# Unsupervised Neural Network Quantifies the Cost of Visual Information Processing

## TL;DR

This study uses unsupervised neural networks to explore how bumblebees naturally prefer certain flower-like visual features and how flowers may have evolved to match these preferences.

## Contribution

The study introduces unsupervised neural network models to quantify unlearned visual preferences in bumblebees and their evolutionary implications.

## Key findings

- Independent Component Analysis better captures bumblebees' unlearned visual preferences compared to other models.
- Flowers may have evolved to align with pollinators' cognitive constraints and processing costs.
- Reconstructed floral images using neural networks closely match original patterns in meaningful ways.

## 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 support 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.