AI

Typicality Effects and the Logic of Reciprocity

Abstract

The variability in the interpretation of reciprocal expressions has been extensively addressed in the literature and received detailed semantic accounts. After pointing out a central empirical limitation of previous logical accounts of reciprocity, we argue that these approaches suffer from inadequacies due to ignoring typicality preferences with binary predicate concepts. We claim that typicality preferences are crucial for interpreting reciprocals and introduce a new principle, the Maximal Typicality Hypothesis (MTH), which analyzes reciprocals using an extension of the Strongest Meaning Hypothesis (SMH) proposed in Dalrymple et al. (1998) Unlike the SMH, which is a principle that implicitly presupposes a classical two-valued (“definitional”) treatment of predicate concepts, the MTH respects the fuzziness of such concepts as manifested by their typicality preferences, and expects strong correlations between these preferences and the range of logical interpretations available for reciprocal expressions. The expected correlations are supported by new empirical results elicited in a series of experiments with speakers of Hebrew.