视觉通路在汉字组块破解过程中的有效连接

The present study used Dynamical Causal Modeling (DCM) to reveal the influence of difficult to decompose Chinese characters on the effective connectivity of “where” and “what” visual stream。 Chunk decomposition is to decompose the familiar items to their components and then to make up new items with the decomposed components。 Some previous studies with eye movements and brain image had revealed that the chunk decomposition was involved visual-spatial information process, and suggested that “what” and “where” visual streams contributed to the course of chunk decomposition。 However, how they worked to complete the chunk decomposition task is still unknown。 The present study has two factors, familiarity and tightness of the spatial structures, each with 2 levels: real words vs. pseudowords and tight chunks vs. loose chunks。 The results indicates that in the loose conditions, familiarity increases the effective connectivity of “where ” stream, while in the pseudowords conditions, tightness of the spatial structures increases the effective connectivity of both “where” and “what” streams, and familiarity and tightness combined to increase not only both the “what” and “where” streams, but also the effective connectivity from the inferior temporal gyrus to the superior parietal lobule.

本研究应用动态因果模型（DCM）探讨不同难度的汉字组块破解任务对于视知觉信息加工中的背侧where通路和腹侧what通路的有效连接强度的影响。组块破解是指将熟悉的项目分解为其组成部分，再以新的方式组成其他项目的过程。尽管以往对于组块破解的眼动和脑成像研究提示了有关组块破解过程的视觉-空间信息加工特点，并提示what通路与where通路可能现时参与组块破解过程，但至今仍不清楚在组块破解过程中这两条视觉通路如何参与并相互协同。本研究包含两个因素，分别是熟悉性和空间结构紧密性，其中熟悉性包含真字和假字两个水平，空间结构包含松散结构和紧密结构两个水平。实验结果表明，松散条件下熟悉性使where通路的有效连接增强，而假字条件下空间结构紧密性使得what通路和where通路的有效连接均增强；熟悉性和空间结构紧密性共同作用不仅使what通路和where通路的有效连接增强，而且使what通路终端到where通路终端之间的有效连接增强。