In this paper, we incorporate a set of principles originated from cognitive psychology into the design of 3-D shape analysis and retrieval algorithms. Based on the "visual salience-guided mesh decomposition" we previously proposed, a 3-D mesh-based shape is first broken up into parts such that human visual perception on parts can be appropriately mimicked. Next, the decomposed parts are individually analyzed and quantified according to the properties of visual salience. To establish the indices of 3-D meshes for shape matching, spherical parameterization is adopted to map the decomposed parts onto the surface of a unit sphere. In this way, the dissimilarity degree between the query acquired from users and a model in database can be calculated. The experimental results have shown that the matching performance of the proposed scheme is indeed efficient and powerful.