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Integrating dual-process decision making and social dynamics: A formal modeling framework for addiction.

Psychological Review, Vol 133(4), Jul 2026, 864-891; doi:10.1037/rev0000584

Currently, formal models of addiction focus either on the complex individual decision-making processes involved in addiction or on the social dynamics of addiction. They do not integrate these two levels, which has been identified as a key shortcoming of current formal models of addiction. To address this, we propose a nonlinear dynamical modeling framework of addiction integrating both the individual level and social level of addictive behavior. The individual level of our modeling framework is a formalization of a dual-process theory, where one type of process increases the consumption of addictive goods, and another type of process limits consumption. For our formalization, we build on a well-studied model from ecology, originally used to model periodic outbreaks of the spruce budworm population. To this model, we add the process of incentive sensitization at the individual level and at the social level, we incorporate the critical processes of selection homophily and peer influence. We show that our integrated modeling framework can be used to explain key phenomena identified in addiction literature: a gradual transition to heavy use, sudden relapse and sudden quitting, relatively stable use states over time (i.e., abstinence moderate use, and heavy use), social contagion and sudden outbreaks, clustering of users, and social aid in recovery. In addition, we demonstrate how our modeling framework can be extended to include mutualistic, competitive, and more complex interactions between different addictive behaviors. Finally, we show how our framework can lead to new insights and predictions and suggest avenues for future research. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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Control adjustment costs limit goal flexibility: Empirical evidence and a computational account.

Psychological Review, Vol 133(4), Jul 2026, 846-863; doi:10.1037/rev0000576

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Different goals require us to engage different control processes, such as adjusting how attentive and cautious we are. Here, we show that performance incurs control adjustment costs when people adjust control to meet changing goals. Across four experiments, we provide evidence of these costs and validate a dynamical systems model explaining the source of these costs. Participants performed a single cognitively demanding task under varying performance goals (e.g., being fast or accurate). We modeled control allocation to include a dynamic process of adjusting from one’s current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model accounts for our empirical finding that people undershoot their target control state more (i.e., exhibit larger adjustment costs) when goals switch rather than remain fixed (Study 1). Further validating our model, we show that the magnitude of this cost is increased when: distances between target states are larger (Study 2), there is less time to adjust to the new goal (Study 3), and goal switches are more frequent (Study 4). Our findings characterize the costs of adjusting control to meet changing goals and show that these costs emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility of goal-directed behavior. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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Cognitive mechanisms of subjective value in multiattribute pricing.

Psychological Review, Vol 133(4), Jul 2026, 892-918; doi:10.1037/rev0000594

Understanding how people assign subjective value to outcomes with multiple attributes, such as risk and delay, is central to understanding the structure and manifestation of economic preferences. However, multiattribute preference has been primarily studied through binary choices. The price at which a person would buy, sell, or equate each prospect offers another measure of subjective value that may diverge from multiattribute choice. In both risky and intertemporal domains, choice and price preferences exhibit systematic preference reversals, where a smaller, sooner, or safer option is chosen while a larger, later, or riskier alternative is assigned a higher price. The present study takes a deep dive into how subjective value is assigned in each case in an attempt to reconcile these diverging measurements and methods of assessing value. To explain how and why preferences change across choice and price, the domains of gains and losses, price frames of buying and selling, and varying levels of time pressure, we develop a two-step neural network–based modeling approach. First, we tested cognitive mechanisms underlying value-based judgments and decisions using a switchboard model comparison. Next, we fit and evaluated individualized joint models, where all data from an individual are modeled using parameters and mechanisms that are specific to their best fitting model structure. While mechanisms like delay discounting and risk aversion are common to both models, our results suggest that anchoring and payoff sensitivity diverged between pricing and choice. Extensive differences across elicitation procedures indicate that a common representation of value may remain elusive. (PsycInfo Database Record (c) 2026 APA, all rights reserved)