Premium
Pertinence Generation in Radiological Diagnosis: Spreading Activation and the Nature of Expertise
Author(s) -
Raufaste Eric,
Eyrolle Hélène,
Mariné Claudette
Publication year - 1998
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/s15516709cog2204_4
Subject(s) - reinterpretation , computer science , medical diagnosis , cognition , process (computing) , artificial intelligence , cognitive psychology , phenomenon , natural language processing , psychology , cognitive science , neuroscience , epistemology , medicine , radiology , philosophy , aesthetics , operating system
An empirical study of human expert reasoning processes is presented. Its purpose is to test a model of how a human expert's cognitive system learns to detect, and does detect, pertinent data and hypotheses. This process is called pertinence generation. The model is based on the phenomenon of spreading activation within semantic networks. Twenty‐two radiologists were asked to produce diagnoses from two very difficult X‐ray films. As the model predicted, pertinence increased with experience and with semantic network integration. However, the experts whose daily work involved explicit reasoning were able, in addition, to go beyond and to generate more pertinence. The results suggest that two qualitatively different kinds of expertise, basic and super, should be distinguished. A reinterpretation of the results of Lesgold et al. (1988) is proposed, suggesting that apparent nonmonotonicities in performance are not representative of common radiological expertise acquisition but result from the inclusion of basic and super expertise on the same curve.