Auswahl der wissenschaftlichen Literatur zum Thema „Sensorimotor decisions“
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Zeitschriftenartikel zum Thema "Sensorimotor decisions"
Felsen, Gidon, und Zachary F. Mainen. „Midbrain contributions to sensorimotor decision making“. Journal of Neurophysiology 108, Nr. 1 (01.07.2012): 135–47. http://dx.doi.org/10.1152/jn.01181.2011.
Der volle Inhalt der QuelleSiegel, M., T. J. Buschman und E. K. Miller. „Cortical information flow during flexible sensorimotor decisions“. Science 348, Nr. 6241 (18.06.2015): 1352–55. http://dx.doi.org/10.1126/science.aab0551.
Der volle Inhalt der QuelleFooken, Jolande, und Miriam Spering. „Eye movements as a readout of sensorimotor decision processes“. Journal of Neurophysiology 123, Nr. 4 (01.04.2020): 1439–47. http://dx.doi.org/10.1152/jn.00622.2019.
Der volle Inhalt der QuelleThura, David, Jean-François Cabana, Albert Feghaly und Paul Cisek. „Integrated neural dynamics of sensorimotor decisions and actions“. PLOS Biology 20, Nr. 12 (15.12.2022): e3001861. http://dx.doi.org/10.1371/journal.pbio.3001861.
Der volle Inhalt der QuelleThura, David, und Paul Cisek. „Microstimulation of dorsal premotor and primary motor cortex delays the volitional commitment to an action choice“. Journal of Neurophysiology 123, Nr. 3 (01.03.2020): 927–35. http://dx.doi.org/10.1152/jn.00682.2019.
Der volle Inhalt der QuelleLekova, Anna K., Paulina Tsvetkova und Anna Andreeva. „Enhancing Brain Health and Cognitive Development Through Sensorimotor Play in Virtual Reality: Uncovering the Neural Correlates“. International Journal of Games and Social Impact 2, Nr. 1 (01.01.2024): 46–70. http://dx.doi.org/10.24140/ijgsi.v2.n1.03.
Der volle Inhalt der QuelleBalsdon, Tarryn, Stijn Verdonck, Tim Loossens und Marios G. Philiastides. „Secondary motor integration as a final arbiter in sensorimotor decision-making“. PLOS Biology 21, Nr. 7 (17.07.2023): e3002200. http://dx.doi.org/10.1371/journal.pbio.3002200.
Der volle Inhalt der QuelleSheppard, William E. A., Polly Dickerson, Rigmor C. Baraas, Mark Mon-Williams, Brendan T. Barrett, Richard M. Wilkie und Rachel O. Coats. „Exploring the effects of degraded vision on sensorimotor performance“. PLOS ONE 16, Nr. 11 (08.11.2021): e0258678. http://dx.doi.org/10.1371/journal.pone.0258678.
Der volle Inhalt der QuelleLiu, Taosheng, und Timothy J. Pleskac. „Neural correlates of evidence accumulation in a perceptual decision task“. Journal of Neurophysiology 106, Nr. 5 (November 2011): 2383–98. http://dx.doi.org/10.1152/jn.00413.2011.
Der volle Inhalt der QuelleCisek, Paul, und Alexandre Pastor-Bernier. „On the challenges and mechanisms of embodied decisions“. Philosophical Transactions of the Royal Society B: Biological Sciences 369, Nr. 1655 (05.11.2014): 20130479. http://dx.doi.org/10.1098/rstb.2013.0479.
Der volle Inhalt der QuelleDissertationen zum Thema "Sensorimotor decisions"
Aguilar, Lleyda David. „Sensorimotor decision-making with moving objects“. Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/461673.
