poses many challenges including which information to use, how to filter out distractions, and how to arrive at a decision strategy that balances effort and accuracy in the face of imperfect information and cognitive constraints. Traditional financial education methods that provide more information to consider and thus require expending additional time and energy have had limited efficacy in improving long-term decision-making capacity. The research presented here takes a different approach by exploring the influence of context on the construction of value to elucidate mechanisms in consumer choice that underlie individual differences in decision-making. This approach uses computational modeling to identify the component parts of decision-making, eye tracking to measure attentional processes and information gathering strategies during choice, and functional neuroimaging (fMRI) to characterize how social networks modulate value representations in the brain. Characterizing the underpinnings of decision-making can help pinpoint which individual differences in the decision process lead to different choices. The research presented reveals that patience in intertemporal choice results from rapid, attribute-wise comparison of amounts with minimal attention paid to time information, whereas impatience results from slower integration of time and amount within options. Furthermore, measuring attention in purchasing behavior shows that budget size can influence the value of items through a comparison process with price. Finally, a public social context influences motivation for rewards for self but does not affect motivation to earn for charity. This mechanistic approach to understanding value construction and evidence accumulation in choice can help offer strategies grounded in human cognition for people to better adapt to their financial situation, hopefully increasing the likelihood of longer-term impact.

Moral cognition represents a foundational faculty of the human species. Our sense of morality develops beginning at a very young age, and its dysfunction can lead to devastating mental disorders. Given its central importance, it has fittingly garnered the attention of thinkers throughout the ages. For millennia, philosophers have pondered what it is to be right or wrong, good or bad, virtuous or vicious. For centuries, psychologists have elucidated how people acquire and act upon a sense of morality. More recently in the last decade, neuroscientists have embarked on a project to study how morality arises from computations in the brain. However, this latest project has been fragmented: researchers have largely studied various neural components of morality - including emotion, value, and mentalizing - in isolation. This has resulted in an informal and disjointed model for the neural mechanisms of morality. This dissertation is concerned with more formally identifying neural components and their influences on each other in the context of moral cognition.

In Chapter 2, I study how the component neural networks of moral cognition may be involved in distinct aspects of a single decision by employing a complex clinical decision making task involving the disclosure of conflicts of interest. I show that for a given decision, the magnitude of conflict of interest is tracked by mentalizing networks, while the degree of disclosure-induced behavioral change exhibited by participants is predicted by value networks. In Chapter 3, I move beyond the informal model of morality used in Chapter 2 and previous literature by devising a methodology to identify hierarchical ontologies of neural circuits; such an approach can have implications on further discussions of morality, and more generally, on other aspects of cognitive neuroscience. From this, I present the 50 elemental neural circuits that are fundamental to human cognition and explore how these elements can differentially combine to form emergent neural circuits. In Chapter 4, I use these advances to address morality, uncovering its relevant component neural networks in a data-driven way. I show that neural circuits important in supporting higher-level moral computations include mentalizing and taste. In Chapter 5, I demonstrate an important complexity in a compositional model of morality. I show that one of the components of moral cognition, mentalizing, can paradoxically be influenced by moral judgments themselves. To conclude, I highlight the implications of both theoretical and methodological advances. The hierarchical ontologies of neural circuits may be a profitable framework for the future characterization and study of mental disorders; and to effectively study these circuits, the use of moral judgment and decision-making paradigms will be effective experimental tasks, considering the centrality of moral cognition to who we are, whether in health or illness.

Humans are able to make adaptive decisions with the goal of obtaining a goal, earning a reward, or avoiding punishment. While much is known about the behavior and corresponding underlying neural mechanism relating to this aspect of decision-making, the field of cognitive neuroscience has focused almost exclusively on how these types of decisions are made in discrete choices where the set of possible actions is comparatively much smaller. We know much less about how human brains are able to make similar types of goal-directed decisions in continuous contexts which are more akin to the types of choices humans make in real-life. Further, how these processes are modified by the presence of other humans whose goals might influence one's own future behavior is currently unknown. Across three empirical studies, I address some of these gaps in the literature by studying human competitive decision-making in a dynamic, control paradigm in which humans interacted with both social and non-social opponents (Chapter 2 and Chapter 4). In Chapter 3, I show that brain regions heavily implicated in social cognition and value-based decision-making also play a role in tracking continuous decision metrics involved in monitoring instantaneous coupling between opponents, advantageous decision timing, and constructing social context. Collectively, the results in this dissertation demonstrate the utility in studying decision-making in less-constrained paradigms with the overall goal of gaining further understanding of how humans make complex, goal-directed decisions closer to real-world conditions.

