Categorization, Memory, and Decision
{'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'}
The research will focus on four outstanding problems in cognitive               psychology, with overall direction and coherence supplied by                    continuing comparisons of memory storage and retrieval models                   with parallel distributed processing models:                                                                                                                    1.  An important form of classification learning is the                         development of skill at judging the probable causes of symptoms,                as in medical diagnosis or trouble shooting of complex equipment.               Such judgments depend both on similarities of new cases to                      previous ones and on frequencies of similar events in one's past                experience.  Analyses of simulated diagnostic situations will                   enable analysis of the way frequency information and similarity                 information combine in such judgments and the conditions for                    approaching optimal decision making.                                                                                                                            2.  Recognition is generally taken to be a more sensitive way of                assessing information stored in memory then recall or other forms               of testing.  Past research has greatly clarified the basis for                  recognition judgments, but there has been little study of the                   underlying learning process.  This deficit will be attacked by                  studies in which recognition tests are preceded by sequences of                 learning experiences, with the results interpreted in terms of                  adaptations of general models for classification learning.                                                                                                      3.  How information acquired by learning fades from memory with                 time has long been studied, but the causes of retention loss are                still not well understood.  The view that a principal factor is                 the displacement of memories by new learning (or unlearning) that               occurs between study and retention testing was undermined by                    numerous apparently negative research results.  In this project,                the hypothesis will be revived in the light of more effective                   models for learning and memory.  Preliminary results indicate                   anew that new learning is an important factor in retention loss.                                                                                                4.  In decision situations such as gambles or choices between                   insurance packages, people normally make choices that tend to                   maximize probable returns; under some circumstances, however,                   conspicuous deviations from that type of rationality have been                  observed.  One, termed the "sure thing" preference, is a tendency               to choose an alternative that yields some certain payoff over                   another alternative whose outcome is uncertain but with a higher                expected value.  This project will investigate the nature of                    previous learning experiences that contribute to the presence or                absence of the "sure thing" phenomenon and related deviations                   from optimal choice behavior.                                                                                                                                   The expected result of this research is convergance on a model                  subsuming several of these types of behavior.  Such a model would               provide a firmer basis for our society's educational enterprise                 than models that only subsume one area of behavior.
{'FirstName': 'William', 'LastName': 'Estes', 'PI_MID_INIT': 'K', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'William K Estes', 'EmailAddress': 'wkestes@indiana.edu', 'NSF_ID': '000380942', 'StartDate': '02/15/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Harvard University', 'CityName': 'CAMBRIDGE', 'ZipCode': '021385366', 'PhoneNumber': '6174955501', 'StreetAddress': '1033 MASSACHUSETTS AVE STE 3', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'MA05', 'ORG_UEI_NUM': 'LN53LCFJFL45', 'ORG_LGL_BUS_NAME': 'PRESIDENT AND FELLOWS OF HARVARD COLLEGE', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '118000', 'Text': 'HUMAN COGNITION & PERCEPTION'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8821029.xml'}