Novel metaphor comprehension: Semantic neighbourhood density interacts with concreteness

Abstract

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 ...

Contributions of semantic richness to the processing of idioms

“…We saw some evidence of this in the interactions with familiarity in our data, where interference with the figurative meaning seemed to be negligible for the most familiar phrases. Comparing our results to other kinds of figurative language, Al-Azary and Buchanan (2017) found an interaction between concreteness and semantic neighbourhood density whereby only metaphorical topic-vehicle pairs from dense semantic neighbourhoods showed an effect of concreteness (more abstract topics were judged as more suitable, and understood more easily), while for less dense neighbourhood, concreteness had no effect. They also discuss their results in terms of a need to suppress irrelevant connections, which is more difficult for concrete topics with many near neighbours in semantic terms.…”

“…Alongside the contribution of semantic richness to the processing of single words, some researchers have also considered how this set of variables may influence processing of metaphors, where the meaning of a word is extended to encompass an additional, figurative meaning. Al-Azary and Buchanan (2017) found that both suitability judgments and online comprehensibility of metaphors were affected by concreteness (more abstract metaphors were facilitated) and semantic neighbourhood density (sparser neighbourhoods were facilitative). They also found an interaction between these two properties which they suggest may explain the variable results obtained for concreteness in previous metaphor studies (e.g.…”

Metaphor Comprehension in Low and High Creative Individuals

“…One such computational model that has been proposed in regard of metaphor comprehension is the predication model ( Kintsch, 2000 , 2001 ; Chiappe and Chiappe, 2007 ; Al-Azary and Buchanan, 2017 ). This computational model assumes that metaphor comprehension is contingent on the activation of semantic neighborhoods in specific context ( Kintsch, 2008 ).…”

“…A comprehensive computational model that can characterize the semantic processing between the topic and the vehicle of a metaphor is the predication model ( Kintsch, 2000 , 2001 , 2008 ; Kintsch and Bowles, 2002 ; Al-Azary and Buchanan, 2017 ). This model is composed of two components: a computational representation of the meanings of words and the application of these representations in computing a contextually appropriate interpretation of statements ( Kintsch, 2000 ).…”

“…According to this process, semantic neighbors that are related to both the topic and the vehicle are strengthened whereas other neighbors of the individual words are inhibited. The semantic neighbors that are activated (i.e., the semantic neighbors of the vehicle which are also related to the topic) along with the topic and vehicle are then used to compute a vector which is taken to represent the metaphor’s meaning ( Al-Azary and Buchanan, 2017 ). Thus, the model can predict performance in comprehension of different metaphor types, based on their novelty ( Kintsch, 2008 ).…”