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Related Experiment Video

Updated: Feb 1, 2026

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Published on: March 22, 2018

Bootstrapped learning of novel objects.

Mark J Brady¹, Daniel Kersten

¹Department of Psychology, University of Minnesota, Minneapolis, MN, USA. brady038@umn.edu

Journal of Vision

|August 7, 2003

Summary

This summary is machine-generated.

Humans can learn to recognize novel camouflaged objects even with ambiguous visual input. This "bootstrapped learning" demonstrates shape model creation from high-ambiguity presentations, challenging opportunistic learning theories.

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Published on: November 2, 2012

Area of Science:

Cognitive Science
Computer Vision
Neuroscience

Background:

Object recognition in cluttered environments is computationally difficult.
Camouflage presents a significant challenge for detection, recognition, and segmentation.
Existing computational models often rely on both low-level features and high-level object models.

Purpose of the Study:

To investigate how the human visual system learns to recognize novel camouflaged objects.
To test the hypothesis that learning is opportunistic, requiring distinct segmentation cues.
To explore the concept of building shape models from ambiguous visual data.

Main Methods:

Human observers were trained to identify and segment novel target shapes.
Training images featured camouflaged targets with high ambiguity.
Performance was assessed for both recognition and segmentation accuracy.

Main Results:

Human observers successfully learned to identify and segment novel camouflaged shapes.
Accurate recognition was achievable even without precise segmentation.
This learning capability was termed "bootstrapped learning."

Conclusions:

The visual system can build object shape models from ambiguous presentations, contradicting opportunistic learning theories.
Learning novel object shapes is possible even when segmentation cues are minimal.
Bootstrapped learning is a key mechanism for acquiring knowledge of novel, camouflaged objects.