Developmental Trajectories & Cognitive Emergence
How experience history, sleep-like consolidation, and sensorimotor interaction shape unique artificial individuals.
The Artificial Individual Hypothesis
In standard machine learning, two models with identical initializations trained on the same shuffled batch converge to functionally identical point estimates. By contrast, in developmental learning, the sequence of interaction experiences fundamentally alters the trajectory of representation formation.
An agent that discovers motor manipulation before spatial navigation will construct different cognitive scaffolds than one exposed to exploratory locomotion first. We study these divergences as empirical evidence of artificial individuality.
Two-Tier Memory Architecture
NeuraBird implements a dual-tier memory system inspired by hippocampal-neocortical interaction in biological systems:
Episodic Buffer
High-resolution, transient recording of sensorimotor streams, proprioceptive feedback, and prediction errors collected during active exploration.
Generative World Model
Enduring structural knowledge crystallized during offline consolidation phases through variational replay, invariant extraction, and counterfactual simulation.
Sleep-Like Replay & Experience Consolidation
Offline replay does not merely repeat past data; it samples from the generative world model with stochastic noise, allowing the agent to mentally rehearse edge-case scenarios that were not encountered directly during wakefulness. This process is critical for achieving zero-shot transfer when facing novel physical obstacles.