Multitask representations in the human cortex transform along a sensory-to-motor hierarchy

• Published in: Nature neuroscience Vol. 26; no. 2; pp. 306 - 315
• Main Authors: Ito, Takuya, Murray, John D.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.02.2023; Nature Publishing Group
• Subjects: 631/378/116/1925; 631/378/2649; Alignment; Animal Genetics and Genomics; Artificial neural networks; Behavioral Sciences; Biological Techniques; Biomedical and Life Sciences; Biomedicine; Brain Mapping; Cognition; Cognition & reasoning; Cognitive ability; Cognitive tasks; Compression; Computer applications; Functional magnetic resonance imaging; Humans; Learning; Magnetic Resonance Imaging; Multilayers; Neural networks; Neurobiology; Neuroimaging; Neurosciences; Representations; Sensorimotor integration; Somatosensory cortex
• DOI: 10.1038/s41593-022-01224-0
• ISSN: 1097-6256; 1546-1726

Human cognition recruits distributed neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multitask representations across the human cortex using functional magnetic resonance imaging during 26 cognitive tasks in the same individuals. We measured the representational similarity across tasks within a region and the alignment of representations between regions. Representational alignment varied in a graded manner along the sensory–association–motor axis. Multitask dimensionality exhibited compression then expansion along this gradient. To investigate computational principles of multitask representations, we trained multilayer neural network models to transform empirical visual-to-motor representations. Compression-then-expansion organization in models emerged exclusively in a rich training regime, which is associated with learning optimized representations that are robust to noise. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multitask representations across the human cortex to support multitask cognition. What are the representations that enable diverse human cognition? The authors investigate cortical representations across 26 tasks and the conditions by which artificial neural network models reproduce these representations.