Home › Analytical Chem › Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Analytical Chem JoVE (Open Access) Citable · DOI

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

DOI: 10.3791/3358-v
What you'll learn
  • ✓ Create naturalistic 3-D virtual objects using morphogenesis simulation (VM)
  • ✓ Generate object categories with controlled feature variations using phylogenesis
  • ✓ Render virtual objects as visual images or haptic stimuli for perception studies
  • ✓ Apply Bayesian inference methods to categorize synthetic object images
Protocol

We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.

Difficulty
advanced
Total time
~2–4 hours per object set (depending on complexity and rendering resolution)

Steps

1
Create naturalistic virtual 3-D objects using VM

Use morphogenesis simulation (VM) to generate biologically realistic 3-D objects with precisely defined morphological features. This computational approach mimics natural growth processes to produce novel object geometries.

▶ 02:12
2
Generate object categories using phylogenesis simulation

Apply phylogenesis simulation (VP) to create naturalistic object categories with controlled, systematic feature variations. This method produces families of related objects suitable for perceptual learning experiments.

▶ 03:16
3
Apply digital morphing to objects

Perform smooth interpolation between synthetic object forms to create continuous feature transitions or intermediate exemplars within object categories.

▶ 04:25
4
Analyze object variation using principal components

Decompose object feature space into principal components to quantify and visualize variation within synthetic object categories in a reduced-dimensionality representation.

▶ 04:50
5
Create haptic versions of 3-D objects

Convert virtual 3-D object models into haptic-compatible formats for tactile presentation, enabling multimodal perception studies combining visual and haptic modalities.

▶ 05:53
6
Perform Bayesian inference on synthetic object categories

Demonstrate a model application using Bayesian inference to classify rendered object images into their source categories, validating the methodology for perception and learning studies.

▶ 06:10
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