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No AccessJournal of Speech and Hearing ResearchResearch Article1 Sep 1958Myoelastic-Aerodynamic Theory of Voice Production Janwillem van den Berg Janwillem van den Berg Google Scholar https://doi.org/10.1044/jshr.0103.227 SectionsAboutPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In Additional Resources FiguresReferencesRelatedDetailsCited by The Journal of the Acoustical Society of America153:5 (2803)1 May 2023 Effect of functional electric stimulation on phonation in an ex vivo aged ovine model Bernhard Jakubaß, Gregor Peters, Stefan Kniesburges, Marion Semmler, Andrijana Kirsch, Claus Gerstenberger, Markus Gugatschka and Michael Döllinger Journal of Voice37:3 (305-313)1 May 2023Integrative Insights into the Myoelastic-Aerodynamic Theory and Acoustics of Phonation. Scientific Tribute to Donald G. MillerJan G. Švec, Harm K. Schutte, C. Julian Chen and Ingo R. Titze Maryam Naghibolhosseini, Ahmed M Yousef, Mohsen Zayernouri, Stephanie RC Zacharias and Dimitar D Deliyski (2023) Deep Learning for High-Speed Laryngeal Imaging Analysis 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)10.1109/ICCIKE58312.2023.10131757979-8-3503-3826-3 Journal of Voice1 Mar 2023Mechanical Parameters Based on High-Speed Videoendoscopy of the Vocal Folds in Patients With Ectodermal DysplasiaFranziska Pelka, Maria Ensthaler, Olaf Wendler, Stefan Kniesburges, Anne Schützenberger and Marion Semmler Journal of Mammalian Evolution30:1 (79-94)1 Mar 2023The vocal apparatus: An understudied tool to reconstruct the evolutionary history of echolocation in bats?Nicolas L. M. Brualla, Laura A. B. Wilson, Michael Doube, Richard T. Carter, Alan G. McElligott and Daisuke Koyabu Journal of Voice1 Feb 2023Dynamic System Coupling in Voice ProductionChristian T. Herbst, Coen P.H. Elemans, Isao T. Tokuda, Vasileios Chatziioannou and Jan G. 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Švec (2017) Numerical Simulation of the Self-oscillating Vocal Folds in Interaction with Vocal Tract Shaped for Particular Czech Vowels Recent Global Research and Education: Technological Challenges10.1007/978-3-319-46490-9_43 MOJ Clinical & Medical Case Reports5:39 Dec 2016Management of Laryngo Tracheal Injury Our ExperienceSanjeev Mohanty The Journal of the Acoustical Society of America140:4 (2614-2635)1 Oct 2016Mechanics of human voice production and controlZhaoyan Zhang Journal of Speech, Language, and Hearing Research59:5 (1002-1017)1 Oct 2016Exploring the Clinical Utility of Relative Fundamental Frequency as an Objective Measure of Vocal HyperfunctionNelson Roy, Rebecca A. Fetrow, Ray M. Merrill and Christopher Dromey The Laryngoscope126:7 (1589-1594)1 Jul 2016Nonstimulated rabbit phonation model: Cricothyroid approximationCarolyn K. Novaleski, Tsuyoshi Kojima, Siyuan Chang, Haoxiang Luo, Carla V. 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Elemans (2016) Embodied Motor Control of Avian Vocal Production Vertebrate Sound Production and Acoustic Communication10.1007/978-3-319-27721-9_5 W. Tecumseh Fitch (2016) Vertebrate Bioacoustics: Prospects and Open Problems Vertebrate Sound Production and Acoustic Communication10.1007/978-3-319-27721-9_10 W. Tecumseh Fitch and Roderick A. Suthers (2016) Vertebrate Vocal Production: An Introductory Overview Vertebrate Sound Production and Acoustic Communication10.1007/978-3-319-27721-9_1 Communication Sciences & Disorders20:4 (607-616)31 Dec 2016Pattern Analysis of Voice Onset and Offset in Normal Adults Using High-Speed Digital Imaging: The Role of Arytenoid Cartilage MovementsSeong Hee Choi, Chi-Sun Oh and Chul-Hee Choi Nature Communications6:11 Dec 2015Universal mechanisms of sound production and control in birds and mammalsC.P.H Elemans, J.H. Rasmussen, C.T. Herbst, D.N. Düring, S.A. Zollinger, H. Brumm, K. Srivastava, N. Svane, M. Ding, O.N. Larsen, S.J. Sober and J.G. Švec Journal of Phonetics52 (35-45)1 Sep 2015Assessing respiratory contributions to f 0 declination in German across varying speech tasks and respiratory demandsSusanne Fuchs, Caterina Petrone, Amélie Rochet-Capellan, Uwe D. Reichel and Laura L. Koenig Ross D. Farhadieh and Wayne A.J. Morrison (2015) Pharyngeal reconstruction Plastic and reconstructive surgery10.1002/9781118655412.ch28 Journal of Experimental Biology218:7 (991-998)1 Apr 2015 Functional morphology of the Alligator mississippiensis larynx with implications for vocal production Tobias Riede, Zhiheng Li, Isao T. Tokuda and Colleen G. Farmer Brad H. Story (2015) Mechanisms of Voice Production The Handbook of Speech Production10.1002/9781118584156.ch3 The Journal of the Acoustical Society of America137:3 (1493-1502)1 Mar 2015Three speech sounds, one motor action: Evidence for speech-motor disparity from English flap productionDonald Derrick, Ian Stavness and Bryan Gick Cancer Research75:1 (31-39)1 Jan 2015A Noninvasive Procedure for Early-Stage Discrimination of Malignant and Precancerous Vocal Fold Lesions Based on Laryngeal Dynamics AnalysisJakob Unger, Jörg Lohscheller, Maximilian Reiter, Christian S. and Maria Computer Methods in Biomechanics and Biomedical Dec of in vocal fold and The Journal of the Acoustical Society of Nov of the of pressure on vocal fold H. and Zhang Journal of Nov of Vocal Fold A Review K. Computer Speech & Sep source analysis to and Journal of Jul Fold Dynamics for Frequency Journal of and Jul of a model of and Jul International Journal of & Jun a