Public health data show that many everyday reckless behaviors reach a developmental peak in adolescence, with adolescents engaging in more reckless behaviors than both children and adults. In contrast, most studies of decision-making across development do not find laboratory risk-taking to peak in adolescence. Here, I focus on two factors that contribute to the discrepancy between public health and laboratory findings: ambiguity and social context. Everyday decisions tend to involve ambiguous decisions (choices with unknown probabilities), while previous laboratory studies have largely focused on risky decisions (choices with known probabilities). Consequently, little is known about the ambiguity preferences of young children. Across three behavioral studies, I show that ambiguity aversion is absent in 5-year-old children (Chapter 2) and 8- and 9-year-old children (Chapter 3) but present in 15- to 18-year-old adolescents (Chapter 4) and adults (Chapters 2 to 4). The results of Chapters 2 through 4 indicate that the willingness to take ambiguous gambles, like the willingness to take risky gambles, does not peak in adolescence. Everyday decisions also often occur in social contexts when friends are present and outcomes can be shared, whereas most laboratory studies occur in social isolation. In Chapter 5, I use functional magnetic resonance imaging to show that neural response to reward for self and for friend are similar in a sample of young adults (ages 18 to 28), and that neural response to reward linearly decreases with age when participants are watched by their friends but not when they are alone. In Chapter 6, I use behavioral modeling to show that adults value rewards similarly for themselves and for their friend. Adolescents, in contrast, value their own rewards more than those of their friend, but the presence of their friend reduces this valuation difference. The results of Chapters 5 and 6 indicate that the presence of friends prompts adolescents and young adults to engage in behavior that benefits both themselves and their friends. Collectively, the results in this dissertation demonstrate the need to consider contextual influences on decision-making in order to better capture everyday decision behavior in the laboratory.

Emotions guide the way individuals interact with the world, influencing nearly every psychological process from attention, to learning, to metacognition. Constructionist models of emotion posit that emotions arise out of combinations of more general psychological ingredients. These psychological ingredients, however, also form the building blocks of other affective responses such as subjective reactions to rewarding and social stimuli. Here, I propose a domain-general account of affective functioning; I contend that subjective responses to emotional, rewarding, and social stimuli all depend on common psychological and neural mechanisms. I support this hypothesis with three independent studies using both a basic science approach and a clinical approach. In the first study (Chapter 2) I demonstrate that the ventromedial prefrontal cortex (vmPFC), which has been implicated in encoding the value of primary, monetary, and social rewards, also encodes the hedonic value of emotional stimuli. In addition to showing that the mechanisms responsible for processing affective information are shared across reward and emotional processing, I also discuss the relevance of a domain-general constructionist account of affect for clinical disorders. In particular, I hypothesize that in anorexia nervosa (AN), affective disturbances should be manifest across responses to emotional, rewarding, and social stimuli (Chapter 3). In Chapter 4, I provide empirical evidence for this conclusion by demonstrating that when viewing social stimuli, women with a history of AN show disturbances in the insula, a brain region that is responsible for interoceptive and affective processing. This suggests that the interpersonal difficulties frequently observed in patients with AN may be due to biases in domain-general affective responses. In Chapter 5, I support this conclusion by showing that individual differences in harm avoidance in healthy women, women with a current diagnosis of AN, and women who have recovered from AN explain the relationship between disordered eating and social dysfunction. Collectively, these results indicate that subjective affective responses to rewarding, emotional, and social information all rely on common mechanisms as would be suggested by a domain-general theory of affect. Furthermore, the application of a constructionist domain-general account of affect can help to explain the fundamental nature of affective disturbances in psychiatric disorders such as AN.

