Abstract
Reduced connectivity between sending and receiving neurons (i.e., synaptic depression) may facilitate change detection by reducing responses for recently viewed objects so new objects can be highlighted. In the experiment reported here, we investigated high-level change detection following semantic satiation, which is the loss of meaning following repetition of a word. A computer simulation of a word-reading neural network with synaptic depression identified key predictions of connectivity reduction. A dynamic-causal-modeling analysis of magnetoencephalography (MEG) responses collected during a category-matching task identified connectivity reduction between a cortical region related to orthography and a cortical region related to semantics as the cause of the reduced MEG response to a repeated word. As predicted, prior repetitions of a category-matching word presented immediately after the repeated word enhanced semantic novelty, as measured with the M400 component. These results demonstrate that a combination of neural-network modeling and connectivity analyses can reveal the manner in which connectivity fluctuations underlie cognitive functions.
Keywords
Get full access to this article
View all access options for this article.
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
