Representational Issues in an Instance Theory of            Automization
{'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'}
Many cognitive activities are automatic, in the sense that they                 are performed quickly and effortlessly, with little conscious                   thought.  Automatic processing provides an important basis for a                wide range of skilled behavior, from perceptual-motor skills,                   such as driving a car, to high-level cognitive skills, such as                  chess and mathematics.  This research project addresses how                     automatic processing is acquired.  Understanding of the nature of               automaticity has implications for the design of training programs               in education, industry, and the military.                                                                                                                       The project concerns a theory of automatization that Logan has                  been developing over the past few years.  The theory assumes that               automatic processing is based on retrieving past solutions                      directly from memory, without any additional computation.                       Automatization involves a transition from processing based on a                 general algorithm to processing based on memory retrieval.  The                 idea is well illustrated in children's acquisition of skill at                  simple addition:  They begin with a general counting algorithm,                 which allows them to add any two numbers, but after sufficient                  exposure, they memorize the basic facts and retrieve sums                       directly from memory without counting.  Logan's past research has               shown that this learning mechanism can account quantitatively for               the increase in speed of processing and reduction in variability                that accompanies automatization, which are the most robust                      phenomena in the literature on automaticity.  This project will                 examine the learning process more directly, investigating the                   kinds of mental representations people acquire as they develop                  automaticity.
{'FirstName': 'Gordon', 'LastName': 'Logan', 'PI_MID_INIT': 'D', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Gordon D Logan', 'EmailAddress': 'gordon.logan@vanderbilt.edu', 'NSF_ID': '000293192', 'StartDate': '09/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'URBANA', 'ZipCode': '618013620', 'PhoneNumber': '2173332187', 'StreetAddress': '506 S WRIGHT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'IL13', 'ORG_UEI_NUM': 'Y8CWNJRCNN91', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ILLINOIS', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '118000', 'Text': 'HUMAN COGNITION & PERCEPTION'}, {'Code': '912000', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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