Weight-Specific Anticipatory Coding of Grip Force in Human Dorsal Premotor Cortex

Nuenen, Bart F.L. van; Kuhtz-Buschbeck, Johann P.; Schulz, Christian; Bloem, Bastiaan R.; Siebner, Hartwig R.

doi:10.1523/jneurosci.5673-11.2012

Cited by 45 publications

(33 citation statements)

References 44 publications

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“…The higher the announced likelihood of a heavy object, the higher the peak GFr and LFr, and the shorter the load phase duration and the interval between peak LFr and liftoff. In a recent study using a grip-lift task (van Nuenen et al 2012), two subsequent visual cues (indicating either high of low weight) were either congruent (75 % of trials), or incongruent (25 % of trials). Since the actual weight was always correctly predicted by the second pre-cue, the first cue can also be seen as a probabilistic pre-cue.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic information on object weight shapes force dynamics in a grip-lift task

2015

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Advance information, such as object weight, size and texture, modifies predictive scaling of grip forces in a grip-lift task. Here, we examined the influence of probabilistic advance information about object weight. Fifteen healthy volunteers repeatedly grasped and lifted an object equipped with a force transducer between their thumb and index finger. Three clearly distinguishable object weights were used. Prior to each lift, the probabilities for the three object weights were given by a visual cue. We examined the effect of probabilistic pre-cues on grip and lift force dynamics. We expected predictive scaling of grip force parameters to follow predicted values calculated according to probabilistic contingencies of the cues. We observed that probabilistic cues systematically influenced peak grip and load force rates, as an index of predictive motor scaling. However, the effects of probabilistic cues on force rates were nonlinear, and anticipatory adaptations of the motor output generally seemed to overestimate high probabilities and underestimate low probabilities. These findings support the suggestion that anticipatory adaptations and force scaling of the motor system can integrate probabilistic information. However, probabilistic information seems to influence motor programs in a nonlinear fashion.

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Section: Discussionmentioning

confidence: 99%

Probabilistic information on object weight shapes force dynamics in a grip-lift task

2015

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“…While SOG is primarily a basic measure of the integrity of the corticospinal tract (Schulz et al, 2012), functional MRI (fMRI) studies also indicate that SOG performance results in some basal ganglia and cerebellar activation (Cramer, et al, 2002; Keisker, Hepp-Reymond, Blickenstorfer, & Kollias, 2010; van Nuenen, Kuhtz-Buschbeck, Schulz, Bloem, & Siebner, 2012). Nonetheless, this SOG network is far more simple than the activation patterns observed during FTT performance as assessed by functional neuroimaging (De Guio, Jacobson, Molteno, Jacobson, & Meintjes, 2012; Lopez-Larson et al, 2012; Witt, Laird, & Meyerand, 2008), where coordinated finger movement has been shown to depend on widespread motor connections (Jin, Lin, & Hallett, 2012; Roessner et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Neuropsychological investigation of motor impairments in autism

Duffield

Trontel

Bigler

et al. 2013

Journal of Clinical and Experimental Neuropsychology

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It is unclear how standardized neuropsychological measures of motor function relate to brain volumes of motor regions in autism spectrum disorder (ASD). An all male sample composed of 59 ASD and 30 controls (ages 5–33 years) completed three measures of motor function: strength of grip (SOG), finger tapping test (FTT), and grooved peg-board test (GPT). Likewise, all participants underwent magnetic resonance imaging with region of interest (ROI) volumes obtained to include the following regions: motor cortex (pre-central gyrus), somatosensory cortex (post-central gyrus), thalamus, basal ganglia, cerebellum and caudal middle frontal gyrus. These traditional neuropsychological measures of motor function are assumed to differ in motor complexity with GPT requiring the most followed by FTT and SOG. Performance by ASD participants on the GPT and FTT differed significantly from controls, with the largest effect size differences observed on the more complex GPT task. Differences on the SOG task between the two groups were non-significant. Since more complex motor tasks tap more complex networks, poorer GPT performance by those with ASD may reflect less efficient motor networks. There was no gross pathology observed in classic motor areas of the brain in ASD, as region of interest (ROI) volumes did not differ, but FTT was negatively related to motor cortex volume in ASD. The results suggest a hierarchical motor disruption in ASD, with difficulties evident only in more complex tasks as well as a potential anomalous size-function relation in motor cortex in ASD.

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“…Although some studies have explored the neural representation of objects' surface and material properties (2-4) and weights (5)(6)(7) or investigated the brain areas involved in explicit, textbook-style physical reasoning (8,9), little is known about the cortical machinery that supports the more implicit perceptual judgments about physical events that are so pervasive in daily life. However, behavioral findings from young children and adults suggest that we use systematic cognitive and neural machinery to make physical inferences.…”

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confidence: 99%

Functional neuroanatomy of intuitive physical inference

Fischer

Mikhael

Tenenbaum

et al. 2016

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

149

159

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To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.physical scene understanding | mental simulation | fMRI | premotor cortex | action planning U nderstanding, predicting, and acting on the world requires an intuitive grasp of physics ( Fig. 1). We see not just a table and a coffee cup, but a table supporting a coffee cup. We see not just a ping pong ball moving after contact with a paddle, but the paddle causing the ball to move by exerting a force through that contact. We use physical intuitions to not just understand the world but predict what will happen next-that a stack of dishes is unstable and likely to fall or that a squash ball is on a trajectory to ricochet off the wall and head in our direction. We also need rich physical knowledge to plan our own actions. Before we pick up an object, we must assess its material and weight and prepare our muscles accordingly. To navigate our environment, we need to determine which surfaces will support us (e.g., a linoleum floor but maybe not the surface of a frozen stream; this tree branch but probably not that one) and what barriers are penetrable (e.g., a beaded curtain but not a glass wall). How do we compute these everyday physical inferences with such apparent ease and speed?Battaglia et al.(1) recently proposed a computational mechanism for how humans can make a wide range of physical inferences in natural scenes via a mental simulation engine akin to the "physics engines" used in many video games. Physics engines are software systems that support efficient but approximate simulations of rigid body, soft body, or fluid mechanics for the purpose of generating realistic interactive gameplay in a virtual physical world. Rather than striving for fine-grained physical accuracy, game physics engines make shortcu...

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Weight-Specific Anticipatory Coding of Grip Force in Human Dorsal Premotor Cortex

Cited by 45 publications

References 44 publications

Probabilistic information on object weight shapes force dynamics in a grip-lift task

Probabilistic information on object weight shapes force dynamics in a grip-lift task

Neuropsychological investigation of motor impairments in autism

Functional neuroanatomy of intuitive physical inference

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