- Title Information
- Title
- Compositionality in Human Structure Learning
- Name:
Personal
- Name Part
- Franklin, Nicholas Thompson
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Frank, Michael
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Badre, David
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Littman, Michael
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Department of Cognitive, Linguistic, and Psychological Sciences
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2018
- Physical Description
- Extent
- 17, 131 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2018
- Genre (aat)
- theses
- Abstract
- Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers. Often, this entails generalizing constituent pieces of experiences that do not fully overlap, but nonetheless share useful similarities with previously acquired knowledge. A young musician may learn to play several instruments in different contexts, for example, learning the flute to play classical and the saxophone to play jazz. Because the fingerings of the saxophone and the flute are nearly the same, a musician can re-use previously learned hand motions for different effect across the two instruments. Conversely, a musician may learn to play a single song across many instruments that require completely distinct physical motions, but yet still transfer knowledge between them. This degree of compositionality is critical for flexible goal-directed behavior but can be difficult for computational frameworks because they often assume an underlying structure of the world that is incompatible with generalization. Here, I propose a novel computational framework that leverages the compositional structure in real-world environments by assuming people generalize goals, or what to do, independently from a word model, or how to do it. I examine the computational framework normatively and ask when it makes sense to generalize goals and world models independently or together. In a series of experiments, I compare human subject behavior to model predictions, showing that people adapt their generalization strategy depending on the environment. Together, these results sug- gest that no one strategy is best across all environments, and that while it is adaptive to represent a complex environment in learnable components, people pay attention to the relationship between components in their environment.
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00866547")
- Topic
- Cognitive science
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20180615
- Identifier:
DOI
- 10.26300/9bkf-a791
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- Collection is open for research.
- Type of Resource (primo)
- dissertations