Abstract

Cimpian et al.(2010) observed that we accept generic statements of the form ‘Gs are f’ on relatively weak evidence, but that if we are unfamiliar with group G and we learn a generic statement about it, we still treat it inferentially in a much stronger way: (almost) all Gs are f. This paper makes use of notions like ‘representativeness’, ‘contingency’ and ‘relative difference’ from (associative learning) psychology to provide a uniform semantics of generics that explains why people accept generics based on weak evidence. The spirit of the approach has much in common with Leslie’s cognition-based ideas about generics, but the semantics will be grounded on a strengthening of Cohen’s(1999) relative readings of generic sentences. In contrast to Leslie and Cohen, we propose a uniform semantic analysis of generics. The basic intuition is that a generic of the form ‘Gs are f’ is true because f is typical for G, which means that f is valuably associated with G. We will make use of Kahneman and Tversky’s Heuristics and Biases approach, according to which people tend to confuse questions about probability with questions about representativeness, to explain pragmatically why people treat many generic statements inferentially in a much stronger way.

Highlights

  • Generic sentences come in very different sorts

  • This paper makes use of notions like ‘representativeness’, ‘contingency’ and ‘relative difference’ from psychology to provide a uniform semantics of generics that explains why people accept generics based on weak evidence

  • We have proposed that generic sentences should be analyzed in terms of representativeness, and that the representativeness of feature f for group G should be measured by ∇ PGf, which is defined making use of contingency, PGf

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Summary

Introduction

Generic sentences come in very different sorts. Consider (1-a) and (1-b). 4. According to our own uniform semantic account, a generic like (1-a) is true basically because relatively many tigers are striped, except when only tigers are considered in which case the (vast) majority of tigers have to be striped. We will argue that such an account is in accordance with the way we inductively learn categories and how we represent them. This semantic analysis will be closely related to Cohen’s treatment of what he calls the ‘relative’ reading of generics, but such that (under certain circumstances) his ‘absolute’ reading comes out as a special case.

Some semantic theories of generics
This equivalence is immediate
Typicality and associative learning
Categorization and typicality
Associative learning
Weak semantics: generics state typicalities
Strong pragmatics: from biases to probabilities
Findings
Conclusion and outlook
Full Text
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