Der volle Inhalt der QuelleMoure’s és essencial per a la nostra supervivència, i en incomptables ocasions ens movem en resposta a informació visual. Tanmateix, aquest procés és incert, donada la variabilitat present tant a l'estadi sensorial com en el motor. Una pregunta crucial, doncs, és com gestionar aquesta incertesa perquè les nostres accions portin a les millors conseqüències possibles. La teoria de la decisió estadística (Statistical decision theory, SDT) és un marc teòric normatiu que estableix com la gent hauria de fer decisions en presència d'incertesa. Aquesta teoria identifica l'acció òptima amb aquella que maximitza la recompensa (entesa com a conseqüència) esperada de la situació. La planificació del moviment pot ser reformulada en termes de SDT, de tal manera que s’emfatitza el component decisional. Diferents treballs experimentals que han fet servir aquesta aproximació teòrica han conclòs que els humans som planificadors de moviment òptims, mentre que altres han identificat situacions on la suboptimalitat sorgeix. No obstant això, la presa de decisions sensoriomotora des de SDT normalment ha ignorat escenaris que requereixen d'interacció com objectes en moviment. Alhora, els treballs dedicats als objectes en moviment no s'han centrat en l'aspecte de decisió. La present tesi es proposa acostar els dos camps, amb cada un dels nostres tres estudis intentant respondre diferents preguntes. L’Estudi I descobrí que, per planificar les nostres decisions, fer servir informació temporal portà a un millor rendiment que fer servir informació espacial, i això fou facilitat per veure l'objecte durant més temps. També vam criticar la limitació de certs models d’SDT per interpretar els nostres dades. L'Estudi II intentà promoure l'ús d'informació temporal, tot i que no s’aconseguí fomentar l’aprenentatge. Finalment, l’'Estudi III trobà que la raó per la qual la gent és subòptima en moltes situacions es deu al fet que representa només la seva variabilitat de mesura, més o menys equivalent al soroll d'execució, mentre que s'exclou la variabilitat creada per sobtats canvis en la planificació de la resposta. També trobàrem que els participants van usar la informació donada per la recompensa tant per evitar ser penalitzats com per escollir el punt on estabilitzar les seves respostes.
Pho, Gerald N. (Gerald Norman). „Sensorimotor transformation and information coding across cortex during perceptual decisions“. Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113919.
Der volle Inhalt der QuelleThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis. "June 2017." Page 206 blank.
Includes bibliographical references.
Perceptual decision-making is an important and experimentally tractable paradigm for uncovering general principles of neural information processing and cognitive function. While the process of mapping sensory stimuli onto motor actions may appear to be simple, its neural underpinnings are poorly understood. The goal of this thesis is to better understand the neural mechanisms underlying perceptual decision-making by exploring three major questions: How is decision-relevant information encoded across the cortex? What cortical areas are necessary for perceptual decision-making? And finally, what neural mechanisms underlie the mapping of sensory percepts to appropriate motor outputs? We investigated the roles of visual (V1), posterior parietal (PPC), and frontal motor (fMC) cortices of mice during a memory-guided visual decision task. Large-scale calcium imaging revealed that neurons in each area were heterogeneous and spanned all task epochs (stimulus, delay, response). However, information encoding was distinct across regions, with V1 encoding stimulus, fMC encoding choice, and PPC multiplexing the two variables. Optogenetic inhibition during behavior showed that all regions were necessary during the stimulus epoch, but only fMC was required during the delay and response epochs. Stimulus information was therefore rapidly transformed into behavioral choice, requiring V1, PPC, and fMC during the transformation period, but only fMC for maintaining the choice in memory prior to execution. We further investigated whether the role of PPC was specific to visual processing or to sensorimotor transformation. Using calcium imaging during both engaged behavior and passive viewing, we found that unlike V1 neurons, most PPC neurons responded exclusively during task performance, although a minority exhibited contrast-dependent visual responses. By re-training mice on a reversed task contingency, we discovered that neurons in PPC but not V1 reflected the new sensorimotor contingency. Population analyses additionally revealed that task-specific information was represented in a dynamic code in PPC but not in V1. The strong task dependence, heterogeneity, and dynamic coding of PPC activity point to a central role in sensorimotor transformation. By measuring and manipulating activity across multiple cortical regions, we have gained insight into how the cortex processes information during sensorimotor decisions, paving the way for future mechanistic studies using the mouse system.
by Gerald N. Pho.
Ph. D. in Neuroscience
Pape, Anna-Antonia [Verfasser], und Markus [Akademischer Betreuer] Siegel. „There is more to decisions than meets the eye : Cortical motor activity and previous motor responses predict sensorimotor decisions / Anna-Antonia Pape ; Betreuer: Markus Siegel“. Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1199354686/34.
Der volle Inhalt der QuellePape, Anna-Antonia Verfasser], und Markus [Akademischer Betreuer] [Siegel. „There is more to decisions than meets the eye : Cortical motor activity and previous motor responses predict sensorimotor decisions / Anna-Antonia Pape ; Betreuer: Markus Siegel“. Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1199354686/34.
Der volle Inhalt der QuelleAcerbi, Luigi. „Complex internal representations in sensorimotor decision making : a Bayesian investigation“. Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16233.
Der volle Inhalt der QuelleHuang, He. „Decision-making and motor control| computational models of human sensorimotor processing“. Thesis, University of California, San Diego, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3673994.
