Recent advancements in deep learning have significantly enhanced our understanding of sensory cognition and perception. This special issue aims to explore the intersection of advanced deep learning technologies and perception science, focusing on how these models enhance our understanding of perceptual mechanisms and their applications, including multisensory integration.
Deep learning models, such as YOLO (You Only Look Once) for real-time object detection and generative adversarial networks (GANs) for high-quality speech synthesis, offer unique opportunities to simulate and study human sensory processes. These technologies allow researchers to model perception at a computational level.
This special issue invites research on how deep learning models can be applied to the study of perception, including vision, hearing, touch, smell, and taste. We particularly encourage studies that explore individual differences, perceptual challenges, sensory rehabilitation, and insights from cognitive neuroscience. The goal is to connect computational models with human perception, advancing both scientific understanding and practical applications.
Authors are encouraged to submit their latest research on the application of deep learning to perception science. Topics of interest include but are not limited to:
• Enhancing YOLO and other object detection models to simulate and study real-time visual perception in dynamic environments.
• Utilizing generative models (GANs, VAEs, and diffusion models) for speech synthesis to investigate auditory perception and multisensory integration.
• Exploring neural network models that emulate sensory processing mechanisms in human and animal perception.
• Applying deep learning to study the development and age-related decline of perceptual abilities.
• Investigating the role of multisensory integration in improving model performance for tasks such as object recognition and speech understanding.
• Addressing challenges in generalization and scalability of deep learning models in perceptual research.
• Developing computationally efficient models for perceptual deficits and sensory rehabilitation applications.
• Advancing deep learning applications to study individual differences in sensory cognition and attention.
Keywords: Deep Learning, Perception Science, Perceptual Mechanisms, Sensory Cognition and Attention, Neural Networks, Cognitive Neuroscience, visual perception, auditory perception
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