This dissertation includes three chapters in behavioral labor economics. In the first chapter I study with Victoria Lee the effect of wage transparency policies on workplace interactions and workplace choice. We run an online experiment in which we manipulate whether wages are known or secret, and also control the wage allocations that participants faced in the teams. The results show that wage transparency can affect the level of hostility as well as the target of the hostility in the teams -- as measured by a punishment game. These treatment effects are moderated by the level of wage inequality within the teams. We also find that wage transparency can alter which job offers participants accept. This result suggests that wage transparency policies can have general equilibrium effects due to workers changed sorting behavior. We argue that these two sets of results can be explained by the presence of social preferences and participants' inaccurate beliefs about the wage allocations that are corrected when wages are transparent.

In the second chapter Nayoung Rim, Roman Rivera, Bocar Ba and myself are studying racial bias within the police. Although there is substantial evidence showing racial bias in firms' hiring decisions, less is known about bias in career recognition. We construct a novel dataset of police award nominations to measure bias against minority employees. Exploiting quasi-random variation in supervisor assignment and randomized timing of annual evaluations, we find that white supervisors are less likely to nominate black officers than white and Hispanic officers leading up to and during the evaluation period. Further, the black-white nomination gap widens with the number of arrests. These patterns suggest that the disparity is not due to in-group favoritism towards white officers but rather bias against black officers. We conduct an online experiment to examine evaluator engagement and find that evaluators are less likely to engage with black officers vs. white officers. Our findings suggest bias in career recognition may have important implications for the black-white promotion gap, the lack of diversity in upper-management positions, and, ultimately, the racial wage gap.

In the last chapter I estimate the effects of behavioral interventions on sleep and cognitive performance among undergraduate students. Three interventions are tested during a three-week long framed field experiment: bedtime text reminders, sleep hygiene tips and an educational meditation video. Throughout the experiment participants' sleep patterns are measured using fitness trackers. The results show that sleep quantity and quality may not be easy to move with mild nudges -- although the estimates are noisy due to the small sample size. Estimates with panel data methods support the positive link between sleep and cognitive outcomes.

Attentional processes are critical aspects of the neural, cognitive, and computational mechanisms of decision-making. However, the role of such processes is often not given much focus in decision-making research, especially for studies involving economic decision-making. Here, I present three studies that evaluated the role of attention during decision making. Study 1 evaluated the role of attentional control, such as top-down and bottom-up control, in mediating conflict between internal and external demands on attention to promote optimal task performance in a discrimination decision task. Results from Study 1 provided novel neural insights into the role of attentional control in processing and resolving conflict between internal representations and external stimuli during everyday decision-making. Studies 2 and 3 evaluated the role of selective attention, namely online feature-based selective attention, underlying mechanisms of delay and effort discounting in economic decision-making. Results from these two studies demonstrated the importance of measuring (online and parametrically) and utilizing feature-based selective attention during comparative decision-making tasks to better quantify the cognitive and computational mechanisms underlying behavior and preferences. Taken together, results from all three studies provide important quantitative and qualitative implications for understanding mechanisms of decision-making through the lens of attention.

A key debate in nutrition research is whether highly processed foods uniquely reinforce consumption and if, in being uniquely reinforcing, highly processed foods cause overeating and obesity. To that end, overarching goal of my dissertation was to examine the mechanisms through which food motivates appetitive behavior and how relative differences in these processes predict differences in eating behavior.

