AUTHOR=Zhang Meichao , Chen Shuang , Wang Lin , Yang Xiaohong , Yang Yufang TITLE=Episodic Specificity in Acquiring Thematic Knowledge of Novel Words from Descriptive Episodes JOURNAL=Frontiers in Psychology VOLUME=8 YEAR=2017 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.00488 DOI=10.3389/fpsyg.2017.00488 ISSN=1664-1078 ABSTRACT=

The current study examined whether thematic relations of the novel words could be acquired via descriptive episodes, and if yes, whether it could be generalized to thematically related words in a different scenario. In Experiment 1, a lexical decision task was used where the novel words served as primes for target words in four conditions: (1) corresponding concepts of the novel words, (2) thematically related words in the same episodes as that in learning condition, (3) thematically related words in different episodes, or (4) unrelated words served as targets. Event related potentials elicited by the targets revealed that compared to the unrelated words, the corresponding concepts and thematically related words in the same episodes elicited smaller N400s with a frontal-central distribution, whereas the thematically related words in different episodes elicited an enhanced late positive component. Experiment 2 further showed a priming effect of the corresponding concepts on the thematically related words in the same episodes as well as in a different episode, indicating that the absence of a priming effect of the learned novel words on the thematically related words in different episode could not be attributed to inappropriate selection of thematically related words in the two conditions. These results indicate that only the corresponding concepts and the thematically related words in the learning episodes were successfully primed, whereas the thematic association between the novel words and the thematically related words in different scenarios could only be recognized in a late processing stage. Our findings suggest that thematic knowledge of novel words is organized via separate scenarios, which are represented in a clustered manner in the semantic network.