Representation is critical in the ARL system since it must be carefully selected to achieve learning. Invariance in the system must be introduced a priori into the representation. For example, if a feature is spurious and contributes no information about the interaction, it will waste resources. The system will wastefully attempt to model and predict it inside the vector. In addition, the representations must be smooth and must not have ambiguities. For example, during initial phases of development, the ARL system employed a different representation of the head and hand blobs. The head and hands were described by their mean, major axis, minor axes and rotation (in radians). Unlike the square-root covariance shape descriptor (our current representation), the rotation value had some unusual singularities. The 0 and values are identical in radian notation. Thus, the system would generate non-linear steps from 0 to as the blobs would rotate and these transitions were difficult to span using the eigenspace temporal processing techniques. Thus, it is critical to pick a representation which contains the desired invariants, is well behaved, is smooth and has no spurious components.