Bayesian decision theory in sensorimotor control

Körding, Konrad P.; Wolpert, Daniel M.

doi:10.1016/j.tics.2006.05.003

Cited by 687 publications

(515 citation statements)

References 55 publications

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“…Several works have reported that humans perform near-optimal cue integration in a variety of settings (1)(2)(3)(4)(5)(6)(7)(8). It is, therefore, essential that the combination of inputs that leads to the multiplicative rule in an attractor network also results in optimal cue integration.…”

Section: Discussionmentioning

confidence: 99%

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Bayesian sampling in visual perception

Moreno-Bote

Knill

Pouget³

2011

Proc. Natl. Acad. Sci. U.S.A.

144

133

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It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to samples from the probability distribution over image interpretations, a form of sampling that we refer to as Bayesian sampling. To test this idea, we manipulated pairs of sensory cues in a bistable display consisting of two superimposed moving drifting gratings, and we asked subjects to report their perceived changes in depth ordering. We report that the fractions of dominance of each percept follow the multiplicative rule predicted by Bayesian sampling. Furthermore, we show that attractor neural networks can sample probability distributions if input currents add linearly and encode probability distributions with probabilistic population codes.Bayesian inference | neuronal network | neuronal noise | perceptual bistability T here is mounting evidence that neural circuits can implement probabilistic inferences over sensory, cognitive, or motor variables. In some cases, humans can perform these inferences optimally, as in multi-cue or multisensory integration (1-8). For complex tasks, such as object recognition, action perception, and object tracking, the computations required for optimal inference are intractable, which implies that humans must use approximate inferences (9-11). One approximate scheme that is particularly appealing from a biological point of view is sampling. Consider as an example the problem of object recognition. The goal of the inference in this case would be to compute the probability over object identities given the image. Although this probability distribution may be difficult to compute explicitly, one can often design algorithms to generate samples from the distribution, allowing one to perform approximate inference (12, 13). Some human cognitive choice behaviors suggest that the nervous system implements sampling. However, whether the same is true for lowlevel perceptual processing is currently unknown.Stimuli that lead to bistable percepts (14-18), like the Necker cube, provide a tractable experimental preparation for testing the sampling hypothesis. With such stimuli, perception alternates stochastically between two possible interpretations, a behavior consistent with sampling as suggested by several works (16,19,20). However, the key question is what probability distribution is being sampled. If the brain uses sampling for Bayesian inference, neural circuits should sample from an internal probability distribution on possible stimulus interpretations that are conditioned on the available sensory data, the so-called posterior distribution. This distribution places important constraints on the distributions of perceptual states for bistable stimuli.To test this idea, we used stimuli composed of two dr...

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Section: Discussionmentioning

confidence: 99%

“…In some cases, humans can perform these inferences optimally, as in multi-cue or multisensory integration (1)(2)(3)(4)(5)(6)(7)(8). For complex tasks, such as object recognition, action perception, and object tracking, the computations required for optimal inference are intractable, which implies that humans must use approximate inferences (9)(10)(11).…”

mentioning

confidence: 99%

Bayesian sampling in visual perception

Moreno-Bote

Knill

Pouget³

2011

Proc. Natl. Acad. Sci. U.S.A.

144

133

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“…Possible muscle synergies can therefore be defined as organizations of muscle forces that stabilize joint torques and motion; or, in other words, alternative solutions to the muscle load-sharing problem. Körding & Wolpert [4] showed that our CNS probably interprets the problem of optimal performance in a statistical fashion by weighting knowledge gathered from previous experiences and information gathered from multiple sensory modalities. By considering both types of information in the form of prior and likelihood, Bayesian statistics have been shown to properly describe the mechanism behind the generation of movement trajectories [7], forces [8] and judgement timing [9].…”