Larynx: A in Clinical and The Apr of and fields in excised Jun and The Journal of the Acoustical Society of Jan and in Sid Khosla and Peters, R. J. Van K. Van and The Jan of and laryngeal D. Journal of Experimental Nov in an T. Herbst, Jan G. Švec, Jörg Lohscheller, S. and W. Tecumseh Fitch Medical Engineering & Aug dynamics of vocal using normal P. and Journal of Jul and Acoustic of Vocal A. and Brad H. Story Audiology - Communication de Carla José and of Biomedical Dec in Vocal Fold the of E. and W. Journal of Nov of Differences in Using High-Speed Videoendoscopy and F. Maria E. and Dimitar D. Deliyski Aug Production of T. Herbst, S. Jörg Lohscheller, Ingo R. Titze, and W. Tecumseh Fitch Research in Jun the Voicing of English Voicing Voicing and Journal of Mar Voice in Digital Signal Mar of dynamics of vocal using and P. David E. D. José C. and The Feb of on acoustic characteristics of A. N. E. and Acoustical Science and of on the of flow the Control in and Speech of Paul and L. T. V. Aerodynamic and Acoustic Theory of Voice Production Forensic The Journal of the Acoustical Society of Dec acoustic correlates of human vocal fold modeling and laryngeal D. Matías Zañartu, F. Dimitar D. Deliyski and E. Oct analysis of speech and Journal of Experimental Sep Subglottal pressure and fundamental frequency control in of Alligator mississippiensis Tobias Riede, Isao T. Tokuda and C. G. Farmer of Voice The Journal of the Acoustical Society of Apr the and as phonation onset Zhang Mar of and Threshold Biomechanical for Voice and The Journal of the Acoustical Society of Mar acoustic model of the subglottal for speech C. Matías and R. Medical Engineering & Mar of the glottal into a C. M. and M. Döllinger The Journal of & Feb S Donald F. of for Phonation and J. of the Respiratory System Journal of the International Dec acoustic correlates of the contrast in M. T. and Journal of Speech, Language, and Hearing Oct of Vocal on Relative Fundamental Frequency Voicing Offset and E. E. and T. Brain and Oct mechanisms for vocal production in birds – and to human speech and and Journal of Experimental Sep and of vocal Tobias Plastic and Reconstructive Aug of the and Christopher J. F. Kim and Hung-Chi Chen on Voice and Voice Jul of Voice R. The Journal of the Acoustical Society of Mar of the glottal and Laryngeal and Pharyngeal - Head and Neck and and of the vocal Handbook of Mammalian Vocalization - An S. and Jennifer L. of the larynx and production of Handbook of Mammalian Vocalization - An The comparison of properties using F. J. and Jack J. Jiang Journal of Jul with vocal properties of their vocal Riede, J. and Ingo R. Titze Tissue Engineering Part B: Sep Engineering for the Vocal Fold K. and Ken Feb tract in and and Journal of Jan of of on acoustic correlates of the contrast in B. Speech Physiology to Journal of Nov and Aerodynamic for Voicing of A L. C. and W. The Oct of Voice in the by an S. and J. Mar in A. P. A. and Paul F. Acoustical Science and the interaction of the flow the of Oct in unilateral laryngeal theoretical Richard and Antoine Giovanni Journal of Sep as a The use of in a J. The Journal of the Acoustical Society of Jul of modeling of and voice with excised larynx T. Tokuda, Jan G. Švec and of & Feb Voice in by Laryngeal of M. and J. The Journal of the Acoustical Society of Feb of acoustic on an model of the vocal Zañartu, and R. Research Mar of voiced using models of the vocal and subglottal R. and EMC - Jan de la and The Journal of the Acoustical Society of Oct

No AccessJournal of Speech and Hearing DisordersArticle1 Jun 1954Systematic Research In Experimental Phonetics:* 1. A Theory Of The Speech Mechanism As A Servosystem Grant Fairbanks Grant Fairbanks Google Scholar https://doi.org/10.1044/jshd.1902.133 SectionsAboutPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In Additional Resources FiguresReferencesRelatedDetailsCited by Scientific Reports12:119 Jan 2022Early auditory responses to speech sounds in Parkinson's disease: preliminary dataFatemeh Mollaei, Douglas M. Shiller, Shari R. Baum and Vincent L. Gracco Augmentative and Alternative Communication38:1 (67-76)2 Jan 2022Code-switching using aided AAC: toward an integrated theoretical frameworkMarika R. King and Gloria Soto Journal of Speech, Language, and Hearing Research64:6S (2261-2275)18 Jun 2021Speech Intensity Response to Altered Intensity Feedback in Individuals With Parkinson's DiseaseAnita Senthinathan, Scott Adams, Allyson D. 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Telage and Joseph Warren Journal of Fluency Jun for speech induced by delayed auditory E. and Perceptual and Motor Feb and Oral Tactile Changes in M. and J. Journal of Human Communication Jun to Control of D. Peter D. Neilson and Peter T. G. and Movement Production Motor Raymond D. of Speech Production in Experimental Journal of Communication Dec between articulation disorders and auditory Annals of the of Sep OF ARTICULATORY IN W. Anderson Perception & Jan production: Evidence for and R. and B. Kim of speech production of Bulletin of the Psychonomic Mar vibrotactile A study of J. Fucci, Patrick P. and Robert and A. P. Van Voice and Sensation and Perceptual and Motor Oct Discrimination Ability of Children with of the I. and Journal of Communication Jan of delayed auditory feedback on children with disorders in auditory B. Garber Psychonomic Aug effects of and on vibrotactile thresholds obtained from the J. Fucci, J. and