Judgment

In everyday life, moral judgments are frequently made in dynamic information environments, in which we are required to revise our first impressions after learning new information. Further, overly harsh moral judgments may damage social relationships. For these reasons, we often need to be cautious in our moral judgments, yet how caution impacts moral decision-making processes remains poorly understood. We investigated how moral valence-driven caution and contextual information expectancy-driven caution affect decision processes underlying moral judgements using the diffusion decision model (DDM) framework. Across two experiments, participants (N = 122) made moral judgements of others’ sharing actions. Prior to judging, participants were informed whether contextual information regarding the deservingness of the recipient would follow. We found that participants slowed their moral judgements when judging negatively valenced actions and when expecting contextual updates. Using a hierarchical Bayesian Markov Chain Monte Carlo estimation of the DDM, we showed that these changes can be accounted for by shifts in drift rate and decision bias (valence) and boundary setting (context), respectively. These findings demonstrate that moral decision caution can be decomposed into distinct aspects of the unfolding decision process: the widening of boundaries in response to contextual update expectancy which may serve to reduce erroneous responding in general; and decision bias shifts, which reflect additional guarding against erroneous judgements which are negative.

Human information processing is naturally limited. To compensate for these limitations, humans rely on contextual information to inform their choices. A classic example of such context-dependence occurs in value-based choice: the relative value of an option depends not only on the option in question but also on the other options in the choice set, or context. While context effects of this sort have been observed primarily in small-scale laboratory studies where choice sets are tightly constrained, it is unknown whether context takes hold of choice “in the wild”. Here, we demonstrate the generality of context-dependent valuation by analyzing a massive real-world restaurant rating dataset (Yelp.com; 4.2 million ratings). We find that Yelp users make fewer ratings-maximizing choices in choice sets with higher overall average ratings. This behavior is quantitatively well-described by a divisive normalization model of choice, wherein the value of available options is scaled to the average of options in a choice set. We follow these analyses up with data from an online experiment, in which we (a) replicate the choice pattern seen in real-world Yelp users and (b) demonstrate that participants’ expectations of an option’s quality are also context dependent, in accordance with the ratings of the options the choice set, even in the absence of explicit choice. The experimental choice data was again well-characterized by a divisive normalization model of valuation. Taken together, we find compelling evidence for context-dependent valuation in behavior, manifesting both in users’ real-world and hypothetical choices and expectations.

The attraction effect occurs when the presence of an inferior option (the decoy) increases the attractiveness of the option that dominates it (the target). Despite its prominence in behavioral science, recent evidence points to the puzzling existence of the opposite phenomenon---a repulsion effect. In this project, we formally develop and experimentally test a normative account of the repulsion effect. This theory is based on the idea that the underlying values of options are uncertain and must be inferred from the available information. A low-value decoy can signal that the target is likely of lower value as well when both are thought to be generated by a similar process. We formalize this logic using a hierarchical Bayesian cognitive model which makes predictions about how the strength of the repulsion effect should vary with properties of the decision problem. Our theory can account for several observed phenomena linked to the repulsion effect across value-based and perceptual decision making, and we find support for its core elements in new experiments. Our results shed light on the key drivers of context-dependent judgment across multiple domains and sharpen our understanding of when decoys can be detrimental.

Superspreading events are the primary mode of infection driving the COVID-19 pandemic, but their effect on risk judgments is currently unknown. More than half a million people in the U.S. died from COVID-19 in one year, yet public risk perceptions of infection and mortality remain variable. Using a combination of epidemiological models and the psychological theory of global-local incompatibility, we theorize that superspreading diseases create a large variance in infections across geographic localities, leading to highly variable and inaccurate risk perceptions. This is problematic because these local infection rates fail to reveal the overall severity of the pandemic, which determines the personal risk of infection at any location in the near future. We test our predictions with a simulation study and a nationally representative study of U.S. citizens (N=3956) conducted in April 2020. Supporting our theory, we find that localized county-level infection rates of COVID-19 are unreliable predictors of national infection rates. However, they explain a significant proportion of variance in judgments of national infection rates, contributing to judgment errors. These results support our theoretical approach for modeling this unique judgment context as an incompatibility between global and local information, providing a framework to predict how citizens will react to novel large scale (global) risks. Our results also help explain the extreme polarization witnessed in the U.S. regarding perceptions of the risks of the COVID-19 pandemic. Accounting for the variability of local experiences with a pandemic can help future generations prepare for how to respond to similar threats more effectively.

We present a new model of decision making under alcohol intoxication. The scope of the model covers binary choice, where choice alternatives are allowed to have any (finite) number of attributes. The acute effects of alcohol intoxication on decision making are accounted for by two parameters, one governing increased choice inconsistency due to “noisier" cognitive representations of the choice attributes, the other governing how attention to choice attributes changes, accounting for the well-known alcohol myopia effect. We demonstrate how our model can account for a variety of alcohol impaired decisions across many different contexts (e.g., decisions to drink and drive, sexual decisions) and be applied using various methodological approaches (e.g., cognitive neuroscience, ecological momentary assessment). We show that our model contains a standard model of multi-attribute decision making, the probit random utility model, as a special case when the alcohol-impairment parameters are set equal to values corresponding to no alcohol impairment.

Preference reversals in risky choice -- where people select low-risk over high-risk prospects in binary choice but assign higher prices to high-risk than low-risk prospects -- have suggested that the valuation processes underlying pricing are distinct from those underlying choice. Despite this, theories of intertemporal choice typically do not distinguish between response processes for pricing and choice, assuming instead that eliciting either response will lead to the same inferences about people’s preferences for delayed outcomes. We show that this assumption is incorrect, and develop a dynamic model of pricing that can account for preference reversals in intertemporal choice. Across two studies, participants showed a preference for smaller sooner options in choice but larger later ones when pricing potential gains (Experiment 1) and losses. This reversal in pricing results in less impulsive behavior, suggesting that pricing frames may reduce choice impulsivity. To explain these diverging price and choice findings in a common framework, we propose a variant of a dynamic price accumulation model that we previously developed to model risky choice. This model is able to predict preference reversals using a common set of parameters for choice and pricing (joint model), providing an account of both response types while extending its account of preference reversals to delayed outcomes.