Lack of Adaptation to Random Conflicting Force Fields of Variable Magnitude

2007 ◽  
Vol 97 (1) ◽  
pp. 738-745 ◽  
Author(s):  
Rahul Gupta ◽  
James Ashe
Keyword(s):  
Motor Behavior ◽  
Force Fields ◽  
Human Subjects ◽  
Large Body ◽  
Internal Models ◽  
Limited Capacity ◽  
Contextual Cues ◽  
Motor Behaviors ◽  
Extended Practice ◽  
The Brain

The concept of internal models has been used to explain how the brain learns and stores a variety of motor behaviors. A large body of work has shown that conflicting internal models could not be learned simultaneously; this suggests either a limited capacity or the unstable nature of short-term motor memories. However, it has been recently shown that multiple conflicting internal models of motor behavior could be acquired simultaneously if associated with appropriate contextual cues and random presentations. We re-examined this issue in a more complex environment in which the magnitude of the conflicting fields could vary randomly. Human subjects failed to show any evidence of learning the force fields themselves or the magnitude of the forces experienced, even with extended practice. Subjects did adapt to the applied perturbation when the field strength was kept constant but still did not form internal models. Our results show that neither random presentation nor specific contextual cues are sufficient for learning conflicting internal models when the magnitude of the forces is also unpredictable. The data suggest that multiple conflicting internal models cannot be learned in all environments, and provide support for the unstable nature or limited capacity of motor memories.

scholarly journals Output Units of Motor Behavior: An Experimental and Modeling Study

2000 ◽  
Vol 12 (1) ◽  
pp. 78-97 ◽  
Author(s):  
E. P. Loeb ◽  
S. F. Giszter ◽  
P. Saltiel and E. Bizzi ◽  
F. A. Mussa-Ivaldi
Keyword(s):  
Motor Control ◽  
Common Property ◽  
Motor Behavior ◽  
Muscle Synergies ◽  
Force Output ◽  
Time Dynamics ◽  
Motor Behaviors ◽  
Distinct Property ◽  
Small Set ◽  
The Brain

Cognitive approaches to motor control typically concern sequences of discrete actions without taking into account the stunning complexity of the geometry and dynamics of the muscles. This begs the question: Does the brain convert the intricate, continuous-time dynamics of the muscles into simpler discrete units of actions, and if so, how? One way for the brain to form discrete units of behavior from muscles is through the synergistic co-activation of muscles. While this possibility has long been known, the composition of potential muscle synergies has remained elusive. In this paper, we have focused on a method that allowed us to examine and compare the limb stabilization properties of all possible muscle combinations. We found that a small set (as few as 23 out of 65,536) of all possible combinations of 16 limb muscles are robust with respect to activation noise: these muscle combinations could stabilize the limb at predictable, restricted portions of the workspace in spite of broad variations in the force output of their component muscles. The locations at which the robust synergies stabilize the limb are not uniformly distributed throughout the leg's workspace, but rather, they cluster at four workspace areas. The simulated robust synergies are similar to the actual synergies we have previously found to be generated by activation of the spinal cord. Thus, we have developed a new analytical method that enabled us to select a few muscle synergies with interesting properties out of the set of possible muscle combinations. Beyond this, the identification of robustness as a common property of the synergies in simple motor behaviors will open the way to the study of dynamic stability, which is an important and distinct property of the vertebrate motor-control system.

scholarly journals Multisensory decisions provide support for probabilistic number representations

2015 ◽  
Vol 113 (10) ◽  
pp. 3490-3498 ◽  
Author(s):  
Ingmar Kanitscheider ◽  
Amanda Brown ◽  
Alexandre Pouget ◽  
Anne K. Churchland
Keyword(s):  
Visual Information ◽  
Human Subjects ◽  
Probability Distributions ◽  
Large Body ◽  
Decision Task ◽  
Probabilistic Representation ◽  
Trial Analysis ◽  
Probabilistic Number ◽  
Fixed Reference ◽  
The Brain

A large body of evidence suggests that an approximate number sense allows humans to estimate numerosity in sensory scenes. This ability is widely observed in humans, including those without formal mathematical training. Despite this, many outstanding questions remain about the nature of the numerosity representation in the brain. Specifically, it is not known whether approximate numbers are represented as scalar estimates of numerosity or, alternatively, as probability distributions over numerosity. In the present study, we used a multisensory decision task to distinguish these possibilities. We trained human subjects to decide whether a test stimulus had a larger or smaller numerosity compared with a fixed reference. Depending on the trial, the numerosity was presented as either a sequence of visual flashes or a sequence of auditory tones, or both. To test for a probabilistic representation, we varied the reliability of the stimulus by adding noise to the visual stimuli. In accordance with a probabilistic representation, we observed a significant improvement in multisensory compared with unisensory trials. Furthermore, a trial-by-trial analysis revealed that although individual subjects showed strategic differences in how they leveraged auditory and visual information, all subjects exploited the reliability of unisensory cues. An alternative, nonprobabilistic model, in which subjects combined cues without regard for reliability, was not able to account for these trial-by-trial choices. These findings provide evidence that the brain relies on a probabilistic representation for numerosity decisions.

scholarly journals Late Bayesian inference in sensorimotor behavior

2017 ◽  
Author(s):  
Evan Remington ◽  
Mehrdad Jazayeri
Keyword(s):  
Bayesian Inference ◽  
Human Subjects ◽  
Internal Models ◽  
Measurement Noise ◽  
Behavioral Variability ◽  
Sensorimotor Transformation ◽  
Bias Variance ◽  
Sensorimotor Skills ◽  
The Brain ◽  
Different Levels

AbstractSensorimotor skills rely on performing noisy sensorimotor computations on noisy sensory measurements. Bayesian models suggest that humans compensate for measurement noise and reduce behavioral variability by biasing perception toward prior expectations. Whether the same holds for noise in sensorimotor computations is not known. Testing human subjects in tasks with different levels of sensorimotor complexity, we found a similar bias-variance tradeoff associated with increased sensorimotor noise. This result was accurately captured by a model which implements Bayesian inference after – not before – sensorimotor transformation. These results indicate that humans perform “late inference” downstream of sensorimotor computations rather than, or in addition to, “early inference” in the perceptual domain. The brain thus possesses internal models of noise in both sensory measurements and sensorimotor computations.

scholarly journals Active neural coordination of motor behaviors with internal states

2021 ◽  
Author(s):  
Yisi S. Zhang ◽  
Daniel Y. Takahashi ◽  
Ahmed El Hady ◽  
Diana A. Liao ◽  
Asif A. Ghazanfar
Keyword(s):  
Motor Behavior ◽  
Cardiac Activity ◽  
Motor Behaviors ◽  
Spontaneous Movements ◽  
Causal Ordering ◽  
Heart Rate Fluctuations ◽  
Internal States ◽  
Network States ◽  
Medial Section ◽  
The Brain

AbstractThe brain continuously coordinates skeletomuscular movements with internal physiological states like arousal, but how is this coordination achieved? One possibility is that brain simply reacts to changes in external and/or internal signals. Another possibility is that it is actively coordinating both external and internal activities. We used functional ultrasound imaging to capture a large medial section of the brain, including multiple cortical and subcortical areas, in marmoset monkeys while monitoring their spontaneous movements and cardiac activity. By analyzing the causal ordering of these different time-series, we found that information flowing from the brain to movements and heart rate fluctuations were significantly greater than in the opposite direction. The brain areas involved in this external versus internal coordination were spatially distinct but also extensively interconnected. Temporally, the brain alternated between network states for this regulation. These findings suggest that the brain’s dynamics actively and efficiently coordinate motor behavior with internal physiology.

scholarly journals Generalization and transfer of contextual cues in motor learning

2015 ◽  
Vol 114 (3) ◽  
pp. 1565-1576 ◽  
Author(s):  
A. M. E. Sarwary ◽  
D. F. Stegeman ◽  
L. P. J. Selen ◽  
W. P. Medendorp
Keyword(s):  
Force Field ◽  
Internal Model ◽  
Force Fields ◽  
Daily Life ◽  
Angular Separation ◽  
Internal Models ◽  
Reaching Movements ◽  
Contextual Cues ◽  
New Models ◽  
The Individual

We continuously adapt our movements in daily life, forming new internal models whenever necessary and updating existing ones. Recent work has suggested that this flexibility is enabled via sensorimotor cues, serving to access the correct internal model whenever necessary and keeping new models apart from previous ones. While research to date has mainly focused on identifying the nature of such cue representations, here we investigated whether and how these cue representations generalize, interfere, and transfer within and across effector systems. Subjects were trained to make two-stage reaching movements: a premovement that served as a cue, followed by a targeted movement that was perturbed by one of two opposite curl force fields. The direction of the premovement was uniquely coupled to the direction of the ensuing force field, enabling simultaneous learning of the two respective internal models. After training, generalization of the two premovement cues' representations was tested at untrained premovement directions, within both the trained and untrained hand. We show that the individual premovement representations generalize in a Gaussian-like pattern around the trained premovement direction. When the force fields are of unequal strengths, the cue-dependent generalization skews toward the strongest field. Furthermore, generalization patterns transfer to the nontrained hand, in an extrinsic reference frame. We conclude that contextual cues do not serve as discrete switches between multiple internal models. Instead, their generalization suggests a weighted contribution of the associated internal models based on the angular separation from the trained cues to the net motor output.

scholarly journals Movement errors during skilled motor performance engage distinct prediction error mechanisms

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ella Gabitov ◽  
Ovidiu Lungu ◽  
Geneviève Albouy ◽  
Julien Doyon
Keyword(s):  
Prediction Error ◽  
Motor Behavior ◽  
Prediction Errors ◽  
Unexpected Events ◽  
Sensorimotor Network ◽  
Motor Behaviors ◽  
Neural Computations ◽  
Adaptive Processes ◽  
Reward Valuation ◽  
The Brain

