Bibliographies thématiques / Sensorimotor decisions

Littérature scientifique sur le sujet « Sensorimotor decisions »

Auteur : Grafiati
Publié le 13 juillet 2024

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Articles de revues sur le sujet "Sensorimotor decisions"

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Felsen, Gidon, et Zachary F. Mainen. « Midbrain contributions to sensorimotor decision making ». Journal of Neurophysiology 108, no 1 (1 juillet 2012) : 135–47. http://dx.doi.org/10.1152/jn.01181.2011.

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Making decisions about future actions is a fundamental function of the nervous system. Classical theories hold that separate sets of brain regions are responsible for selecting and implementing an action. Traditionally, action selection has been considered the domain of high-level regions, such as the prefrontal cortex, whereas action generation is thought to be carried out by dedicated cortical and subcortical motor regions. However, increasing evidence suggests that the activity of individual neurons in cortical motor structures reflects abstract properties of “decision variables” rather than conveying simple motor commands. Less is known, though, about the role of subcortical structures in decision making. In particular, the superior colliculus (SC) is critical for planning and initiating visually guided, gaze-displacing movements and selecting visual targets, but whether and how it contributes more generally to sensorimotor decisions are unclear. Here, we show that the SC is intimately involved in orienting decisions based on odor cues, even though the SC does not explicitly process olfactory stimuli. Neurons were recorded from the intermediate and deep SC layers in rats trained to perform a delayed-response, odor-cued spatial choice task. SC neurons commonly fired well in advance of movement initiation, predicting the chosen direction nearly 1 s before movement. Moreover, under conditions of sensory uncertainty, SC activity varied with task difficulty and reward outcome, reflecting the influence of decision variables on the intercollicular competition thought to underlie orienting movements. These results indicate that the SC plays a more general role in decisions than previously appreciated, extending beyond visuomotor functions.
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Siegel, M., T. J. Buschman et E. K. Miller. « Cortical information flow during flexible sensorimotor decisions ». Science 348, no 6241 (18 juin 2015) : 1352–55. http://dx.doi.org/10.1126/science.aab0551.

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Fooken, Jolande, et Miriam Spering. « Eye movements as a readout of sensorimotor decision processes ». Journal of Neurophysiology 123, no 4 (1 avril 2020) : 1439–47. http://dx.doi.org/10.1152/jn.00622.2019.

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Real-world tasks, such as avoiding obstacles, require a sequence of interdependent choices to reach accurate motor actions. Yet, most studies on primate decision making involve simple one-step choices. Here we analyze motor actions to investigate how sensorimotor decisions develop over time. In a go/no-go interception task human observers ( n = 42) judged whether a briefly presented moving target would pass (interceptive hand movement required) or miss (no hand movement required) a strike box while their eye and hand movements were recorded. Go/no-go decision formation had to occur within the first few hundred milliseconds to allow time-critical interception. We found that the earliest time point at which eye movements started to differentiate actions (go versus no-go) preceded hand movement onset. Moreover, eye movements were related to different stages of decision making. Whereas higher eye velocity during smooth pursuit initiation was related to more accurate interception decisions (whether or not to act), faster pursuit maintenance was associated with more accurate timing decisions (when to act). These results indicate that pursuit initiation and maintenance are continuously linked to ongoing sensorimotor decision formation. NEW & NOTEWORTHY Here we show that eye movements are a continuous indicator of decision processes underlying go/no-go actions. We link different stages of decision formation to distinct oculomotor events during open- and closed-loop smooth pursuit. Critically, the earliest time point at which eye movements differentiate actions preceded hand movement onset, suggesting shared sensorimotor processing for eye and hand movements. These results emphasize the potential of studying eye movements as a readout of cognitive processes.
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Thura, David, Jean-François Cabana, Albert Feghaly et Paul Cisek. « Integrated neural dynamics of sensorimotor decisions and actions ». PLOS Biology 20, no 12 (15 décembre 2022) : e3001861. http://dx.doi.org/10.1371/journal.pbio.3001861.

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Recent theoretical models suggest that deciding about actions and executing them are not implemented by completely distinct neural mechanisms but are instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows meaningful differences between regions but no distinct clusters within each region, consistent with an integrated dynamical system. During deliberation, cortical activity unfolds on a two-dimensional “decision manifold” defined by sensory evidence and urgency and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd, it is curved; in M1, it is nearly perfectly flat; and in dlPFC, it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct functional contributions of different brain regions to an integrated dynamical system governing action selection and execution.
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Thura, David, et Paul Cisek. « Microstimulation of dorsal premotor and primary motor cortex delays the volitional commitment to an action choice ». Journal of Neurophysiology 123, no 3 (1 mars 2020) : 927–35. http://dx.doi.org/10.1152/jn.00682.2019.

