The phonological congruency modulated long-term form priming of Chinese characters.
Mem Cognit
; 52(2): 312-333, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-37782444
Elucidating the interaction between lexical processing and word learning is essential for a complete understanding of the underlying mechanisms of each of them. Long-term priming for words reflects an interplay between lexical processing and word learning. Although robust long-term priming effects have been found between two occurrences of the same word and between semantically similar words, it remains unclear whether long-term priming between orthographically similar words (i.e., long-term form priming) is a reliable effect. Following the theoretical analysis based on the connectionist framework, we articulated the possibility that long-term form priming might be modulated by the phonological congruency between the prime and target words, and that if this modulator was under control, reliable effects of long-term form priming would emerge. However, this hypothesis has not been adequately tested empirically. The present study tested this hypothesis by using Chinese phonograms and the phonetic radicals embedded in them as the prime and target items. In three experiments that varied in the types of stimuli and testing tasks, we consistently found that when the prime and target had the same phonology, naming the prime facilitated later processing of the target, while when they had different phonologies, the priming effect was inhibitory. These observations were consistent with the connectionist account of long-term priming for words. Our findings help confirm the reliability, generalizability, and robustness of long-term form priming and elucidate its underlying mechanisms, and suggesting promising future directions on the interactions between lexical processing and word learning.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizagem Verbal
/
Fonética
Limite:
Humans
Idioma:
En
Revista:
Mem Cognit
Ano de publicação:
2024
Tipo de documento:
Article