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Towards Modeling False Memory With Computational Knowledge Bases
Author(s) -
Li Justin,
Kohanyi Emma
Publication year - 2017
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12245
Subject(s) - computer science , wordnet , cognition , semantic memory , artificial intelligence , cognitive model , task (project management) , semantics (computer science) , computational model , natural language processing , semantic network , machine learning , cognitive science , psychology , programming language , management , neuroscience , economics
One challenge to creating realistic cognitive models of memory is the inability to account for the vast common–sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese–Roediger–McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling.