• August 5, 2025
• Article

Computational models of reinforcement learning (RL) have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, potentially oversimplifying the nuanced relationship between human behavior and rewards. To address these challenges and explore models of RL, we utilized a method of model discovery using equation discovery algorithms. This method, currently used mainly in physics and biology, attempts to capture data by proposing a differential equation from an array of suggested linear and nonlinear functions. Using this method, we were able to identify a model of RL which we termed the Quadratic Q-Weighted model. The model suggests that reward prediction errors obey nonlinear dynamics and exhibit negativity biases, resulting in an underweighting of reward when expectations are low, and an overweighting of the absence of reward when expectations are high. We tested the generalizability of our model by comparing it to classical models used in nine published studies. Our model surpassed traditional models in predictive accuracy across eight out of these nine published datasets, demonstrating not only its generalizability but also its potential to offer insights into the complexities of human learning. This work showcases the integration of a behavioral task with advanced computational methodologies as a potent strategy for uncovering the intricate patterns of human cognition, marking a significant step forward in the development of computational models that are both interpretable and broadly applicable.

• May 2025
• Article

The desire to appear virtuous can motivate people to punish wrongdoers, a desirable outcome when punishment is clearly deserved. Yet claims that “virtue signaling” is fueling a culture of outrage suggest that reputation concerns may inspire even potentially unmerited punishment. Moreover, might reputation do more to drive punishment in ambiguous situations, where punishment is less clearly deserved, eroding punishers’ sensitivity to moral nuance? Across eight studies focused on the U.S. political context (total n = 15,472 Americans from MTurk and Prolific), we show that reputation can drive ambiguously-deserved punishment. In situations involving politicized moral transgressions, including those where the case for punishing the transgressor is judged to be relatively ambiguous, subjects expect punishers to be perceived positively by co-partisans, and punish at higher rates when punishing is observable to a co-partisan audience. Moreover, reputation can drive punishment in ambiguous situations even among individuals who personally question the morality of punishment, highlighting the power of reputation to push people away from their values. Yet we find no evidence that reputation erodes sensitivity to nuance by doing more to drive punishment in more ambiguous situations. Instead, subjects expect punishment to look better when more unambiguously deserved, and making punishment observable does as much or more to drive punishment in unambiguous than ambiguous situations—even when the co-partisan audience is strongly ideological (and so might have been expected to encourage undiscerning punishment). We thus suggest that reputation can make people more punitive, even in ambiguous situations, but does not diminish sensitivity to nuance.

• June 4, 2025
• Editorial

Workplace stress, on the rise for decades, has been treated by many organizations as a personal issue instead of a business-critical risk that merits executive oversight. This is likely due in part to the fact that companies have not effectively quantified and tracked the cost stress poses to integral business outcomes. Companies can take charge of the avoidable costs of stress by surveying their workforce and mapping the stress they report onto quantifiable outcomes like revenue, customer satisfaction, and performance evaluations. Understanding how fluctuations in stress impact these outcomes can help businesses come up with and initiate targeted solutions to reduce the likelihood of disruption, protect workforce health, and unlock long-term competitive advantage.