An official website of the United States government
Do children use objects to infer the people and actions that created them? We ask how children judge whether designs were socially transmitted (copied), asking if children use a simple perceptual heuristic (more similar = more likely copied), or make a rational, flexible inference (Bayesian inverse planning). We found evidence that children use inverse planning to reason about artifacts’ designs: When children saw two identical designs, they did not always infer copying occurred. Instead, similarity was weaker evidence of copying when an alternative explanation ‘explained away’ the similarity. Thus, children inferred copying had occurred less often when designs were efficient (Exp1, age 7-9; N=52), and when there was a constraint that limited the number of possible designs (Exp2, age 4-5; N=160). When thinking about artifacts, young children go beyond perceptual features and use a process like inverse planning to reason about the generative processes involved in design.
more »
« less
Detecting social transmission in the design of artifacts via inverse planning
How do people use human-made objects (artifacts) to learn about the people and actions that created them? We test the richness of people’s reasoning in this domain, focusing on the task of judging whether social transmission has occurred (i.e. whether one person copied another). We develop a formal model of this reasoning process as a form of rational inverse planning, which predicts that rather than solely focusing on artifacts’ similarity to judge whether copying occurred, people should also take into account availability constraints (the materials available), and functional constraints (which materials work). Using an artifact-building task where two characters build tools to solve a puzzle box, we find that this inverse planning model predicts trial-by-trial judgments, whereas simpler models that do not consider availability or functional constraints do not. This suggests people use a process like inverse planning to make flexible inferences from artifacts’ features about the source of design ideas.
more »
« less
- Award ID(s):
- 1749551
- PAR ID:
- 10101000
- Date Published:
- Journal Name:
- The Proceedings of the Annual Meeting of the Cognitive Science Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.more » « less
-
In radiation therapy treatment plan optimization, selecting a set of clinical objectives that are tractable and parsimonious yet effective is a challenging task. In clinical practice, this is typically done by trial and error based on the treatment planner’s subjective assessment, which often makes the planning process inefficient and inconsistent. We develop the objective selection problem that infers a sparse set of objectives for prostate cancer treatment planning based on historical treatment data. We formulate the problem as a nonconvex bilevel mixed-integer program using inverse optimization and highlight its connection with feature selection to propose multiple solution approaches, including greedy heuristics and regularized problems and application-specific methods that use anatomical information of the patients. Our results show that the proposed heuristics find objectives that are near optimal. Via curve analysis on dose-volume histograms, we show that the learned objectives closely represent latent clinical preferences.more » « less
-
Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K. (Ed.)Listening to music activates representations of movement and social agents. Why? We ask whether high-level causal reasoning about how music was generated can lead people to link musical sounds with animate agents. To test this, we asked whether people (N=60) make flexible inferences about whether an agent caused musical sounds, integrating information from the sounds’ timing and from the visual context in which it was produced. Using a 2x2 within-subject design, we found evidence of causal reasoning: In a context where producing a musical sequence would require self-propelled movement, people inferred that an agent had been present causing the sounds. When the context provided an alternative possible explanation, this ‘explained away’ the agent, reducing the tendency to infer an agent was present for the same acoustic stimuli. People can use causal reasoning to infer whether an agent produced musical sounds, suggesting that high-level cognition can link music with social concepts.more » « less
-
When people make decisions, they act in a way that is either automatic (“rote”), or more thoughtful (“reflective”). But do people notice when others are behaving in a rote way, and do they care? We examine the detection of rote behavior and its consequences in U.S. adults, focusing specifically on pedagogy and learning. We establish repetitiveness as a cue for rote behavior (Experiment 1), and find that rote people are seen as worse teachers (Experiment 2). We also find that the more a person's feedback seems similar across groups (indicating greater rote‐ness), the more negatively their teaching is evaluated (Experiment 3). A word‐embedding analysis of an open‐response task shows people naturally cluster rote and reflective teachers into different semantic categories (Experiment 4). We also show that repetitiveness can be decoupled from perceptions of rote‐ness given contextual explanation (Experiment 5). Finally, we establish two additional cues to rote behavior that can be tied to quality of teaching (Experiment 6). These results empirically show that people detect and care about scripted behaviors in pedagogy, and suggest an important extension to formal frameworks of social reasoning.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