Here we cover three studies where participants are asked to make choices to earn food rewards. In each study, we examine how participants form action-reward contingency estimations and reward magnitude estimations through learning and how their choices reflect differences in the underlying decision processes. In chapter two, we show that when you reinforce behavior with a food rather than money, differences in reward magnitude estimation predict differences in behavior. Our results demonstrate that a key feature of natural reinforcers like food – that reward magnitude diminishes as a function of consumption due to changes in internal state – is observable in participants’ decision-making. In chapter three, we find that a large subset of children, whose choices appear as if they are trying to lose, were using a choice strategy that would be nearly optimal given a plausible (albeit false) assumption about the experimental environment. Our results indicate that children can form computationally complex models of their environment; however, their choices can be maladaptive if those models are incorrect. In chapter four, we examined how individual differences in action-reward contingency estimation and estimated reward magnitude for food rewards predicted differences on validated clinical measures associated with weight gain in children. We show preliminary evidence that overall reward magnitude for snack foods, as well as change in reward magnitude, are associated with outcomes on the Child Eating Behavior Questionnaire and the Eating in the Absence of Hunger Task.

Taken together, our results demonstrate that highly processed foods could affect how people make reward magnitude estimations and ultimately make decisions about what and how much to eat. However, the extent to which decision processes affect clinical outcomes like risk for weight gain is unclear. We show that while differences in reward magnitude estimation may not affect cognitive processes underlying the choice of when to stop eating, it may affect choices about portion. Highly processed foods could meaningfully affect portion size selection by altering expectations about how consumption affects internal state. Given the strong association between portion sizes and overall consumption, this suggests a that highly processed foods could trigger meaningful differences in caloric intake by changing what and how much people put on their plates.

Seeking information is fundamental not only for making adaptive choices but also for extending our knowledge both individually and as a collective society. Understanding how people organize and carry out seeking information, remains an open and critical challenge for the field of psychology and neuroscience. In this dissertation, I approach this challenge by examining the organization of information seeking through the lens of the motivational states of an individual. I leverage behavioral, computational modeling, and neuroimaging methods to provide a multi-faceted understanding of the relationship amongst motivation, information seeking, and memory formation. In the first study, I used computational models to examine latent sampling strategies as people gathered information to guide subsequent action. These results demonstrated that unlike the optimal strategy, people perform simple accumulations instead of computing expected values, and that unlike a simple heuristic strategy, people dynamically integrate multiple sources of information in lieu of using only one source, highlighting a computationally efficient strategy that balances competing motivations for accuracy and cost minimization. In the second study, I explore how curiosity evaluates information as it unfolds over time, reporting that curiosity can promote the patience to prolong uncertainty and such patience culminates in experiencing discovery. In the third study, I extend these findings to capture the rewarding process of discovery and its impact on memory using functional magnetic resonance imaging. In the fourth study, I investigate how distinct motivational forces can drive information seeking and learning through distinct mechanisms. Collectively, the work presented in this dissertation reveals that our motivational states play a significant role in shaping our information seeking behaviors, working to balance competing goal states, and pushing us to confront and even enjoy the experience of uncertainty.

Few aspects of human cognition are more personal than the choices we make. Our decisions — from the mundane to the impossibly complex — continually shape the courses of our lives. In recent years, researchers have applied the tools of neuroscience to understand the mechanisms that underlie decision making, as part of the new discipline of decision neuroscience. A primary goal of this emerging field has been to identify the processes that underlie specific decision variables, including the value of rewards, the uncertainty associated with particular outcomes, and the consequences of social interactions. Here, across three independent studies, I focus on the neural circuitry supporting social valuation — which shapes our social interactions and interpersonal choices. In the first study (Chapter 2), I demonstrate that social valuation relies on the posterior ventromedial prefrontal cortex (pVMPFC). Extending these findings, I next show that idiosyncratic responses within pVMPFC predict individual differences in complex social decision scenarios (Chapter 3). In addition, I also demonstrate that decisions involving other people (e.g., donations to a charitable organization) produce increased activation in brain regions associated with social cognition, particularly the temporal-parietal junction (TPJ). Finally, in my last study (Chapter 4), I employ functional connectivity analyses and show that social cognition regions — including the TPJ — exhibit increased connectivity with pVMPFC during social valuation, an effect that depends upon individual differences in preferences for social stimuli. Collectively, these results demonstrate that the computation of social value relies on distributed neural circuitry, including both value regions and social cognition regions. Future research on social valuation and interpersonal choice must build upon this emerging theme by linking neural circuits and behavior.

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.

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.

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.

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.

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.