Brain processing motion and curves

Motion and Perception: How Your Brain Interprets Movement

Nico Varela in Mind & Education August 2025 • 4 min read.

"Uncover the fascinating link between curvature coding and motion direction to understand how your brain processes visual motion."

Our perception of the world relies heavily on two key visual processes: shape recognition and motion detection. For a long time, scientists believed these were handled by separate pathways in the brain—shape primarily through the ventral stream and motion via the dorsal stream. However, mounting evidence suggests that these processes are far more interconnected than previously thought.

Neurophysiological studies, for instance, have revealed that neurons in areas V4 and IT (both ventral stream regions) respond to shapes defined solely by the coherent motion of random dots. Additionally, anatomical studies have demonstrated strong connections between the motion-sensitive dorsal area MT and the contour/shape-sensitive ventral area V4, painting a picture of constant communication and integration.

This article delves into a specific aspect of this interaction: how curvature, a fundamental element of shape, is encoded in relation to motion direction. By examining the motion direction tuning of curvature processing, we can better understand the mechanisms that allow us to perceive and interact with a dynamic environment.

What is Curvature Coding?

Curvature coding refers to the way our brains process and interpret curves and contours within visual stimuli. It's a critical aspect of shape recognition, allowing us to differentiate between objects and understand their forms. Researchers have long been intrigued by how the brain achieves this, particularly in the context of movement.

To explore the link between curvature coding and motion direction, scientists often use shape after-effects, such as the shape-frequency after-effect (SFAE) and the shape-amplitude after-effect (SAAE). These after-effects occur when adapting to a specific shape causes a shift in the perceived frequency or amplitude of a subsequently viewed test shape. By manipulating the motion of these shapes, researchers can gain insights into the motion sensitivity of the underlying curvature-coding mechanisms.

Here are the key questions that researchers aim to address:

Are curvature-encoding mechanisms selective for global motion direction (the overall direction of a shape)?
Are these mechanisms sensitive to shape temporal frequency (the rate at which the shape changes over time)?
How do luminance properties, such as spatial frequency (the fineness of detail) and temporal frequency (the rate at which luminance changes), affect the interaction between motion and curvature?
Do local motion cues (motion of individual components within a shape) play a role in curvature coding?

To answer these questions, a series of experiments were conducted using moving sine-wave-shaped contours. These contours were designed to drift within a fixed stimulus window, either with global motion (the entire contour moving) or local motion (only the internal elements moving). The researchers then measured the SFAEs and SAAEs under various conditions, manipulating motion direction, shape temporal frequency, and luminance properties.

Key Findings and Implications

The experiments revealed several key findings: Global Motion Selectivity: Both SFAEs and SAAEs exhibited selectivity for global motion direction, meaning the after-effects were stronger when adaptor and test contours moved in the same direction. Temporal Frequency Dependence: For the same-motion-direction condition, both after-effects increased with shape temporal frequency. Luminance Influence: Luminance temporal frequency partially explained the increase in after-effect magnitude with shape temporal frequency. Lack of Local Motion Selectivity: Local motion after-effects did not show selectivity for motion direction. These results suggest that curvature is encoded by mechanisms that are selective for motion direction, and that this directional selectivity is most apparent when the stimulus provides sufficient spatio-temporal coverage. In simpler terms, when your brain has enough visual information over a short period, it can better determine the direction in which a curve is moving.

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.

Everything You Need To Know

What is curvature coding, and why is it important for understanding how we see movement?

Curvature coding is the brain's method for processing and interpreting the curves and contours within visual stimuli. This is a fundamental aspect of shape recognition, enabling us to distinguish between objects and grasp their forms. It's crucial for motion perception because the brain utilizes curvature information to understand how shapes move and change over time. Scientists investigate this using shape after-effects like shape-frequency after-effect (SFAE) and shape-amplitude after-effect (SAAE), which help uncover the mechanisms of motion direction tuning of curvature processing.

How do the brain's shape recognition and motion detection processes interact, and where do key brain regions like V4, IT, and MT come into play?

The article highlights that shape recognition (primarily handled by the ventral stream) and motion detection (primarily handled by the dorsal stream) are interconnected. Brain areas V4 and IT (both in the ventral stream) respond to shapes defined by motion. Anatomical studies show strong connections between the motion-sensitive dorsal area MT and the contour/shape-sensitive ventral area V4. These connections indicate constant communication and integration between shape and motion processing. Curvature coding is one area where these two processes interact.

Can you explain the experiments mentioned that explore the link between curvature coding and motion direction, and what key questions were they designed to answer?

The experiments involved using moving sine-wave-shaped contours with global or local motion. Researchers measured the SFAEs and SAAEs under various conditions. The key questions addressed were: Are curvature-encoding mechanisms selective for global motion direction? Are these mechanisms sensitive to shape temporal frequency? How do luminance properties affect motion and curvature interaction? Do local motion cues play a role? These experiments aimed to understand how our brains perceive and interpret moving shapes, focusing on curvature's role.

What were the main findings of the experiments, and what do they tell us about how the brain processes movement in relation to shape?

The experiments revealed that both SFAEs and SAAEs showed selectivity for global motion direction, meaning the after-effects were stronger when adaptor and test contours moved in the same direction. They also showed that for the same-motion-direction condition, the after-effects increased with shape temporal frequency. Luminance temporal frequency partially explained this increase. Local motion after-effects didn't show selectivity for motion direction. These results suggest that curvature is encoded by mechanisms selective for motion direction and that directional selectivity is most apparent with sufficient spatio-temporal coverage.

Why is understanding curvature coding and its relationship to motion direction important for our understanding of visual perception, and what are the broader implications?

Understanding curvature coding is crucial because it reveals how our brains extract meaningful information from the visual world. It helps explain how we perceive and interact with a dynamic environment. It provides insights into how the brain integrates shape and motion, demonstrating that these processes are not entirely separate. The broader implications extend to fields like computer vision and robotics, where understanding how the brain processes visual information can inspire more sophisticated algorithms and systems for object recognition and motion tracking. The knowledge helps us comprehend the underlying mechanisms of visual perception and potential impairments in individuals with visual processing disorders.