Inside the Mind
Effort norms encourage more exertion but not less
Emily Zohar & Michael Inzlicht
Journal of Experimental Psychology: General, forthcoming
Abstract:
People are lazy. According to the law of least effort, people generally prefer to exert less rather than more effort to achieve the same reward. However, this research often isolates individuals from social influences, overlooking the fact that we are inherently social beings whose behavior is shaped by the norms and information we gather from others. Here, we examine whether individuals conform to both high-effort and low-effort norms equally or whether the strength of normative influence on effort choices depends on the direction of the norm. Across 12 studies (N = 1,957), participants completed a demand selection task where they repeatedly chose between a hard or easy task. While people generally avoid effort, results revealed that participants exerted significantly more effort after learning that previous participants consistently chose the harder task, compared to a control group who received no information about others' choices. Participants who were informed that others typically opted for the easier task, however, did not exert less effort than the control group and in fact exerted more effort. Even after increasing the acceptability of low effort — by enhancing the value of low effort and the psychological closeness to past participants — individuals still opposed the low-effort norm, exerting no less effort than the control group. These findings suggest that while others' behavior can inspire us to work harder, individuals show resistance to lowering their effort below what they would typically exert. While we consistently found conformity to high-effort norms, effort preferences were not influenced when hearing about others completing an unrelated task, pointing to a possible boundary condition for norm effects.
Social information creates self-fulfilling prophecies in judgments of pain, vicarious pain, and cognitive effort
Aryan Yazdanpanah et al.
Proceedings of the National Academy of Sciences, 17 February 2026
Abstract:
Expectations can shape perception and potentially lead to self-fulfilling prophecies such as placebo effects that persist or grow over time. Nonetheless, whether and how unreinforced and unconditioned social cues (i.e., suggestions about future experiences that have not been reinforced with reward or punishment) can create and sustain such effects is unknown. We conducted a set of experiments in which participants (N = 111) experienced stimuli eliciting somatic pain (heat), vicarious pain (videos of others in pain), and cognitive effort (a mental-rotation task), at three intensity levels each. Before each stimulus, participants viewed a social cue that ostensibly indicated ratings from 10 other participants but was in fact randomized to a high or low mean aversiveness level independent of actual stimulus intensity. Across all tasks, participants' expectations and experience ratings shifted in line with the cues, with high-aversive cues leading to higher perceived aversiveness. Computational modeling and behavioral analysis revealed lower learning rates for prediction errors inconsistent with the trial's cue value (e.g., better than expected for high-aversive cues) and higher learning rates for prediction errors consistent with the cue value (e.g., worse than expected for high-aversive cues). These findings reveal a confirmation bias in learning: people update more when outcomes align with expectations. Combined with expectation effects on perception, this bias helps sustain social cue effects. Together, these mechanisms show how social information can shape perception and learning, giving rise to self-fulfilling prophecies.
Fleeting generalization: How unstable belief updating keeps people overly pessimistic about talking to strangers
Stav Atir & Nicholas Epley
Journal of Personality and Social Psychology, forthcoming
Abstract:
Conversations with strangers and weak ties tend to be positive experiences, and yet research suggests a reliable tendency to hold overly pessimistic expectations about such conversations. We examine how people update their beliefs after talking with strangers to understand how people's miscalibrated social expectations could persist even in the presence of more positive social experiences. In three longitudinal experiments, having a conversation led to more optimistic (and better calibrated) expectations about a future conversation, especially with the same person, but updating was fleeting. Within 1 or 2 weeks, expectations reverted to a more pessimistic baseline similar to those who had no conversation to learn from in the first place. This fleeting generalization was unique to conversation (compared to a noninteractive control condition). It emerged both when a future conversation was with the same person and when it was with a different person, when people were explicitly asked to predict their experience before having it and when they were not, and across both relatively shallow and deeper conversations. Fleeting generalization stems partly (but not entirely) from recalling conversations as less positive than they felt immediately after having them. These findings suggest that miscalibrated social beliefs can persist even with unbiased experience to learn from.
Understanding human metacontrol and its pathologies using deep neural networks
Kai Sandbrink, Laurence Hunt & Christopher Summerfield
Proceedings of the National Academy of Sciences, 3 March 2026
Abstract:
Error monitoring is crucial for inferring how controllable an environment is, and thus for estimating the value of control processes (metacontrol). In this study, we use computational simulations with deep neural networks to investigate its behavioral and neural correlates. We trained both humans and deep reinforcement learning (RL) agents to perform a reward-guided learning task that required adaptation to changes in action controllability. Deep RL agents could only solve the task when designed to explicitly predict action prediction errors that fire in the medial prefrontal cortex. When trained this way, they displayed signatures of metacontrol that closely resembled those observed in humans. Moreover, when deep RL agents were trained to over- or underestimate controllability, they developed behavioral pathologies partially matching those of humans who reported depressive, anxious, or compulsive traits on transdiagnostic questionnaires. These findings open up avenues for studying metacontrol using deep neural networks.
Lay beliefs of willpower shape individuals' propensity to approach or avoid effortful tasks
Christopher Mlynski et al.
Journal of Experimental Psychology: General, forthcoming
Abstract:
Research on individuals' lay beliefs of willpower — beliefs on whether demanding tasks drain a limited resource or are rather energizing — has shown that they can influence self-control performance on consecutive tasks and everyday self-regulation in the context of high demands. However, no research has examined whether these beliefs of willpower affect individuals' willingness to self-select into or avoid effortful tasks in the first place. The present study addresses this gap through three correlational studies (N = 1,461) and one preregistered experiment (N = 442). The correlational studies demonstrated that the more participants endorsed a nonlimited-resource belief, the more likely they were to choose higher difficulty levels on a mental arithmetic task, even when controlling for math self-concept. Further analyses revealed that individuals with nonlimited-resource beliefs steadily increased their difficulty choices over the course of the task, while those with limited-resource beliefs consistently chose easier problems. Study 2 provided causal evidence showing that individuals induced to adopt a nonlimited-resource belief selected more difficult math problems than those induced to hold a limited-resource belief. These findings highlight the significant role of lay beliefs of willpower in shaping individuals' willingness to self-select into or avoid effortful tasks, illustrating how these underlying beliefs can have large-scale implications for goal setting and effort-based decision-making processes.