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

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.

Automated summary

The study reveals the computational and functional architectures of human cognition by characterizing the geometry and topography of multitask representations in the human cortex using functional magnetic resonance imaging. The results show that multitask representations are organized along a gradient from sensory to association to motor processing, and the dimensions of these representations undergo compression-then-expansion. Neural network models trained in a rich learning regime replicate the compression-then-expansion organization observed in empirical data, suggesting that optimized representations and noise robustness play a crucial role in multitask cognition.