Neural network processing of natural language: II. Towards a unified model of corticostriatal function in learning sentence comprehension and non-linguistic sequencing Export

@article{citeulike:3383631, abstract = {A central issue in cognitive neuroscience today concerns how distributed neural networks in the brain that are used in previous termlanguagenext term learning and processing can be involved in non-linguistic cognitive sequence learning. This issue is informed by a wealth of functional neurophysiology studies of sentence comprehension, along with a number of recent studies that examined the brain processes involved in learning non-linguistic sequences, or artificial grammar learning (AGL). The current research attempts to reconcile these data with several current neurophysiologically based models of sentence processing, through the specification of a neural network model whose architecture is constrained by the known cortico-striato-thalamo-cortical (CSTC) neuroanatomy of the human previous termlanguagenext term system. The challenge is to develop simulation models that take into account constraints both from neuranatomical connectivity, and from functional imaging data, and that can actually learn and perform the same kind of previous termlanguagenext term and artificial previous termsyntaxnext term tasks. In our proposed model, structural cues encoded in a recurrent cortical network in BA47 activate a CSTC circuit to modulate the flow of lexical previous termsemanticnext term information from BA45 to an integrated representation of meaning at the sentence level in BA44/6. During previous termlanguagenext term acquisition, corticostriatal plasticity is employed to allow closed class structure to drive thematic role assignment. From the AGL perspective, repetitive internal structure in the AGL strings is encoded in BA47, and activates the CSTC circuit to previous termpredictnext term the next element in the sequence. Simulation results from Caplan's [Caplan, D., Baker, C., \& Dehaut, F. (1985). Syntactic determinants of sentence comprehension in aphasia. Cognition, 21, 117–175] test of syntactic comprehension, and from Gomez and Schvaneveldts' [Gomez, R. L., \& Schvaneveldt, R. W. (1994). What is learned from artificial grammars?. Transfer tests of simple association. Journal of Experimental Psychology: Learning, Memory and Cognition, 20, 396–410] artificial grammar learning experiments are presented. These results are discussed in the context of a brain architecture for learning grammatical structure for multiple natural previous termlanguages,next term and non-linguistic sequences.}, author = {Dominey, P. and Inui, T. and Hoen, M.}, citeulike-article-id = {3383631}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.bandl.2008.08.002}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0093934X08001090}, day = {05}, doi = {10.1016/j.bandl.2008.08.002}, issn = {0093934X}, journal = {Brain and Language}, keywords = {comprehension, computationalmodel, neuralnetwork, psycholinguistics, sentence, simulation}, month = {October}, posted-at = {2008-10-07 16:38:24}, priority = {2}, title = {Neural network processing of natural language: II. Towards a unified model of corticostriatal function in learning sentence comprehension and non-linguistic sequencing}, url = {http://dx.doi.org/10.1016/j.bandl.2008.08.002}, year = {2008} }

TY - JOUR ID - citeulike:3383631 L3 - citeulike-article-id:3383631 N2 - A central issue in cognitive neuroscience today concerns how distributed neural networks in the brain that are used in previous termlanguagenext term learning and processing can be involved in non-linguistic cognitive sequence learning. This issue is informed by a wealth of functional neurophysiology studies of sentence comprehension, along with a number of recent studies that examined the brain processes involved in learning non-linguistic sequences, or artificial grammar learning (AGL). The current research attempts to reconcile these data with several current neurophysiologically based models of sentence processing, through the specification of a neural network model whose architecture is constrained by the known cortico-striato-thalamo-cortical (CSTC) neuroanatomy of the human previous termlanguagenext term system. The challenge is to develop simulation models that take into account constraints both from neuranatomical connectivity, and from functional imaging data, and that can actually learn and perform the same kind of previous termlanguagenext term and artificial previous termsyntaxnext term tasks. In our proposed model, structural cues encoded in a recurrent cortical network in BA47 activate a CSTC circuit to modulate the flow of lexical previous termsemanticnext term information from BA45 to an integrated representation of meaning at the sentence level in BA44/6. During previous termlanguagenext term acquisition, corticostriatal plasticity is employed to allow closed class structure to drive thematic role assignment. From the AGL perspective, repetitive internal structure in the AGL strings is encoded in BA47, and activates the CSTC circuit to previous termpredictnext term the next element in the sequence. Simulation results from Caplan’s [Caplan, D., Baker, C., & Dehaut, F. (1985). Syntactic determinants of sentence comprehension in aphasia. Cognition, 21, 117–175] test of syntactic comprehension, and from Gomez and Schvaneveldts’ [Gomez, R. L., & Schvaneveldt, R. W. (1994). What is learned from artificial grammars?. Transfer tests of simple association. Journal of Experimental Psychology: Learning, Memory and Cognition, 20, 396–410] artificial grammar learning experiments are presented. These results are discussed in the context of a brain architecture for learning grammatical structure for multiple natural previous termlanguages,next term and non-linguistic sequences. JF - Brain and Language SN - 0093934X TI - Neural network processing of natural language: II. Towards a unified model of corticostriatal function in learning sentence comprehension and non-linguistic sequencing KW - comprehension KW - computationalmodel KW - neuralnetwork KW - psycholinguistics KW - sentence KW - simulation AU - Dominey, P AU - Inui, T AU - Hoen, M PY - 2008/10/05/ UR - http://dx.doi.org/10.1016/j.bandl.2008.08.002 ER -