scholarly journals Isolating syntax in natural language: MEG evidence for an early contribution of left posterior temporal cortex

2018 ◽  
Author(s):  
Graham Flick ◽  
Liina Pylkkänena
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Sentence Processing ◽  
Temporal Dynamics ◽  
Temporal Cortex ◽  
Activity Increase ◽  
Semantic Composition ◽  
Left Posterior ◽  
Spatio Temporal ◽  
Sparse Set

ABSTRACTSyntax is the engine that allows us to create an infinitude of linguistic expressions, and the construction of syntactic structures, such as noun phrases and verb phrases, is considered a fundamental component of language processing. Nevertheless, insights concerning the neurobiological basis of syntax have remained elusive, in part because it is difficult to isolate syntax from semantic composition. Consequently, many studies of syntax have relied on meaningless artificial stimuli, such as jabberwocky expressions or artificial grammars. However, while pure manipulations of syntax are challenging to design, natural language grammars do have a sparse set of constructions presenting this possibility. Here we examined one such case, English post-nominal adjectives (mountain TALL enough for a strenuous hike), which were contrasted with semantically parallel but structurally simpler noun-adjective sequences in an MEG experiment. We observed a sharp activity increase in the left posterior temporal lobe (PTL) when syntactic composition was more straightforward, approximately 200 ms after adjective onset. The semantic fit between the noun and adjective was also varied, but this affected anterior temporal cortex, consistent with prior work. These findings offer a unique demonstration of the relevance of posterior temporal cortex for syntactic processing in natural language. We also present connectivity evidence that the syntax-related PTL responses were relayed to ipsilateral inferior frontal and anterior temporal regions. The combined results draw an initial picture of the rapid spatio-temporal dynamics of the syntactic and semantic composition network in sentence processing.

scholarly journals Supramodal Sentence Processing in the Human Brain: Fmri Evidence for the Influence of Syntactic Complexity in More Than 200 Participants

2019 ◽  
Author(s):  
Julia Uddén ◽  
Annika Hultén ◽  
Jan-Mathijs Schoffelen ◽  
Nietzsche Lam ◽  
Karin Harbusch ◽  
...  
Keyword(s):  
Language Processing ◽  
Sentence Processing ◽  
Parietal Lobe ◽  
Inferior Frontal Gyrus ◽  
Middle Temporal Gyrus ◽  
Syntactic Complexity ◽  
Input Modality ◽  
Middle Temporal ◽  
Left Posterior ◽  
Brain Fmri

ABSTRACTThis study investigated two questions. One is to which degree sentence processing beyond single words is independent of the input modality (speech vs. reading). The second question is which parts of the network recruited by both modalities is sensitive to syntactic complexity. These questions were investigated by having more than 200 participants read or listen to well-formed sentences or series of unconnected words. A largely left-hemisphere fronto-temporoparietal network was found to be supramodal in nature, i.e. independent of input modality. In addition, the left inferior frontal gyrus (LIFG) and the left posterior middle temporal gyrus (LpMTG) were most clearly associated with left-branching complexity. The left anterior middle temporal gyrus (LaMTG) showed the greatest sensitivity to sentences that differed in right-branching complexity. Moreover, activity in LIFG and LpMTG increased from sentence onset to end, in parallel with an increase of the left-branching complexity. While LIFG, bilateral anterior and posterior MTG and left inferior parietal lobe (LIPL) all contribute to the supramodal unification processes, the results suggest that these regions differ in their respective contributions to syntactic complexity related processing. The consequences of these findings for neurobiological models of language processing are discussed.

scholarly journals Mapping the plague through natural language processing

2021 ◽  
Author(s):  
Fabienne Krauer ◽  
Boris V. Schmid
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Specific Information ◽  
Location Data ◽  
Quantitative Analyses ◽  
Overall Performance ◽  
Spatio Temporal ◽  
Temporal Extent

AbstractPlague has caused three major pandemics with millions of casualties in the past centuries. There is a substantial amount of historical and modern primary and secondary literature about the spatial and temporal extent of epidemics, circumstances of transmission or symptoms and treatments. Many quantitative analyses rely on structured data, but the extraction of specific information such as the time and place of outbreaks is a tedious process. Machine learning algorithms for natural language processing (NLP) can potentially facilitate the establishment of datasets, but their use in plague research has not been explored much yet. We investigated the performance of five pre-trained NLP libraries (Google NLP, Stanford CoreNLP, spaCy, germaNER and Geoparser.io) for the extraction of location data from a German plague treatise published in 1908 compared to the gold standard of manual annotation. Of all tested algorithms, we found that Stanford CoreNLP had the best overall performance but spaCy showed the highest sensitivity. Moreover, we demonstrate how word associations can be extracted and displayed with simple text mining techniques in order to gain a quick insight into salient topics. Finally, we compared our newly digitised plague dataset to a re-digitised version of the famous Biraben plague list and update the spatio-temporal extent of the second pandemic plague mentions. We conclude that all NLP tools have their limitations, but they are potentially useful to accelerate the collection of data and the generation of a global plague outbreak database.

scholarly journals Fast temporal dynamics and causal relevance of face processing in the human temporal cortex

