Cognitive theories based on a fixed feature set suffer from
frame and symbol grounding problems. Flexible features and other
empirically acquired constraints (e.g., analog-to-analog mappings)
provide a framework for letting extrinsic relations influence symbol
manipulation. By offering a biologically plausible basis for feature
learning, nonorthogonal multiresolution analysis and dimensionality
reduction, informed by functional constraints, may contribute to a
solution to the symbol grounding problem.