  In this study, the authors propose a computational cognitive model for sign
language (SL) perception and comprehension with detailed algorithmic
descriptions based on cognitive functionalities in human language processing.
The semantic network model (SNM) that represents semantic relations between
concepts, it is used as a form of knowledge representation. The proposed model
is applied in the comprehension of sign language for classifier predicates. The
spreading activation search method is initiated by labeling a set of source
nodes (e.g. concepts in the semantic network) with weights or "activation" and
then iteratively propagating or "spreading" that activation out to other nodes
linked to the source nodes. The results demonstrate that the proposed search
method improves the performance of sign language comprehension in the SNM.
