Animals solve new, complex tasks by reusing and adapting prior knowledge. This flexibility depends not only on the content of experience but also on its structure. Early training curricula are especially important: poorly structured experiences can hinder abstraction and limit generalization.
However, the neural mechanisms through which experience shapes future learning remain unclear. Here, we trained recurrent neural networks (RNNs) on an odor timing task used to study complex timing behavior in mice and then tested the model predictions with mouse behavior and medial entorhinal cortex recordings. Without structured early experience, both RNNs and mice developed rigid, error-prone strategies, whereas structured training promoted neural activity reflecting the task’s temporal structure.
Using dynamical systems analysis, we examined how different training curricula shaped network dynamics and whether these dynamics supported abstraction and generalization as task complexity increased. These findings demonstrate that the structure of prior experience governs how flexible, generalizable knowledge emerges in biological systems and computational models. Learning enables animals to construct flexible knowledge structures, allowing them to adapt to new tasks by reconfiguring prior experiences into novel strategies.
This compositional nature of learning suggests that the structure of prior experiences—the curriculum—is critical for future problem-solving. Poorly structured curricula, however, can cause animals to become ‘stuck’, perseverating on suboptimal solutions that only partially achieve their goals1. The neural mechanisms through which the organization of past experience shapes future learning remain poorly understood.
Therefore, we asked: How does the structure of past experience shape both the cognitive strategies and the underlying neural circuit dynamics that support flexible task performance? Past experience is often conceptualized as a library of atomic units of learned behaviors, called task primitives, each accomplishing a basic goal. Complex behaviors are thought to arise by compiling these primitives into behavioral programs2,3,4,5, making learning inherently compositional.
In laboratory and real-world settings alike, simpler shaping tasks or experiences often break down complex behaviors into subunits that can later be assembled, or ‘composed’, into strategies capable of achieving broader goals6,7,8,9. However, it is often unclear how the structure of prior training alters cognition1. Moreover, shaping protocols—or the natural structure of experience—inevitably embed assumptions about how tasks should be solved, biasing the strategies that emerge.
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