Work in human infancy and behavior-based robotics that grounds intelligent abilities in sensorimotor exchanges between a system and its environment shares recurrent problems of when, whether, and how scaling up from basic to supposedly higher abilities is possible. An action-based model of the infant is introduced that converges with features of independently motivated animat models exploiting emergent functionality and challenges alternatives that invoke conceptual representations. Adaptive change routinely exhibited in infants' everyday activities outstrips the scaling-up potential of current robotic systems and clarifies effective principles obeyed by naturally intelligent systems. A general form is outlined to subject-environment interaction that "engineers" restructuring of early abilities in the direction of greater anticipation (considered an upper boundary for the competence of concept-free human and animat systems); and an action-based account of the phenomena is provided. This emphasizes the relationship between representation and situated inference and the role of reciprocal constraints between cognitive and physical-motor mechanisms. Finally, this article questions how far typical self organizing connectionist networks take us toward understanding a system that is capable of mapping recurrent viable patterns of activity into more permanent adaptive changes.
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