Yang Xiang’s research while affiliated with Harvard University and other places

People use various strategies to bolster the perception of their competence. One strategy is self-handicapping, by which people deliberately impede their performance in order to protect or enhance perceived competence. Despite much prior research, it is unclear why, when, and how self-handicapping occurs. We develop a formal theory that chooses the optimal degree of self-handicapping based on its anticipated performance and signaling effects. We test the theory’s predictions in two experiments (𝑁 = 400), showing that self-handicapping occurs more often when it is unlikely to affect the outcome and when it increases the perceived competence in the eyes of a naive observer. With sophisticated observers (who consider whether a person chooses to self-handicap), self-handicapping is less effective when followed by failure. We show that the theory also explains the findings of several past studies. By offering a systematic explanation of self-handicapping, the theory lays the groundwork for developing effective interventions.

The gambler’s fallacy is typically defined as the false belief that a random event is less likely to occur if it has occurred recently, even when the probability of the event is known to be independent across trials. While forms of this fallacy have been documented numerous times, past work suffers from two problems. First, most studies have not actually measured probabilistic predictions (“60% likely to be heads”), but rather point predictions (“Heads”). Second, the few studies that did measure probabilistic beliefs used sequences that were not independent, and therefore not a strong test of the fallacy. To address these problems, we conducted a series of high-powered, preregistered studies. We asked participants to report probabilistic predictions for truly independent sequences. In contrast to point predictions, which (as in previous research) generated a significant gambler’s fallacy, we found no evidence for a gambler’s fallacy in probabilistic predictions. Moreover, the point predictions could not be reconstructed by sampling from the probability judgments. This suggests that the gambler’s fallacy originates at the decision stage rather than in probabilistic reasoning, as posited by several leading theories. In a separate experiment, we replicate the results of a previous study showing a small gambler’s fallacy for probabilistic predictions with non-independent sequences. However, to the extent that the gambler’s fallacy manifests for probabilistic predictions, it seems to be driven by a small proportion of the participants. Taken together, these findings demonstrate that the gambler’s fallacy is real and robust for point predictions, but not for probabilistic predictions with independent sequences. New theories of the gambler’s fallacy may be needed to explain these findings.

Individual contributors to a collaborative task are often rewarded for going above and beyond—salespeople earn commissions, athletes earn performance bonuses, and companies award special parking spots to their employee of the month. How do we decide when to reward collaborators, and are these decisions closely aligned with how responsible they were for the outcome of a collaboration? In Experiments 1a and 1b (𝑁 = 360), we tested how participants give bonuses, using stimuli and an experiment design that has previously been used to elicit responsibility judgments (Xiang et al., 2023a). Past work has found that responsibility judgments are driven both by how much effort people actually contributed and how much they could have contributed (Xiang et al., 2023a). In contrast, here we found that participants allocated bonuses based only on how much effort agents actually contributed. In Experiments 2a and 2b (𝑁 = 358), participants allocated bonuses to agents who were instructed to exert a particular level of effort; participants rewarded these agents more for complying with instructions, and their rewards were less sensitive to the precise level of effort exerted. Together, these findings suggest that people reward collaborators based on their willingness to exert effort, and point to a difference between decisions about how to assign responsibility to collaborators and how to incentivize them. One possible explanation for this difference is that responsibility judgments may reflect causal inference about past collaborations, whereas providing incentives may motivate collaborators to keep exerting effort in the future. Our work sheds light on how we understand and formalize the cognitive capacities that underlie collaboration.

In order to efficiently divide labor with others, it’s important to understand what our collaborators can do (i.e., their competence). However, competence is not static—people get better at particular jobs the more often they perform them. This plasticity of competence creates a challenge for collaboration: For example, is it better to assign tasks to whoever is most competent now, or to the person who can be trained most efficiently “on-the-job”? We conducted two experiments (N = 199) that examine how people make decisions about whom to train (Experiment 1) and whom to recruit (Experiment 2) to a collaborative task, based on the collaborators’ starting expertise, the training opportunities available, and the goal of the task. We found that participants’ decisions were best captured by a planning model that attempts to maximize the returns from collaboration while minimizing the costs of hiring and training individual collaborators. This planning model outperformed alternative myopic models that based these decisions on the agents’ current strengths, or on how much agents stood to improve in a single training step, without considering whether this training would enable agents to succeed at the task in the long run. Our findings suggest that people do not recruit and train collaborators based solely on their current competence, nor solely on the opportunities for their collaborators to improve. Instead, people use an intuitive theory of competence to balance the costs of hiring and training others against the benefits to the collaboration.

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the produced effort or force (individuals are more responsible if they produce more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N = 717), we found that participants’ judgments are best described by a model that considers both produced and counterfactual effort. This finding generalized to an independent validation data set (N = 99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

By collaborating with others, humans can pool their limited knowledge, skills, and resources to achieve goals that outstrip the abilities of any one person. What cognitive capacities make human collaboration possible? Here, we propose that collaboration is grounded in an intuitive understanding of how others think and of what they can do-in other words, of their mental states and competence. We present a belief-desire-competence framework that formalizes this proposal by extending existing models of commonsense psychological reasoning. Our framework predicts that agents recursively reason how much effort they and their partner will allocate to a task, based on the rewards at stake and on their own and their collaborator's competence. Across three experiments (N = 249), we show that the belief-desire-competence framework captures human judgments in a variety of contexts that are critical to collaboration, including predicting whether a joint activity will succeed (Experiment 1), selecting incentives for collaborators (Experiment 2), and choosing which individuals to recruit for a collaborative task (Experiment 3). Our work provides a theoretical framework for understanding how commonsense psychological reasoning contributes to collaborative achievements. (PsycInfo Database Record (c) 2023 APA, all rights reserved).