AbstractThe brain detects deviations from intended behaviors by estimating the mismatch between predicted and actual outcomes. Axiomatic to these computations are salience and valence prediction error signals, which alert the brain to the occurrence and value of unexpected events. Despite the theoretical assertion of these prediction error signals, it is unknown whether and how brain mechanisms underlying their computations support error processing during skilled motor behavior. Here we demonstrate, with functional magnetic resonance imaging, that internal detection, i.e., without externally-provided feedback, of self-generated movement errors evokes instantaneous activity increases within the salience network and delayed lingering decreases within the nucleus accumbens – a key structure in the reward valuation pathway. A widespread suppression within the sensorimotor network was also observed. Our findings suggest that neural computations of salience and valence prediction errors during skilled motor behaviors operate on different time-scales and, therefore, may contribute differentially to immediate and longer-term adaptive processes.

scholarly journals Hypothalamic dopamine neurons control sensorimotor behavior by modulating brainstem premotor nuclei

2020 ◽  
Author(s):  
Joshua P. Barrios ◽  
Wei-Chun Wang ◽  
Roman England ◽  
Erica Reifenberg ◽  
Adam D. Douglass
Keyword(s):  
Sensorimotor Integration ◽  
Motor Behavior ◽  
Third Ventricle ◽  
Dopamine Neurons ◽  
Preoptic Nucleus ◽  
Anatomy And Physiology ◽  
Motor Behaviors ◽  
Hypothalamic Nuclei ◽  
The Brain ◽  
Hypothalamic Dopamine

SummaryDopamine (DA)-producing neurons are critically involved in the production of motor behaviors in multiple circuits that are conserved from basal vertebrates to mammals. While there is increasing evidence that DA neurons in the hypothalamus play a locomotor role, their precise contributions to behavior and the circuit mechanisms by which they are achieved remain unclear. Here we demonstrate that tyrosine hydroxylase 2-expressing (th2+) DA neurons in the zebrafish hypothalamus fire phasic bursts of activity to acutely promote swimming and modulate audiomotor behaviors on fast timescales. Their anatomy and physiology reveal two distinct functional DA modules within the hypothalamus. The first comprises an interconnected set of cerebrospinal fluid-contacting DA nuclei surrounding the third ventricle, which lack distal projections outside of the hypothalamus and influence locomotion through unknown means. The second includes neurons in the preoptic nucleus, which send long-range projections to targets throughout the brain, including the mid- and hindbrain, where they activate premotor circuits involved in swimming and sensorimotor integration. These data suggest a broad regulation of motor behavior by DA neurons within multiple hypothalamic nuclei and elucidate a novel functional mechanism for the preoptic DA neurons in the initiation of movement.

The human brain: Chairman’s introduction

1981 ◽  
Vol 292 (1057) ◽  
pp. 151-153
Keyword(s):  
Human Brain ◽  
Cultural Evolution ◽  
Positive Feedback ◽  
Time Scale ◽  
Biological Evolution ◽  
Internal Models ◽  
Adaptive Behaviour ◽  
Myelin Sheaths ◽  
The Brain

Much has been said at the symposium about the pre-eminent role of the brain in the continuing emergence of man. Tobias has spoken of its explosive enlargement during the last 1 Ma, and how much of its enlargement in individual ontogeny is postnatal. We are born before our brains are fully grown and ‘wired up ’. During our long adolescence we build up internal models of the outside world and of the relations of parts of our bodies to it and to one another. Neurons that are present at birth spread their dendrites and project axons which acquire their myelin sheaths, and establish innumerable contacts with other neurons, over the years. New connections are formed; genetically endowed ones are stamped in or blanked off. People born without arms may grow up to use their toes in skills that are normally manual. Tobias, Darlington and others have stressed the enormous survival value of adaptive behaviour and the ‘positive feedback’ relation between biological and cultural evolution. The latter, the unique product of the unprecedentedly rapid biological evolution of big brains, advances on a time scale unknown to biological evolution.

scholarly journals Multifaceted origins of sex differences in the brain

2016 ◽  
Vol 371 (1688) ◽  
pp. 20150106 ◽  
Author(s):  
Margaret M. McCarthy
Keyword(s):  
Sex Differences ◽  
State Of The Art ◽  
Human Subjects ◽  
Special Issue ◽  
Molecular Analyses ◽  
The Past ◽  
Current State ◽  
Biological Variable ◽  
Males And Females ◽  
The Brain

Studies of sex differences in the brain range from reductionistic cell and molecular analyses in animal models to functional imaging in awake human subjects, with many other levels in between. Interpretations and conclusions about the importance of particular differences often vary with differing levels of analyses and can lead to discord and dissent. In the past two decades, the range of neurobiological, psychological and psychiatric endpoints found to differ between males and females has expanded beyond reproduction into every aspect of the healthy and diseased brain, and thereby demands our attention. A greater understanding of all aspects of neural functioning will only be achieved by incorporating sex as a biological variable. The goal of this review is to highlight the current state of the art of the discipline of sex differences research with an emphasis on the brain and to contextualize the articles appearing in the accompanying special issue.

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