Understanding Advances in Computational Cognitive Neuroscience Over the Past Ten Years: A Scoping Review

Computational Cognitive Neuroscience (CCN) is an interdisciplinary field that uses computational models to understand how the brain gives rise to cognition. Although the field has grown substantially over the past ten years, no review has systematically mapped the major advances achieved during this period. This review therefore aims to map advances in CCN over the past ten years by analyzing the most frequently used computational models, the cognitive domains investigated, and the computational tools employed, and by determining the predominant emphasis of the studies, whether theoretical, translational, or integrated. This scoping review was conducted in accordance with the methodology proposed by the Joanna Briggs Institute (JBI) and reported following PRISMA-ScR. Searches were performed in PubMed, Embase and IEEE Xplore between 24 and 28 April 2025, and were complemented by a supplementary relevance-ranked Scopus search and by expert-informed identification of additional records. A total of 104 studies were included. Recent progress in CCN was found to be predominantly theoretical, with research focused on memory, learning, language and communication, and decision-making. Recurrent neural networks and Bayesian models emerged as the most widely adopted frameworks. Although CCN has advanced rapidly over the past ten years, the field continues to face critical challenges, notably concerning neurobiological plausibility, model integration, limited external generalizability, and a pressing need for more translational research. Beyond the reviewed corpus, the review discusses emerging AI paradigms, transformers, foundation models, large language models, and neuromorphic computing, as prospective directions informed by complementary literature.