Unsupervised Neural Network Quantifies the Cost of Visual Information Processing

Orban, Levente and Chartier, Sylvain (2015) Unsupervised Neural Network Quantifies the Cost of Visual Information Processing. PLoS ONE, 10 (7). NA. ISSN 1932-6203

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Abstract

Untrained,“flower-naïve” bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees’ unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-
patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to sup-
port a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators’ cognitive constraints.

Item Type:	Journal Article
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Law and Social Sciences (SoLaSS)
Depositing User:	Levente Orban
Date Deposited:	10 Feb 2026 22:52
Last Modified:	10 Feb 2026 22:52
URI:	https://repository.usp.ac.fj/id/eprint/15258

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