Stochastic General Recognition Theory
{'Code': '04040500', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'BCS', 'LongName': 'Division Of Behavioral and Cognitive Sci'}}
{'SignBlockName': 'Jasmine Young', 'PO_EMAI': 'jyoung@nsf.gov', 'PO_PHON': '7032924833'}
Stochastic general recognition theory is a precise mathematical                 formalization of several ideas that have been popular in                        perceptual and cognitive psychology for many years.  One of these               is that repeated exposure to any complex stimulus does not always               elicit the same perceptual state; a second is that the perceptual               system that analyzes such a stimulus is composed of several                     mutually interacting channels that operate in parallel.  The                    theory makes specific numerical predictions about response                      probabilities and response times in a wide variety of cognitive                 and perceptual experiments, such as identification and                          categorization.  In addition, it has the potential to account for               the effects of varying stimulus exposure duration, the masking of               one stimulus with another, and the effects of short interstimulus               intervals.  This research will include a number of experiments                  whose results are predicted by the theory; thus, these                          experiments will provide a rigorous test of the theory.  One set                of experiments will investigate how the structure of a category                 affects categorization time; another set will investigate how                   different types of interactions during the perceptual processing                of separate stimulus components affect categorization and                       identification performance.  These experiments will increase our                understanding of how humans identify and categorize complex                     patterns.  This, in turn, may improve our understanding of some                 reading and learning disabilities and may also lead to more                     accurate pattern recognition by machine.
{'FirstName': 'F. Gregory', 'LastName': 'Ashby', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'F. Gregory Ashby', 'EmailAddress': 'ashby@psych.ucsb.edu', 'NSF_ID': '000433336', 'StartDate': '02/15/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of California-Santa Barbara', 'CityName': 'SANTA BARBARA', 'ZipCode': '931060001', 'PhoneNumber': '8058934188', 'StreetAddress': '3227 CHEADLE HALL', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '24', 'CONGRESS_DISTRICT_ORG': 'CA24', 'ORG_UEI_NUM': 'G9QBQDH39DF4', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF CALIFORNIA, SANTA BARBARA', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '118000', 'Text': 'HUMAN COGNITION & PERCEPTION'}
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