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Abstract temporal recurrent network (ATRN) (Dominey et al.)
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The abstract temporal recurrent network (ATRN) extends the previous temporal recurrent network (TRN) designed from primate neurophysiological data to account for the non-human primate cognitive capacities to process sequences of actions. The model, which includes modules for the prefrontal cortex, the basal ganglia and the anterior cortex (short term memory), was proved to be capable of detecting the serial, temporal and importantly abstract structures of sequences. This last capacity enabled then the network to learn syntactic rules. The authors proved that the ATRN could assign accurately thematic roles to nouns in a sentence, therefore proving that syntactic processing could at least in part rely on cognitive sequencing abilities. Two predictions followed that the authors verified: in some patients syntactic impairments is correlated with poor sequencing abilities, and EEG reveals common ERP for both syntactic and sequence processing, fostering the idea of potential common neurophysiological substrates for both.
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Abstract Temporal Recurrent Network ( Dominey - Hoen - Blanc - Lelkov-Boissard) (Dominey et al.)
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A model that describes the cognitive sequence processing based on the functional neuroanatomy of the primate cortex and basal ganglia. This model illustrates neurologically plausible system for extracting serial, temporal, and abstract structure in both linguistic and non-linguistic cognitive sequencing tasks.
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An Integrative Account Model of Language Learning (Rodriguez-Fornells et al.)
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Initial language acquisition at infants and learning second languages at adult level are two different subjects that have been widely scrutinized both by neurophysiologically and neuropsychologically. But still there are much more unknown parts about the brain mechanisms involved in these processes. Several researchers employed brain imaging techniques such as ERPs and magnetic resonance to find out some cues about the interaction of brain regions in achieving and then maintain this complex task of language learning. Current study focuses on this issue in terms of word extraction from speech, figuring out the grammatical construction of them and finally understanding the mechanism which conveys meaning of verbal context. Several other related brain imaging data have been reviewed during the current research and this data is used in the justification of the proposed model with respect to interaction of different paths in the brain for language processing.
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Cognitive sequence processing model (Dominey et al.)
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A cognitive sequence processing model that was developed based on the functional neuroanatomy of primate cortex and
basal ganglia is used to simulate the behavior of human infants in extracting serial, temporal and abstract structure from language-like sound sequences. This model can, with training, perform adult level syntactic comprehension, based on dissociated processing streams for open vs. closed class words
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Syntactic Comprehension and Artificial Grammar Learning (Dominey,Inui,Hoen) (Dominey - Inui - Hoen)
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The organization of brain in terms of distributed neural networks and how this structure can be used in nonlinguistic sequence cognition as well as being used in language acquisition is a valuable area to study profoundly. Important researches have been made about important aspects of this issue such as sentence comprehension, nonlinguistic sequences and artificial grammar learning (AGL) in order to examine the brain mechanisms and related processes involved in these tasks. Current research accommodates data obtained from those studies and several other neurophysiological models of sentence processing and specifies a new architecture using known human language cortico-striato-thalamo-cortical (CSTC) constraints within a neural network model. One important aspect of evaluating the proposed system in respect to human performance is developing simulations that display the system is able to learn and carry out similar language and artificial syntax tasks while considering the actual neuroanatomical connectivity boundaries and real world brain imaging data. In current model a recurrent cortical network in Broadmann Area 47(BA47) encodes structural cues which later activate above mentioned CSTC circuit. This circuit modulates lexical semantic information flow between Broadmann Area 45 (BA45) and Broadmann Areas 44 and 6 (BA44/6). The latter one is the locus of sentence level meaning representation. In language learning process closed class words control thematic role assignment (TRA) trough corticostriatal plasticity. For Artificial Grammar Learning (AGL) tasks, repetitive internal structures are encoded in BA47 and activate CSTC circuit to anticipate the next element in the sequence. The model is a syntactic comprehension architecture that depicts interaction of BA44/45/47/6, Basal Ganglia (Caudate and SNr) and Thalamus. Two main tasks performed for performance evaluation of this developed model are Caplan's Test for thematic role assignment and syntactic comprehension (Caplan, D., Baker, C., & Dehaut, F., 1985) and Artificial Grammar Learning (AGL) study of Gomez and Schvaneveldt. (Gomez and Schvaneveldt, 1994)
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