M. Telage Perceptual and Motor Jun An Investigation of for Donald J. Fucci and J. Jan Communication for Evaluation and Perceptual and Motor Dec of Oral and Vibrotactile J. Fucci, Donald and F. Perceptual and Motor Dec Discrimination and Discrimination in to Speech L. and Donald J. Fucci Journal of Motor Jun Theory of Motor A. Central Speech Mar study of two for of oral tactile J. E. and Marcia P. Journal of in to the reading of W. Perceptual and Motor Oct on Theory in Children's and John L. Perceptual and Motor Apr Language Stuttering Delayed Auditory G. Journal of Learning Apr A. and E. Journal of Speech and Hearing Sep Among Auditory and L. and A. Perception & May with an Effects of on stuttering delayed auditory G. MacKay Jan Perceptual and Motor Oct of Movement to Auditory and Tactile in M. and Patricia A. Journal of Communication Oct of articulation responses in sound B. Adaptation adaptation to visual feedback from Speech Nov of speech in conditions of delayed and John W. Perceptual and Motor Oct of the to Auditory and Tactile M. and Psychonomic Jan in delayed auditory I. and J. J. and Theoretical to the Experimental of Speech in Normal and in in Research and and theoretical to the of speech in and in J. on of speech and a from Review of Dec The Speech T. Review of Dec The and of M. E. Speech Nov of analysis of the of R. Review of Apr Volume to in Jun &

Previous article Next article Circular LawV. L. GirkoV. L. Girkohttps://doi.org/10.1137/1129095PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] V. L. Girko, Random Matrices, Vishcha Shkola, Kiev, 1975, (In Russian.) 0344.60001 Google Scholar[2] V. L. Girko, Theory of Random Determinants, Vishcha Shkola, Kiev, 1980, (In Russian.) Google Scholar[3] L. A. Pastur, Spectra of random selfadjoint operators, Uspehi Mat. Nauk, 28 (1973), 3–64, (In Russian.) 53:10042 Google Scholar[4] V. L. Girko, V-transformation, Dokl. Akad. Nauk Ukrain. SSR Ser. A, (1982), 5–6, 86, (In Russian.) 83e:60032 0509.60032 Google Scholar[5] M. L. Mehta, Random matrices and the statistical theory of energy levels, Academic Press, New York, 1967x+259, London 36:3554 Google Scholar[6] V. L. Girko, The central limit theorem for random determinants, Theory Prob. Appl., 26 (1981), 521–531 0487.60031 LinkGoogle Scholar[7] V. L. Girko, General limit theorems for spectral functions of random matrices, Theory Prob. Appl., 22 (1977), 156–160 0374.60038 LinkGoogle Scholar[8] F. R. Gantmacher, The theory of matrices, Chelsea, New York, 1989 Google Scholar[9] B. V. Ryaznov, Spectra of random Gaussian g-cyclic matrices, Theory Prob. Appl., 23 (1978), 543–558 LinkGoogle Scholar[10] Tosio Kato, Perturbation theory for linear operators, Springer-Verlag, Berlin, 1976xxi+619 53:11389 0342.47009 CrossrefGoogle Scholar[11] V. V. 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Previous article Next article Non-Parametric Estimation of a Multivariate Probability DensityV. A. EpanechnikovV. A. Epanechnikovhttps://doi.org/10.1137/1114019PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] Emanuel Parzen, On estimation of a probability density function and mode, Ann. Math. Statist., 33 (1962), 1065–1076 MR0143282 0116.11302 CrossrefGoogle Scholar[2] Murray Rosenblatt, Remarks on some nonparametric estimates of a density function, Ann. Math. Statist., 27 (1956), 832–837 MR0079873 0073.14602 CrossrefGoogle Scholar[3] G. M. Manija, Remarks on non-parametric estimates of a two-dimensional density function, Soobšč. Akad. Nauk Gruzin. SSR, 27 (1961), 385–390 MR0143303 Google Scholar[4] E. A. Nadaraya, Estimation of a bivariate probability density, Soobshch. Akad. Nauk Gruz. SSR, 36 (1964), 267–268 Google Scholar[5] R. E. Bellman, , I. Glicksberg and , O. A. 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No. R-313, 1958rm xix+244 MR0094281 0086.11703 Google Scholar Previous article Next article FiguresRelatedReferencesCited byDetails Kernel-based learning of birth process from evolving spatiotemporal RFS data stream in SMC CPHD filter for multi-target trackingSignal Processing, Vol. 203 Cross Ref Assessing spatial connectivity effects on daily streamflow forecasting using Bayesian-based graph neural networkScience of The Total Environment, Vol. 855 Cross Ref Joint Source-Channel Decoding of Polar Codes for HEVC-Based Video StreamingACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 18, No. 4 Cross Ref A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecastingApplied Energy, Vol. 325 Cross Ref A novel structure adaptive fractional discrete grey forecasting model and its application in China’s crude oil production predictionExpert Systems with Applications, Vol. 207 Cross Ref Age-varying effects of repeated emergency department presentations for children in Canada6 May 2022 | Journal of Health Services Research & Policy, Vol. 27, No. 4 Cross Ref Probabilistic adaptive power pinch analysis for islanded hybrid energy storage systemsJournal of Energy Storage, Vol. 54 Cross Ref Should I stay or should I fly? 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Cyberpsychology, Behavior, and Social NetworkingVol. 23, No. 7 EditorialConnecting Through Technology During the Coronavirus Disease 2019 Pandemic: Avoiding “Zoom Fatigue”Brenda K. WiederholdBrenda K. WiederholdBrenda K. Wiederhold, Editor-in-Chief Search for more papers by this authorPublished Online:10 Jul 2020https://doi.org/10.1089/cyber.2020.29188.bkwAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View article"Connecting Through Technology During the Coronavirus Disease 2019 Pandemic: Avoiding “Zoom Fatigue”." 