How mental representations are constructed and how they evolve are central problems for cognitive science. Representation decisions help determine what computations are hard or easy. Structured, relational representations are a hallmark of human cognition. Developmental studies show that children do not perform as well as adults in tasks that require noticing relational similarity. What drives this development? Gentner and her colleagues have argued that comparison and language are two forces driving this change. This thesis explores these ideas further by presenting a computational model of forced choice tasks to illuminate the roles of comparison and language in driving representational change. The model simulates the following roles of comparison. First, comparison can be used to make selections in forced-choice tasks. Second, comparisons from recent positive experiences are assimilated as interim generalizations which are retrieved for subsequent tasks and influence encoding by highlighting relevant structure. Third, comparisons suggest opportunities for re-representation. Finally, verifying candidate inferences resulting from a comparison provides a way to augment encodings with background knowledge, thus enriching representations. The model simulates the role of language in facilitating the creation and enrichment of generalizations as follows. When two objects are given the same label, the model compares them. This leads to an interim generalization associated with that label, enriched with commonalities from background knowledge. We tested these hypotheses by extending the Companion cognitive architecture and simulating three developmental studies. To reduce tailorability, the visual stimuli were provided as sketches and the objects were labeled using simplified English. The model was evaluated by comparing its behavior and learning trajectory to that of children in the developmental studies. The performance of the model in the simulations provide evidence for the claims of this thesis.

Last modified

• 02/20/2018

Creator

• Balasubramanian Kandaswamy

DOI

• https://doi.org/10.21985/N25Q4T

Subject

• Computer Science

Keyword

• Representation learning
• developmental model
• relational representation
• structure mapping

Date created

• 2016-01-01

Resource type

• Dissertation

Rights statement

• In Copyright