To study the different mechanisms of understanding figurative language in a speaker’s native language and their second language, this study investigated how scientific metaphors in Chinese and English are electrophysiologically processed via event-related potential experimentation. Compared with the metaphors from daily life or in literary works, scientific metaphors tend to involve both a more complicated context structure and a distinct knowledge-inferencing process. During the N400 time window, English scientific metaphors elicited more negative N400s than Chinese ones at the parietal region. (...) In the late positive component time window, English scientific metaphors elicited less positive LPCs than Chinese ones at the parietal region, and larger late negativities encompassing smaller areas of the brain. The findings might indicate that for late unbalanced bilingual speakers, L2 scientific metaphor comprehension requires more effort in information retrieval or access to the non-literal route. Altogether, the possible findings are that non-native and non-dominant language processing involves decreased automaticity of cognitive mechanisms, and decreased sensitivity to the levels of conventionality of metaphoric meanings. (shrink)

The role of the two hemispheres in processing metaphoric language is controversial. In order to complement current debates, the current divided visual field study introduced scientific metaphors as novel metaphors, presenting orientation mapping from the specific and familiar domains to the abstract and unfamiliar domains, to examine hemispheric asymmetry in metaphoric processing. Twenty-four Chinese native speakers from science disciplines took part in the experiment. The participants were presented with four types of Chinese word pairs: scientific metaphors, conventional metaphors, literal word (...) pairs, and unrelated word pairs. The first word in each pair was presented centrally, and the second was presented to the left visual field or the right visual field. Event-related potentials were recorded when participants read the target words and judged whether words in each pair were related. The data demonstrated that both hemispheres were involved at the initial stage of metaphor processing, but the right hemisphere took a more privileged role. The significant activation of the left hemisphere for scientific metaphoric processing supports the fine-coarse coding hypothesis. During right-visual-field presentation, the left hemisphere, responsible for the processing of closely related domains, has to integrate the loosely associated domains of scientific metaphor, which greatly increased cognitive taxes. Moreover, the data of late positive components revealed different hemispheric activation between scientific metaphors and conventional metaphors. Compared with literal pairs, conventional metaphors elicited significantly higher LPCs during right visual field presentation, while the scientific metaphor elicited significantly lower LPCs during left visual field presentation. These results suggest different processing mechanisms between novel metaphors and conventional metaphors and the special role of the right hemisphere in novel metaphoric processing at the later mapping stage. (shrink)

Reinforcement learning relies on the reward prediction error (RPE) signals conveyed by the midbrain dopamine system. Previous studies showed that dopamine plays an important role in both positive and negative reinforcement. However, whether various reinforcement processes will induce distinct learning signals is still unclear. In a probabilistic learning task, we examined RPE signals in different reinforcement types using an electrophysiology index, namely, the feedback-related negativity (FRN). Ninety-four participants were randomly assigned into four groups: base (no money incentive), positive reinforcement (presentation (...) of money rewards), negative reinforcement (removal of money losses), and combined reinforcement (money rewards and removal of money losses) groups. In addition, in order to evaluate the engagement of emotional activity in the different reinforcement processes, Positive and Negative Affect Schedule-Expanded Form (PANAS-X) scales were applied before and after the experiment to detect the emotional changes. The results showed that there was no difference between groups in the dopamine-related learning bias. However, compared to the other three groups, negative reinforcement elicited smaller FRN (the difference-wave measure) during the learning, stronger positive affect and joviality, and less fatigue after the learning, in which the difference between the negative and positive reinforcement groups was smaller. The results indicated that pure avoidance motivation may induce distinct emotional fluctuations, which influence the feedback processing. (shrink)