Visual Encoding Model

Visual encoding models aim to computationally represent how the brain processes visual information, often by predicting brain activity (e.g., fMRI signals) from visual stimuli. Current research focuses on improving model accuracy through various architectures, including those leveraging large language models for multimodal integration of image and textual data, and exploring the impact of model size and training data on predictive performance. These advancements are significant for neuroscience, offering insights into visual perception and individual differences in brain function, and potentially enabling personalized applications such as optimized stimulus design for brain stimulation or neurofeedback.