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Humans and other animals are faced with decisions about actions on a daily basis. These typically include a period of deliberation that ends with the commitment to a choice, which then leads to the overt expression of that choice through action. Previous studies with monkeys have demonstrated that neural activity in sensorimotor areas correlates with the deliberation process and reflects the moment of commitment before movement initiation, but the causal roles of these regions are challenging to establish. Here, we tested whether dorsal premotor (PMd) and primary motor cortex (M1) are causally involved in the volitional commitment to a reaching choice. We found that brief subthreshold microstimulation in PMd or M1 delayed commitment to an action but not the initiation of the action itself. Importantly, microstimulation only had a significant effect when it was delivered close to and before commitment time. These results are consistent with the proposal that PMd and M1 participate in the commitment process, which occurs when a critical firing rate difference is reached between cells voting for the selected option and those voting for the competing one. NEW & NOTEWORTHY The neural substrates of decisions between actions are typically investigated by correlating neural activity and subjects’ decision behavior, but this does not establish causality. In a reaching decision task, we demonstrate that subthreshold microstimulation of the monkey dorsal premotor cortex or primary motor cortex delays the deliberation duration if applied shortly before choice commitment. This result suggests a causal role of the sensorimotor cortex in the determination of decisions between actions.
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Lekova, Anna K., Paulina Tsvetkova et 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, no 1 (1 janvier 2024) : 46–70. http://dx.doi.org/10.24140/ijgsi.v2.n1.03.

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Brain health is a critical part of well-being because it is a foundation for the ability to communicate, make decisions and solve real-life problems. Virtual reality games involve motor and sensory activities that can help to improve brain connectivity by providing an immersive and interactive experience that engages multiple brain regions simultaneously. Reinforcing sensorimotor activities influences cognitive skills and improves brain health. Sensorimotor play in virtual reality is a relatively new concept that is gaining attention as a tool for promoting brain health and cognitive abilities. It is believed that this type of play can have positive impact on brain health and cognitive function, such as improving memory, enhancing focus, and reducing stress and anxiety. The aims of the current paper are (1) – to present evidence, based on neuro correlates, of the importance of the sensorimotor play to the brain health and (2) – to propose a conceptual model for a personalized VR game design using neurocognitive feedback obtained through Brain-Computer Interface that assesses brain areas during sensorimotor stimulation.
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Balsdon, Tarryn, Stijn Verdonck, Tim Loossens et Marios G. Philiastides. « Secondary motor integration as a final arbiter in sensorimotor decision-making ». PLOS Biology 21, no 7 (17 juillet 2023) : e3002200. http://dx.doi.org/10.1371/journal.pbio.3002200.

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Sensorimotor decision-making is believed to involve a process of accumulating sensory evidence over time. While current theories posit a single accumulation process prior to planning an overt motor response, here, we propose an active role of motor processes in decision formation via a secondary leaky motor accumulation stage. The motor leak adapts the “memory” with which this secondary accumulator reintegrates the primary accumulated sensory evidence, thus adjusting the temporal smoothing in the motor evidence and, correspondingly, the lag between the primary and motor accumulators. We compare this framework against different single accumulator variants using formal model comparison, fitting choice, and response times in a task where human observers made categorical decisions about a noisy sequence of images, under different speed–accuracy trade-off instructions. We show that, rather than boundary adjustments (controlling the amount of evidence accumulated for decision commitment), adjustment of the leak in the secondary motor accumulator provides the better description of behavior across conditions. Importantly, we derive neural correlates of these 2 integration processes from electroencephalography data recorded during the same task and show that these neural correlates adhere to the neural response profiles predicted by the model. This framework thus provides a neurobiologically plausible description of sensorimotor decision-making that captures emerging evidence of the active role of motor processes in choice behavior.
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Sheppard, William E. A., Polly Dickerson, Rigmor C. Baraas, Mark Mon-Williams, Brendan T. Barrett, Richard M. Wilkie et Rachel O. Coats. « Exploring the effects of degraded vision on sensorimotor performance ». PLOS ONE 16, no 11 (8 novembre 2021) : e0258678. http://dx.doi.org/10.1371/journal.pone.0258678.

