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      The revolution will not be controlled: natural stimuli in speech neuroscience

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          ABSTRACT

          Humans have a unique ability to produce and consume rich, complex, and varied language in order to communicate ideas to one another. Still, outside of natural reading, the most common methods for studying how our brains process speech or understand language use only isolated words or simple sentences. Recent studies have upset this status quo by employing complex natural stimuli and measuring how the brain responds to language as it is used. In this article we argue that natural stimuli offer many advantages over simplified, controlled stimuli for studying how language is processed by the brain. Furthermore, the downsides of using natural language stimuli can be mitigated using modern statistical and computational techniques.

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          Most cited references55

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          Identifying natural images from human brain activity.

          A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.
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            People thinking about thinking peopleThe role of the temporo-parietal junction in “theory of mind”

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              Predicting human brain activity associated with the meanings of nouns.

              The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.
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                Author and article information

                Journal
                Lang Cogn Neurosci
                Lang Cogn Neurosci
                PLCP
                plcp21
                Language, Cognition and Neuroscience
                Routledge
                2327-3798
                2327-3801
                2020
                22 July 2018
                : 35
                : 5 , The Neuroscience of Natural Language Processing (Part 1), edited by Olaf Hauk and Bela Weiss
                : 573-582
                Affiliations
                [a ]Communication Sciences & Disorders, Moody College of Communication, The University of Texas at Austin , Austin, USA
                [b ]Department of Neurology, Dell Medical School, The University of Texas at Austin , Austin, USA
                [c ]Department of Neuroscience, The University of Texas at Austin , Austin, USA
                [d ]Department of Computer Science, The University of Texas at Austin , Austin, USA
                Author notes
                Article
                1499946
                10.1080/23273798.2018.1499946
                7324135
                32656294
                4566de7c-3d4b-46c4-a710-e35e826e1c1c
                © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

                History
                : 21 February 2018
                : 03 July 2018
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 77, Pages: 10
                Funding
                Funded by: Burroughs Wellcome Fund 10.13039/100000861
                This work was supported by Burroughs Wellcome Fund.
                Categories
                Review Articles

                natural language,encoding models,fmri,ecog,eeg
                natural language, encoding models, fmri, ecog, eeg

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