harnad@elbereth.rutgers.edu (Stevan Harnad) (11/11/88)
Below is the abstract of a forthcoming target article to appear in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. To be considered as a commentator or to suggest other appropriate commentators, please send email to: harnad@confidence.princeton.edu or write to: BBS, 20 Nassau Street, #240, Princeton NJ 08542 [tel: 609-921-7771] ____________________________________________________________________ A SOLUTION TO THE TAG-ASSIGNMENT PROBLEM FOR NEURAL NETWORKS KEYWORDS: affordance; attention; connectionist network; eye movements; illusory conjunction; neural network; object recognition; retinotopic representations; saccades; spatial localization Gary W. Strong Bruce A. Whitehead College of Information Studies Computer Science Program Drexel University University of Tennessee Space Institute Philadelphia, PA 19104 USA Tullahoma, TN 37388 USA ABSTRACT: Purely parallel neural networks can model object recognition in brief displays -- the same conditions under which illusory conjunctions (the incorrect combination of features into perceived objects in a stimulus array) have been demonstrated empirically (Treisman & Gelade 1980; Treisman 1986). Correcting errors of illusory conjunction is the "tag-assignment" problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations and objects. This problem must be solved to model human object recognition over a longer time scale. A neurally plausible model has been constructed which simulates both the parallel processes that may give rise to illusory conjunctions and the serial processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that can correct illusory conjunctions. Our approach addresses two questions: (i) How can objects be identified from simultaneously attended features in a parallel, distributed representation? (ii) How can the spatial selection requirements of such an attentional process be met by a separation of pathways between spatial and nonspatial processing? Analysis of these questions yields a neurally plausible simulation model of tag assignment, based on synchronization of neural activity for features within a spatial focus of attention. -- Stevan Harnad ARPA/INTERNET: harnad@mind.princeton.edu harnad@princeton.edu harnad@confidence.princeton.edu srh@flash.bellcore.com harnad@princeton.uucp CSNET: harnad%mind.princeton.edu@relay.cs.net UUCP: princeton!mind!harnad BITNET: harnad@pucc.bitnet harnad@pucc.princeton.edu (609)-921-7771