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Purpose Many people experience unilateral degraded vision, usually owing to a developmental or age-related disorder. There are unresolved questions regarding the extent to which such unilateral visual deficits impact on sensorimotor performance; an important issue as sensorimotor limitations can constrain quality of life by restricting ‘activities of daily living’. Examination of the relationship between visual deficit and sensorimotor performance is essential for determining the functional implications of ophthalmic conditions. This study attempts to explore the effect of unilaterally degraded vision on sensorimotor performance. Methods In Experiment 1 we simulated visual deficits in 30 participants using unilateral and bilateral Bangerter filters to explore whether motor performance was affected in water pouring, peg placing, and aiming tasks. Experiment 2 (n = 74) tested the hypothesis that kinematic measures are associated with visuomotor deficits by measuring the impact of small visual sensitivity decrements created by monocular viewing on sensorimotor interactions with targets presented on a planar surface in aiming, tracking and steering tasks. Results In Experiment 1, the filters caused decreased task performance—confirming that unilateral (and bilateral) visual loss has functional implications. In Experiment 2, kinematic measures were affected by monocular viewing in two of three tasks requiring rapid online visual feedback (aiming and steering). Conclusions Unilateral visual loss has a measurable impact on sensorimotor performance. The benefits of binocular vision may be particularly important for some groups (e.g. older adults) where an inability to complete sensorimotor tasks may necessitate assisted living. There is an urgent need to develop rigorous kinematic approaches to the quantification of the functional impact of unilaterally degraded vision and of the benefits associated with treatments for unilateral ophthalmic conditions to enable informed decisions around treatment.
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Liu, Taosheng, et Timothy J. Pleskac. « Neural correlates of evidence accumulation in a perceptual decision task ». Journal of Neurophysiology 106, no 5 (novembre 2011) : 2383–98. http://dx.doi.org/10.1152/jn.00413.2011.

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Sequential sampling models provide a useful framework for understanding human decision making. A key component of these models is an evidence accumulation process in which information is accrued over time to a threshold, at which point a choice is made. Previous neurophysiological studies on perceptual decision making have suggested accumulation occurs only in sensorimotor areas involved in making the action for the choice. Here we investigated the neural correlates of evidence accumulation in the human brain using functional magnetic resonance imaging (fMRI) while manipulating the quality of sensory evidence, the response modality, and the foreknowledge of the response modality. We trained subjects to perform a random dot motion direction discrimination task by either moving their eyes or pressing buttons to make their responses. In addition, they were cued about the response modality either in advance of the stimulus or after a delay. We isolated fMRI responses for perceptual decisions in both independently defined sensorimotor areas and task-defined nonsensorimotor areas. We found neural signatures of evidence accumulation, a higher fMRI response on low coherence trials than high coherence trials, primarily in saccade-related sensorimotor areas (frontal eye field and intraparietal sulcus) and nonsensorimotor areas in anterior insula and inferior frontal sulcus. Critically, such neural signatures did not depend on response modality or foreknowledge. These results help establish human brain areas involved in evidence accumulation and suggest that the neural mechanism for evidence accumulation is not specific to effectors. Instead, the neural system might accumulate evidence for particular stimulus features relevant to a perceptual task.
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Cisek, Paul, et Alexandre Pastor-Bernier. « On the challenges and mechanisms of embodied decisions ». Philosophical Transactions of the Royal Society B : Biological Sciences 369, no 1655 (5 novembre 2014) : 20130479. http://dx.doi.org/10.1098/rstb.2013.0479.

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Neurophysiological studies of decision-making have focused primarily on elucidating the mechanisms of classic economic decisions, for which the relevant variables are the values of expected outcomes and action is simply the means of reporting the selected choice. By contrast, here we focus on the particular challenges of embodied decision-making faced by animals interacting with their environment in real time. In such scenarios, the choices themselves as well as their relative costs and benefits are defined by the momentary geometry of the immediate environment and change continuously during ongoing activity. To deal with the demands of embodied activity, animals require an architecture in which the sensorimotor specification of potential actions, their valuation, selection and even execution can all take place in parallel. Here, we review behavioural and neurophysiological data supporting a proposed brain architecture for dealing with such scenarios, which we argue set the evolutionary foundation for the organization of the mammalian brain.
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Thèses sur le sujet "Sensorimotor decisions"

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Aguilar, Lleyda David. « Sensorimotor decision-making with moving objects ». Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/461673.

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Moving is essential for us to survive, and in countless occasions we move in response to visual information. However, this process is characterized as uncertain, given the variability present both at the sensory and motor stages. A crucial question, then, is how to deal with this uncertainty in order for our actions to lead to the best possible outcomes. Statistical decision theory (SDT) is a normative framework that establishes how people should make decisions in the presence of uncertainty. This theory identifies the optimal action as that which maximizes the expected reward (outcome) of the situation. Movement planning can be reformulated in terms of SDT, so that the focus is placed on the decisional component. Some experimental work making use of this theoretical approach has concluded that humans are optimal movement planners, while other has identified situations where suboptimality arises. However, sensorimotor decision-making within SDT has commonly eluded scenarios of interaction with moving objects. At the same time, the work devoted to moving objects has not focused on the decisional aspect. The present thesis aims at bridging both fields, with each of our three studies trying to answer different questions. Given the spatiotemporal nature of situations with moving objects, we can plan our actions by relying on both temporal and spatial cues provided by the object. In Study I we investigated whether exploiting more one type of these visual cues led to a better performance, as defined by the reward given after each action. In our task we presented a target, which could vary in speed and motion time, approaching a line. Participants responded to stop the target and were rewarded according to its proximity to the line. Responding after the target crossed the line was penalized. We discovered that those participants planning their responses based on time-based motion cues had a better performance than those monitoring the target’s changing spatial position. This was due to the former approach circumventing a limitation imposed by the resolution of the visual system. We also found that viewing the object for longer favored time-based responses, as mediated by longer integration time. Finally, we used existing SDT models to obtain a reference of optimality, but we defend that these models are limited to interpret our data. Study II built on our previous findings to explore whether the use of temporal cues could be learnt. We took our previous paradigm and adapted it so that reward was manipulated after each task in order to foster exploiting temporal information. There was no evidence for learning taking place, since participants using temporal cues did so from the start of the experiment. Whether other methods reward can shape the use of certain cues, and why some people naturally tend to make more use of temporal information, still remain elusive. Study III deepened our knowledge on which variability people consider when planning their responses. We hypothesized that the reason why people are suboptimal (as defined by SDT) in many situations is because they represent only their measurement variability, roughly equivalent to the execution noise, while excluding the variability created by sudden changes in their planning. We took previous data and used a Kalman filter to extract each participant’s measurement variability. We then used it to compute SDT-derived optimal responses, and discovered that they explained well our data, giving support to our hypothesis. We also found evidence for participants using the information provided by reward both to avoid being penalized and to choose the point at which to stabilize their responses. Taken together, our experimental work presents interaction with moving objects as a complex set of situations where different information guides our response planning. Firstly, visual cues of different origin. Secondly, our variability, coming from many sources, some of which may not be considered. Finally, the outcomes related to each action.
Moure’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.
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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.

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Thesis: Ph. D. in Neuroscience, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2017.
This 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
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Pape, Anna-Antonia [Verfasser], et 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.

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Pape, Anna-Antonia Verfasser], et 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.

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Acerbi, Luigi. « Complex internal representations in sensorimotor decision making : a Bayesian investigation ». Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16233.

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The past twenty years have seen a successful formalization of the idea that perception is a form of probabilistic inference. Bayesian Decision Theory (BDT) provides a neat mathematical framework for describing how an ideal observer and actor should interpret incoming sensory stimuli and act in the face of uncertainty. The predictions of BDT, however, crucially depend on the observer’s internal models, represented in the Bayesian framework by priors, likelihoods, and the loss function. Arguably, only in the simplest scenarios (e.g., with a few Gaussian variables) we can expect a real observer’s internal representations to perfectly match the true statistics of the task at hand, and to conform to exact Bayesian computations, but how humans systematically deviate from BDT in more complex cases is yet to be understood. In this thesis we theoretically and experimentally investigate how people represent and perform probabilistic inference with complex (beyond Gaussian) one-dimensional distributions of stimuli in the context of sensorimotor decision making. The goal is to reconstruct the observers’ internal representations and details of their decision-making process from the behavioural data – by employing Bayesian inference to uncover properties of a system, the ideal observer, that is believed to perform Bayesian inference itself. This “inverse problem” is not unique: in principle, distinct Bayesian observer models can produce very similar behaviours. We circumvented this issue by means of experimental constraints and independent validation of the results. To understand how people represent complex distributions of stimuli in the specific domain of time perception, we conducted a series of psychophysical experiments where participants were asked to reproduce the time interval between a mouse click and a flash, drawn from a session-dependent distribution of intervals. We found that participants could learn smooth approximations of the non-Gaussian experimental distributions, but seemed to have trouble with learning some complex statistical features such as bimodality. To investigate whether this difficulty arose from learning complex distributions or computing with them, we conducted a target estimation experiment in which “priors” where explicitly displayed on screen and therefore did not need to be learnt. Lack of difference in performance between the Gaussian and bimodal conditions in this task suggests that acquiring a bimodal prior, rather than computing with it, is the major difficulty. Model comparison on a large number of Bayesian observer models, representing different assumptions about the noise sources and details of the decision process, revealed a further source of variability in decision making that was modelled as a “stochastic posterior”. Finally, prompted by a secondary finding of the previous experiment, we tested the effect of decision uncertainty on the capacity of the participants to correct for added perturbations in the visual feedback in a centre of mass estimation task. Participants almost completely compensated for the injected error in low uncertainty trials, but only partially so in the high uncertainty ones, even when allowed sufficient time to adjust their response. Surprisingly, though, their overall performance was not significantly affected. This finding is consistent with the behaviour of a Bayesian observer with an additional term in the loss function that represents “effort” – a component of optimal control usually thought to be negligible in sensorimotor estimation tasks. Together, these studies provide new insight into the capacity and limitations people have in learning and performing probabilistic inference with distributions beyond Gaussian. This work also introduces several tools and techniques that can help in the systematic exploration of suboptimal behaviour. Developing a language to describe suboptimality, mismatching representations and approximate inference, as opposed to optimality and exact inference, is a fundamental step to link behavioural studies to actual neural computations.
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Huang, 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.

