Multi-task representations in human cortex transform along a sensory-to-motor hierarchy

Human cognition recruits diverse neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multi-task representations across human cortex using functional MRI during 26 cognitive tasks in the same subjects. We measured the representational similarity across tasks within a region, and the alignment of representations between regions. We found a cortical topography of representational alignment following a hierarchical sensory-association-motor gradient, revealing compression-then-expansion of multi-task dimensionality along this gradient. To investigate computational principles of multi-task representations, we trained multi-layer neural network models to transform empirical visual to motor representations. Compression-then-expansion organization in models emerged exclusively in a training regime where internal representations are highly optimized for sensory-to-motor transformation, and not under generic signal propagation. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multi-task representations across human cortex to support flexible cognition.