Humans can seamlessly infer other people's preferences, based on what they do. Broadly, two types of accounts have been proposed to explain different aspects of this ability. The first account focuses on spatial information: Agents' efficient navigation in space reveals what they like. The second account focuses on statistical information: Uncommon choices reveal stronger preferences. Together, these two lines of research suggest that we have two distinct capacities for inferring preferences. Here we propose that this is not the case, and (...) that spatial-based and statistical-based preference inferences can be explained by the assumption that agents are efficient alone. We show that people's sensitivity to spatial and statistical information when they infer preferences is best predicted by a computational model of the principle of efficiency, and that this model outperforms dual-system models, even when the latter are fit to participant judgments. Our results suggest that, as adults, a unified understanding of agency under the principle of efficiency underlies our ability to infer preferences. (shrink)

Many actions have instrumental aims, in which we move our bodies to achieve a physical outcome in the environment. However, we also perform actions with epistemic aims, in which we move our bodies to acquire information and learn about the world. A large literature on action recognition investigates how observers represent and understand the former class of actions; but what about the latter class? Can one person tell, just by observing another person’s movements, what they are trying to learn? Here, (...) 5 experiments explore epistemic action understanding. We filmed volunteers playing a ‘physics game’ consisting of two rounds: Players shook an opaque box and attempted to determine (i) the number of objects hidden inside, or (ii) the shape of the objects inside. Then, independent subjects watched these videos and were asked to determine which videos came from which round: Who was shaking for number and who was shaking for shape? Across several variations, observers successfully determined what an actor was trying to learn, based only on their actions (i.e., how they shook the box)—even when the box’s contents were identical across rounds. These results demonstrate that humans can infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding. (shrink)

Research has shown that both adults and children organize familiar activity into discrete units with consistent boundaries, despite the dynamic, continuous nature of everyday experiences. However, less is known about how observers segment unfamiliar event sequences. In the current study, we took advantage of the novelty that is inherent in modern dance. Modern dance features natural human motion but does not contain canonical goals—therefore, observers cannot recruit prior goal‐related knowledge to segment it. Our main aims were to identify whether observers (...) segment modern dance into the steps intended by the dancers, and what types of cues contribute to segmentation under these circumstances. Experiment 1 used a classic event segmentation task and found that adults were able to consistently identify only a few of the dancers’ intended steps. Experiment 2 tested adults in an offline labeling task. Results showed that steps which could more easily be labeled offline in Experiment 2 were more likely to be segmented online in Experiment 1. (shrink)

Speakers often use gesture to demonstrate how to perform actions—for example, they might show how to open the top of a jar by making a twisting motion above the jar. Yet it is unclear whether listeners learn as much from seeing such gestures as they learn from seeing actions that physically change the position of objects (i.e., actually opening the jar). Here, we examined participants' implicit and explicit understanding about a series of movements that demonstrated how to move a set (...) of objects. The movements were either shown with actions that physically relocated each object or with gestures that represented the relocation without touching the objects. Further, the end location that was indicated for each object covaried with whether the object was grasped with one or two hands. We found that memory for the end location of each object was better after seeing the physical relocation of the objects, that is, after seeing action, than after seeing gesture, regardless of whether speech was absent (Experiment 1) or present (Experiment 2). However, gesture and action built similar implicit understanding of how a particular handgrasp corresponded with a particular end location. Although gestures miss the benefit of showing the end state of objects that have been acted upon, the data show that gestures are as good as action in building knowledge of how to perform an action. (shrink)