Decisions
The (Better) Road Not Taken: Setting a Goal Reduces Switching to More Effective Alternatives
Elizabeth Friedman, Guy Voichek & Ravi Dhar
Journal of Consumer Research, forthcoming
Abstract:
We identify a novel way in which setting goals can backfire. In 13 studies and four supplemental studies, using both incentive-compatible and hypothetical designs across a range of domains, we demonstrate that setting an explicit goal and making progress towards it decreases the likelihood of switching to alternative means of pursuit. This occurs because means seem more effective relative to alternatives if they have been used to progress toward a reference point. Consistent with this mechanism, we show that the perceived effectiveness of the means used to pursue a goal, relative to an alternative, partially mediates the effect of setting a goal on the decision to switch means. Further, the effect occurs only after people make initial progress towards a specified reference point. Setting a goal does not decrease switching if people are reminded to consider the advantages of both the initial and alternative means. We conclude with a discussion of the theoretical and practical implications of our findings.
A large-scale comparison of divergent creativity in humans and large language models
Dawei Wang et al.
Nature Human Behaviour, forthcoming
Abstract:
Human–machine partnerships are increasingly used to address grand societal challenges, yet knowledge of the comparative strengths of humans and machines to innovate is nascent. Here we compare the ability of humans (N = 9,198) and large language models (LLMs, N = 215,542 observations) to generate novel ideas in an established creativity task. We present three key results. First, human creativity on average is slightly higher than that of LLMs. Second, creativity differences are pronounced at the extremes of the distribution, with humans exhibiting greater variability and higher levels of creativity in the right-hand tail of the distribution. Third, attempts to increase the creativity of LLMs through instructing LLMs to take on genius personas or different demographic roles lifted performance up to a threshold beyond which the output became opposite real-life patterns, whereas strategic prompt-engineering efforts yielded mixed to negative results. We discuss the implications of our findings for human–machine collaboration and problem solving.
Didn’t Have Time or Didn’t Make Time? How Language Shapes Perceived Control over Time and Motivation
Luis Abreu, Jordan Etkin & Holly Howe
Journal of Marketing Research, forthcoming
Abstract:
Goal failure is an important problem that is costly for both companies and consumers. Consumers often purchase products, subscribe to services, and download apps in support of valued goals, yet fail to use these tools as much as intended. But might the language consumers use to describe such goal failures affect how they subsequently pursue those goals? Nine experiments demonstrate that, compared to saying “didn’t have time”, saying “didn’t make time” increases subsequent motivation. This is driven by perceived control over time. Specifically, saying “didn’t make” (vs. “didn’t have”) time makes consumers feel more in control of their time, which increases their subsequent motivation to reengage with the goal. Notably, such make-time framing has downstream implications for consumer evaluations of goal-related products and services. Further, it can be manipulated directly, as well as through firms’ promotional activities (i.e., featuring make-time language on social media). Importantly, make-time (vs. have-time) framing may be particularly beneficial in the context of goal failure, when consumers are less inclined to adopt this perspective naturally. Together, the findings shed light on how language shapes motivation, deepen understanding of time’s role in goal pursuit, and have important implications for how companies manage consumer goal failure.
Understanding People’s Preferences for Predictions: People Prioritize Being Right over Minimizing How Wrong They Are in Expectation
Berkeley Dietvorst
Management Science, forthcoming
Abstract:
This work explores the preferences that laypeople exhibit when making and evaluating predictions in the form of point estimates (e.g., the high temperature will be 66°). I propose that people typically have diminishing sensitivity to prediction error: the absolute difference between a prediction and a realized outcome. As a result, people often prioritize “being right,“ focusing on achieving near perfect predictions and placing less emphasis on the magnitude of errors when errors occur. Across 16 studies using varying methods and stimuli, participants exhibited multiple behaviors consistent with diminishing sensitivity to prediction error: (i) predicting the mode of distributions, (ii) restricting predictions to possible outcomes, (iii) reporting decreasing reactions to increasing marginal units of error, and (iv) preferring predictive models built with diminishing sensitivity to error. This behavior diverges from traditional methods of building predictive models and common interpretations of people’s predictions, which often prioritize avoiding large errors and assume that people are predicting the mean. Ultimately, this work not only highlights the discrepancies between our current practices and people’s preferences for predictions but also calls for a more thorough exploration of human objectives before we build models for them to use or make inferences about their beliefs in light of a decision they made.
Estrogen modulates reward prediction errors and reinforcement learning
Carla Golden et al.
Nature Neuroscience, December 2025, Pages 2502-2514
Abstract:
Gonadal hormones act throughout the brain and modulate psychiatric symptoms. Yet how hormones influence cognitive processes is unclear. Exogenous 17β-estradiol, the most potent estrogen, modulates dopamine in the nucleus accumbens core, which instantiates reward prediction errors (RPEs), the difference between received and expected reward. Here we show that following endogenous increases in 17β-estradiol, dopamine RPEs and behavioral sensitivity to previous rewards are enhanced, and nucleus accumbens core dopamine reuptake proteins are reduced. Rats adjusted how quickly they initiated trials in a task with varying reward states, balancing effort against expected rewards. Nucleus accumbens core dopamine reflected RPEs that influenced rats’ initiation times. Higher 17β-estradiol predicted greater sensitivity to reward states and larger RPEs. Proteomics revealed reduced dopamine transporter expression following 17β-estradiol increases. Finally, knockdown of midbrain estrogen receptors suppressed sensitivity to reward states. Therefore, endogenous 17β-estradiol predicts dopamine reuptake and RPE signaling, and causally dictates the impact of previous rewards on behavior.