Encoding Categorical and Coordinate Spatial Relations Without Input-Output Correlations: New Simulation Models |
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| Authors: | David P. Baker Christopher F. Chabris Stephen M. Kosslyn |
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| Affiliation: | 1. Department of Thoracic Surgery, Nancy Regional University Hospital, Nancy, France;2. Thoracic Surgery Unit, University of Torino, Torino, Italy;3. Department of Pathology, Nancy Regional University Hospital, Nancy, France;4. Department of Thoracic Surgery, Strasbourg University Hospital, Strasbourg, France;5. Department of Thoracic Surgery and Upper Gastrointestinal Surgery, McGill University, Montreal, Canada;1. School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire LE12 5RD, UK;2. Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia;3. School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK |
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| Abstract: | Cook (1995) criticized Kosslyn, Chabris, Marsolek & Koenig's (1992) network simulation models of spatial relations encoding in part because the absolute position of a stimulus in the input array was correlated with its spatial relation to a landmark; thus, on at least some trials, the networks did not need to compute spatial relations. The network models reported here include larger input arrays, which allow stimuli to appear in a large range of locations with an equal probability of being above or below a “bar,” thus eliminating the confound present in earlier models. The results confirm the original hypothesis that as the size of the network's receptive fields increases, performance on a coordinate spatial relations task (which requires computing precise, metric distance) will be relatively better than on a categorical spatial relations task (which requires computing above/below relative to a landmark). |
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