AI Insight
This study extends cognitive taskonomy research by examining how multiple brain tasks can work together to support learning new tasks, rather than just looking at single task-to-task transfer. Using fMRI data from 23 different cognitive tasks in the Human Connectome Project, researchers trained over 1,000 neural network models and found that motor tasks transfer well within motor domains but poorly to other cognitive areas, while working memory tasks emerged as high-priority sources when optimizing resource allocation across multiple tasks. The findings reveal that many-to-one task relationships cannot be fully understood by examining task pairs alone.
Why it matters
Understanding how cognitive tasks share neural processes could improve brain-computer interfaces, inform more efficient training protocols for cognitive rehabilitation, and guide experimental design by identifying which tasks provide the most informative data for understanding broader brain function.
Understand the Science
Abstract: Cognitive tasks are organized by shared and specialized neural processes. Masked fMRI reconstruction provides a common self-supervised objective for quantifying transfer relations among task states, but existing reconstruction-based taskonomies mainly study one-to-one transfer from a single source task to a target. Here, we extend an fMRI cognitive taskonomy from single-source to multi-source transfer across 23 Human Connectome Project task states and use Boolean Integer Programming (BIP) to analyze budget-constrained task allocation. We train 1,127 task-specific and transfer models. Single-source transfer is directional and paradigm structured: motor states transfer well within the motor paradigm but provide limited support to most non-motor targets, consistent with a shared sensorimotor execution system and effector-specific representations. Multi-source transfer depends on the composition of the source set, suggesting that many-to-one task relations are not fully captured by pairwise taskonomy alone. Across supervision budgets, BIP repeatedly allocates direct supervision to several 0-back and 2-back working-memory states, although these states are not consistently the strongest individual sources. This pattern may reflect the integration of perceptual, attentional, and executive processes in working-memory tasks. Together, these findings reveal a cross-paradigm-limited motor cluster and working-memory states with high priority under the specified global allocation objective. Our study extends reconstruction-based fMRI taskonomy from one-to-one transfer to many-to-one task relations and budget-constrained task dependencies.
Source: Beyond Single-Source Cognitive Taskonomy:Multi-Source Task Relations through fMRI Transfer Learning