Relational representations in classification: comparing similarity and structure-based approaches

Classification has become an important analytical tool in psychology, yet methodological research has focused primarily on improving learning algorithms, while the representation of psychological information has received comparatively less attention. This study examines how three representational strategies affect classification performance: a similarity-based representation derived from multidimensional scaling, an intra-stimulus relational representation (structure-based classification, SBC), and a conventional feature-based representation. Across three simulation studies and two empirical datasets from emotion perception, we evaluated these representations under a common classification framework. When class structure was defined purely by the location of stimuli in a latent space, similarity-based and feature-based representations performed comparably, and both significantly outperformed the relational representation. Under the primary linear-SVM analyses, the relational representation became more effective at low separation when classes differed in dispersion, but declined disproportionately as task-irrelevant dimensions increased. Nonlinear RBF-SVM robustness analyses showed that both effects were partly classifier-dependent: dispersion-related information could also be recovered from feature- and similarity-based representations, and the dimensionality-related SBC decline was substantially attenuated. Empirical applications showed a similarly mixed pattern, with the relative performance of each representation varying across datasets. These findings indicate that no single representation is universally optimal; rather, each representation is differentially sensitive to specific statistical properties of class structure, including location, dispersion, and dimensionality. Representational choice should therefore be treated as a substantive modeling decision in psychological classification rather than a routine preprocessing step.