2018 ◽  
Author(s):  
Jessica Schrouff ◽  
Omri Raccah ◽  
Sori Baek ◽  
Vinitha Rangarajan ◽  
Sina Salehi ◽  
...  
Keyword(s):  
Human Brain ◽  
Face Processing ◽  
Common Ground ◽  
Temporal Dynamics ◽  
Time Window ◽  
Temporal Cortex ◽  
Surrogate Marker ◽  
Marine Animal ◽  
Distributed Models ◽  
Spatio Temporal

ABSTRACTRecordings with a large number of intracranial electrodes in eight neurosurgical subjects offered a unique opportunity to examine the fast temporal dynamics of face processing simultaneously across a relatively large extent of the human temporal cortex (TC). Measuring the power of slow oscillatory bands of activity (θ, α, β, and γ) as well as High-Frequency Broadband (HFB, 70-177 Hz) signal, we found that the HFB showed the strongest univariate and multivariate changes in response to face compared to non-face stimuli. Using the HFB signal as a surrogate marker for local cortical engagement, we identified recording sites with selective responses to faces that were anatomically consistent across subjects and responded with graded strength to human, mammal, bird, and marine animal faces. Importantly, the most face selective sites were located more posteriorly and responded earlier than those with less selective responses to faces. Using machine learning based methods, we demonstrated that a sparse model focusing on information from the human face selective sites performed as well as, or better than, anatomically distributed models of face processing when discriminating faces from non-faces stimuli. Lastly, we identified the posterior fusiform (pFUS) site as causally the most relevant node for inducing distortion of face perception by direct electrical stimulation. Our findings support the notion of face information being processed first in the most selective sites - that are anatomically discrete and localizable within individual brains and anatomically consistent across subjects – which is then distributed in time to less selective anterior temporal sites within a time window that is too fast to be detected by current neuroimaging methods. The new information about the fast spatio-temporal dynamics of face processing across multiple sites of the human brain provides a new common ground for unifying the seemingly contradictory modular and distributed models of face processing in the human brain.

scholarly journals Mapping Violent Presence of Armed Actors in Colombia

2019 ◽  
Vol 1 ◽  
pp. 1-9 ◽  
Author(s):  
Javier Osorio ◽  
Mohamed Mohamed ◽  
Viveca Pavon ◽  
Susan Brewer-Osorio
Keyword(s):  
Civil War ◽  
Political Violence ◽  
Language Processing ◽  
Temporal Dynamics ◽  
Early Stage ◽  
Temporal Trends ◽  
Early Warning Systems ◽  
Spatio Temporal ◽  
Interactive Map ◽  
Armed Actors

<p><strong>Abstract.</strong> Heat maps and Early Warning Systems have traditionally contributed to identifying and managing risks associated with crime trends, health hazards, and natural disasters. However, their application to analyzing civil war dynamics still is at an early stage. To address this need, this research integrates Natural Language Processing (NLP) tools and Geographic Information Systems (GIS) to generate an interactive map of the violent presence of armed actors in the Colombian civil war between 1988 and 2017. The NLP component generates fine-grained geo-location data of armed actors' violent presence. The GIS component then uses the geo-referenced data to present dynamic clusters of four main types of actors: Government forces, Insurgent organizations, Paramilitary groups, and Criminal organizations. Each type of actors is further disaggregated into a multitude of specific armed organizations. Based on anomalies in the spatio-temporal trends identified in the data, we develop an EWS methodology to detect “emerging”, “intense”, and “critical” cases. This application contributes to the efforts of the academic and policy communities to understanding the spatio-temporal dynamics of political violence and promoting sustainable peace in civil war settings.</p>

scholarly journals Cortical Effects of Shifting Letter Position in Letter Strings of Varying Length

2003 ◽  
Vol 15 (5) ◽  
pp. 731-746 ◽  
Author(s):  
Piers Cornelissen ◽  
Antti Tarkiainen ◽  
Päivi Helenius ◽  
Riitta Salmelin
Keyword(s):  
Word Length ◽  
Right Hemisphere ◽  
Word Reading ◽  
Temporal Dynamics ◽  
Temporal Cortex ◽  
Letter Position ◽  
Opposite Pattern ◽  
Spatio Temporal ◽  
Single Word Reading ◽  
The Right

Neuroimaging and lesion studies suggest that occipitotemporal brain areas play a necessary role in recognizing a wide variety of objects, be they faces, letters, numbers, or household items. However, many questions remain regarding the details of exactly what kinds of information are processed by the occipito-temporal cortex. Here, we address this question with respect to reading. Ten healthy adult subjects performed a single word reading task. We used whole-head magnetoencephalography to measure the spatio-temporal dynamics of brain responses, and investigated their sensitivity to: (1) lexicality (defined here as the difference between words and consonant strings), (2) word length, and (3) variation in letter position. Analysis revealed that midline occipital activity around 100 msec, consistent with low-level visual feature analysis, was insensitive to lexicality and variation in letter position, but was slightly affected by string length. Bilateral occipito-temporal activations around 150 msec were insensitive to lexicality and reacted to word length only in the timing (and not strength) of activation. However, vertical shifts in letter position revealed a hemispheric imbalance: The right hemisphere activation increased with the shifts, whereas the opposite pattern was evident in the left hemisphere. The results are discussed in the light of Caramazza and Hillis's (1990) model of early reading.

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