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VIEWPOINTPerception of effort during exercise is independent of afferent feedback from skeletal muscles, heart, and lungsSamuele MarcoraSamuele MarcoraPublished Online:01 Jun 2009https://doi.org/10.1152/japplphysiol.90378.2008This is the final version - click for previous versionMoreSectionsPDF (55 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations perception of effort, also known as perceived exertion or sense of effort, is a major feature of fatigue (7), and it is widely used to monitor and prescribe exercise intensity (18). However, despite its importance, the neurophysiological bases of this atypical sensation are poorly understood. A model popular among physiologists investigating central regulation of exercise performance is that perceived exertion results from the complex integration of different inputs to the central nervous system (CNS). These inputs include afferent feedback from the peripheral organs most active during aerobic exercise (i.e., skeletal muscles, heart, and lungs) and other interoceptors, with or without additional inputs from the CNS itself, such as knowledge of the exercise task endpoint (3, 6, 28).The purpose of this article is to challenge this view by reviewing evidence that perception of effort during dynamic whole body exercise is independent of afferent feedback from small-diameter muscle afferents, heart, and lungs. For isolated and/or isometric exercise, the reader is referred to other reviews demonstrating that the sense of effort is independent of sensory inputs from muscle spindles and Golgi tendon organs (15, 24).Definitions.The common assumption that afferent feedback contributes significantly to perception of effort may derive from a too broad operational definition. Indeed, if subjects are instructed that rating of perceived exertion (RPE) refers to the discomfort experienced during exercise, then muscle pain, thermal discomfort, and thirst can be appropriately included in the perceptual rating because hedonicity is one of the four dimensions of any sensation (quality, intensity, duration, and hedonicity) (3). However, these and other unpleasant sensations experienced during exercise have their own specific neurophysiological mechanisms and verbal descriptors (i.e., quality) and can be differentiated from perception of effort (e.g., Ref. 19). Therefore, we believe that a narrower definition of exertion as “the effort expended in performing a physical activity” (Oxford Dictionary of Sports Science and Medicine) is physiologically more appropriate. Furthermore, this definition is in line with the verbal descriptors chosen by Borg for his RPE scale (“heavy/hard” and “light/easy”) (18) rather than ratings of hedonicity (“pleasant/unpleasant,” “feeling good/feeling bad,” “comfortable/uncomfortable”) (3).Perceived exertion and afferent feedback from skeletal muscles.Skeletal muscles are richly innervated by group III and IV fibers that provide afferent feedback related to metabolic, ionic, thermal, and mechanical stress (12). These small-diameter afferent fibers project, through lamina I neurons, to various spinal and supraspinal sites including the sensory cortex (5). Given the existence of such sensory system and the well-known increases in muscle metabolic by-products, potassium, temperature, and mechanical stress during intense exercise (12), it seems logical to assume that perception of effort is based on these afferent stimuli (Fig. 1A). Furthermore, there are high correlations between markers of metabolic stress (e.g., blood lactate concentration and %V̇o2max) with RPE during incremental exercise (18). However, the presence of the sensory machinery and a high correlation between the proposed metabolic stimuli and perceived exertion does not necessarily mean that the two are causally related. Indeed, several experimental studies in which perception of effort and metabolic stress have been dissociated argue against such common assumption.Fig. 1.Simplified afferent feedback (A) and corollary discharge (B) models of perceived exertion.Download figureDownload PowerPointFor example, when skeletal muscle weakness is induced by a small dose of curare (a drug that blocks transmission at the neuromuscular junction without affecting afferent pathways) there is a significant increase in RPE during cycling exercise (9). In theory, recruitment of glycolytic fibers due to the selective effect of curare on oxidative fibers may increase metabolic stimuli and perception of effort. However, the small increase in blood lactate concentration (0.36 mM) compared with the disproportionate increase in perceived exertion (4.8 points on Borg's 15-point RPE scale) argue against this hypothesis. Similarly, changes in afferent feedback from prefatigued locomotor muscles cannot explain the significant increase in perceived exertion we measured during a high-intensity cycling test (14). Indeed, we induced a significant reduction in locomotor muscle force using an eccentric exercise protocol known to affect primarily type II fibers without any metabolic stress or changes in muscle receptors sensitivity. Increased afferent feedback related to inflammation (which manifests itself at perceptual level as delayed-onset muscle soreness) was also controlled by performing