Our ability to produce and understand language involves a complex, dynamic interaction between different types of knowledge, involving orthographic, phonological, semantic, syntactic, and pragmatic representations, as well as knowledge of the world. Moreover, given that discourse rapidly unfolds at the rate of several words per second, these representations need to be activated, retrieved and/or computed in real time. Informed by behavioral and neuroimaging data, explicit neurocomputational models of language processing seek to offer mechanistic explanations of the representations and computations that underlie online language production and comprehension.
Neural models from the field of machine learning and particularly deep learning are only the most recent developments in this field. Localist and distributed connectionist models, advanced measurement models like diffusion models, and expert systems are alternative formal approaches able to capture various aspects of language processing. Finally, probabilistic language models as well as corpus-based approaches are powerful computational techniques, which, taken together, may enhance our understanding of language in terms of how it is represented and processed in the human brain.
In this Research Topic, we invite submissions that combine such neurocomputational models of language processing with human neuroimaging and behavioral data. The manuscripts can be submitted to Frontiers in Human Neuroscience or Frontiers in Artificial Intelligence and may contain sophisticated neural simulations of specific aspects of language processing. These submissions can either deepen our understanding of unimpaired language processing or shed light on language disorders or developmental aspects of language. Alternatively, the papers can be submitted to Frontiers in Artificial Intelligence and thus may highlight the state-of-the-art in natural language processing (NLP). Given this broad spectrum, topics may range from models that seek to explain electrophysiological and functional imaging data to neurally inspired computational models explaining eye-tracking eye-tracking or reading time data. With this special issue, we hope to provide a comprehensive overview of the latest developments in the neurocomputational modelling of human language production and comprehension.
Our ability to produce and understand language involves a complex, dynamic interaction between different types of knowledge, involving orthographic, phonological, semantic, syntactic, and pragmatic representations, as well as knowledge of the world. Moreover, given that discourse rapidly unfolds at the rate of several words per second, these representations need to be activated, retrieved and/or computed in real time. Informed by behavioral and neuroimaging data, explicit neurocomputational models of language processing seek to offer mechanistic explanations of the representations and computations that underlie online language production and comprehension.
Neural models from the field of machine learning and particularly deep learning are only the most recent developments in this field. Localist and distributed connectionist models, advanced measurement models like diffusion models, and expert systems are alternative formal approaches able to capture various aspects of language processing. Finally, probabilistic language models as well as corpus-based approaches are powerful computational techniques, which, taken together, may enhance our understanding of language in terms of how it is represented and processed in the human brain.
In this Research Topic, we invite submissions that combine such neurocomputational models of language processing with human neuroimaging and behavioral data. The manuscripts can be submitted to Frontiers in Human Neuroscience or Frontiers in Artificial Intelligence and may contain sophisticated neural simulations of specific aspects of language processing. These submissions can either deepen our understanding of unimpaired language processing or shed light on language disorders or developmental aspects of language. Alternatively, the papers can be submitted to Frontiers in Artificial Intelligence and thus may highlight the state-of-the-art in natural language processing (NLP). Given this broad spectrum, topics may range from models that seek to explain electrophysiological and functional imaging data to neurally inspired computational models explaining eye-tracking eye-tracking or reading time data. With this special issue, we hope to provide a comprehensive overview of the latest developments in the neurocomputational modelling of human language production and comprehension.