People’s evaluations of events often depend on their expectations about those events. There is often disappointment when events fail to meet expectations—sometimes even when the events are still positive overall—and there is a special thrill to having one’s expectations exceeded. In this dissertation I propose a theory of event evaluation based on contrasts between global representations of states of the world that accounts for the role of expectations in evaluations. I then present four studies showing evidence for this model in moral and non-moral domains. In Study 1, I generated quantitative predictions from the model to fit existing data describing people’s emotional reactions to risky monetary gambles. Study 2 is a naturalistic study examining football fans’ reactions after wins and losses based on their Twitter activity. Fans’ reactions are influenced by their prior expectations of winning or losing each game. The evaluation of events is intimately linked to the evaluation of actions, in that one mode for evaluating action is simply to evaluate the events brought about by those actions. Consequently, Studies 3 and 4 shift focus to judgments of moral actions. In Study 3, participants were asked to contrast pairs of identical actions taken against victims in different circumstances. Participants’ expectations about victims’ prior risk affected their moral judgments of the actions so that a victim’s greater prior risk mitigated the severity of their moral judgments. Study 4 examined a naturalistic dataset of global Twitter activity in response to terrorism events. Terrorism events evoked larger public reactions when those events were surprising, with weaker reactions resulting when terrorism occurred in countries that suffer from high rates of terrorism. Finally, I discuss the implications of these findings for the normative status of human moral judgments. I discuss how expectations may play a role in the omission-commission distinction, moral luck, and victim blaming. Although it seems justifiable that expectations influence evaluations of non-moral events, in judgments of moral actions this tendency may lead to pervasive moral error.

The study of how children and adults learn mathematics has given rise to a rich set of psychological phenomena involving mental representation, conceptual understanding, working memory, relational reasoning and problem solving. The subfield of understanding rational number processing and reasoning focuses on mental representation and conceptual understanding of rational numbers, and in particular fractions. Fractions differ from other number types, such as whole numbers, both conceptually and in format. Previous research has highlighted the extent to which fractions and other rational numbers pose challenges for children and adults with respect to magnitude estimation and misconceptions. The goal of this dissertation is to highlight the distinct differences in reasoning with different types of rational numbers. First, a neuroimaging study provides evidence that fractions yield a distinct pattern of neural activation during magnitude estimation that differs from both decimals and integers (Chapter 2). Second, a set of behavioral studies with adults highlights the affordances of the bipartite format of fractions for relational reasoning tasks (Chapter 3). Finally, a developmental study with pre-algebra students provides evidence for a significant relationship between relational understanding of fractions and algebra performance, and specifically algebraic modeling. This work is presented in the context of viewing mathematical notation as a type of conceptual modeling. In particular, decimals have advantages in measurement and representing magnitude. Fractions, on the other hand, have advantages in relational contexts, due to the fact that fractions, with their bipartite (a/b) format, inherently specify a relation between the cardinalities of two sets. When mathematics is viewed as a type of relational modeling, rational expressions provide a gateway to more complex mathematical notations and concepts, such as those in algebra.

Machines curate our news narratives to weaponize our minds against us, making commonsense politics impossible because we no longer share a common baseline of facts. Many helplessly accept this alienating and accelerating reality as the new normal. However, technology guided by humanistic principles and cognitive science may restore our vitality. My dissertation integrates psychology experiments on individuals and networked-groups, computational cognitive modeling, large-scale analyses of social media, open-source software development, and citizen science principles to illustrate how online networks and generative artificial intelligence can promote healthier attitudes towards established evidence and each other.

In Chapter 2, I analyze networked behavior and narrative agency across a series of online social network experiments (total N = 660, 13,200 interactions). Through a hashtag generation game I manipulated communication patterns within a group to encourage shared or polarized beliefs. Entropy dynamics of generated hashtags and language data revealed that belief and behavioral coherence vary according to neighborhood topology at both local and global levels. Rewards for aligning hashtags also shifted participants' causal language use when writing personal narratives about a disaster event. Given these findings, Chapter 3 introduces a computational framework rooted in Bayesian decision theory to disentangle how rewards (e.g., engagement metrics embedded in social media) ought to influence our beliefs about evidence, and describes how the framework can guide interventions on networked-groups.

For better and worse, people's beliefs are sensitive to online interactions. Chapter 4 integrates large-scale analyses of internet discourse (100,000 interactions), belief-updating experiments (total N = 2,676), and interactive data visualizations to (1) identify features of persuasion in naturalistic online interactions, and (2) extend those features into a crowdsourced data narrative that countered misconceptions about structural racism in a random sample of Americans (Cohen’s d = .4). Chapter 5 describes a state-of-the-art Large Language Model system that models causal beliefs from natural language data, which I applied in previous chapters.

The first and final chapters expand on my vision for the future of cognitive and behavioral science, and sketch a trajectory for human-oriented technology development and academic achievement. As practicing scientists we must systemically reorient our goals if we are to conquer the networks that radicalize and atomize society.