| Paper: | PS-1A.38 |
| Session: | Poster Session 1A |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Human Priors in Hierarchical Program Induction |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1265-0 |
| Authors: |
Mark Ho, Sophia Sanborn, Fred Callaway, David Bourgin, Tom Griffiths, UC Berkeley, United States |
| Abstract: |
People impose structure onto other agents' sequential problem-solving behavior. That is, they interpret actions in terms of a \textit{likely program} that the observed agent was executing to solve a problem. But what prior expectations do people have about these programs? For example, in both cognitive science and computer science, \textit{shortest description length} has been proposed as a general principle for inducing a program. However, there may be other criteria that bias how people reconstruct others' solutions: That they are symmetric, balanced, or organize child and parent processes in particular ways. Here, we report preliminary experiments and models that investigate peoples' priors on others' problem-solving programs. We first present a novel experimental paradigm in which participants were given examples of how a problem was solved and needed to reconstruct the program that generated the solution. Then, we discuss the application of our model of human program priors to these data. We find that shortest description length inadequately explains how people reconstruct others' problem solving programs. |
