What is Cognitive Science
Generics and the Structure of the Mind
Dr. Sarah-Jane Leslie
Thursday, November 30, 2006, 12:00pm - 07:00pm
Princeton University, Department of Philosophy
I consider bare plural generics and argue that they give voice to our cognitive system�s most primitive generalizations. Explicitly quantified statements, in contrast, invoke more taxing, higher-level cognitive processes. I begin by briefly considering the leading semantic accounts of generics, and find that they are vulnerable to counterexamples. I then remark on the sheer complexity of these accounts, which contrasts sharply with the simplicity of semantic accounts of explicit quantifiers. This contrast becomes quite striking when we consider data from language acquisition; young children actually find generics considerably easier to master than quantifiers. I argue that this is because generics express our most cognitively basic generalizations. I claim further that the strange truth-conditional behavior of generics can be traced to what are quite plausibly quirks and biases associated with these cognitively primitive generalizations. Having laid out my account, I consider whether generics can be considered quantificational in any sense, and conclude that they cannot. Generic generalizations simply do not depend on considerations of quantity, or on any considerations describable in the language of set-theory. While this is a surprising result from the point of view of semantics, it is to be expected once we recognize that generics express our most primitive generalizations. I discuss this observation in the context of the Two Systems view of cognition set forth by Daniel Kahneman and his colleagues, and propose that generic generalizations exhibit the characteristics of the lower-level System 1, while quantificational generalizations exhibit the characteristics of the higher-level System 2.
"What is Cognitive Science?"
This lunchtime talks series is designed to introduce the University community to issues in Cognitive Science. Cognitive Science is one the the few fields where modern developments in computer science and artificial intelligence promise to shed light on classical problems in psychology and the philosophy of the mind. Ancient questions of how we see the world, understand language, and reason, and questions such as 'how a material system can know about the outside world', are being explored with the powerful new conceptual prosthetics of computer modeling.
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