Efficient coding in active perception: A developmental perspective on autonomous control

The efficient coding hypothesis states that biological perceptual systems adapt to the statistics of the sensory signals arising in their natural environments. Because infants actively shape these sensory statistics through their own behavior, perception and action form a tightly coupled developmental loop. We present an integrative review of recent extensions of efficient coding into the domain of active perception, with a particular focus on the Active Efficient Coding (AEC) framework. AEC explains the development of perceptions and actions through a unifying computational principle: encoding sensory observations as efficiently as possible. We introduce a novel formalism that frames AEC within rate-distortion theory, interpreting active perception as a problem of lossy compression. We then re-examine AEC models of the autonomous learning and calibration of active binocular vision in a simulated infant embodiment. This work shows how active perception can emerge without external supervision, providing a foundation for the development of complex behaviors and higher cognition.