Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks.

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'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 actual 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.

Teachers recruit mentalizing regions to represent learners' beliefs.

Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers' 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' examples were best described by a model that selects evidence that maximizes the learner's belief in the correct answer. Consistent with this idea, participants' 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' posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.

Representational exchange in social learning: Blurring the lines between the ritual and instrumental.

We propose that human social learning is subject to a trade-off between the cost of performing a computation and the flexibility of its outputs. Viewing social learning through this lens sheds light on cases that seem to violate bifocal stance theory (BST) - such as high-fidelity imitation in instrumental action - and provides a mechanism by which causal insight can be bootstrapped from imitation of cultural practices.

The role of causal structure in implicit evaluation.

Causal relationships, unlike mere co-occurrence, allow humans to obtain rewards and avoid punishments by intervening on their environment. Accordingly, explicit (controlled) evaluations of stimuli encountered in the environment are known to be sensitive to causal relationships above and beyond mere co-occurrence. In this project, we conduct stringent tests of whether implicit (automatic) evaluation also reflects causal relationships and begin to probe the representational mechanisms underlying such sensitivity. Participants (N = 4836) observed causal events during which two stimuli were equally contingent with positive or negative outcomes but only one of them was causally responsible for these outcomes. Across 6 studies, varying in design and amount of verbal scaffolding provided, differences in causal status consistently guided not only explicit measures of evaluation (Likert and slider scales; Bayes Factor meta-analysis: Cohen's d = 0.28, BF10 > 1046) but also their implicit counterparts (Implicit Association Tests; Bayes Factor meta-analysis: Cohen's d = 0.22, BF10 > 1029). However, unlike their explicit counterparts, implicit evaluations were not sensitive to causal relationships that had to be flexibly derived by combining disparate past experiences. Taken together, these studies suggest that implicit evaluations are sensitive to causal information. Such sensitivity seems to be mediated via precompiled, causally informed value representations rather than online computations over a causal model.

Punishment is Organized around Principles of Communicative Inference.

Humans use punishment to influence each other's behavior. Many current theories presume that this operates as a simple form of incentive. In contrast, we show that people infer the communicative intent behind punishment, which can sometimes diverge sharply from its immediate incentive value. In other words, people respond to punishment not as a reward to be maximized, but as a communicative signal to be interpreted. Specifically, we show that people expect harmless, yet communicative, punishments to be as effective as harmful punishments (Experiment 1). Under some situations, people display a systematic preference for harmless punishments over more canonical, harmful punishments (Experiment 2). People readily seek out and infer the communicative message inherent in a punishment (Experiment 3). And people expect that learning from punishment depends on the ease with which its communicative intent can be inferred (Experiment 4). Taken together, these findings demonstrate that people expect punishment to be constructed and interpreted as a communicative act.

Planning Complexity Registers as a Cost in Metacontrol.

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.

Is utilitarian sacrifice becoming more morally permissible?

A central tenet of contemporary moral psychology is that people typically reject active forms of utilitarian sacrifice. Yet, evidence for secularization and declining empathic concern in recent decades suggests the possibility of systematic change in this attitude. In the present study, we employ hypothetical dilemmas to investigate whether judgments of utilitarian sacrifice are becoming more permissive over time. In a cross-sectional design, age negatively predicted utilitarian moral judgment (Study 1). To examine whether this pattern reflected processes of maturation, we asked a panel to re-evaluate several moral dilemmas after an eight-year interval but observed no overall change (Study 2). In contrast, a more recent age-matched sample revealed greater endorsement of utilitarian sacrifice in a time-lag design (Study 3). Taken together, these results suggest that today's younger cohorts increasingly endorse a utilitarian resolution of sacrificial moral dilemmas.

Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.

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'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.

Plans, Habits, and Theory of Mind.

Human success and even survival depends on our ability to predict what others will do by guessing what they are thinking. If I accelerate, will he yield? If I propose, will she accept? If I confess, will they forgive? Psychologists call this capacity "theory of mind." According to current theories, we solve this problem by assuming that others are rational actors. That is, we assume that others design and execute efficient plans to achieve their goals, given their knowledge. But if this view is correct, then our theory of mind is startlingly incomplete. Human action is not always a product of rational planning, and we would be mistaken to always interpret others' behaviors as such. A wealth of evidence indicates that we often act habitually-a form of behavioral control that depends not on rational planning, but rather on a history of reinforcement. We aim to test whether the human theory of mind includes a theory of habitual action and to assess when and how it is deployed. In a series of studies, we show that human theory of mind is sensitive to factors influencing the balance between habitual and planned behavior.

When Does Model-Based Control Pay Off?

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.

Bad actions or bad outcomes? Differentiating affective contributions to the moral condemnation of harm.

Moral condemnation of harmful behavior is influenced by both cognitive and affective processes. However, despite much recent research, the proximate source of affect remains unclear. One obvious contender is empathy; simulating the victim's pain could lead one to judge an action as wrong ("outcome aversion"). An alternative, less obvious source is one's own aversion to performing the action itself ("action aversion"). To dissociate these alternatives, we developed a scale that assessed individual aversions to (a) witnessing others experience painful outcomes (e.g., seeing someone fall down stairs); and (b) performing actions that are harmless yet aversive (e.g., stabbing a fellow actor with a fake stage knife). Across 4 experiments, we found that moral condemnation of both first-person and third-party harmful behavior in the context of moral dilemmas is better predicted by one's aversion to action properties than by an affective response to victim suffering. In a fifth experiment, we manipulated both action aversion and the degree of expected suffering across a number of actions and found that both factors make large, independent contributions to moral judgment. Together, these results suggest we may judge others' actions by imagining what it would feel like to perform the action rather than experience the consequences of the action. Accordingly, they provide a counterpoint to a dominant but largely untested assumption that empathy is the key affective response governing moral judgments of harm.

Pushing moral buttons: the interaction between personal force and intention in moral judgment.

In some cases people judge it morally acceptable to sacrifice one person's life in order to save several other lives, while in other similar cases they make the opposite judgment. Researchers have identified two general factors that may explain this phenomenon at the stimulus level: (1) the agent's intention (i.e. whether the harmful event is intended as a means or merely foreseen as a side-effect) and (2) whether the agent harms the victim in a manner that is relatively "direct" or "personal". Here we integrate these two classes of findings. Two experiments examine a novel personalness/directness factor that we call personal force, present when the force that directly impacts the victim is generated by the agent's muscles (e.g., in pushing). Experiments 1a and b demonstrate the influence of personal force on moral judgment, distinguishing it from physical contact and spatial proximity. Experiments 2a and b demonstrate an interaction between personal force and intention, whereby the effect of personal force depends entirely on intention. These studies also introduce a method for controlling for people's real-world expectations in decisions involving potentially unrealistic hypothetical dilemmas.