Reconstructing mental object representations: a machine vision approach to human visual recognition

This paper introduces a new approach to assess visual representations underlying the recognition of objects. Human performance is modeled by CLARET, a machine learning and matching system, based on inductive logic programming and graph matching principles. The model is applied to data of a learning experiment addressing the role of prior experience in the ontogenesis of mental object representations. Prior experience was varied in terms of sensory modality, i.e. visual versus haptic versus visuohaptic. The analysis revealed distinct differences between the representational formats used by subjects with haptic versus those with no prior object experience. These differences suggest that prior haptic exploration stimulates the evolution of object representations which are characterized by an increased differentiation between attribute values and a pronounced structural encoding.