Literature DB >> 26447569

The Neural Representation of Sequences: From Transition Probabilities to Algebraic Patterns and Linguistic Trees.

Stanislas Dehaene1, Florent Meyniel2, Catherine Wacongne3, Liping Wang4, Christophe Pallier2.   

Abstract

A sequence of images, sounds, or words can be stored at several levels of detail, from specific items and their timing to abstract structure. We propose a taxonomy of five distinct cerebral mechanisms for sequence coding: transitions and timing knowledge, chunking, ordinal knowledge, algebraic patterns, and nested tree structures. In each case, we review the available experimental paradigms and list the behavioral and neural signatures of the systems involved. Tree structures require a specific recursive neural code, as yet unidentified by electrophysiology, possibly unique to humans, and which may explain the singularity of human language and cognition.
Copyright © 2015 Elsevier Inc. All rights reserved.

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Year:  2015        PMID: 26447569     DOI: 10.1016/j.neuron.2015.09.019

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  100 in total

1.  fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.

Authors:  Cory Shain; Idan Asher Blank; Marten van Schijndel; William Schuler; Evelina Fedorenko
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Review 2.  Empirical approaches to the study of language evolution.

Authors:  W Tecumseh Fitch
Journal:  Psychon Bull Rev       Date:  2017-02

3.  Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data.

Authors:  Noslen Hernández; Aline Duarte; Guilherme Ost; Ricardo Fraiman; Antonio Galves; Claudia D Vargas
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4.  Neural substrates of word category information as the basis of syntactic processing.

Authors:  Luyao Chen; Junjie Wu; Yongben Fu; Huntae Kang; Liping Feng
Journal:  Hum Brain Mapp       Date:  2018-09-21       Impact factor: 5.038

Review 5.  Language as a biomarker for psychosis: A natural language processing approach.

Authors:  Cheryl M Corcoran; Vijay A Mittal; Carrie E Bearden; Raquel E Gur; Kasia Hitczenko; Zarina Bilgrami; Aleksandar Savic; Guillermo A Cecchi; Phillip Wolff
Journal:  Schizophr Res       Date:  2020-06-01       Impact factor: 4.939

6.  Transitional Probabilities Are Prioritized over Stimulus/Pattern Probabilities in Auditory Deviance Detection: Memory Basis for Predictive Sound Processing.

Authors:  Maria Mittag; Rika Takegata; István Winkler
Journal:  J Neurosci       Date:  2016-09-14       Impact factor: 6.167

7.  Toward an Integration of Deep Learning and Neuroscience.

Authors:  Adam H Marblestone; Greg Wayne; Konrad P Kording
Journal:  Front Comput Neurosci       Date:  2016-09-14       Impact factor: 2.380

8.  Long-term implicit memory for sequential auditory patterns in humans.

Authors:  Roberta Bianco; Peter Mc Harrison; Mingyue Hu; Cora Bolger; Samantha Picken; Marcus T Pearce; Maria Chait
Journal:  Elife       Date:  2020-05-18       Impact factor: 8.140

9.  How humans learn and represent networks.

Authors:  Christopher W Lynn; Danielle S Bassett
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-24       Impact factor: 11.205

10.  Cingulate and cerebellar beta oscillations are engaged in the acquisition of auditory-motor sequences.

Authors:  María Herrojo Ruiz; Burkhard Maess; Eckart Altenmüller; Gabriel Curio; Vadim V Nikulin
Journal:  Hum Brain Mapp       Date:  2017-07-13       Impact factor: 5.038

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