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To 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.

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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.

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Robots currently recognise and use objects through algorithms that are hand-coded or specifically trained. Such robots can operate in known, structured environments but cannot learn to recognise or use novel objects as they appear. This thesis demonstrates that a robot can develop meaningful object representations by learning the fundamental relationship between action and change in sensory state; the robot learns sensorimotor coordination. Methods based on Markov Decision Processes are experimentally validated on a mobile robot capable of gripping objects, and it is found that object recognition and manipulation can be learnt as an emergent property of sensorimotor coordination.
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LA, TONA Giuseppe. « An Architecture for Observational Learning ». Doctoral thesis, Università degli Studi di Palermo, 2014. http://hdl.handle.net/10447/91227.

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In this thesis I present an architecture that learns new skills through observation and adapts to the environment through situated experience in the world. Such an architectural growth is bootstrapped from a minimal initial knowledge and the architecture itself is built around the biologically-inspired notion of internal models. The key idea, supported by findings in cognitive neuroscience, is that the same internal models used in overt goal-directed action execution can be covertly re-enacted in simulation to observe and understand the actions of others. The system applies these concepts to learning higher order cognitive functions like learning problem solving skills and social interaction skills. Rather than rea- soning over abstract symbols, the system relies on biologically plausible processes firmly grounded in the actual sensori-motor experience of the agent. The system continuously learns new models and revises existing ones through the observation of other intentional agents in the world and through direct indivi- dual experience. The learning process accumulates knowledge about causal rela- tions between observed events, and acquires complex skills observing and abstract- ing the goals of a demonstrator. To reach its goals, the system exploits the acquired knowledge and uses its internal models to reason about future consequences of its actions. The architec- ture anticipates future needs and perils and through its internal models used in simulation it reasons about the future in a way detached from the current situation. This thesis presents also two case studies used to test the ideas constituting the architecture. The first is a classical AI problem-solving domain: the Sokoban puzzle; the second is the domain of social interaction.
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Mihoub, 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.

