The impact of variety of episodic contexts on the integration of novel words into semantic network

ABSTRACT

The current study examined whether and how the variety of episodic contexts influences the integration of novel words into semantic network via thematic and taxonomic relations in a semantic priming task with event-related potential (ERP) technique. The novel words were acquired from discourses containing either single or multiple episodes. We found that corresponding concepts, targets thematic-related to learning episodes (Experiment 1) and taxonomic-related targets (Experiment 2) elicited semantic-priming N400s/LPCs effects compared to the unrelated targets in both conditions, whereas the targets thematic-related to unlearned episodes (Experiment 1) and feature-related targets (Experiment 2) elicited semantic-priming N400s/LPCs effects only in the multiple episodic condition. These results indicated that only the novel words learned from variable episodes could successfully prime thematically related words in unlearned episodes and feature-related words. Our findings suggest that the variety of episodic contexts contributes to establishing more stable and richer semantic representations of the novel words.

KEYWORDS:

• Novel word learning
• semantic integration
• variety of episodic contexts
• ERP

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Numbers: 61433018), and Funded by CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows (Grant No. 2016LH0015), as well as the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Grant No. Y6CX212007).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant number 61433018), and Funded by CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows (grant number 2016LH0015), as well as the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (grant number Y6CX212007).