This article presents a case study in examining the bias of two particular formalisms: decision trees and linear threshold units. The immediate result is a new hybrid representation, called a ‘perceptron tree’, and an associated learning algorithm called the ‘percepton tree error correction procedure’. The longer term result is a model for exploring issues related to understanding representational bias and constructing other useful hybrid representations.