A decade of comparing brains to DNNs: Progress and perspectives

Over the past decade, comparisons between deep neural networks (DNNs) and the human brain have become central to cognitive neuroscience. Early work focused on vision, driven by the success of convolutional neural networks in object recognition, before such comparisons later gained traction in language with the rise of large-scale language models. These comparisons have validated existing hypotheses and generated new ones, challenging views of information processing, connectivity, and computational goals. Despite progress, debates persist over the interpretability and validity of mapping DNNs to brains, underscoring the need for more refined models and methods. Looking ahead, integrating cross-modal insights from vision and language, together with improved modeling and experimental frameworks, promises to advance the mechanistic understanding of cognition.