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L'interaction face-à-face représente une des formes les plus fondamentales de la communication humaine. C'est un système dynamique multimodal et couplé – impliquant non seulement la parole mais de nombreux segments du corps dont le regard, l'orientation de la tête, du buste et du corps, les gestes faciaux et brachio-manuels, etc – d'une grande complexité. La compréhension et la modélisation de ce type de communication est une étape cruciale dans le processus de la conception des agents interactifs capables d'engager des conversations crédibles avec des partenaires humains. Concrètement, un modèle de comportement multimodal destiné aux agents sociaux interactifs fait face à la tâche complexe de générer un comportement multimodal étant donné une analyse de la scène et une estimation incrémentale des objectifs conjoints visés au cours de la conversation. L'objectif de cette thèse est de développer des modèles de comportement multimodal pour permettre aux agents artificiels de mener une communication co-verbale pertinente avec un partenaire humain. Alors que l'immense majorité des travaux dans le domaine de l'interaction humain-agent repose essentiellement sur des modèles à base de règles, notre approche se base sur la modélisation statistique des interactions sociales à partir de traces collectées lors d'interactions exemplaires, démontrées par des tuteurs humains. Dans ce cadre, nous introduisons des modèles de comportement dits "sensori-moteurs", qui permettent à la fois la reconnaissance des états cognitifs conjoints et la génération des signaux sociaux d'une manière incrémentale. En particulier, les modèles de comportement proposés ont pour objectif d'estimer l'unité d'interaction (IU) dans laquelle sont engagés de manière conjointe les interlocuteurs et de générer le comportement co-verbal du tuteur humain étant donné le comportement observé de son/ses interlocuteur(s). Les modèles proposés sont principalement des modèles probabilistes graphiques qui se basent sur les chaînes de markov cachés (HMM) et les réseaux bayésiens dynamiques (DBN). Les modèles ont été appris et évalués – notamment comparés à des classifieurs classiques – sur des jeux de données collectés lors de deux différentes interactions face-à-face. Les deux interactions ont été soigneusement conçues de manière à collecter, en un minimum de temps, un nombre suffisant d'exemplaires de gestion de l'attention mutuelle et de deixis multimodale d'objets et de lieux. Nos contributions sont complétées par des méthodes originales d'interprétation et d'évaluation des propriétés des modèles proposés. En comparant tous les modèles avec les vraies traces d'interactions, les résultats montrent que le modèle HMM, grâce à ses propriétés de modélisation séquentielle, dépasse les simples classifieurs en terme de performances. Les modèles semi-markoviens (HSMM) ont été également testé et ont abouti à un meilleur bouclage sensori-moteur grâce à leurs propriétés de modélisation des durées des états. Enfin, grâce à une structure de dépendances riche apprise à partir des données, le modèle DBN a les performances les plus probantes et démontre en outre la coordination multimodale la plus fidèle aux évènements multimodaux originaux
Face 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
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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.

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Chapitres de livres sur le sujet "Sensorimotor decisions"

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Körding, Konrad P., et Daniel M. Wolpert. « Probabilistic Mechanisms in Sensorimotor Control ». Dans Percept, Decision, Action : Bridging the Gaps, 191–202. Chichester, UK : John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470034989.ch15.

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Barash, Shabtai, et Mingsha Zhang. « Switching of Sensorimotor Transformations : Antisaccades and Parietal Cortex ». Dans Percept, Decision, Action : Bridging the Gaps, 59–74. Chichester, UK : John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470034989.ch6.

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Bittencourt, Juliana, Bruna Velasques, Silmar Teixeira, Danielle Aprígio, Mariana Gongora, Mauricio Cagy, Thayaná Fernandes, Pedro Ribeiro et Victor Marinho. « Schizophrenia : A Disorder of Timing and Sensorimotor Integration During Decision-Making ». Dans Integrated Science, 123–41. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96814-4_6.

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Duysens, Jacques, Geert Verheyden, Firas Massaad, Pieter Meyns, Bouwien Smits-Engelsman et Ilse Jonkers. « Rehabilitation of gait and balance after CNS damage ». Dans Oxford Textbook of Neurorehabilitation, sous la direction de Volker Dietz, Nick S. Ward et Christopher Kennard, 239–52. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198824954.003.0018.

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This chapter discusses the importance of basic and clinical science for the rehabilitation of patients with stroke or SCI (spinal cord injury). Examples are given from novel approaches in rehabilitation. Insights can be obtained by investigating normal locomotor or postural activity but it is argued that the use of more challenging locomotor tasks can provide much needed additional information. To update rehabilitation tools it is also essential to keep track of new technological developments and new assessment tools. Careful assessments, based on advanced 3D recordings of posture and gait, can assist in making proper decisions about planned interventions, aimed at alleviating some clinical symptoms such as spasticity. With respect to training, the focus here is on general principles of sensorimotor rehabilitation of gait and posture. Learning is context dependent. The consequence is that current rehabilitation approaches need to emphasize that training should be ‘task-oriented training’, and should involve the whole body.
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Duysens, Jacques, Geert Verheyden, Firas Massaad, Pieter Meyns, Bouwien Smits-Engelsman et Ilse Jonkers. « Rehabilitation of gait and balance after CNS damage ». Dans Oxford Textbook of Neurorehabilitation, 211–23. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199673711.003.0018.

