This chapter presents an innovative approach to understanding system-detectable error in mental representations. Through integration of Millikan's producer–consumer framework and Bickhard's conception of system-detectable error, it develops a coherence-based model that explains how cognitive systems can recognize and utilize their own representational errors without requiring meta-representational capabilities. The analysis demonstrates how inconsistencies between representations enable error detection, while the ability to differentiate information reliability allows for error correction. The discussion systematically evaluates the model's application to key examples from cognitive science, including concept learning, mental imagery, and cognitive maps. By examining both simple and complex representational systems, it reveals how the coherence-based approach can account for error detection across different levels of cognitive sophistication while avoiding the pitfalls of previous teleosemantic theories. The chapter demonstrates that this framework successfully addresses major criticisms of teleosemantics. Through detailed analysis of learning mechanisms and the role of representational error in adaptation, the chapter establishes that the coherence-based model satisfies core requirements for a naturalistic theory of mental representation while maintaining sufficient explanatory power for empirical research. This synthesis of teleosemantic insights with system-detectable error provides a foundation for understanding how cognitive systems can recognize and learn from their own mistakes.