the high-intensity cycling test 30 min after the intermittent 100 drop jumps.So what is the neural signal underlying perception of effort in these experimental conditions? The findings reviewed above support the hypothesis suggested more than 150 years ago that perception of effort is a “sensation of innervation” (24). Basically, it is proposed that the sense of effort is centrally generated by forwarding neural signals, termed corollary discharges or efference copies, from motor to sensory areas of the cerebral cortex (Fig. 1B). Therefore, it is not surprising that the increase in central motor command necessary to exercise at the same workload with muscles weakened by partial neuromuscular blockade or locomotor muscle fatigue is perceived as increased effort. Despite this kind of experimental evidence in favor of the corollary discharge model of perceived exertion (15), lack of a neurobiological mechanism and the discovery of muscle receptor function by Sherrington made most people believe that these receptors were responsible for all sensations related to muscle contraction (24). However, in recent years, corollary discharge pathways have been described in animals, including a single interneuron forwarding inhibitory signals from motor to sensory areas of the male singing cricket nervous system (23). This important discovery provides a “proof-of-principle” for the existence of neurons and pathways serving other corollary discharge functions including perception of effort.Lastly, convincing evidence against the widespread assumption that perceived exertion arises from afferent muscle feedback is provided by experiments using epidural anesthesia. Indeed, despite a marked reduction in sensory inputs from the lower limbs, RPE during cycling exercise is either unchanged or increased by spinal blockade (13, 27). The latter effect is due to the muscle weakness induced by the epidural anesthesia used in these studies and the compensatory increase in central neural drive to the motoneurons innervating the locomotor muscles. Even when this reduction in muscle strength is taken into account by exercising at the same relative intensity, RPE is not reduced by blockade of sensory inputs from the lower limbs (16). This is strong experimental evidence against the notion that somatosensory feedback from active muscles is important for perception of effort during exercise (6).Perceived exertion and afferent feedback from the heart.The correlation between perception of effort and heart rate (HR) during incremental exercise is so strong that the 6–20 RPE scale was originally developed by Borg to reflect the range of HR found in young and fit subjects (60 beats/min at rest, 200 beats/min during maximal exercise) (18). There is also evidence consistent with an afferent cortical representation of cardiac action (10), which is not surprising given the existence of both sympathetic and vagal afferent pathways from the heart (8), and chest wall mechanoreceptors (4). Indeed, some subjects can accurately report cardiac activity (4), and the crushing, burning, and squeezing sensations of angina pectoris are far too familiar to patients with ischemic heart disease (8). However, various studies in which cardiac function has been manipulated experimentally do not support a causal relationship between HR and perception of effort (18). For example, RPE during cycling exercise is unchanged or even increased when HR is reduced by calcium channel and β-adrenergic blockade (17). Therefore, it seems that normal subjects do not attend to afferent cardiac signals when rating perceived exertion. Furthermore, cardiac transplant patients (who have a denervated heart) report normal perception of effort during an incremental exercise test (1). Indeed, RPE is recommended by the American Heart Association for prescription of exercise intensity after transplantation (22).Perceived exertion and afferent feedback from the lungs.Dyspnea, from the Greek “difficult breathing,” is a major component of overall perceived exertion (18) and it has been extensively investigated because of its clinical relevance (20). As for the discomfort associated with intense exercise, the word “dyspnea” can refer to several sensations with different verbal descriptors and neurophysiological mechanisms. Inhibitory and excitatory afferent inputs from central and peripheral chemoreceptors and mechanoreceptors in the chest wall, lungs, and airways are thought to play an important role in generating air hunger, chest tightness, and unsatisfied inspiration (20). However, the dyspnea experienced during aerobic exercise by healthy individuals without restricted breathing and normal blood gases largely reflects the sense of respiratory effort (11, 20, 21). This respiratory sensation is generated by corollary discharges of the central motor commands to the respiratory muscles (11, 20), and it is clearly independent of feedback from pulmonary vagal afferents. Indeed, patients with double lung transplantation can estimate the magnitude of inspiratory resistive loads based on their normal sense of effort (31). Similar results have been obtained in healthy subjects with anesthetized airways (2). Importantly, the common neurophysiological mechanism shared by perceptions of effort referred to breathing and locomotor muscle contraction justifies their integration in the overall rating of perceived exertion.Conclusions.Although essential for a variety of other physiological and perceptual processes such as cardiorespiratory regulation (12) and sense of position and movement (24), afferent feedback from skeletal muscles, heart, and lungs does not contribute significantly to perception of effort during exercise, which is the conscious awareness of the central motor commands to the locomotor and respiratory muscles. 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Marcora, School of Sport, Health and Exercise Sciences, Bangor Univ., George Bldg., Normal Site, Holyhead Road, Bangor, Gwynedd, LL57 2PZ United Kingdom (e-mail: [email protected]) Download PDF Previous Back to Top Next FiguresReferencesRelatedInformation Cited ByA lab-based comparison of differential ratings of perceived exertion between a run and jump protocol involving low or high impacts on the lower extremities25 April 2022 | European Journal of Sport Science, Vol. 23, No. 5Effects of Sex and Cuff Pressure on Physiological Responses during Blood Flow Restriction Resistance Exercise in Young Adults13 December 2022 | Medicine & Science in Sports & Exercise, Vol. 55, No. 5Application of the Ratings of Perceived Exertion-Clamp Model to Examine the Effects of Joint Angle on the Time Course of Torque and Neuromuscular Responses During a Sustained, Isometric Forearm Flexion to Task Failure17 November 2022 | Journal of Strength and Conditioning Research, Vol. No. of changes physiological during a time to | European Journal of Vol. and heart rate responses to of April | European Journal of Vol. and muscle during of April | European Journal of Vol. of a perceived effort cycling November 2022 | European Journal of Vol. No. of fatigue and neuromuscular of performance during prolonged | Vol. No. the model to among associated with perceived and performance in and | European Journal of Vol. Exertion Responses to Exercise for and of A | of Sport Science, Vol. No. of Muscle and of A with November 2022 | Sports Vol. No. control of respiratory and during November 2022 | European Journal of Vol. No. Neural in 2022 | Journal of Science in Sport and Exercise, Vol. No. of effort and the of physical A to motor | in Vol. and Human 2022 | Sports Vol. No. effect of on physical performance and effort perception during maximal dynamic exercise in Vol. effects of muscle on of endurance A and & Vol. of the Task to December 2022 | and Vol. of and in 2022 | Journal of Sports Vol. No. effects of on exercise performance and physiological of exercise performance during cycling A & Vol. No. during the the November Effect of Resistance Blood Flow Restriction on A and November 2022 | Sports A Vol. the and the and the November 2022 | Journal of and Vol. No. of breathing air during cycling on and performance in the Journal of Sports Medicine and Physical Vol. No. when it The effects of on at high 2022 | Journal of Sports Sciences, Vol. No. of on and exercise performance in | European Journal of Sport Science, Vol. No. patients with neurocognitive are more and 2022 | and Research, Vol. No. I feedback the effects of fatigue on endurance exercise of Sport and Exercise, Vol. Perceived for and Resistance Exercise | Sports A Vol. No. of Perceived Exertion or April | Journal of Strength and Conditioning Research, Vol. No. be the to the and performance in fatigue A and 2022 | in Vol. and perceptual mechanisms of fatigue task in response to and M. and 2022 | Journal of Vol. No. the of and on the of April 2022 | and Vol. No. of on and During a | Journal of Strength and Conditioning Research, Vol. No. of on perceived exertion during endurance A with of Exercise Science & Vol. 20, No. Model for and of 2022 | Journal of and Vol. No. effects of pain induced by blood in one on exercise and and inhibition of the in and Vol. No. of action on the for action April 2022 | Journal of Sport and Exercise Vol. A of to Human 2022 | in Vol. as a 2022 | Journal of Vol. No. central fatigue after a December | Journal of Medicine & Science in Vol. No. to the Effect of on during and 2022 | Vol. No. effects of exercise intensity and on the relationship between the component of and peripheral 2022 | Vol. No. that Neuromuscular not a in with | Medicine & Science in Sports & Exercise, Vol. No. of the intensity of fatigue and on perceptual regulation and performance during physical 2022 | Vol. No. on and in | for Exercise and Sport, Vol. No. effort for is associated with | Journal of Vol. No. and by and movement descriptors in with during 2022 | Vol. and perceptual responses during at and in normal and | Journal of Vol. No. and a on Neural and Vol. the of Perceived Exertion and Heart to in and of Strength and Conditioning Research, Vol. No. of for November workload during in comparison between heart rate and perceived exertion based 2022 | & Sport - Science & Vol. No. and to Additional in 2022 | Journal of Human Kinetics, Vol. Vol. and to in Perceived | and Vol. No. and to Vol. and fatigue and the April | Vol. No. and mechanisms of fatigue and in | Vol. No. the hypothesis to an Journal of Vol. to Resistance Exercise of Strength and Conditioning Research, Vol. No. of maximal lactate by a lactate test in & Vol. No. of effort during an isometric contraction is by muscle or | European Journal of Vol. No. exertion and performance effects of and Journal of Sports Medicine and Physical Vol. No. muscle and perceptual responses during and cycling to task Martin and | American Journal of and Vol. No. of on | Journal of Vol. No.

Previous article Next article Prescribing a System of Random Variables by Conditional DistributionsR. L. DobrushinR. L. Dobrushinhttps://doi.org/10.1137/1115049PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] V. V. Sazonov, On perfect measures, Izv. Akad. Nauk SSSR Ser. Mat., 26 (1962), 391–414, (In Russian.) MR0181729 Google Scholar[2] K. R. Parthasarathy, Probability measures on metric spaces, Probability and Mathematical Statistics, No. 3, Academic Press Inc., New York, 1967xi+276 MR0226684 0153.19101 CrossrefGoogle Scholar[3] M. B. Averincev, A certain method of describing random fields with a discrete argument, Problemy Peredači Informacii, 6 (1970), 100–108, (In Russian.) MR0329038 Google Scholar[4] R. L. Dobrushin, The description of a random field by means of conditional probabilities and conditions of its regularity, Theory Prob. Applications, 13 (1968), 197–224 10.1137/1113026 LinkGoogle Scholar[5] Yu. A. 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Previous article Next article Fractional Brownian Motions, Fractional Noises and ApplicationsBenoit B. Mandelbrot and John W. Van NessBenoit B. Mandelbrot and John W. Van Nesshttps://doi.org/10.1137/1010093PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] I. Adelman, Long cycles—fact or artifact?, Amer. Economic Rev., 60 (1965), 444–463 Google Scholar[2] William Feller, The asymptotic distribution of the range of sums of independent random variables, Ann. Math. Statistics, 22 (1951), 427–432 MR0042626 0043.34201 CrossrefISIGoogle Scholar[3] I. M. Gel'fand and , N. Ya. Vilenkin, Generalized functions. Vol. 4, Academic Press [Harcourt Brace Jovanovich Publishers], New York, 1964 [1977]xiv+384 MR0435834 Google Scholar[4] C. W. J. Granger, The typical spectral shape of an economic variable, Econometrica, 34 (1966), 150–161 CrossrefISIGoogle Scholar[5] G. A. Hunt, Random Fourier transforms, Trans. Amer. Math. 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Previous article Next article Heuristic Methods for Location-Allocation ProblemsLeon CooperLeon Cooperhttps://doi.org/10.1137/1006005PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] Leon Cooper, Location-allocation problems, Operations Res., 11 (1963), 331–343, No. 3 MR0152365 0113.14201 CrossrefISIGoogle Scholar[2] Google Scholar[3] D. H. Lehmer, Teaching combinatorial tricks to a computerProc. Sympos. Appl. Math., Vol. 10, American Mathematical Society, Providence, R.I., 1960, 179–193, Combinatorial Analysis MR0113289 0096.00504 CrossrefGoogle Scholar[4] J. Heller, Some numerical experiments for an $M\times J$ flow shop and its decision-theoretical aspects, Operations Res., 8 (1960), 178–184, No. 2 MR0111626 0092.27910 CrossrefISIGoogle Scholar[5] J. Moshman, The Application of Sequential Estimation To Computer Simulation and Monte Carlo Procedures, J. Association for Computing Machinery, 5 (1958), 0086.11603 CrossrefISIGoogle Scholar[6] A. 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Previous article Next article Approximate Integration of Stochastic Differential EquationsG. N. Mil'shtejnG. N. Mil'shtejnhttps://doi.org/10.1137/1119062PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] I. I. Gikhman and , A. V. Skorokhod, Introduction to the Theory of Random Processes, Izd-vo "Nauka", Moscow, 1965, (In Russian.) Google Scholar[2] I. I. Gikhman and , A. V. Skorokhod, Stochastic Differential Equations, Springer, New York, 1972 0242.60003 CrossrefGoogle Scholar[3] R. Z. Khas'minskii, Stability of Systems of Differential Equations under Random Perturbations of their Parameters, Izd-vo "Nauka", Moscow, 1969, (In Russian.) Google Scholar[4] E. Hills and , R. Phillips, Functional Analysis and Semigroups, American Mathematical Society, Providence, RI., 1957 0078.10004 Google Scholar[5] R. L. Stratonovich, Conditional Markov processes and their application to the theory of optimal control, Translated from the Russian by R. N. and N. 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Mil'shtein17 July 2006 | Theory of Probability & Its Applications, Vol. 23, No. 2AbstractPDF (451 KB)Brownian dynamics study of transitions in a polymer chain of bistable oscillatorsThe Journal of Chemical Physics, Vol. 69, No. 3 Cross Ref Computational field theory and pattern formation Cross Ref Volume 19, Issue 3| 1975Theory of Probability & Its Applications History Submitted:23 September 1973Published online:28 July 2006 InformationCopyright © 1975 © Society for Industrial and Applied MathematicsPDF Download Article & Publication DataArticle DOI:10.1137/1119062Article page range:pp. 557-562ISSN (print):0040-585XISSN (online):1095-7219Publisher:Society for Industrial and Applied Mathematics

PreviousNext No AccessSEG Technical Program Expanded Abstracts 1992Electronic documents give reproducible research a new meaningAuthors: Jon F. ClaerboutMartin KarrenbachJon F. ClaerboutStanford Univ. and Martin KarrenbachStanford Univ.https://doi.org/10.1190/1.1822162 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1822162FiguresReferencesRelatedDetailsCited ByPackaging research artefacts with RO-CrateData Science, Vol. 46Reproducibility in Evolutionary ComputationACM Transactions on Evolutionary Learning and Optimization, Vol. 1, No. 4Reproducible Research in R: A Tutorial on How to Do the Same Thing More Than Once9 December 2021 | Psych, Vol. 3, No. 4Preregistration in experimental linguistics: applications, challenges, and limitations24 March 2021 | Linguistics, Vol. 59, No. 5Practical Reproducibility in Geography and Geosciences13 October 2020 | Annals of the American Association of Geographers, Vol. 111, No. 5Tool-based Support for the FAIR Principles for Control Theoretic Results: The "Automatic Control Knowledge Repository"30 June 2021 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Claerbout and Martin Karrenbach, (1992), "Electronic documents give reproducible research a new meaning," SEG Technical Program Expanded Abstracts : 601-604. https://doi.org/10.1190/1.1822162 Plain-Language Summary PDF DownloadLoading ...

