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Shaklai, S.; Mimouni-Bloch, A.; Levin, M.; Friedman, J. |
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Title |
Development of finger force coordination in children |
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2017 |
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Experimental Brain Research |
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235 |
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12 |
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3709–3720 |
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Coordination is often observed as body parts moving together. However, when producing force with multiple fingers, the optimal coordination is not to produce similar forces with each finger, but rather for each finger to correct mistakes of other fingers. In this study, we aim to determine whether and how this skill develops in children aged 4-12 years. We measured this sort of coordination using the uncontrolled manifold hypothesis (UCM). We recorded finger forces produced by 60 typically developing children aged between 4 and 12 years in a finger-pressing task. The children controlled the height of an object on a screen by the total amount of force they produced on force sensors. We found that the synergy index, a measure of the relationship between “good” and “bad” variance, increased linearly as a function of age. This improvement was achieved by a selective reduction in “bad” variance rather than an increase in “good” variance. We did not observe differences between males and females, and the synergy index was not able to predict outcomes of upper limb behavioral tests after controlling for age. As children develop between the ages of 4 and 12 years, their ability to produce negative covariation between their finger forces improves, likely related to their improved ability to perform dexterous tasks. |
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Shaklai2017 |
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86 |
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Awasthi, Bhuvanesh; Friedman, Jason; Williams, Mark A |
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Title |
Reach Trajectories Reveal Delayed Processing of Low Spatial Frequency Faces in Developmental Prosopagnosia |
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2012 |
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Cognitive Neuroscience |
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3 |
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2 |
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120-130 |
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Developmental prosopagnosia (DP) is characterized by a selective deficit in face recognition despite normal cognitive and neurological functioning. Previous research has established configural processing deficits in DP subjects. Low spatial frequency (LSF) information subserves configural face processing. Using hybrid stimuli, here we examined the evolution of perceptual dynamics and integration of LSF information by DP subjects while they pointed to high spatial frequency (HSF) face targets. Permutation analysis revealed a 230-ms delay in LSF processing by DP subjects as compared to controls. This delayed processing is likely to contribute to the difficulties associated with face recognition in DP subjects and is reflective of their alleged reliance on local rather than global features in face perception. These results suggest that quick and efficient processing of LSF information is critical for the development of normal face perception. |
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Penn State @ write.to.jason @ |
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27 |
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Awasthi, Bhuvanesh; Friedman, Jason; Williams, Mark |
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Faster, stronger, lateralized: Low spatial frequency information supports face processing |
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2011 |
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Neuropsychologia |
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49 |
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13 |
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3583-3590 |
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Distinct visual pathways are selectively tuned for processing specific spatial frequencies. Recently, Awasthi, Friedman and Williams (2011) reported fast categorisation of faces at periphery, arguing for primacy of low spatial frequency (LSF) information in face processing. However, previous studies have also documented rapid categorization of places and natural scenes. Here, we tested if the LSF advantage is face specific or also involved in place perception. We used visually guided reaching as a continuous behavioral measure to examine the processing of LSF and high spatial frequency (HSF) hybrids, presented at the periphery. Subjects reached out and touched targets and their movements were recorded. The trajectories revealed that LSF interference was both 95 ms earlier and stronger for faces than places and was lateralized to the left visual field. The early processing of LSF information supports the assumption that faces are prioritised and provides a (neural) framework for such specialised processing. |
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Penn State @ write.to.jason @ |
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25 |
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Friedman, Jason; SKM, Varadhan; Zatsiorsky, Vladimir M.; Latash, Mark L. |
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Title |
The sources of two components of variance: an example of multifinger cyclic force production tasks at different frequencies |
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2009 |
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Experimental Brain Research |
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Exp Brain Res |
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196 |
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2 |
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263-277 |
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In a multifinger cyclic force production task, the finger force variance measured across trials can be decomposed into two components, one that affects the combined force output (“bad variance”) and one that does not (“good variance”). Previous studies have found similar time patterns of “bad variance” and force rate leading to an approximately linear relationship between them. Based on this finding and a recently developed model of multifinger force production, we expected the “bad variance” during cyclic force production to increase monotonically with the rate of force change, both within a cycle and across trials at different frequencies. Alternatively, “bad variance” could show a dependence on task frequency, not on actual force derivative values. Healthy subjects were required to produce cyclic force patterns to prescribed targets by pressing on unidimensional force sensors, at a frequency set by a metronome. The task was performed with only the index finger, and with all four fingers. In the task with all four fingers, the “good variance” increased approximately linearly with an increase in the force magnitude. The “bad variance” showed within-a-cycle modulation similar to that of the force rate. However, an increase in the frequency did not lead to an increase in the “bad variance” that could be expected based on the natural relationships between action frequency and the rate of force change modulation. The results have been interpreted in the framework of an earlier model of multifinger force production where “bad variance” is a result of variance of the timing parameter. The unexpected lack of modulation of the “bad variance” with frequency suggests a drop in variance of the timing parameter with increased frequency. This mechanism may serve to maintain a constant acceptable level of variance under different conditions. |
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Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802, USA |
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1432-1106 |
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PMID:19468721 |
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Penn State @ write.to.jason @ |
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15 |
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Friedman, J.; Amiaz, A.; Korman, M. |
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Title |
The online and offline effects of changing movement timing variability during training on a finger-opposition task |
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Year |
2022 |
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Scientific Reports |
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Sci Rep |
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12 |
Issue |
1 |
Pages |
13319 |
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Fingers; Humans; *Learning; *Motor Skills; Movement; Psychomotor Performance; Upper Extremity |
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In motor learning tasks, there is mixed evidence for whether increased task-relevant variability in early learning stages leads to improved outcomes. One problem is that there may be a connection between skill level and motor variability, such that participants who initially have more variability may also perform worse on the task, so will have more room to improve. To avoid this confound, we experimentally manipulated the amount of movement timing variability (MTV) during training to test whether it improves performance. Based on previous studies showing that most of the improvement in finger-opposition tasks comes from optimizing the relative onset time of the finger movements, we used auditory cues (beeps) to guide the onset times of sequential movements during a training session, and then assessed motor performance after the intervention. Participants were assigned to three groups that either: (a) followed a prescribed random rhythm for their finger touches (Variable MTV), (b) followed a fixed rhythm (Fixed control MTV), or (c) produced the entire sequence following a single beep (Unsupervised control MTV). While the intervention was successful in increasing MTV during training for the Variable group, it did not lead to improved outcomes post-training compared to either control group, and the use of fixed timing led to significantly worse performance compared to the Unsupervised control group. These results suggest that manipulating MTV through auditory cues does not produce greater learning than unconstrained training in motor sequence tasks. |
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2045-2322 |
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PMID:35922460; PMCID:PMC9349301 |
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115 |
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