AI-Driven Information Reconstruction: Bridging Visual Perception and Memory

Zhang Guoxin; Wang Wenqiang; Li Feng; Wang Benchi

doi:10.16719/j.cnki.1671-6981.20250605

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Journal of Psychological Science ›› 2025, Vol. 48 ›› Issue (6) : 1346-1358. DOI: 10.16719/j.cnki.1671-6981.20250605

Computational Modeling and Artificial Intelligence

AI-Driven Information Reconstruction: Bridging Visual Perception and Memory

Zhang Guoxin^1,2, Wang Wenqiang^1,2, Li Feng³, Wang Benchi^1,2

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Abstract

This systematic review traces the evolution of neural decoding methodologies, from early visual feature extraction to the cutting-edge frontier of reconstructing memory and mental states. Advances in computational neuroscience and artificial intelligence have driven this progress, with sophisticated generative models redefining how neural signals are interpreted and translated.
Early pioneering studies in visual perception laid the groundwork for neural decoding by demonstrating the reconstruction of basic visual elements—such as oriented edges and simple geometric shapes—from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. These foundational efforts, while groundbreaking, were limited by simple stimulus designs and reliance on handcrafted feature representations. The advent of deep learning marked a transformative shift, enabling more flexible and nuanced interpretations of neural activity. Initial applications of variational autoencoders and generative adversarial networks established frameworks for mapping neural signals to latent representations, though challenges in reconstruction accuracy and model stability persisted.
Recent advances in diffusion models have ushered in a new era of precision, significantly enhancing the translation of neural signals into detailed visual outputs. Modern frameworks now leverage biologically inspired, hierarchical architectures that separately process low-level visual features and high-level semantic content, closely mirroring the organization of the human visual system. These developments have enabled the reconstruction of complex naturalistic scenes, human faces, and dynamic visual experiences, bringing neural signal interpretation closer to practical applications.
Venturing further, the field now confronts the more ambitious challenge of reconstructing memory and mental imagery—a task far more complex than visual perception decoding. Unlike perception studies that rely on controlled external stimuli, memory decoding must grapple with internally generated, highly subjective, and context-dependent neural signals. Preliminary research reveals intriguing parallels between neural patterns associated with recalled and perceived experiences, yet significant hurdles remain. These include the noisy and variable nature of memory-related signals, individual differences in neural encoding, and the dynamic, context-sensitive character of memory retrieval.
Technical challenges in memory reconstruction remain formidable. Current approaches struggle to generalize across diverse memory types and individual neural encoding variations, limiting their applicability. Non-invasive neuroimaging techniques suffer from insufficient temporal resolution and signal clarity, obscuring the fine-grained dynamics of memory processes. Furthermore, existing neural signal encoders—typically optimized for simpler, stimulus-driven tasks like visual perception—often fail to capture the complex, distributed brain activity underlying memory. New theoretical frameworks are needed to model the reconstructive, dynamic, and context-dependent nature of human memory, which fundamentally differs from the linear stimulus-response dynamics characteristic of visual perception.
Addressing these challenges demands several critical research priorities. First, developing advanced neuroimaging technologies with improved spatiotemporal resolution is essential to capture intricate, dynamic patterns of memory encoding. Second, designing more sophisticated neural signal encoders will better model the distributed and context-sensitive nature of memory processes. Third, creating large-scale, standardized neuroimaging datasets is vital for training robust, generalizable models that account for individual variability and diverse memory types. These efforts collectively aim to bridge current limitations and achieve accurate, generalizable memory reconstruction.
Successful reconstruction of memory and mental states could fundamentally transform our understanding of human cognition, enable novel treatments for memory-related disorders, and revolutionize human-computer interfaces. However, these advancements also raise profound philosophical questions regarding the nature of memory, personal identity, and consciousness. As neural decoding approaches this pivotal juncture, researchers must carefully balance its transformative potential against technical, ethical, and societal challenges. The journey from visual reconstruction to comprehensive mental state decoding represents a groundbreaking endeavor at the intersection of neuroscience and artificial intelligence, with the potential to reshape our understanding of the human mind.

Key words

artificial intelligence / visual reconstruction / memory reconstruction / generative model

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Zhang Guoxin, Wang Wenqiang, Li Feng, Wang Benchi. AI-Driven Information Reconstruction: Bridging Visual Perception and Memory[J]. Journal of Psychological Science. 2025, 48(6): 1346-1358 https://doi.org/10.16719/j.cnki.1671-6981.20250605

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