TY - GEN ID - cogprints2019 UR - http://cogprints.org/2019/ A1 - Martinez, Guillermina A1 - Cangelosi, Angelo A1 - Coventry, Kenny Y1 - 2001/// N2 - This paper describes a neural network model for the study of spatial language. It deals with both geometric and functional variables, which have been shown to play an important role in the comprehension of spatial prepositions. The network is integrated with a virtual reality interface for the direct manipulation of geometric and functional factors. The training uses experimental stimuli and data. Results show that the networks reach low training and generalization errors. Cluster analyses of hidden activation show that stimuli primarily group according to extra-geometrical variables. TI - A Hybrid Neural Network and Virtual Reality System for Spatial Language Processing SP - 16 AV - public EP - 21 ER -