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A Cognitive Theory of Graphical and Linguistic Reasoning: Logic and Implementation
Author(s) -
Stenning Keith,
Oberlander Jon
Publication year - 1995
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/s15516709cog1901_3
Subject(s) - computer science , cognitive architecture , mental representation , venn diagram , inference , abstraction , construals , cognitive science , cognition , representation (politics) , graphics , equivalence (formal languages) , artificial intelligence , natural language processing , theoretical computer science , linguistics , mathematics , psychology , construal level theory , philosophy , neuroscience , social psychology , mathematics education , epistemology , politics , political science , law , computer graphics (images)
We discuss external and internal graphical and linguistic representational systems. We argue that a cognitive theory of peoples' reasoning performance must account for (a) the logical equivalence of inferences expressed in graphical and linguistic form, and (b) the implementational differences that affect facility of inference. Our theory proposes that graphical representation limit abstraction and thereby aid “processibility”. We discuss the ideas of specificity and abstraction, and their cognitive relevance. Empirical support both comes from tasks which involve the manipulation of external graphics and tasks that do not. For the former, we take Euler's (1772) circles, provide a novel computational reconstruction, show how it captures abstractions, and contrast it with earlier construals and with Johnson‐Laird's (1983) mental models representations. We demonstrate equivalence of the graphical Euler system, and the nongraphical mental models system. For tasks not involving manipulation of external graphics, we discuss text comprehension, and the mental performance of syllogisms. By positing an internal system with the same specificity as Euler's circles, we cover the mental models data, and generate new empirical predictions. Finally, we consider how the architecture of working memory explains why such specific representations are relatively easy to store.