Neural representation dynamics reveal computational principles of cognitive task learning.

During cognitive task learning, neural representations must be rapidly constructed for novel task performance, then optimized for robust practiced task performance. How the geometry of neural representations changes to enable this transition from novel to practiced performance remains unknown. We hypothesized that practice involves a shift from compositional representations (task-general activity patterns that can be flexibly reused across tasks) to conjunctive representations (task-specific activity patterns specialized for the current task). Functional MRI during learning of multiple complex tasks substantiated this dynamic shift from compositional to conjunctive representations, which was associated with reduced cross-task interference (via pattern separation) and behavioral improvement. Further, we found that conjunctions originated in subcortex (hippocampus and cerebellum) and slowly spread to cortex, extending multiple memory systems theories to encompass cognitive task learning. The strengthening of conjunctive representations hence serves as a computational signature of learning, reflecting cortical-subcortical dynamics that optimize task representations in the human brain.

Highlights: Learning shifts multi-task representations from compositional to conjunctive formatsCortical conjunctions uniquely associate with improved behavior and pattern separationThese conjunctions strengthen over separated learning events and index switch costsSubcortical regions are critical for cross-region binding of task rule information.