A brain composed of shapes and neural connections, symbolizing shape learning.

Unlock Your Brain's Hidden Potential: How Shape Learning Rewires Your Visual Perception

Avery Sinclair in Mind & Education August 2025 • 4 min read.

"New EEG research reveals how our brains learn to see shapes holistically, opening doors to enhanced learning and visual processing."

Vision is more than just seeing; it's a complex process involving multiple levels of understanding. Our brains take in basic features, integrate them, and construct meaningful object representations. A key concept in this process is the idea that complex visual structures possess a unique quality that differs from simply adding up their individual components. Think of it as recognizing a face, not just individual eyes, nose, and mouth.

For decades, vision research has explored this idea, often referred to as "Gestalt" psychology. The concept suggests that our brains are wired to see the whole before the parts. This holistic approach is evident in how we perceive everything from simple dot patterns to complex shapes, objects, faces, and scenes. But what exactly happens in our brains as we learn to recognize and categorize shapes?

Now, groundbreaking research is shedding light on the neural mechanisms behind holistic shape perception. By using advanced brain imaging techniques, scientists are uncovering how our brains learn to integrate visual information, paving the way for new insights into learning, visual processing, and even potential therapies for visual impairments.

The Science of Seeing Shapes: How Your Brain Builds a Holistic View

A brain composed of shapes and neural connections, symbolizing shape learning.

Researchers at KU Leuven, Belgium, and the University of Minnesota Twin Cities conducted a study to explore the neural markers of holistic shape representations acquired through learning. Their work, published in Vision Research, uses EEG (electroencephalography) frequency tagging to reveal how the brain integrates individual shape components into a unified whole.

The study focused on "Fourier Boundary Descriptors" (FBDs), mathematically defined shapes that consist of combined sinusoidal radial frequency components (RFCs). Participants were trained to distinguish between highly similar shapes, effectively learning to categorize them based on subtle feature combinations. Here’s how they did it:

Shape Tagging: Two parts of each shape were 'tagged' by changing their contrast at different temporal frequencies.
EEG Monitoring: Brain activity was monitored using EEG to detect emergent frequency components, known as intermodulation responses (IMs).
Training Sessions: Participants underwent four training sessions to learn the shape categorization.
Post-Training EEG: EEG recordings were taken while participants viewed both trained and untrained shapes.

The researchers hypothesized that learning to categorize shapes would strengthen the neural connections responsible for integrating shape features, leading to stronger IM responses in the EEG. This would indicate that the brain is processing the shapes more holistically after learning.

Unlocking the Potential of Visual Learning

This research offers a compelling glimpse into the brain's remarkable ability to learn and adapt its visual processing. By understanding how shape learning reshapes neural connections, we can potentially develop new strategies to enhance visual skills, improve learning outcomes, and even address visual impairments. The next time you see a shape, remember that your brain is doing far more than just seeing – it's actively building a holistic understanding of the world around you.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.visres.2018.01.007, Alternate LINK

Title: Eeg Frequency Tagging Reveals Higher Order Intermodulation Components As Neural Markers Of Learned Holistic Shape Representations

Subject: Sensory Systems

Journal: Vision Research

Publisher: Elsevier BV

Authors: Mark Vergeer, Naoki Kogo, Andrey R. Nikolaev, Nihan Alp, Veerle Loozen, Brenda Schraepen, Johan Wagemans

Published: 2018-11-01

Everything You Need To Know

What is Gestalt psychology and how does it relate to visual perception?

Gestalt psychology is a concept that suggests our brains are wired to see the whole before the parts. This means we perceive objects and shapes holistically, integrating individual components to form a unified representation. In the context of vision, it implies that we recognize a face not just by its individual features like eyes, nose, and mouth, but as a complete, recognizable whole. This holistic approach is fundamental to understanding how we process complex visual information, as highlighted by the research on shape learning and how our brains integrate visual information to perceive objects.

How did the researchers at KU Leuven and the University of Minnesota Twin Cities study shape learning using EEG?

The researchers utilized EEG (electroencephalography) frequency tagging to investigate how the brain integrates visual information. They used 'Fourier Boundary Descriptors' (FBDs) as shapes and 'tagged' two parts of each shape by altering their contrast at different temporal frequencies. They monitored brain activity to detect intermodulation responses (IMs), which would indicate that the brain is processing the shapes more holistically after learning. Participants underwent training sessions to learn shape categorization. The researchers hypothesized that learning to categorize shapes would strengthen the neural connections responsible for integrating shape features, leading to stronger IM responses in the EEG.

What are Fourier Boundary Descriptors (FBDs), and why were they used in the study?

Fourier Boundary Descriptors (FBDs) are mathematically defined shapes consisting of combined sinusoidal radial frequency components (RFCs). They were used in the study because they allowed researchers to control and manipulate the shapes' features systematically. By using FBDs, the researchers could train participants to distinguish between highly similar shapes, and then use EEG to observe how the brain learns to integrate the individual components of these shapes into a unified whole. This controlled approach allowed them to isolate and study the neural mechanisms behind holistic shape perception.

What are the potential implications of this research on shape learning for visual skills and learning outcomes?

The research on shape learning has significant implications for enhancing visual skills and improving learning outcomes. By understanding how the brain reshapes neural connections during shape learning, researchers can potentially develop new strategies to enhance visual skills. This could involve targeted training programs that improve the brain's ability to integrate visual information, ultimately leading to better visual processing. Furthermore, this knowledge may offer insights into addressing visual impairments, creating opportunities for therapies to help individuals with visual challenges to improve their perception and understanding of shapes.

What are the key steps involved in the shape categorization training and EEG monitoring in the study?

The study involved several key steps. First, the researchers used Fourier Boundary Descriptors (FBDs) and tagged parts of each shape by changing their contrast at different temporal frequencies. Next, EEG monitoring was employed to detect emergent frequency components, known as intermodulation responses (IMs), to track brain activity. Participants then underwent four training sessions to learn shape categorization. Finally, EEG recordings were taken while participants viewed both trained and untrained shapes. This comprehensive approach allowed researchers to observe how the brain learns to integrate individual shape components into a unified whole and to assess the impact of training on neural connections involved in visual processing.