@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.} } @article {5525, title = {Mental control of uncertainty}, journal = {Cognitive, Affective, \& Behavioral Neuroscience}, volume = {23}, year = {2022}, month = {09/2023}, pages = {465 - 475}, abstract = {

Can you reduce uncertainty by thinking? Intuition suggests that this happens through the elusive process of attention: if we expend mental effort, we can increase the reliability of our sensory data. Models based on {\textquotedblleft}rational inattention{\textquotedblright} formalize this idea in terms of a trade-off between the costs and benefits of attention. This paper surveys the origin of these models in economics, their connection to rate-distortion theory, and some of their recent applications to psychology and neuroscience. We also report new data from a numerosity judgment task in which we manipulate performance incentives. Consistent with rational inattention, people are able to improve performance on this task when incentivized, in part by increasing the reliability of their sensory data.

}, issn = {1530-7026}, doi = {10.3758/s13415-022-01034-8}, url = {https://link.springer.com/10.3758/s13415-022-01034-8}, author = {Gershman, Samuel J. and Burke, Taylor} }