Neural Correlates of Internal Models: Figure 1.

Xivry, Jean-Jacques Orban de; Ethier, Vincent

doi:10.1523/jneurosci.2426-08.2008

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…To guide these eye movements, it is useful to be able to anticipate the future location of moving targets (Orban de Xivry et al 2008.) Humans, chimpanzees, and monkeys are able to predict target motion even when the target is rendered temporarily invisible (Bennett and Barnes 2006; Churchland et al 2003; Filion et al 1996; Collier-Baker et al 2005), although it is unclear whether macaques possess this ability in the same degree as apes (Natale et al, 1986; de Blois and Novak, 1994).…”

Section: Introductionmentioning

confidence: 99%

Internally Generated Error Signals in Monkey Frontal Eye Field during an Inferred Motion Task

Ferrera

Barborică²

2010

J. Neurosci.

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An internal model for predictive saccades in frontal cortex was investigated by recording neurons in monkey frontal eye field (FEF) during an inferred motion task. Monkeys were trained to make saccades to the extrapolated position of a small moving target that was rendered temporarily invisible and whose trajectory was altered. On approximately two-thirds of the trials, monkeys made multiple saccades while the target was invisible. Primary saccades were correlated with extrapolated target position. Secondary saccades significantly reduced residual errors resulting from imperfect accuracy of the first saccade. These observations suggest that the second saccade was corrective. Because there was no visual feedback, corrective saccades could only be driven by an internally generated error signal. Neuronal activity in the frontal eye field was directionally tuned before both primary and secondary saccades. Separate subpopulations of cells encoded either saccade direction or direction error before the second saccade. These results suggest that FEF neurons encode the error after the first saccade, as well as the direction of the second saccade. Hence, FEF appears to contribute to detecting and correcting movement errors based on internally generated signals.

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Section: Introductionmentioning

confidence: 99%

Internally Generated Error Signals in Monkey Frontal Eye Field during an Inferred Motion Task

Ferrera

Barborică²

2010

J. Neurosci.

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“…While there is consensus that some inverse kinematic computation likely occurs in the cerebellum -not the focus of the present study as cerebellum is usually argued not to influence awareness (Koch, 2018)-little neuroimaging work has studied cortical inverse models. Some researchers argue such computations are performed directly in motor cortex (Schweighofer et al, 1998), but experimental evidence suggests this occurs upstream of premotor cortex in visuomotor pathways (Ghasia et al, 2008;Xivry & Ethier, 2008). There is controltheoretic justification for inverse kinematic computation to occur near primary sensory areas; fast "suboptimal" control policies often benchmark better than theoretically optimal but slower policies in practice, as the benefits of more rapidly updating (imperfect) actions given feedback seem to outstrip that of step-by-step optimality (Howell et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Overlapping Cortical Substrate of Biomechanical Control and Subjective Agency

Veillette,

Chao,

Nith

et al. 2024

Preprint

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Every movement requires the nervous system to solve a complex biomechanical control problem, but this process is mostly veiled from one's conscious awareness. Simultaneously, we also have conscious experience of controlling our movements - our sense of agency (SoA). Whether SoA corresponds to those neural representations that implement actual neuromuscular control is an open question with ethical, medical, and legal implications. If SoA is the conscious experience of control, this predicts that SoA can be decoded from the same brain structures that implement the so-called "inverse kinematic" computations for planning movement. We correlated human fMRI measurements during hand movements with the internal representations of a deep neural network (DNN) performing the same hand control task in a biomechanical simulation - revealing detailed cortical encodings of sensorimotor states, idiosyncratic to each subject. We then manipulated SoA by usurping control of participants' muscles via electrical stimulation, and found that the same voxels which were best explained by modeled inverse kinematic representations - which, strikingly, were located in canonically visual areas - also predicted SoA. Importantly, model-brain correspondences and robust SoA decoding could both be achieved within single subjects, enabling relationships between motor representations and awareness to be studied at the level of the individual.

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“…An alternative explanation for these improvements is that while using AOMI, the children were provided with the opportunity to pair their MI-generated forward model with an accurate real-time visual display [ 8 , 36 ]. The cerebellum is believed to generate forward models allowing the prediction of the sensory consequences of movements [ 65 ]. In accordance with the prominent IMD hypothesis, this could explain the improved performance and techniques on shoelace tying and cup stacking tasks.…”

Section: Discussionmentioning

confidence: 99%

Combined action observation and motor imagery improves learning of activities of daily living in children with Developmental Coordination Disorder

et al. 2023

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Developmental coordination disorder (DCD) is characterised by poor motor coordination, which interferes with the ability to execute activities of daily living (ADLs). Combined action observation and motor imagery (AOMI) involves observing movement videos whilst imagining simultaneously the sensations of executing the same movement. Laboratory-based research indicates that AOMI can help improve movement coordination in children with DCD, but no previous research had investigated the efficacy of AOMI interventions for learning ADLs. This study investigated the efficacy of a home-based, parent-led, AOMI intervention for learning ADLs in children with DCD. Children with confirmed (n = 23) or suspected (n = 5) DCD (total sample n = 28), aged 7–12 years, were assigned to either an AOMI intervention or a control intervention (both n = 14). Participants attempted the following ADLs at pre-test (week 1), post-test (week 4), and retention test (week 6): shoelace tying, cutlery use, shirt buttoning, and cup stacking. Task completion times and movement techniques were recorded. The AOMI intervention produced significantly faster task completion times than the control intervention at post-test for shoelace tying, and significantly improved movement techniques for shoelace tying and cup stacking. Importantly, for children who could not tie shoelaces at pre-test (n = 9 per group), 89% of those following the AOMI intervention learnt the skill successfully by the end of the study, compared to only 44% of those following the control intervention. The findings indicate that home-based, parent-led, AOMI interventions can aid the learning of complex ADLs in children with DCD, and may be particularly effective for facilitating the learning of motor skills that do not currently exist within these children’s motor repertoire.

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Neural Correlates of Internal Models: Figure 1.

Cited by 8 publications

References 9 publications

Internally Generated Error Signals in Monkey Frontal Eye Field during an Inferred Motion Task

Internally Generated Error Signals in Monkey Frontal Eye Field during an Inferred Motion Task

Overlapping Cortical Substrate of Biomechanical Control and Subjective Agency

Combined action observation and motor imagery improves learning of activities of daily living in children with Developmental Coordination Disorder

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