Credit assignment is a fundamental issue for the Learning Classifier Systems literature. We engage in a detailed investigation of credit assignment in one recent system called UCS, and in the process uncover two previously undocumented features. We draw on techniques from the classical pattern recognition literature, showing how to analytically derive an optimal credit assignment system, given certain assumptions. Our primary aim is not to improve accuracy, but to better understand the system and put it on a more solid theoreticalfoundation. Nonetheless, empirical results on benign data demonstrate our new system, called UCSpv (UCS with principled voting), can match or exceed the original UCS. Further, its fitness function is principled, and, unlike that of UCS, requires no tuning. However, on more difficult data it seems UCSpv does need some form of tuning or correction. We believe the framework we adopt offers a promising new direction for LCS research, providing principled methods for action selection and bringing LCS closer to the mainstream pattern recognition literature.