Evidence for two types of spatial representations: Hemispheric specialization for categorical and coordinate relations.
Abstract
Analyses of human object recognition abilities led to the hypothesis that 2 kinds of spatial relation representations are used in human vision. Evidence for the distinction between abstract categorical spatial relation representations and specific coordinate spatial relation representations was provided in 4 experiments. These results indicate that Ss make categorical judgments--on/off, left/right, and above/below--faster when stimuli are initially presented to the left cerebral hemisphere, whereas they make evaluations of distance--in relation to 2 mm, 3 mm, or 1 in. (2.54 cm)--faster when stimuli are initially presented to the right cerebral hemisphere. In addition, there was evidence that categorical representations developed with practice.
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Cited by (6)
- Cerebral specialization and interhemispheric communication: does the corpus callosum enable the human condition?Gazzaniga, M. S. · 2000 · Brain
- Distinct prefrontal cortex activity associated with item memory and source memory for visual shapesSlotnick, Scott D., Moo, Lauren R., Segal, Jessica B., Hart, John · 2003 · Cognitive Brain Research
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Evidence for two types of spatial representations: Hemispheric specialization for categorical and coordinate relations. https://app.cochat.ai/papers/10.1037//0096-1523.15.4.723
- The paper
- Kosslyn, S. M., Koenig, O., Barrett, A., & Cave, C. B. (1989). Evidence for two types of spatial representations: Hemispheric specialization for categorical and coordinate relations. Journal of Experimental Psychology: Human Perception and Performance. https://doi.org/10.1037//0096-1523.15.4.723
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.