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Encoding Categorical and Coordinate Spatial Relations Without Input‐Output Correlations: New Simulation Models
Author(s) -
Baker David P.,
Chabris Christopher F.,
Kosslyn Stephen M.
Publication year - 1999
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2301_2
Subject(s) - categorical variable , spatial relation , landmark , computer science , receptive field , metric (unit) , spatial organization , spatial analysis , encoding (memory) , pattern recognition (psychology) , artificial intelligence , mathematics , machine learning , statistics , ecology , operations management , economics , biology
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).