This chapter provides a critical analysis of Mark Bickhard's interactivist model of representation, focusing on his innovative concept of system-detectable error and anticipatory functions. Through examination of his biological model of representation, it demonstrates how cognitive systems must be capable of detecting their own errors to engage in genuine representation and learning. The analysis reveals how Bickhard’s approach differs from previous teleosemantic theories by emphasizing internal detection of representational adequacy rather than external correspondence or proper function. The discussion evaluates both minimal and comprehensive versions of the interactivist model, showing how anticipatory organization enables cognitive systems to test their representations against the world through action. While successfully addressing the system-detectability of error, the chapter demonstrates that Bickhard's framework faces challenges in accounting for social representations and risks being simultaneously too broad and too narrow in its attribution of representational capacities. Through detailed examination of examples ranging from bacterial behavior to complex cognitive abilities, the chapter establishes that although Bickhard's model effectively captures individual anticipatory representation, it struggles to fully account for social representations and semantic correspondence. This analysis reveals both the strengths and limitations of action-oriented approaches to mental content, while setting up examination of potential synthetic approaches combining teleosemantic and interactivist insights.