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This chapter discusses the importance of basic and clinical science for the rehabilitation of patients with stroke or SCI (spinal cord injury). Examples are given from novel approaches in rehabilitation. Insights can be obtained by investigating normal locomotor or postural activity but it is argued that the use of more challenging locomotor tasks can provide much needed additional information. To update rehabilitation tools it is also essential to keep track of new technological developments and new assessment tools. Careful assessments, based on advanced 3-D recordings of posture and gait, can assist in making proper decisions about planned interventions, aimed at alleviating some clinical symptoms such as spasticity. With respect to training, the focus here is on general principles of sensorimotor rehabilitation of gait and posture. Learning is context dependent. The consequence is that current rehabilitation approaches need to emphasize that training should be ‘task-oriented training’, and should involve the whole body.
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Jaramillo, Jorge, et Zengcai V. Guo. « Thalamocortical Contributions to Neural Dynamics and Behavior ». Dans The Cerebral Cortex and Thalamus, sous la direction de Adam W. Hantman et Kevin P. Cross, 367–80. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197676158.003.0035.

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Abstract Neurons in the frontal cortex exhibit intricate dynamics during cognitive functions such as perception, motor planning, and decision-making. Recent studies have demonstrated contributions from non-sensory thalamic nuclei to cortical neural dynamics underlying cognitive and sensorimotor computations. In a memory-guided motor-planning task, interactions between motor thalamus and frontal cortex maintain low-dimensional cortical dynamics (dynamical modes). In this chapter, the authors propose a circuit-level computational framework, whereby interaction between excitatory and inhibitory assemblies in the cortex support dynamical modes, while subcortical structures, including the basal ganglia and brainstem, modulate thalamocortical circuits to control these dynamical modes for successful behavior. The authors review results supporting this computational framework and outline open questions for future work.
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Faix, Marvin, Emmanuel Mazer, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy et Jorge Lobo. « Cognitive Computation ». Dans Robotic Systems, 906–29. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch045.

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Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.
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« Cognitive Architecture With Episodic Memory ». Dans Reductive Model of the Conscious Mind, 243–82. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5653-5.ch008.

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The authors chose a provocative title for this book. In this provocation, there is an incentive for those who would like to understand what consciousness is. Their goal was to explain the phenomenon, which is perhaps even harder to understand than the emergence of life from inanimate matter. Through this work, they developed and described a reductive model of conscious mind named motivated emotional mind. Although the basis for episodic memory are real events that were observed by the agent, memorized episodes can also be generated in the agent's mind. The working memory supports explanation of the meaning of the whole scene by combining the meanings of its constituent elements and their relations. The observed scenes are stored in the episodic memory. An agent can build its value system to assess the significance of observed events and later use it to influence its behavior and its emotional states. Only the conscious being has the ability to remember episodes from its experiences. The conscious system must be able to imagine a hypothetical situation and plan its activities. Because episodic memories require the structures of the hippocampus or its equivalent, if the body has a hippocampus, it is potentially conscious. Working memory is responsible for temporarily storing information that has been perceived in the environment or retrieved from long-term memory. It is important for reasoning, decision-making, and behavioral control. It records stimuli processed in the deeper layers of the brain. In addition, working memory combines temporary storage and manipulates selected information to support cognitive functions. Embodied intelligence architecture discussed in this chapter is aimed at building an intelligent and conscious machines and its ability to learn is recognized as the most important feature of intelligence. Authors show that embodied minds contain certain memory structures, and it is through them that machines can be conscious. The organization of brain structures and their functions constitute a functional, reductive model of the conscious mind, called motivated emotional mind. Different functional blocks of this architecture process information simultaneously, sending interrupt signals to direct attention, change plans, monitor activities, and respond to external threats and opportunities. They also provide a conscious agent with personal memories, accumulated knowledge, skills, and desires, making the agent act fully autonomously. What is needed to build embodied, conscious machines? First of all, their sensing must be based on the observations and predictions of results of their own actions in the real world. This requires the development of sensorimotor coordination integrated with the machine value system. The second requirement is the development of learning methods and control of the robot's movements. This includes the development of motoric functions, activators, grippers, methods of movement, and navigation. The chapter ends with predictions for future development of conscious robots and elaboration on the life and death cycles for conscious minds.
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Actes de conférences sur le sujet "Sensorimotor decisions"

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Kolesovs, Aleksandrs, Klavs Evelis, Liga Ozolina-Molla, Liga Plakane, Juris Porozovs et Viktors Veliks. « Exploration of EEG Markers of Sensorimotor Functioning During Incorrect versus Correct Decisions ». Dans 81th International Scientific Conference of the University of Latvia. University of Latvia Press, 2023. http://dx.doi.org/10.22364/htqe.2023.42.

