Grossberg S, Myers C W
Department of Cognitive and Neural Systems, Boston University, Massachusetts 02215, USA.
Psychol Rev. 2000 Oct;107(4):735-67. doi: 10.1037/0033-295x.107.4.735.
How do listeners integrate temporally distributed phonemic information into coherent representations of syllables and words? For example, increasing the silence interval between the words "gray chip" may result in the percept "great chip," whereas increasing the duration of fricative noise in "chip" may alter the percept to "great ship" (B. H. Repp, A. M. Liberman, T. Eccardt, & D. Pesetsky, 1978). The ARTWORD neural model quantitatively simulates such context-sensitive speech data. In ARTWORD, sequentially stored phonemic items in working memory provide bottom-up input to unitized list chunks that group together sequences of items of variable length. The list chunks compete with each other. The winning groupings feed back to establish a resonance which temporarily boosts the activation levels of selected items and chunks, thereby creating an emergent conscious percept whose properties match such data.
听众如何将时间上分布的音素信息整合为音节和单词的连贯表征?例如,增加“gray chip”(灰芯片)两个单词之间的静音间隔可能会导致听成“great chip”(大芯片),而增加“chip”中摩擦音的时长可能会使听感变为“great ship”(大船)(B. H. 雷普、A. M. 利伯曼、T. 埃卡特和D. 佩塞茨基,1978年)。ARTWORD神经模型对这类上下文敏感的语音数据进行了定量模拟。在ARTWORD中,工作记忆中顺序存储的音素项目为组合不同长度项目序列的统一列表块提供自下而上的输入。列表块相互竞争。获胜的分组反馈以建立一种共振,该共振会暂时提高所选项目和块的激活水平,从而产生一种涌现的意识感知,其属性与这类数据相匹配。