Lexical meaning contains literal meaning and non-literal meaning, and the metaphorical meaning is an important part of non-literal meaning. Previous lexical meaning learning studies mainly focus on the literal meaning and the establishment of feature representation. Little is known about the metaphorical meaning learning. The current study adopted the contextual learning paradigm, which was widely used in literal meaning studies, to explore whether participants were able to get the metaphorical meaning of the novel word, and whether the metaphorical meaning could establish feature representation.
First, Experiment 1 and Experiment 2 were designed to explore the first question.In Experiment 1，the novel words were embedded in two types of learning contexts: one supported the metaphorical meaning (metaphorical meaning learning condition: ML condition), and the other supported the literal meaning (literal meaning learning condition: LL condition). The learning effect was assessed via a lexical decision task with event-related potentials (ERPs) being recorded. In this task, the novel words served as primes, the literal corresponding concepts of the novel words (CC targets),the topic words (TO targets) and unrelated words (NR targets) served as target words.The results showed that the novel words could prime could prime the CC targets, butthey could not prime the TO targets, indicating that participants could not construct the relation between the novel words and topic words. We thought that the task might affect the demand for cognitive effort. In Experiment 2, a semantic-relatedness judgment task was adopted to explore the novel words' metaphorical meaning learning. The results found that novel words could prime the topic words, indicating that learners could get the metaphorical meaning of newly learned words. In a word, the results of two experiments indicated that participants could get the metaphorical meaning of novel words, and the detection of the learning effect was affected by the task settings. The metaphorical meaning learning was more demanding than literal meaning learning.
Second, the formation of metaphor is based on similarities of properties between the topic and the vehicle. In metaphorical meaning learning, can participants establish the feature representation? The related feature of vehicle can be divided into two types:the first one is related to the meaning of whole metaphor, while the second one is only related to the literal meaning of the vehicle. In the psycholinguistic literature on metaphor, two models have been proposed to describe the different features processing in metaphor: property comparison model and property attribution model. Whether the novel word can establish feature representation in metaphorical meaning learning? Do the two properties have different activation mode? Which model can explain the feature representation in metaphorical meaning learning? Experiment 3 was designed to explore these questions. In Experiment 3, we adopted the contextual learning paradigm which was used in Experiment 2. In the learning phase, participants were asked to guess the novel word's meaning from discourses which supported the novel words'metaphorical meaning. The learning effect was assessed via a semantic-relatedness judgment task. In this task, the novel words served as primes, the literal corresponding concepts of the novel words (CC targets), the metaphor-related feature words (MR targets), the vehicle-related feature words (VR targets) and unrelated words (NR targets) served as target words. ERP responses to the targets were recorded. The ERP results showed that participants could establish the feature representation in metaphorical meaning leaning. Furthermore, there was no difference between these two related words (MR targets and VR targets), which was consistent with the property comparison model.
In conclusion, we explored the lexical metaphorical meaning learning and the establishment of feature representation in contextual learning. There were two main findings: the first was that learners could get the metaphorical meaning of novel words. The second was that learners could establish feature representation in metaphorical meaning leaning, which was consistent with the property comparison model.