The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.

This study investigated links between working memory and speech processing systems. We used delayed pseudoword repetition in fMRI to investigate the neural correlates of sublexical structure in phonological working memory (pWM). We orthogonally varied the number of syllables and consonant clusters in auditory pseudowords and measured the neural responses to these manipulations under conditions of covert rehearsal (Experiment 1). A left-dominant network of temporal and motor cortex showed increased activity for longer items, with motor cortex only showing greater activity concomitant with adding consonant clusters. An individual-differences analysis revealed a significant positive relationship between activity in the angular gyrus and the hippocampus, and accuracy on pseudoword repetition. As models of pWM stipulate that its neural correlates should be activated during both perception and production/rehearsal [Buchsbaum, B. R., & D'Esposito, M. The search for the phonological store: From loop to convolution. Journal of Cognitive Neuroscience, 20, 762-778, 2008; Jacquemot, C., & Scott, S. K. What is the relationship between phonological short-term memory and speech processing? Trends in Cognitive Sciences, 10, 480-486, 2006; Baddeley, A. D., & Hitch, G. Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47-89). New York: Academic Press, 1974], we further assessed the effects of the two factors in a separate passive listening experiment (Experiment 2). In this experiment, the effect of the number of syllables was concentrated in posterior-medial regions of the supratemporal plane bilaterally, although there was no evidence of a significant response to added clusters. Taken together, the results identify the planum temporale as a key region in pWM; within this region, representations are likely to take the form of auditory or audiomotor "templates" or "chunks" at the level of the syllable [Papoutsi, M., de Zwart, J. A., Jansma, J. M., Pickering, M. J., Bednar, J. A., & Horwitz, B. From phonemes to articulatory codes: an fMRI study of the role of Broca's area in speech production. Cerebral Cortex, 19, 2156-2165, 2009; Warren, J. E., Wise, R. J. S., & Warren, J. D. Sounds do-able: auditory-motor transformations and the posterior temporal plane. Trends in Neurosciences, 28, 636-643, 2005; Griffiths, T. D., & Warren, J. D. The planum temporale as a computational hub. Trends in Neurosciences, 25, 348-353, 2002], whereas more lateral structures on the STG may deal with phonetic analysis of the auditory input [Hickok, G. The functional neuroanatomy of language. Physics of Life Reviews, 6, 121-143, 2009].

The National Institute of Mental Health's Research Domain Criteria (RDoC) Initiative "calls for the development of new ways of classifying psychopathology based on dimensions of observable behavior." As a result of this ambitious initiative, language has been identified as an independent construct in the RDoC matrix. In this article, we frame language within an evolutionary and neuropsychological context and discuss some of the limitations to the current measurements of language. Findings from genomics and the neuroimaging of performance during language tasks are discussed in relation to serious mental illness and within the context of caveats regarding measuring language. Indeed, the data collection and analysis methods employed to assay language have been both aided and constrained by the available technologies, methodologies, and conceptual definitions. Consequently, different fields of language research show inconsistent definitions of language that have become increasingly broad over time. Individually, they have also shown significant improvements in conceptual resolution, as well as in experimental and analytic techniques. More recently, language research has embraced collaborations across disciplines, notably neuroscience, cognitive science, and computational linguistics and has ultimately re-defined classical ideas of language. As we move forward, the new models of language with their remarkably multifaceted constructs force a re-examination of the NIMH RDoC conceptualization of language and thus the neuroscience and genetics underlying this concept. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

The Hodgkin-Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modelling in neuroscience, which has expanded to encompass a variety of species and components of the nervous system. Here we review the developments in this area with a focus on efforts in the community towards modelling the mammalian neocortex using spatially extended conductance-based neuronal models. The Hodgkin-Huxley formalism and related foundational contributions, such as Rall's cable theory, remain widely used in these efforts to the current day. We argue that at present the field is undergoing a qualitative change due to new very rich datasets describing the composition, connectivity and functional activity of cortical circuits, which are being integrated systematically into large-scale network models. This trend, combined with the accelerating development of convenient software tools supporting such complex modelling projects, is giving rise to highly detailed models of the cortex that are extensively constrained by the data, enabling computational investigation of a multitude of questions about cortical structure and function.

OBJECTIVE: Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. METHODS: Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. RESULTS: Building on these standard practices, model-sharing sites and tools have been developed that fit into several categories: 1) standardized neural simulators; 2) shared computational resources; 3) declarative model descriptors, ontologies, and standardized annotations; and 4) model-sharing repositories and sharing standards. CONCLUSION: A number of complementary innovations have been proposed to enhance sharing, transparency, and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation, and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. SIGNIFICANCE: Model management will become increasingly important as multiscale models become larger, more detailed, and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.