@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 {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 {4655, title = {The logic of universalization guides moral judgment}, journal = {Proceedings of the National Academy of Sciences (PNAS)}, year = {2020}, month = {Feb-10-2020}, pages = {202014505}, abstract = {

To explain why an action is wrong, we sometimes say, {\textquotedblleft}What if everybody did that?{\textquotedblright} In other words, even if a single person{\textquoteright}s behavior is harmless, that behavior may be wrong if it would be harmful once universalized. We formalize the process of universalization in a computational model, test its quantitative predictions in studies of human moral judgment, and distinguish it from alternative models. We show that adults spontaneously make moral judgments consistent with the logic of universalization, and report comparable patterns of judgment in children. We conclude that, alongside other well-characterized mechanisms of moral judgment, such as outcome-based and rule-based thinking, the logic of universalizing holds an important place in our moral minds.

}, issn = {0027-8424}, doi = {10.1073/pnas.2014505117}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2014505117}, author = {Levine, Sydney and Max Kleiman-Weiner and Laura Schulz and Joshua B. Tenenbaum and Fiery A Cushman} } @conference {4449, title = {Hard choices: Children{\textquoteright}s understanding of the cost of action selection. }, booktitle = {Cognitive Science Society}, year = {2019}, author = {Shari Liu and Fiery A Cushman and Samuel J Gershman and Kool, Wouter and Elizabeth S Spelke} } @article {4190, title = {Planning Complexity Registers as a Cost in Metacontrol}, journal = {Journal of Cognitive Neuroscience}, volume = {30}, year = {2018}, month = {10/2018}, pages = {1391 - 1404}, abstract = {

Decision-making algorithms face a basic tradeoff between accuracy and effort (i.e., computational demands). It is widely agreed that humans can choose between multiple decision-making processes that embody different solutions to this tradeoff: Some are computationally cheap but inaccurate, whereas others are computationally expensive but accurate. Recent progress in understanding this tradeoff has been catalyzed by formalizing it in terms of model-free (i.e., habitual) versus model-based (i.e., planning) approaches to reinforcement learning. Intuitively, if two tasks offer the same rewards for accuracy but one of them is much more demanding, we might expect people to rely on habit more in the difficult task: Devoting significant computation to achieve slight marginal accuracy gains would not be "worth it." We test and verify this prediction in a sequential reinforcement learning task. Because our paradigm is amenable to formal analysis, it contributes to the development of a computational model of how people balance the costs and benefits of different decision-making processes in a task-specific manner; in other words, how we decide when hard thinking is worth it.

}, issn = {0898-929X}, doi = {10.1162/jocn_a_01263}, url = {https://www.mitpressjournals.org/doi/abs/10.1162/jocn_a_01263}, author = {Kool, Wouter and Samuel J Gershman and Fiery A Cushman} } @article {3442, title = {Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.}, journal = {Psychol Sci}, volume = {28}, year = {2017}, month = {2017 Sep}, pages = {1321-1333}, abstract = {

Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system{\textquoteright}s task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.

}, issn = {1467-9280}, doi = {10.1177/0956797617708288}, author = {Kool, Wouter and Samuel J Gershman and Fiery A Cushman} } @article {2646, title = {Thinking fast or slow? A reinforcement-learning approach}, year = {2017}, address = {San Antonio, TX}, author = {Kool, W and Samuel J Gershman and Fiery A Cushman} } @article {2645, title = {When Does Model-Based Control Pay Off?}, journal = {PLoS Comput Biol}, volume = {12}, year = {2016}, month = {2016 Aug}, pages = {e1005090}, abstract = {

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1005090}, author = {Kool, Wouter and Fiery A Cushman and Samuel J Gershman} }