Bibliographic Details
| Title: |
Foveal Word Reading Requires Inter hemispheric Communication. |
| Authors: |
Hunter, Zoë R.1, Brysbaert, Marc1, Knecht, Stefan2 |
| Source: |
Journal of Cognitive Neuroscience. Aug2007, Vol. 19 Issue 8, p1373-1387. 15p. 1 Black and White Photograph, 1 Diagram, 4 Charts, 5 Graphs. |
| Subjects: |
Word recognition ability testing, Visual perception, Motion perception (Vision), Cerebral dominance, Language & languages, Cognition |
| Abstract: |
The left cerebral hemisphere is dominant for language processing in most individuals. It has been suggested that this asymmetric language representation can influence behavioral performance in foveal word-naming tasks. We carried out two experiments in which we obtained laterality indices by means of functional imaging during a mental word-generation task, using functional transcranial Doppler sonography and functional magnetic resonance imaging, respectively. Subsequently, we administered a behavioral word-naming task, where participants had to name foveally presented words of different lengths shown in different fixation locations shifted horizontally across the screen. The optimal viewing position for left language dominant individuals is located between the beginning and the center of a word. It is shifted toward the end of a word for right language dominant individuals and, to a lesser extent, for individuals with bilateral language representation. These results demonstrate that interhemispheric communication is required for foveal word recognition. Consequently, asymmetric representations of language and processes of interhemispheric transfer should be taken into account in theoretical models of visual word recognition to ensure neurological plausibility. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Cognitive Neuroscience is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |