@article {5533, title = {Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks}, journal = {Cognition}, volume = {241}, year = {2023}, month = {12/2023}, pages = {105609}, abstract = {

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 actual effort or force (individuals are more responsible if they put forth more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator{\textquoteright}s part could have altered the outcome). Across four experiments (), we found that participants{\textquoteright} judgments are best described by a model that considers both actual and counterfactual effort. This finding generalized to an independent validation data set (). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

}, issn = {00100277}, doi = {10.1016/j.cognition.2023.105609}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0010027723002433}, author = {Xiang, Yang and Landy, Jenna and Cushman, Fiery A. and V{\'e}lez, Natalia and Gershman, Samuel J.} } @article {5534, title = {Collaborative decision making is grounded in representations of other people{\textquoteright}s competence and effort.}, journal = {Journal of Experimental Psychology: General}, volume = {152}, year = {2023}, month = {06/2023}, pages = {1565 - 1579}, abstract = {

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{\textemdash}in other words, of their mental states and competence. We present a belief{\textendash}desire{\textendash}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{\textquoteright}s competence. Across three experiments (N = 249), we show that the belief{\textendash}desire{\textendash}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.

}, issn = {0096-3445}, doi = {10.1037/xge0001336}, url = {https://doi.apa.org/doi/10.1037/xge0001336}, author = {Xiang, Yang and V{\'e}lez, Natalia and Gershman, Samuel J.} } @article {5549, title = {Teachers recruit mentalizing regions to represent learners{\textquoteright} beliefs}, journal = {Proceedings of the National Academy of Sciences}, volume = {120}, year = {2023}, month = {05/2023}, abstract = {

Significance

Humans are remarkably efficient teachers. For example, an experienced forager can teach a novice how to distinguish delicious morels from similar, toxic mushrooms by pointing out a few distinctive features. These features constitute a tiny fraction of what a knowledgeable teacher could possibly point out. Out of all the things we could teach, how do our brains compute what information is most helpful to communicate? Here, we find that specialized regions in the brains of teachers track learners{\textquoteright} beliefs during teaching. These results shed light on the neural mechanisms that support our extraordinary abilities as teachers.

Abstract

Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers{\textquoteright} decisions about what information to communicate. Participants (N = 28) played the role of teachers while being scanned using fMRI; their task was to select examples that would teach learners how to answer abstract multiple-choice questions. Participants{\textquoteright} examples were best described by a model that selects evidence that maximizes the learner{\textquoteright}s belief in the correct answer. Consistent with this idea, participants{\textquoteright} predictions about how well learners would do closely tracked the performance of an independent sample of learners (N = 140) who were tested on the examples they had provided. In addition, regions that play specialized roles in processing social information, namely the bilateral temporoparietal junction and middle and dorsal medial prefrontal cortex, tracked learners{\textquoteright} posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.

}, issn = {0027-8424}, doi = {10.1073/pnas.2215015120}, url = {https://pnas.org/doi/10.1073/pnas.2215015120}, author = {V{\'e}lez, Natalia and Chen, Alicia M. and Burke, Taylor and Cushman, Fiery A. and Gershman, Samuel J.} }