Computational mechanisms for context-based behavioral interventions: A large-scale analysis.

Choice context influences decision processes and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and to design behavioral interventions to influence and improve choices. We analyzed the effects of context-based behavioral interventions on the computational mechanisms underlying decision-making. We collected data from two large laboratory studies involving 19 prominent behavioral interventions, and we modeled the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allowed us to parametrize the biases induced by each intervention, to interpret these biases in terms of underlying decision mechanisms and their properties, to quantify similarities between interventions, and to predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision-making.

Decomposing loss aversion from gaze allocation and pupil dilation.

Loss-averse decisions, in which one avoids losses at the expense of gains, are highly prevalent. However, the underlying mechanisms remain controversial. The prevailing account highlights a valuation bias that overweighs losses relative to gains, but an alternative view stresses a response bias to avoid choices involving potential losses. Here we couple a computational process model with eye-tracking and pupillometry to develop a physiologically grounded framework for the decision process leading to accepting or rejecting gambles with equal odds of winning and losing money. Overall, loss-averse decisions were accompanied by preferential gaze toward losses and increased pupil dilation for accepting gambles. Using our model, we found gaze allocation selectively indexed valuation bias, and pupil dilation selectively indexed response bias. Finally, we demonstrate that our computational model and physiological biomarkers can identify distinct types of loss-averse decision makers who would otherwise be indistinguishable using conventional approaches. Our study provides an integrative framework for the cognitive processes that drive loss-averse decisions and highlights the biological heterogeneity of loss aversion across individuals.

Psychological mechanisms of loss aversion: A drift-diffusion decomposition.

Decision makers often reject mixed gambles offering equal probabilities of a larger gain and a smaller loss. This important phenomenon, referred to as loss aversion, is typically explained by prospect theory, which proposes that decision makers give losses higher utility weights than gains. In this paper we consider alternative psychological mechanisms capable of explaining loss aversion, such as a fixed utility bias favoring rejection, as well as a bias favoring rejection prior to gamble valuation. We use a drift diffusion model of decision making to conceptually distinguish, formally define, and empirically measure these mechanisms. In two preregistered experiments, we show that the pre-valuation bias provides a very large contribution to model fits, predicts key response time patterns, reflects prior expectations regarding gamble desirability, and can be manipulated independently of the valuation process. Our results indicate that loss aversion is the result of multiple different psychological mechanisms, and that the pre-valuation bias is a fundamental determinant of this well-known behavioral tendency. These results have important implications for how we model behavior in risky choice tasks, and how we interpret its relationship with various psychological, clinical, and neurobiological variables.

Process and content in decisions from memory.

Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this article we present a general framework that can address the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to naturalistic binary choices from domains such as risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and social judgment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An ontology of decision models.

Decision models are essential theoretical tools in the study of choice behavior, but there is little consensus about the best model for describing choice, with different fields and different research programs favoring their own idiosyncratic sets of models. Even within a given field, decision models are seldom studied alongside each other, and insights obtained using 1 model are not typically generalized to others. We present the results of a large-scale computational analysis that uses landscaping techniques to generate a representational structure for describing decision models. Our analysis includes 89 prominent models of risky and intertemporal choice, and results in an ontology of decision models, interpretable in terms of model spaces, clusters, hierarchies, and graphs. We use this ontology to measure the properties of individual models and quantify the relationships between different models. Our results show how decades of quantitative research on human choice behavior can be synthesized within a single representational framework. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Cognitive models of optimal sequential search with recall.

Many everyday decisions require sequential search, according to which available choice options are observed one at a time, with each observation involving some cost to the decision maker. In these tasks, decision makers need to trade-off the chances of finding better options with the cost of search. Optimal strategies in such tasks involve threshold decision rules, which terminate the search as soon as an option exceeding a reward value is found. Threshold rules can be seen as special cases of well-known algorithmic decision processes, such as the satisficing heuristic. Prior work has found that decision makers do use threshold rules, however the stopping thresholds observed in data are typically smaller than the (expected value maximizing) optimal threshold. We put forward an array of cognitive models and use parametric model fits on participant-level search data to examine why decision makers adopt seemingly suboptimal thresholds. We find that people's behavior is consistent with optimal search if we allow participants to display risk aversion, psychological effort cost, and decision error. Thus, decision makers appear to be able to search in a resource-rational manner that maximizes stochastic risk averse utility. Our findings shed light on the psychological factors that guide sequential decision making, and show how threshold models can be used to describe both computational and algorithmic aspects of search behavior.

A Dynamic Dual Process Model of Intertemporal Choice.

Dual process theories of decision making describe choice as the result of an automatic System 1, which is quick to activate but behaves impulsively, and a deliberative System 2, which is slower to activate but makes decisions in a rational and controlled manner. However, most existent dual process theories are verbal descriptions and do not generate testable qualitative and quantitative predictions. In this paper, we describe a formalized dynamic dual process model framework of intertemporal choice that allows for precise, experimentally testable predictions regarding choice probability and response time distributions. The framework is based on two-stage stochastic process models to account for the two postulated systems and to capture the dynamics and uncertainty involved in decision making. Using quasi closed form solutions, we illustrate how different factors (timing of System 1, time constraint, and preferences in both systems), which are reflected in the model parameters, influence qualitative and quantitative model predictions. Furthermore, we show how an existing static-deterministic model on intertemporal choice can be implemented in the framework allowing for testable predictions. The proposed framework can bring novel insights into the processes underlying intertemporal choices.

Automatic biases in intertemporal choice.

Dual process theories of intertemporal decision making propose that decision makers automatically favor immediate rewards. In this paper, we use a drift diffusion model to implement these theories, and empirically investigate the role of their proposed automatic biases. Our model permits automatic biases in the response process, in the form of a shifted starting point, as well as automatic biases in the evaluation process, in the form of an additive drift rate intercept. We fit our model to individual-level choice and response time data, and find that automatic biases (as measured though the starting point and drift rate intercept in our model) are prevalent in intertemporal choice, but that the type, magnitude, and direction of these biases vary greatly across individuals. Our results pose new challenges for theories of intertemporal choice behavior.