An Internal Model for Sensorimotor Integration

Wolpert, Daniel M.; Ghahramani, Zoubin; Jordan, Michael I.

doi:10.1126/science.7569931

Cited by 2,930 publications

(2,245 citation statements)

References 7 publications

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“…Importantly, these predictions not only allow humans to maintain and correct on-going movements but also to effectively separate their own actions from the environment (Jeannerod, 2003) and from actions of other agents (Daprati et al, 1997;Jeannerod, 2004). One approach to understanding the mechanisms underlying agency, the central monitoring framework (Frith et al, 2000b), is hence based on the internal model for sensorimotor control proposed by Wolpert et al (Miall and Wolpert, 1996;Wolpert et al, 1995).…”

Section: Agencymentioning

confidence: 99%

“…Motor corrections reflect how the current estimate and the predictions of future motor states are affected by the feedback and whether such motor corrections precede motor awareness (agency). Analysing motor performance across several experimental conditions is further necessary in order to relate it to the prevalent theoretical frameworks for agency (Blakemore et al, 1998a;Frith, 2005) and the aforementioned computational models of sensorimotor control (Todorov and Jordan, 2002;Wolpert et al, 1995).…”

Section: Agencymentioning

confidence: 99%

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Agency, gait and self-consciousness

Kannape

Blanke

2012

International Journal of Psychophysiology

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Agency is an important aspect of bodily self-consciousness, allowing us to separate own movements from those induced by the environment and to distinguish own movements from those of other agents. Unsurprisingly, theoretical frameworks for agency such as central monitoring are closely tied to computational models of sensorimotor control. Until recently agency research has largely focussed on goal-directed movements of the upper limbs. In particular, the influence of performance-related sensory cues and the relevance of prediction signals for agency judgements have been studied through a variety of spatio-temporal mismatches between movement and the sensory consequences of movement. However, agents often perform a different type of movement; highly automated movements that involve the entire body such as walking, cycling, and swimming with potentially different agency mechanisms. Here, we review recent work about agency for full-body movements such as gait, highlighting the effects of performance-related visual and auditory cues on gait agency. Gait movements differ from upper limb actions. Gait is cyclic, more rarely immediately goal-directed, and is generally considered one of the most automatic and unconscious actions. We discuss such movement differences with respect to the functional mechanisms of full-body agency and body-part agency by linking these gait agency paradigms to computational models of motor control. This is followed by a selective review of gait control, locomotion, and models of motor control relying on prediction signals and underlining their relevance for full-body agency.

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Section: Agencymentioning

confidence: 99%

Section: Agencymentioning

confidence: 99%

Agency, gait and self-consciousness

Kannape

Blanke

2012

International Journal of Psychophysiology

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“…Humans and animals associate sensory events (stimuli, rewards or behavioral responses) with other sensory events and use these associations to form novel associative chains (Craik 1943;Piaget 1954;Mackintosh 1974;Arbib 1972;Dickinson 1980;Wolpert et al 1995). However, the TD model is limited to associations between stimuli and one type of reward and does not form novel associative chains.…”

Section: Extended Td Modelmentioning

confidence: 99%

“…Planning may not only be involved in "cognitive" tasks but also in "motor" tasks that cannot be solved with sensorimotor learning (Wolpert et a., 1995). Pure sensorimotor learning fails to explain behavior in tasks that require novel movements to reach a goal.…”

Section: Saccadic Eye Movementsmentioning

confidence: 99%

Modeling functions of striatal dopamine modulation in learning and planning

Suri¹,

Bargas

Arbib³

2001

Neuroscience

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The activity of midbrain dopamine neurons is strikingly similar to the reward prediction error of TD reinforcement learning models. Experimental evidence and simulation studies suggest that dopamine neuron activity serves as an effective reinforcement signal for learning of sensorimotor associations in striatal matrisomes.In the current study, we simulate dopamine neuron activity with the Extended TD model (Suri and Schultz, submitted) and examine the influence of this signal on medium spiny neurons in striatal matrisomes. This model includes transient membrane effects of dopamine, dopamine-dependent longterm adaptations of corticostriatal transmission, and rhythmic fluctuations of the membrane potential between an elevated "up-state" and a hyperpolarized "down-state." The most dominant activity in the striatal matrisomes elicits behaviors via projections from the basal ganglia to the thalamus and the cortex. This "standard model" performs successfully when tested for sensorimotor learning and goaldirected behavior (planning). To investigate the contributions of these model assumptions to learning and planning, we test the performance of several model variants that lack one of these mechanisms. These simulations show that the adaptation of the dopamine-like signal is necessary for planning and for sensorimotor learning. Lack of dopamine-like novelty responses decreases the number of exploratory acts, which deteriorates planning capabilities. Sensorimotor learning requires dopamine-dependent long-term adaptation of corticostriatal transmission. The model loses its planning capabilities if the dopamine-like signal is simulated with the original TD model. The capability for planning is improved by transient dopamine membrane effects, dopamine-dependent long-term effects on corticostriatal transmission, dopamine-and input-dependent influences on the durations of membrane potential fluctuations, and manipulations that prolong the reaction time of the model. These simulation results suggest that striatal dopamine is important for sensorimotor learning, exploration, and planning.

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“…Computational neuroscience has provided an important theoretical concept for motor control research -that of internal models (see, for example, Wolpert, 1997;Wolpert, Ghahramani, & Jordan, 1995;Wolpert, Miall, & Kawato, 1998). Forward internal models predict the outcome of a motor command, allowing individuals to select the most appropriate motor plan for a particular movement (Wolpert, 1997).…”

Section: Introductionmentioning

confidence: 99%