Der volle Inhalt der QuelleTo survive and effectively interact with the environment, human sensorimotor control system collects sensory information and acts based on the state of the world. Human behavior can be considered and studied at discrete time or continuous time. For the former, human makes discrete categorical decisions when presented with different alternative choices (e.g. choose Left or Right at an intersection). For the later, humans plan and execute continuous movements when instructed to perform a motor task (e.g. drive to a destination). In this dissertation we examine human behavior at both levels. Part I focuses on understanding decision-making at discrete time using Bayesian Models. We start by investigating the influence of environmental statistics in a saccadic visual search ask, in which we use a dynamic belief model to describe subjects' learning process of the environment statistics cross-trials. Then we look at a special effect of decision- making, the sequential effect, and apply the dynamic belief model to explain subjects' cross-trial learning and a drift diffusion model to explain their within-trial decision- making process. Part II focuses on examining motor control at continuous time using Optimal Control Theory. We start by investigating the objective functions in oculomotor control (saccadic eye movement, smooth pursuit, and applications in eye-hand coordination) with an infomax model. Then we apply inverse optimal control model to study impaired motor behavior in depressed individuals. In particular, we present a framework based on optimal control theory, which can distinguish the effects of sensorimotor speed, goal setting and motivational factors in goal-directed motor tasks. Finally, we propose to use facial expression as another measure of the emotional state in depressed individuals, which can be used to provide further understanding of the behavior and model parameters estimated from the proposed inverse framework.
Glover, Arren John. „Developing grounded representations for robots through the principles of sensorimotor coordination“. Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71763/1/Arren_Glover_Thesis.pdf.
Der volle Inhalt der QuelleLA, TONA Giuseppe. „An Architecture for Observational Learning“. Doctoral thesis, Università degli Studi di Palermo, 2014. http://hdl.handle.net/10447/91227.
Der volle Inhalt der QuelleMihoub, Alaeddine. „Apprentissage statistique de modèles de comportement multimodal pour les agents conversationnels interactifs“. Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT079/document.
Der volle Inhalt der QuelleFace to face interaction is one of the most fundamental forms of human communication. It is a complex multimodal and coupled dynamic system involving not only speech but of numerous segments of the body among which gaze, the orientation of the head, the chest and the body, the facial and brachiomanual movements, etc. The understanding and the modeling of this type of communication is a crucial stage for designing interactive agents capable of committing (hiring) credible conversations with human partners. Concretely, a model of multimodal behavior for interactive social agents faces with the complex task of generating gestural scores given an analysis of the scene and an incremental estimation of the joint objectives aimed during the conversation. The objective of this thesis is to develop models of multimodal behavior that allow artificial agents to engage into a relevant co-verbal communication with a human partner. While the immense majority of the works in the field of human-agent interaction (HAI) is scripted using ruled-based models, our approach relies on the training of statistical models from tracks collected during exemplary interactions, demonstrated by human trainers. In this context, we introduce "sensorimotor" models of behavior, which perform at the same time the recognition of joint cognitive states and the generation of the social signals in an incremental way. In particular, the proposed models of behavior have to estimate the current unit of interaction ( IU) in which the interlocutors are jointly committed and to predict the co-verbal behavior of its human trainer given the behavior of the interlocutor(s). The proposed models are all graphical models, i.e. Hidden Markov Models (HMM) and Dynamic Bayesian Networks (DBN). The models were trained and evaluated - in particular compared with classic classifiers - using datasets collected during two different interactions. Both interactions were carefully designed so as to collect, in a minimum amount of time, a sufficient number of exemplars of mutual attention and multimodal deixis of objects and places. Our contributions are completed by original methods for the interpretation and comparative evaluation of the properties of the proposed models. By comparing the output of the models with the original scores, we show that the HMM, thanks to its properties of sequential modeling, outperforms the simple classifiers in term of performances. The semi-Markovian models (HSMM) further improves the estimation of sensorimotor states thanks to duration modeling. Finally, thanks to a rich structure of dependency between variables learnt from the data, the DBN has the most convincing performances and demonstrates both the best performance and the most faithful multimodal coordination to the original multimodal events
Suriya-Arunroj, Lalitta. „Neural basis of rule-based decisions with graded choice biases“. Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0028-87D1-D.
Der volle Inhalt der QuelleBuchteile zum Thema "Sensorimotor decisions"
Körding, Konrad P., und Daniel M. Wolpert. „Probabilistic Mechanisms in Sensorimotor Control“. In Percept, Decision, Action: Bridging the Gaps, 191–202. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470034989.ch15.
Der volle Inhalt der QuelleBarash, Shabtai, und Mingsha Zhang. „Switching of Sensorimotor Transformations: Antisaccades and Parietal Cortex“. In Percept, Decision, Action: Bridging the Gaps, 59–74. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470034989.ch6.
