Cross-Modal Object Recognition Is Viewpoint-Independent

Journal article

BACKGROUND: Previous research suggests that visual and haptic object recognition are viewpoint-dependent both within- and cross-modally. However, this conclusion may not be generally valid as it was reached using objects oriented along their extended y-axis, resulting in differential surface processing in vision and touch. In the present study, we removed this differential by presenting objects along the z-axis, thus making all object surfaces more equally available to vision and touch. METHODOLOGY/PRINCIPAL FINDINGS: Participants studied previously unfamiliar objects, in groups of four, using either vision or touch. Subsequently, they performed a four-alternative forced-choice object identification task with the studied objects presented in both unrotated and rotated (180 degrees about the x-, y-, and z-axes) orientations. Rotation impaired within-modal recognition accuracy in both vision and touch, but not cross-modal recognition accuracy. Within-modally, visual recognition accuracy was reduced by rotation about the x- and y-axes more than the z-axis, whilst haptic recognition was equally affected by rotation about all three axes. Cross-modal (but not within-modal) accuracy correlated with spatial (but not object) imagery scores. CONCLUSIONS/SIGNIFICANCE: The viewpoint-independence of cross-modal object identification points to its mediation by a high-level abstract representation. The correlation between spatial imagery scores and cross-modal performance suggest that construction of this high-level representation is linked to the ability to perform spatial transformations. Within-modal viewpoint-dependence appears to have a different basis in vision than in touch, possibly due to surface occlusion being important in vision but not touch

Authors: Lacey, S; Peters, A; Sathian, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS ONE
• Volume: 2
• Issue: 9
• Pages: e890-e890
• Publication date: 2007-09-11
• DOI: 10.1371/journal.pone.0000890
• EISSN: 1932-6203
• ISSN: 1932-6203
• Language: English
• Keywords: Pattern recognition (psychology); Feature (linguistics); Object (grammar); Computer science; Representation (politics); Artificial intelligence; Modal; Computer vision; Rotation (mathematics); Cognitive neuroscience of visual object recognition