心理学
召回
最佳显著性理论
认知心理学
脑电图
大声朗读
识别记忆
听力学
发展心理学
阅读(过程)
认知
神经科学
社会心理学
语言学
医学
哲学
作者
Bohua Zhang,Zong Meng,Qing Li,Antao Chen,Glen E. Bodner
出处
期刊:Cortex
[Elsevier]
日期:2023-05-05
卷期号:165: 57-69
被引量:4
标识
DOI:10.1016/j.cortex.2023.04.006
摘要
The production effect (PE) is the finding that reading words aloud rather than silently during study leads to improved memory. We used electroencephalography (EEG) techniques to detect the contributions of recollection, familiarity, and attentional processes to the PE in recognition memory, using Chinese stimuli. During the study phase, participants encoded each list item aloud, silently, or by performing a non-unique aloud (control) task. During the test phase, they made remember/know/new recognition judgments. We recorded EEG data in both phases. The behavioral results replicated the typical pattern with English stimuli: Recognition was better in the aloud condition than in the silent (and control) condition, and this PE was due to enhanced recollection and familiarity. At study, the amplitude of the P3b ERP component was greater in the aloud than in the silent/control conditions, suggesting that reading aloud increases attention or preparatory processing during the intention phase. At test, the recollection-based LPC old/new effect was largest in the aloud condition; however, the familiarity-based FN400 old/new effect was equivalent between the aloud condition and the silent/control conditions. Only the LPC effect correlated with the behavioral effect. Moreover, multivariate pattern analysis (MVPA) showed that accurate classification of items as 'aloud' versus 'new' mainly occurred in the later period of the recognition response, consistent with the LPC old/new effect. Our findings suggest that the within-subject PE in recognition memory reflects enhanced attention and distinctiveness, rather than increased memory strength. More broadly, our findings suggest that encoding strategies such as production enhance recollection more than familiarity.
科研通智能强力驱动
Strongly Powered by AbleSci AI