Der volle Inhalt der QuelleBittencourt, Juliana, Bruna Velasques, Silmar Teixeira, Danielle Aprígio, Mariana Gongora, Mauricio Cagy, Thayaná Fernandes, Pedro Ribeiro und Victor Marinho. „Schizophrenia: A Disorder of Timing and Sensorimotor Integration During Decision-Making“. In Integrated Science, 123–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96814-4_6.
Der volle Inhalt der QuelleDuysens, Jacques, Geert Verheyden, Firas Massaad, Pieter Meyns, Bouwien Smits-Engelsman und Ilse Jonkers. „Rehabilitation of gait and balance after CNS damage“. In Oxford Textbook of Neurorehabilitation, herausgegeben von Volker Dietz, Nick S. Ward und Christopher Kennard, 239–52. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198824954.003.0018.
Der volle Inhalt der QuelleDuysens, Jacques, Geert Verheyden, Firas Massaad, Pieter Meyns, Bouwien Smits-Engelsman und Ilse Jonkers. „Rehabilitation of gait and balance after CNS damage“. In Oxford Textbook of Neurorehabilitation, 211–23. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199673711.003.0018.
Der volle Inhalt der QuelleJaramillo, Jorge, und Zengcai V. Guo. „Thalamocortical Contributions to Neural Dynamics and Behavior“. In The Cerebral Cortex and Thalamus, herausgegeben von Adam W. Hantman und Kevin P. Cross, 367–80. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197676158.003.0035.
Der volle Inhalt der QuelleFaix, Marvin, Emmanuel Mazer, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy und Jorge Lobo. „Cognitive Computation“. In Robotic Systems, 906–29. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch045.
Der volle Inhalt der Quelle„Cognitive Architecture With Episodic Memory“. In Reductive Model of the Conscious Mind, 243–82. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5653-5.ch008.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Sensorimotor decisions"
Kolesovs, Aleksandrs, Klavs Evelis, Liga Ozolina-Molla, Liga Plakane, Juris Porozovs und Viktors Veliks. „Exploration of EEG Markers of Sensorimotor Functioning During Incorrect versus Correct Decisions“. In 81th International Scientific Conference of the University of Latvia. University of Latvia Press, 2023. http://dx.doi.org/10.22364/htqe.2023.42.
Der volle Inhalt der QuelleTatai, Fabian, Dominik Straub und Constantin Rothkopf. „Humans use Newtonian physics in intuitive sensorimotor decisions under risk“. In 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1580-0.
Der volle Inhalt der QuelleMartinez-Rodriguez, L. Alexandra, Elaine A. Corbett und Simon P. Kelly. „Effects of value on early sensory activity and motor preparation during rapid sensorimotor decisions“. In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1171-0.
Der volle Inhalt der QuelleUsenkova, Ekaterina V., und Olga A. Shirokova. „The use of information and communication technologies in the development of sensorimotor skills in preschool children with speech disorders“. In Специальное образование: методология, практика, исследования. Yaroslavl state pedagogical university named after К. D. Ushinsky, 2021. http://dx.doi.org/10.20323/978-5-00089-532-0-2021-53-58.
Der volle Inhalt der QuelleReddy, P. V., E. W. Justh und P. S. Krishnaprasad. „Motion camouflage with sensorimotor delay“. In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434522.
Der volle Inhalt der QuelleBatta, Erasmo, und Christopher Stephens. „Heuristics as Decision-making Habits of Autonomous Sensorimotor Agents“. In The 2019 Conference on Artificial Life. Cambridge, MA: MIT Press, 2019. http://dx.doi.org/10.1162/isal_a_00144.
Der volle Inhalt der QuelleBatta, Erasmo, und Christopher Stephens. „Heuristics as Decision-making Habits of Autonomous Sensorimotor Agents“. In The 2019 Conference on Artificial Life. Cambridge, MA: MIT Press, 2019. http://dx.doi.org/10.1162/isal_a_00144.xml.
Der volle Inhalt der QuelleZhong, Junpei, Rony Novianto, Mingjun Dai, Xinzheng Zhang und Angelo Cangelosi. „A hierarchical emotion regulated sensorimotor model: Case studies“. In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531882.
Der volle Inhalt der QuelleKarg, Philipp, Simon Stoll, Simon Rothfus und Soren Hohmann. „Inverse Stochastic Optimal Control for Linear-Quadratic Gaussian and Linear-Quadratic Sensorimotor Control Models“. In 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992798.
Der volle Inhalt der QuelleNakahira, Yorie, Nikolai Matni und John C. Doyle. „Hard limits on robust control over delayed and quantized communication channels with applications to sensorimotor control“. In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7403407.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Sensorimotor decisions"
Alwan, Iktimal, Dennis D. Spencer und Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, Dezember 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.
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