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Open-skill sports are demanding for athletes’ decision-making. Incorrect decisions can have substantial consequences. Complex programs for elite athletes include improvement of their neurocognitive functioning. Simultaneously, making errors remains underrepresented in broader sports science. The present study explored EEG markers of sensorimotor reactions under incorrect versus correct responses to visual stimuli. Seven male participants (24.2±2.5 years) completed the Choice Response Time task (CRT) with simultaneous EEG registration. Two color stimuli (red and green) and a discriminative stimulus (black) were presented on an LCD screen, using PSYCHOTOOLBOX coded CRT task. Stimulus and response events were synchronized with EEG amplifier NVX-136, and 32 channels of EEG were recorded. Data were preprocessed in EEGLAB, and event-related potential (ERP) calculations were performed in ERPLAB. ERP was analyzed for correct and incorrect color choices and reactions to the discriminative stimulus. The electrodes represented the visual (O1, Oz, O2), frontal (F3, Fz, F4), and sensorimotor (C3, Cz, C4) cortex. Behavior data revealed a shorter reaction time during the incorrect decision (4.6% of cases) than during the correct one, 398.1±55 ms vs. 456.8±96 ms. In the N2 peak area, the incorrect color stage differed significantly from the two correct stages (e.g., the amplitude of -1.624 mkV at a latency of 264 ms for the correct color stage and -1.779 mkV at a latency of 254 ms for the correct discriminating stage vs. -3.716 mkV at a latency of 300 ms for the incorrect color stage for channel F3, peak N2). Correct decision stages had similar ERP wave peak patterns. Incorrect decisions deviate from functioning during correct ones. Differences in the N2 peak area represented conflict in decision-making during incorrect decisions. Simultaneously, the shorter latency of a motor reaction requires investigating the role of decision-making conflicts in impulse control and behavioral consequences.
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Tatai, Fabian, Dominik Straub et Constantin Rothkopf. « Humans use Newtonian physics in intuitive sensorimotor decisions under risk ». Dans 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom : Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1580-0.

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Martinez-Rodriguez, L. Alexandra, Elaine A. Corbett et Simon P. Kelly. « Effects of value on early sensory activity and motor preparation during rapid sensorimotor decisions ». Dans 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA : Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1171-0.

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Usenkova, Ekaterina V., et Olga A. Shirokova. « The use of information and communication technologies in the development of sensorimotor skills in preschool children with speech disorders ». Dans 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.

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The dissemination of information and communication technologies in speech therapy practice makes it possible to open up new training options, which makes it possible to achieve better results in correctional work in order to eliminate violations of speech and sensory development of children. Children develop a holistic view of the world around them, social and cultural life, and their own personality. The formation of knowledge concerning a variety of areas is carried out, the ability to make decisions in accordance with their own knowledge and experience is improved, skills of interaction with other people of different ages are created. The article offers various options for the use of information and communication technologies in the formation of sensorimotor skills in children with speech disorders.
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Reddy, P. V., E. W. Justh et P. S. Krishnaprasad. « Motion camouflage with sensorimotor delay ». Dans 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434522.

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Batta, Erasmo, et Christopher Stephens. « Heuristics as Decision-making Habits of Autonomous Sensorimotor Agents ». Dans The 2019 Conference on Artificial Life. Cambridge, MA : MIT Press, 2019. http://dx.doi.org/10.1162/isal_a_00144.

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Batta, Erasmo, et Christopher Stephens. « Heuristics as Decision-making Habits of Autonomous Sensorimotor Agents ». Dans The 2019 Conference on Artificial Life. Cambridge, MA : MIT Press, 2019. http://dx.doi.org/10.1162/isal_a_00144.xml.

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Zhong, Junpei, Rony Novianto, Mingjun Dai, Xinzheng Zhang et Angelo Cangelosi. « A hierarchical emotion regulated sensorimotor model : Case studies ». Dans 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531882.

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Karg, Philipp, Simon Stoll, Simon Rothfus et Soren Hohmann. « Inverse Stochastic Optimal Control for Linear-Quadratic Gaussian and Linear-Quadratic Sensorimotor Control Models ». Dans 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992798.

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Nakahira, Yorie, Nikolai Matni et John C. Doyle. « Hard limits on robust control over delayed and quantized communication channels with applications to sensorimotor control ». Dans 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7403407.

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Rapports d'organisations sur le sujet "Sensorimotor decisions"

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Alwan, Iktimal, Dennis D. Spencer et Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, décembre 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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