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 Computational Approaches to Spatial Orientation: From Transfer Functions to Dynamic Bayesian Inference

2008 ◽  
Vol 100 (6) ◽  
pp. 2981-2996 ◽  
Author(s):  
Paul R. MacNeilage ◽  
Narayan Ganesan ◽  
Dora E. Angelaki
Keyword(s):  
Bayesian Inference ◽  
Spatial Orientation ◽  
Particle Filtering ◽  
Transfer Functions ◽  
Linear Acceleration ◽  
Frequency Domain Analysis ◽  
Internal Models ◽  
Velocity Storage ◽  
Computational Approaches ◽  
The Brain

Spatial orientation is the sense of body orientation and self-motion relative to the stationary environment, fundamental to normal waking behavior and control of everyday motor actions including eye movements, postural control, and locomotion. The brain achieves spatial orientation by integrating visual, vestibular, and somatosensory signals. Over the past years, considerable progress has been made toward understanding how these signals are processed by the brain using multiple computational approaches that include frequency domain analysis, the concept of internal models, observer theory, Bayesian theory, and Kalman filtering. Here we put these approaches in context by examining the specific questions that can be addressed by each technique and some of the scientific insights that have resulted. We conclude with a recent application of particle filtering, a probabilistic simulation technique that aims to generate the most likely state estimates by incorporating internal models of sensor dynamics and physical laws and noise associated with sensory processing as well as prior knowledge or experience. In this framework, priors for low angular velocity and linear acceleration can explain the phenomena of velocity storage and frequency segregation, both of which have been modeled previously using arbitrary low-pass filtering. How Kalman and particle filters may be implemented by the brain is an emerging field. Unlike past neurophysiological research that has aimed to characterize mean responses of single neurons, investigations of dynamic Bayesian inference should attempt to characterize population activities that constitute probabilistic representations of sensory and prior information.

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 Internal Models in Biological Control

2019 ◽  
Vol 2 (1) ◽  
pp. 339-364 ◽  
Author(s):  
Daniel McNamee ◽  
Daniel M. Wolpert
Keyword(s):  
Biological Control ◽  
Bayesian Inference ◽  
Feedback Control ◽  
Motor System ◽  
Sensorimotor Control ◽  
Internal Models ◽  
Optimal Feedback Control ◽  
Probabilistic Framework ◽  
Behavioral Evidence ◽  
The Brain

Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

scholarly journals Using electrodermal activity to validate multilevel pain stimulation in healthy volunteers evoked by thermal grills

2020 ◽  
Vol 319 (3) ◽  
pp. R366-R375
Author(s):  
Hugo F. Posada-Quintero ◽  
Youngsun Kong ◽  
Kimberly Nguyen ◽  
Cara Tran ◽  
Luke Beardslee ◽  
...  
Keyword(s):  
Tissue Injury ◽  
Electrodermal Activity ◽  
Pain Scale ◽  
Potential Harm ◽  
Electric Pulses ◽  
Stimulation Level ◽  
Highly Correlated ◽  
The Brain ◽  
Different Levels ◽  
Assessment Research

We have tested the feasibility of thermal grills, a harmless method to induce pain. The thermal grills consist of interlaced tubes that are set at cool or warm temperatures, creating a painful “illusion” (no tissue injury is caused) in the brain when the cool and warm stimuli are presented collectively. Advancement in objective pain assessment research is limited because the gold standard, the self-reporting pain scale, is highly subjective and only works for alert and cooperative patients. However, the main difficulty for pain studies is the potential harm caused to participants. We have recruited 23 subjects in whom we induced electric pulses and thermal grill (TG) stimulation. The TG effectively induced three different levels of pain, as evidenced by the visual analog scale (VAS) provided by the subjects after each stimulus. Furthermore, objective physiological measurements based on electrodermal activity showed a significant increase in levels as stimulation level increased. We found that VAS was highly correlated with the TG stimulation level. The TG stimulation safely elicited pain levels up to 9 out of 10. The TG stimulation allows for extending studies of pain to ranges of pain in which other stimuli are harmful.

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.

Variables That Influence the Generation of Random Sequences: An Update

1997 ◽  
Vol 84 (2) ◽  
pp. 627-661 ◽  
Author(s):  
Peter Brugger
Keyword(s):  
Short Term Memory ◽  
Human Subjects ◽  
Complete Suppression ◽  
Random Generation ◽  
Short Term ◽  
Random Sequences ◽  
Potential Applications ◽  
Basic Experiments ◽  
The Brain ◽  
Relevant Work

This article updates Tune's 1964 review of variables influencing human subjects' attempts at generating random sequences of alternatives. It also covers aspects not included in the original review such as randomization behavior by patients with neurological and psychiatric disorders. Relevant work from animal research (spontaneous alternation paradigm) is considered as well. It is conjectured that Tune's explanation of sequential nonrandomness in terms of a limited capacity of short-term memory can no longer be maintained. Rather, interdependence among consecutive choices is considered a consequence of an organism's natural susceptibility to interference. Random generation is thus a complex action which demands complete suppression of any rule-governed behavior. It possibly relies on functions of the frontal lobes but cannot otherwise be “localized” to restricted regions of the brain. Possible developments in the field are briefly discussed, both with respect to basic experiments regarding the nature of behavioral nonrandomness and to potential applications of random-generation tasks.

scholarly journals Attention controls multisensory perception via 2 distinct mechanisms at different levels of the cortical hierarchy

PLoS Biology ◽  
2021 ◽  
Vol 19 (11) ◽  
pp. e3001465
Author(s):  
Ambra Ferrari ◽  
Uta Noppeney
Keyword(s):  
Causal Inference ◽  
Causal Structure ◽  
Multisensory Perception ◽  
Spatial Representations ◽  
Perceptual Inference ◽  
Sensory Signals ◽  
Cortical Hierarchy ◽  
Parietal Cortices ◽  
The Brain ◽  
Different Levels

To form a percept of the multisensory world, the brain needs to integrate signals from common sources weighted by their reliabilities and segregate those from independent sources. Previously, we have shown that anterior parietal cortices combine sensory signals into representations that take into account the signals’ causal structure (i.e., common versus independent sources) and their sensory reliabilities as predicted by Bayesian causal inference. The current study asks to what extent and how attentional mechanisms can actively control how sensory signals are combined for perceptual inference. In a pre- and postcueing paradigm, we presented observers with audiovisual signals at variable spatial disparities. Observers were precued to attend to auditory or visual modalities prior to stimulus presentation and postcued to report their perceived auditory or visual location. Combining psychophysics, functional magnetic resonance imaging (fMRI), and Bayesian modelling, we demonstrate that the brain moulds multisensory inference via 2 distinct mechanisms. Prestimulus attention to vision enhances the reliability and influence of visual inputs on spatial representations in visual and posterior parietal cortices. Poststimulus report determines how parietal cortices flexibly combine sensory estimates into spatial representations consistent with Bayesian causal inference. Our results show that distinct neural mechanisms control how signals are combined for perceptual inference at different levels of the cortical hierarchy.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

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