Section: Introductionmentioning

confidence: 99%

Stochastic modelling of muscle recruitment during activity

et al. 2015

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One contribution of 11 to a theme issue 'Multiscale modelling in biomechanics: theoretical, computational and translational challenges'. Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo (MCMC) methods, whereas EMG-driven muscle forces were calculated using EMG-driven modelling. We calculated the differences between the spectrum and EMG-driven muscle force for 1-15 input EMGs, and we identified the muscle strategy that best matched the recorded EMG pattern. The best-fit strategy, static optimization solution and EMG-driven force data were compared using correlation analysis. Possible and plausible muscle forces were defined as within physiological boundaries and within EMG boundaries. Possible muscle and joint forces were calculated by constraining the muscle forces between zero and the peak muscle force. Plausible muscle forces were constrained within six selected EMG boundaries. The spectrum to EMG-driven force difference increased from 40 to 108 N for 1-15 EMG inputs. The best-fit muscle strategy better described the EMG-driven pattern (R 2 ¼ 0.94; RMSE ¼ 19 N) than the static optimization solution (R 2 ¼ 0.38; RMSE ¼ 61 N). Possible forces for 27 of 34 muscles varied between zero and the peak muscle force, inducing a peak hip force of 11.3 body-weights. Plausible muscle forces closely matched the selected EMG patterns; no effect of the EMG constraint was observed on the remaining muscle force ranges. The model can be used to study alternative muscle recruitment strategies in both physiological and pathophysiological neuromotor conditions.

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“…However, this research leaves open the question whether value is presented in the brain very coarsely for discriminating among a small number of different choices or is represented in full detail in a topographic map. We therefore devised a saccade task in which the expected gain varied smoothly as a function of saccadic end point, similar to tasks that have been used to study the effect of value on pointing behavior (18)(19)(20). Value has been shown to influence the fine-tuning of motor actions (e.g., pointing).…”

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confidence: 99%

Dynamic integration of information about salience and value for saccadic eye movements

Schütz

Trommershäuser

Gegenfurtner

2012

Proc. Natl. Acad. Sci. U.S.A.

114

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Humans shift their gaze to a new location several times per second. It is still unclear what determines where they look next. Fixation behavior is influenced by the low-level salience of the visual stimulus, such as luminance, contrast, and color, but also by highlevel task demands and prior knowledge. Under natural conditions, different sources of information might conflict with each other and have to be combined. In our paradigm, we trade off visual salience against expected value. We show that both salience and value information influence the saccadic end point within an object, but with different time courses. The relative weights of salience and value are not constant but vary from eye movement to eye movement, depending critically on the availability of the value information at the time when the saccade is programmed. Shortlatency saccades are determined mainly by salience, but value information is taken into account for long-latency saccades. We present a model that describes these data by dynamically weighting and integrating detailed topographic maps of visual salience and value. These results support the notion of independent neural pathways for the processing of visual information and value.neuroeconomics | decision-making | cue combination | visual perception B ecause of foveal specialization for high acuity and color vision, humans frequently move their eyes to project different parts of the visual scene on the fovea. Although the basic networks for the programming and execution of saccades have been studied for decades (1, 2), surprisingly little is known about the neural processes that underlie selection of the point of fixation of the next saccade. To some degree, the weighted combination of basic visual-stimulus features can predict saccadic eye movements in natural scenes (3-5). These basic stimulus features are, among others, local differences in luminance, color, or orientation and are combined by the visual system in a bottom-up image-based salience map. However, the salience difference between fixated and nonfixated image locations is typically rather small (6, 7), indicating that the influence of salience may be modulated by other factors. Visual salience, by definition, is determined by features of the visual scene alone and therefore is determined exclusively by visual bottom-up processing. Other factors reflect the influence of top-down processing. Task demands, for example, exhibit constraints on gaze patterns in different activities such as visual searching (8), manipulating an object (9), playing ball sports, preparing a cup of tea (10), and navigating between obstacles (11). In all these examples, gaze is concentrated on objects that are relevant for the task.Along different lines, recent research in neuroeconomics has used saccadic eye movements as a tool to uncover the neural bases of primate choice behavior. The results of these experiments indicate that value can be an important determinant of the neural activity underlying the selection of a saccadic target when one object bear...

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Bayesian decision theory in sensorimotor control

Cited by 687 publications

References 55 publications

Bayesian sampling in visual perception

Bayesian sampling in visual perception

Stochastic modelling of muscle recruitment during activity

Dynamic integration of information about salience and value for saccadic eye movements

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