Statistical learning of shape-conjunctions: (Higher) order from chaos

Richard N. Aslin, Jozsef Fiser

Research output: Contribution to journalArticle


Learning new visual features involves the encoding of local spatial correlations from the statistics of images. Previously, we showed that human observers can learn the spatial configuration of shape-pairs embedded within multiple exemplars of complex displays. Passive observation of several dozen exemplars was sufficient for learning both single-shape frequency and higher-order (conditional probability) information about shape-pairs in the displays. In the present study, these same 12 simple shapes were grouped into four 3-element base-triplets, each with a specific I- or V-shaped spatial arrangement. During the learning phase, subjects passively viewed displays (2 sec each) in which two of the four base-triplets were pseudorandomly arranged in a 5 × 5 grid. These two base-triplets created cross-triplet shape-pairs that were superficially indistinguishable from base-triplet shape-pairs. Some of these cross-triplet shape-pairs had the same probability of occurrence as some base-triplet shape-pairs. However, all four of the base-triplets were more predictable (they had higher conditional probabilities) than any non-base-triplets. After the learning phase, subjects were unable to discriminate cross-triplet shape-pairs from base-triplet shape-pairs [t(20)=1.18, n.s.], but they were able to discriminate base-triplets from non-base-triplets [t(20)=4.86, p< .001]. These results replicate our earlier findings by showing that subjects can learn higher-order spatial statistics across multiple images. More importantly, these results demonstrate that the statistically based coherence of scenes can operate on at least triplets of shapes which define a coherent object configuration, and that triplet statistics are not necessarily bootstrapped from pair-wise statistics.

Original languageEnglish
Pages (from-to)468a
JournalJournal of Vision
Issue number3
Publication statusPublished - Dec 1 2001

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems

Fingerprint Dive into the research topics of 'Statistical learning of shape-conjunctions: (Higher) order from chaos'. Together they form a unique fingerprint.

  • Cite this