Perceptual Space

Perceptual space research investigates how humans and machines organize sensory information, aiming to understand the underlying representations and processes. Current studies focus on how different model architectures, including convolutional neural networks (CNNs) and transformers, create and utilize perceptual spaces, often comparing these to human perception using similarity judgments and class activation maps. This research is crucial for improving machine learning models' ability to interpret sensory data, impacting fields like object recognition, speech processing, and autonomous driving by enabling more robust and human-like performance. Furthermore, understanding perceptual spaces sheds light on fundamental cognitive processes in humans.