Previous research suggests that metaphor comprehension is affected both by the concreteness of the topic and vehicle and their semantic neighbours (Kintsch, 2000;Xu, 2010). However, studies have yet to manipulate these 2 variables simultaneously. To that end, we composed novel metaphors manipulated on topic concreteness and semantic neighbourhood density (SND) of topic and vehicle. In Experiment 1, participants rated the metaphors on the suitability (e.g. sensibility) of their topic-vehicle pairings. Topic concreteness interacted with SND such that participants rated metaphors from sparse semantic spaces to be more sensible than those from dense semantic spaces and preferred abstract topics over concrete topics only for metaphors from dense semantic spaces. In Experiments 2 and 3, we used presentation deadlines and found that topic concreteness and SND affect the online processing stages associated with metaphor comprehension. We discuss how the results are aligned with established psycholinguistic models of metaphor comprehension.Keywords Language comprehension . Psycholinguistics .
Semantics . Metaphor processingIn metaphor, unrelated concepts are paired to create a meaningful relation. For example, in time is money, the topic, or first concept, time and the vehicle, or second concept, money, are semantically distinct but the statement is nonetheless comprehensible even though literally untrue. Given that the topic and vehicle are unrelated, they each refer to a wide range of properties which vary in their relevance to the created meaning of the metaphor. Understanding a metaphor requires emphasizing the properties relevant to its meaning while suppressing the irrelevant ones (Black, 1962). For example, when reading a metaphor such as some lawyers are sharks, irrelevant property primes (e.g. sharks can be blue) hinder processing whereas relevant property primes (e.g. sharks can be ruthless) facilitate processing, as compared to no primes (McGlone & Manfredi, 2001). Moreover, metaphor primes (e.g. that defense lawyer is a shark) facilitate reading target sentences that refer to properties which are relevant to the metaphor's meaning (e.g. sharks are tenacious) but not sentences that refer to properties that are irrelevant to the metaphor's meaning (e.g. sharks are good swimmers; Gernsbacher, Keysar, Robertson, & Werner, 2001). Taken together, the aforementioned studies suggest that online metaphor comprehension hinges on accessing the appropriate sense of the vehicle. Psycholinguistic models characterize the semantic processing of the topic and vehicle needed for metaphor comprehension (see Gibbs & Colston, 2012, for a review of such models). Of these models, one of the most comprehensive is Kintsch's (2000Kintsch's ( , 2001Kintsch's ( , 2008) predication algorithm, which we will discuss below.The predication algorithm models the meaning of argument-predicate sentences, of which metaphor is an example, in semantic space (Kintsch, 2001). In this approach, latent semantic analysis (LSA) is used to model ...