skip to main content

Title: What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others' Actions Based on Relative Reward and Danger

When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years of experience making such decisions, and 6‐ and 7‐year‐old children, who have less experience and are demonstrably worse at judging the consequences of their own actions, conceive others' actions as motivated both by reward (how good reaching one's intended goal would be), and by what we call “danger” (how badly one's action could end). In two pre‐registered experiments, we tested whether adults and 6‐ and 7‐year‐old children tailor their predictions and explanations of an agent's action choices to the specific degree of danger and reward entailed by each action. Across four different tasks, we found that children and adults expected others to negatively appraise dangerous situations and minimize the danger of their actions. Children's and adults' judgments varied systematically in accord with both the degree of danger the agent faced and the value the agent placed on the goal state it aimed to achieve. However, children did not calibrate their inferences abouthow muchan agent valued the goal state of a successful action in accord with the degree of danger the action entailed, and adults calibrated these inferences more weakly than inferences concerning the agent's future action choices. These results suggest that from childhood, people use a degree of danger and reward to make quantitative, fine‐grained explanations and predictions about other people's behavior, consistent with computational models on theory of mind that contain continuous representations of other agents' action plans.

more » « less
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
Date Published:
Journal Name:
Cognitive Science
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Highlights

    In the present experiments, 3‐month‐old prereaching infants learned to attribute either object goals or place goals to other people's reaching actions.

    Prereaching infants view agents’ actions as goal‐directed, but do not expect these acts to be directed to specific objects, rather than to specific places.

    Prereaching infants are open‐minded about the specific goal states that reaching actions aim to achieve.

    more » « less
  2. Abstract Research Highlights

    Children in our sample extracted patterns from an agent's positive social choices between multiple groups to reason about groups’ relative social standing.

    Children used the pattern of an agent's positive social choices to guide their reasoning about which groups were likely to be “leaders” and “helpers” in a fictional town.

    The pattern that emerged in an agent's choices of friends shaped children's thinking about groups’ relativesocialbut notphysicalpower.

    Children tracked social choices to reason about group‐based hierarchies at the individual level (which groups an agent prefers) and societal level (which groups are privileged).

    more » « less
  3. We pose and study the problem of planning in Markov decision processes (MDPs), subject to participation constraints as studied in mechanism design. In this problem, a planner must work with a self-interested agent on a given MDP. Each action in the MDP provides an immediate reward to the planner and a (possibly different) reward to the agent. The agent has no control in choosing the actions, but has the option to end the entire process at any time. The goal of the planner is to find a policy that maximizes her cumulative reward, taking into consideration the agent's ability to terminate. We give a fully polynomial-time approximation scheme for this problem. En route, we present polynomial-time algorithms for computing (exact) optimal policies for important special cases of this problem, including when the time horizon is constant, or when the MDP exhibits a "definitive decisions" property. We illustrate our algorithms with two different game-theoretic applications: the problem of assigning rides in ride-sharing and the problem of designing screening policies. Our results imply efficient algorithms for computing (approximately) optimal policies in both applications. 
    more » « less
  4. This review presents a theoretical account of the development of possibility beliefs in childhood through two developmental pathways, centered around the experience and understanding of our intentional, goal-directed actions. Pathway 1 (Naïve Optimism to Calibrated Realism) can be seen as early as the first year, as increased coordination of action through motor experience leads infants to a graded notion of what is possible and how much effort is required to achieve goals. Infants also incorporate social information into their earliest possibility beliefs, referencing caregivers to guide them in uncertain situations and learning from role models to effectively calibrate effort. Pathway 2 (Naïve Pessimism to Creative Transcendence) emerges from ages 4 to 7. At first, preschoolers correctly distinguish possible and impossible actions but are overly pessimistic about limits on possibility. With age, children use their imaginations to overcome hypothetical limits. This account suggests that realistic beliefs about what we can possibly do are in place in early childhood, preceding later developmental milestones in self-concept, identity, self-efficacy, achievement-orientation, and self-goals. This leaves open questions about mechanisms of change, how possibility beliefs contribute to later self-beliefs, and whether interventions that combine action experience with creative idea generation can increase the sense of the possible in children and adults.

    more » « less
  5. Abstract

    Beliefs about the controllability of positive or negative events in the environment can shape learning throughout the lifespan. Previous research has shown that adults’ learning is modulated by beliefs about the causal structure of the environment such that they update their value estimates to a lesser extent when the outcomes can be attributed to hidden causes. This study examined whether external causes similarly influenced outcome attributions and learning across development. Ninety participants, ages 7 to 25 years, completed a reinforcement learning task in which they chose between two options with fixed reward probabilities. Choices were made in three distinct environments in which different hidden agents occasionally intervened to generate positive, negative, or random outcomes. Participants’ beliefs about hidden-agent intervention aligned with the true probabilities of the positive, negative, or random outcome manipulation in each of the three environments. Computational modeling of the learning data revealed that while the choices made by both adults (ages 18–25) and adolescents (ages 13–17) were best fit by Bayesian reinforcement learning models that incorporate beliefs about hidden-agent intervention, those of children (ages 7–12) were best fit by a one learning rate model that updates value estimates based on choice outcomes alone. Together, these results suggest that while children demonstrate explicit awareness of the causal structure of the task environment, they do not implicitly use beliefs about the causal structure of the environment to guide reinforcement learning in the same manner as adolescents and adults.

    more » « less