Semantic processing of English sentences using statistical computation based on neurophysiological models (original) (raw)

Modelling of syntactical processing in the cortex

Biosystems, 2007

Probably the hardest test for a theory of brain function is the explanation of language processing in the human brain, in particular the interplay of syntax and semantics. Clearly such an explanation can only be very speculative, because there are essentially no animal models and it is hard to study detailed neural processing in humans. The approach presented in this paper uses well established basic neural mechanisms in a plausible global network architecture that is formulated essentially in terms of cortical areas and their intracortical and corticocortical interconnections. The neural implementation of this system shows that the comparatively intricate logical task of understanding semantico-syntactical structures can be mastered by a neural network architecture. The system presented also shows additional context awareness, in particular the model is able to correct ambiguous input to a certain degree, e.g. the input "bot show/lift green wall" with an artificial ambiguity between "show" and "lift" is correctly interpreted as "bot show green wall" since a wall is not liftable. Furthermore, the system is able to learn new object words during runtime.

Language and the rest of cognition: computational, algorithmic and possible neural commonalities

2003

Abstract The computational program for theoretical neuroscience proposed by Marr and Poggio [1] calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels���computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational���significant progress has been made in the understanding of cognition.

Bridging language with the rest of cognition: computational, algorithmic and neurobiological issues and methods

2004

Abstract The computational program for theoretical neuroscience initiated by Marr and Poggio (1977) calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels���computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational���significant progress has been made in the understanding of cognition.

Neurolinguistic computational models

2008

ABSTRACT It is tempting to think of the brain as functioning very much like a computer. Like the digital computer, the brain takes in data and outputs decisions and conclusions. However, unlike the computer, the brain does not store precise memories at specific locations. Instead, the brain reaches decisions through the dynamic interaction of diverse areas operating in functional neural circuits. The role of specific local areas in these functional neural circuits appears to be highly flexible and dynamic.

Towards understanding of natural language: Neurocognitive inspirations

Artificial Neural Networks–ICANN …, 2007

Neurocognitive processes responsible for representation of meaning and understanding of words are investigated. First a review of current knowledge about word representation, recent experiments linking it to associative memory and to right hemisphere synchronous activity is presented. Various conjectures on how meaning arises and how reasoning and problem solving is done are presented. These inspirations are used to make systematic approximation to spreading activation in semantic memory networks. Using hierarchical ontologies representations of short texts are enhanced and it is shown that highdimensional vector models may be treated as a snapshot approximation of the neural activity. Clustering short medical texts into different categories is greatly enhanced by this process, thus facilitating understanding of the text.

Predicting neural activity patterns associated with sentences using a neurobiologically motivated model of semantic representation

We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.

Tracking neural coding of perceptual and semantic features of concrete nouns

NeuroImage, 2012

We present a methodological approach employing magnetoencephalography (MEG) and machine learning techniques to investigate the flow of perceptual and semantic information decodable from neural activity in the half second during which the brain comprehends the meaning of a concrete noun. Important information about the cortical location of neural activity related to the representation of nouns in the human brain has been revealed by past studies using fMRI. However, the temporal sequence of processing from sensory input to concept comprehension remains unclear, in part because of the poor time resolution provided by fMRI. In this study, subjects answered 20 questions (e.g. is it alive?) about the properties of 60 different nouns prompted by simultaneous presentation of a pictured item and its written name. Our results show that the neural activity observed with MEG encodes a variety of perceptual and semantic features of stimuli at different times relative to stimulus onset, and in different cortical locations. By decoding these features, our MEG-based classifier was able to reliably distinguish between two different concrete nouns that it had never seen before. The results demonstrate that there are clear differences between the time course of the magnitude of MEG activity and that of decodable semantic information. Perceptual features were decoded from MEG activity earlier in time than semantic features, and features related to animacy, size, and manipulability were decoded consistently across subjects. We also observed that regions commonly associated with semantic processing in the fMRI literature may not show high decoding results in MEG. We believe that this type of approach and the accompanying machine learning methods can form the basis for further modeling of the flow of neural information during language processing and a variety of other cognitive processes.

Syntax meets semantics during brain logical computations

Progress in Biophysics & Molecular Biology, 2018

The discrepancy between syntax and semantics is a painstaking issue that hinders a better comprehension of the underlying neuronal processes in the human brain. In order to tackle the issue, we at first describe a striking correlation between Wittgenstein's Tractatus, that assesses the syntactic relationships between language and world, and Perlovsky's joint language-cognitive computational model, that assesses the semantic relationships between emotions and "knowledge instinct". Once we have established a correlation between a purely logical approach to the language and computable psychological activities, we aim to find the neural correlates of syntax and semantics in the human brain. Starting from topological arguments, we suggest that the semantic properties of a proposition are processed at a level that is higher dimensional than the functional level used by the brain for syntactical properties. In a fully reversible process, the syntactic elements embedded in Broca's area project into multiple scattered semantic cortical zones. The presence of higher functional dimensions gives rise to the increase in informational content that takes place in semantic expressions. Therefore, diverse features of human language and cognitive world can be assessed in terms of both the logic armor described by the Tractatus, and the neurocomputational techniques at hand. One of our motivations is to build a neurocomputational framework able to provide a feasible explanation for brain's semantic processing, in preparation for novel computers with nodes built into higher dimensions.

Intelligent Computing Methods in Language Processing by Brain

Communications in Computer and Information Science, 2017

Language processing by brain focuses on experimental, theoretical and psychological study of the brain functioning while processing language. Techniques of dynamic brain imaging and behavioral study require mathematical modeling and methods to explore the scenario. Intelligent computing methods model the observed behavior and process images to obtain clear picture of the brain. This paper illustrates the various models and methodology of neurolinguistic with special emphasis on intelligent computing methods in the field. Finally a comparative study of research going on aphasia and dyslexia has been done.