Neural mechanisms of semantic memory

Kraut, Michael A.; Pitcock, Jeffery A.; Hart, John

doi:10.1007/s11910-004-0069-6

Cited by 16 publications

(11 citation statements)

References 38 publications

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“…Furthermore, the hippocampoparahippocampal area (medial temporal lobe system) plays an important role in human memory [10] and learning [11]. As for the pulvinar nucleus of the thalamus, it was reported to be associated with sensory-motor integrative functions [12], auditory unilateral neglect or extinction [13], language processing [14], spatial attention [15] and visually triggered semantic memory [16]. Therefore, the associated functions for speech perception might be interfered with by white noise, but this requires further study.…”

Section: Discussionmentioning

confidence: 99%

The effects of masking on the activation of auditory-associated cortex during speech listening in white noise

Hwang

Chen

et al. 2006

Acta Oto-Laryngologica

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In humans, the activation of several regions including the middle parts of the superior and middle temporal gyri, parahippocampal gyrus, cuneus and thalamus of the left hemisphere was significantly reduced under the masking paradigm with +5 dB signal to noise ratio. In addition, reduced activation was also found at the lingual gyrus, anterior and middle parts of the superior temporal gyrus (STG), uncus, fusiform gyrus, and inferior frontal gyrus of the right hemisphere during masking.

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Section: Discussionmentioning

confidence: 99%

The effects of masking on the activation of auditory-associated cortex during speech listening in white noise

Hwang

Chen

et al. 2006

Acta Oto-Laryngologica

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“…This aspect shares some ideas with the ''Neural Hybrid Model of semantic Object Memory'' by Hart et al (2007), Kraut et al (2003Kraut et al ( , 2004.…”

Section: Discussionmentioning

confidence: 80%

“…An oscillator may be silent, if it does not receive enough excitation, or may oscillate in the c-frequency band, if excited by a sufficient input. c-band synchronization has been proposed as an important mechanism in highlevel cognitive tasks, including language recognition and semantic processing (Kraut et al 2004; Slotnick et al Fig. 1 Schematic diagram describing the general structure of the network.…”

Section: Qualitative Descriptionmentioning

confidence: 99%

“…Finally, Hart and coworkers formulated a complex theory, which incorporates several previous aspects: Their model (named ''hybrid model of semantic object memory''), assumes that different cortical regions embody not only sensory-motor representations of objects, but also higher cognitive aspects (lexical, emotional,…). This information is then integrated by means of synchronized neural activity in the gamma-range modulated by the thalamus Kraut et al 2003Kraut et al , 2004.…”

Section: Introductionmentioning

confidence: 99%

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An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation

2011

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This work presents a connectionist model of the semantic-lexical system. Model assumes that the lexical and semantic aspects of language are memorized in two distinct stores, and are then linked together on the basis of previous experience, using physiological learning mechanisms. Particular characteristics of the model are: (1) the semantic aspects of an object are described by a collection of features, whose number may vary between objects. (2) Individual features are topologically organized to implement a similarity principle. (3) Gamma-band synchronization is used to segment different objects simultaneously.(4) The model is able to simulate the formation of categories, assuming that objects belong to the same category if they share some features. (5) Homosynaptic potentiation and homosynaptic depression are used within the semantic network, to create an asymmetric pattern of synapses; this allows a different role to be assigned to shared and distinctive features during object reconstruction. (6) Features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. (7) Features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that, after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Word presentation, in turn, activates the corresponding features in the sensorymotor areas, recreating the same conditions occurred during learning, according to a grounded cognition viewpoint. Several words and their conceptual description can coexist in the lexical-semantic system exploiting gamma-band time division. Schematic exempla are shown, to illustrate the possibility to distinguish between words representing a category, and words representing individual members and to evaluate the role of gamma-band synchronization in priming. Finally, the model is used to simulate patients with focalized lesions, assuming a damage of synaptic strength in specific feature areas. Results are critically discussed in view of future model extensions and application to real objects. The model represents an original effort to incorporate many basic ideas, found in recent conceptual theories, within a single quantitative scaffold.

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“…Hart et al (2002) and Kraut et al (2004) proposed the “Neural Hybrid Model of Semantic Object Memory” in which different regions encode not only sensorimotor but also high-order cognitive information (language, emotion, …). This information is then bound by synchronization of gamma rhythms modulated by the thalamus.…”

Section: Introductionmentioning

confidence: 99%

A Computational Model of the Lexical-Semantic System Based on a Grounded Cognition Approach

Ursino¹,

Cuppini²,

Magosso³

2010

Front. Psychology

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This work presents a connectionist model of the semantic-lexical system based on grounded cognition. The model assumes that the lexical and semantic aspects of language are memorized in two distinct stores. The semantic properties of objects are represented as a collection of features, whose number may vary among objects. Features are described as activation of neural oscillators in different sensory-motor areas (one area for each feature) topographically organized to implement a similarity principle. Lexical items are represented as activation of neural groups in a different layer. Lexical and semantic aspects are then linked together on the basis of previous experience, using physiological learning mechanisms. After training, features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. The model also includes some inhibitory synapses: features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Moreover, different objects and the corresponding words can be simultaneously retrieved and segmented via a time division in the gamma-band. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurring during learning. The model simulates the formation of categories, assuming that objects belong to the same category if they share some features. Simple exempla are shown to illustrate how words representing a category can be distinguished from words representing individual members. Finally, the model can be used to simulate patients with focalized lesions, assuming an impairment of synaptic strength in specific feature areas.

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Neural mechanisms of semantic memory

Cited by 16 publications

References 38 publications

The effects of masking on the activation of auditory-associated cortex during speech listening in white noise

The effects of masking on the activation of auditory-associated cortex during speech listening in white noise

An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation

A Computational Model of the Lexical-Semantic System Based on a Grounded Cognition Approach

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