The ability to reason about our own and others’ competenceinforms our everyday decisions. However, competence is anabstract concept which manifests in the objective properties ofthe task completed by an agent (i.e., task-based features, suchas quality of outcome or task difficulty) as well as the sub-jective properties of the agent (i.e., agent-based features, suchas dexterity, speed, focus). Thus, acquiring an integrated no-tion of competence may be a nontrivial challenge for youngchildren. Prior work on children’s understanding of compe-tence has often used explicit verbal cues to describe the rele-vant features, or experimental tasks that confounded these fea-tures. Here we examine how preschool-aged children evalu-ate the relative competence of two agents by systematicallymanipulating task-based and agent-based features without ex-plicit linguistic or gestural support. We find that 4- and 5-year-olds readily use perceptual cues to task-based (i.e., taskdifficulty) and agent-based (i.e., agent speed) features to in-fer competence (Exp.1-3) but not when when these perceptualcues are closely matched (Exp.4). These results suggest thata basic understanding of relative competence emerges earlierthan previously believed, but an abstract, adult-like concept ofcompetence may continue to develop throughout childhood.

The ability to reason about task difficulty is critical for manyreal-world decisions. Building on prior work on preschoolers’inferences about the difficulty of novel physical tasks (Gweonet al., 2017), here we ask whether this ability further supportsrational allocation of effort in collaborative and individual con-texts. When an agent could offer help to someone who had tocomplete a hard task versus someone who had to complete aneasy task, adults and preschoolers offered help with the hardertask (Collaborative Goal). When an agent could choose tocomplete a hard task or an easy task to achieve the same out-come, adults and preschoolers preferentially chose the easiertask (Individual Goal). In the absence of explicit informationabout the relative difficulty of tasks, even young children in-ferred the expected difficulty of tasks and appropriately allo-cated effort across agents and across tasks. Beyond expectingagents to choose actions that maximize their own utility in in-dividual contexts, our results show that even preschool-agedchildren readily understand how deviating from this choice canbe desirable in cooperative contexts.

The ability to reason about the difficulty of novel tasks is criti-cal for many real-world decisions. To decide whether to tacklea task or how to divide labor across people, we must estimatethe difficulty of the goal in the absence of prior experience.Here we examine adults’ and preschoolers’ inferences aboutthe difficulty of simple block-building tasks. Exp.1 first estab-lished that building time is a useful proxy for difficulty. Exp.2asked participants to view the initial and final states of variousblock-building tasks and judge their relative difficulty. Whileadults were near-ceiling on all trials, children showed varyinglevels of performance depending on the nature of the dimen-sions that varied across structures. Exp. 3 replicated the pat-tern. These results suggest that children can reverse-engineerthe process of goal-directed actions to infer the relative diffi-culty of novel tasks, although their ability to incorporate morenuanced factors may continue to develop.

The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselvesor appreciating others’ accomplishments. Here we give a computational account of how humans judge the difficultyof a range of physical construction tasks (e.g., moving 10 loose blocks from their initial configuration to their targetconfiguration, such as a vertical tower) by quantifying two key factors that influence construction difficulty: physical effortand physical risk. Physical effort captures the minimal work needed to transport all objects to their final positions, and iscomputed using a hybrid task-and-motion planner. Physical risk corresponds to stability of the structure, and is computedusing noisy physics simulations to capture the costs for precision (e.g., attention, coordination, fine motor movements)required for success. We show that the full effort-risk model captures human estimates of difficulty and construction timebetter than either component alone. Preprint link https://arxiv.org/abs/1905.04445.