By middle childhood, humans are able to learn abstract semantic relations (e.g., antonym, synonym, category membership) and use them to reason by analogy. A deep theoretical challenge is to show how such abstract relations can arise from nonrelational inputs, thereby providing key elements of a protosymbolic representation system. We have developed a computational model that exploits the potential synergy between deep learning from “big data” (to create semantic features for individual words) and supervised learning from “small data” (to create representations of semantic relations between words). Given as inputs labeled pairs of lexical representations extracted by deep learning, the modelmore »
This content will become publicly available on July 1, 2022
Visual analogy: Deep learning versus compositional models
Is analogical reasoning a task that must be learned to solve
from scratch by applying deep learning models to massive
numbers of reasoning problems? Or are analogies solved by
computing similarities between structured representations of
analogs? We address this question by comparing human
performance on visual analogies created using images of
familiar three-dimensional objects (cars and their subregions)
with the performance of alternative computational models.
Human reasoners achieved above-chance accuracy for all
problem types, but made more errors in several conditions
(e.g., when relevant subregions were occluded). We compared
human performance to that of two recent deep learning models
(Siamese Network and Relation Network) directly trained to
solve these analogy problems, as well as to that of a
compositional model that assesses relational similarity
between part-based representations. The compositional model
based on part representations, but not the deep learning models,
generated qualitative performance similar to that of human
reasoners.
- Editors:
- Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K.
- Award ID(s):
- 1827374
- Publication Date:
- NSF-PAR ID:
- 10231806
- Journal Name:
- Proceedings of the 43rd Annual Meeting of the Cognitive Science Society
- Sponsoring Org:
- National Science Foundation
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