Parts beget parts: Bootstrapping hierarchical object representations through visual statistical learning

Alan L.F. LEE, Zili LIU, Hongjing LU

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Previous research has shown that humans are able to acquire statistical regularities among shape parts that form various spatial configurations, via exposure to these configurations without any task or feedback. The present study extends this approach of visual statistical learning to examine whether prior knowledge of parts, acquired in a separate learning context, facilitates acquisition of multi-layer hierarchical representations of objects. After participants had learned to encode a shape-pair as a chunk into memory, they viewed cluttered scenes containing multiple shape chunks. One of the larger configurations was constructed by combining the learned shape-pair with an unfamiliar, complementary shape-pair. Although the complementary shape-pair had never been presented separately during learning, it was remembered better than other shape pairs that were parts of larger configurations. The greater perceived familiarity of the complementary shape-pair depended on the encoding strength of the previously learned shape-pair. This “parts-beget-parts” effect suggests that statistical learning, in combination with prior knowledge, can represent objects as a coherent whole and also as a spatial configuration of parts by bootstrapping multi-layer hierarchical structures.
Original languageEnglish
Article number104515
JournalCognition
Early online date23 Dec 2020
DOIs
Publication statusE-pub ahead of print - 23 Dec 2020

Bibliographical note

This research was supported by NSF grant BCS-165530 to Hongjing Lu.

Keywords

  • Compositionality
  • Hierarchical structure
  • Object representation
  • Statistical learning
  • Visual chunks

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