Limits...
Mapping Muscles Activation to Force Perception during Unloading.

Toma S, Lacquaniti F - PLoS ONE (2016)

Bottom Line: In fact a global measure of the muscles considered was able to predict approximately 60% of the perceptual decisions total variance.Moreover the inter-subjects differences in psychophysical sensitivity showed high correlation with both participants' muscles sensitivity and participants' joint torques.Overall, our findings gave insights into both the role played by the corticospinal motor commands while performing a force detection task and the influence of the gravitational muscular torque on the estimation of vertical forces.

View Article: PubMed Central - PubMed

Affiliation: Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy.

ABSTRACT
It has been largely proved that while judging a force humans mainly rely on the motor commands produced to interact with that force (i.e., sense of effort). Despite of a large bulk of previous investigations interested in understanding the contributions of the descending and ascending signals in force perception, very few attempts have been made to link a measure of neural output (i.e., EMG) to the psychophysical performance. Indeed, the amount of correlation between EMG activity and perceptual decisions can be interpreted as an estimate of the contribution of central signals involved in the sensation of force. In this study we investigated this correlation by measuring the muscular activity of eight arm muscles while participants performed a quasi-isometric force detection task. Here we showed a method to quantitatively describe muscular activity ("muscle-metric function") that was directly comparable to the description of the participants' psychophysical decisions about the stimulus force. We observed that under our experimental conditions, muscle-metric absolute thresholds and the shape of the muscle-metric curves were closely related to those provided by the psychophysics. In fact a global measure of the muscles considered was able to predict approximately 60% of the perceptual decisions total variance. Moreover the inter-subjects differences in psychophysical sensitivity showed high correlation with both participants' muscles sensitivity and participants' joint torques. Overall, our findings gave insights into both the role played by the corticospinal motor commands while performing a force detection task and the influence of the gravitational muscular torque on the estimation of vertical forces.

Show MeSH

Related in: MedlinePlus

Threshold and slope ratios (muscle/behavior) of experimental data and two simulated models.Upper graph shows the ratio between the psychophysical threshold and the muscle-metric PSE from the empirical muscular model with 8 predictors (EXP), the simplest and reliable model selected with 3 predictors (BEST) and the model with just one predictors (LEAST). Data samples were obtained by merging 11 subjects (same as in Fig 4) PSEs from muscle and psycho -metric measures calculated across bootstrap procedure. Lower graph depicts the ratio between the slopes of the two metrics curves for the three muscular models mentioned above. Data samples were obtained by merging 9 subjects (same as in Fig 4) slopes muscle and psycho -metric measures calculated across bootstrap procedure. In each graphs gray lines indicate the medians of the ratio sample (50° percentile). Boxes represent data between the 25°, Q1, and 75°, Q3, percentile of the whole sample. Upper and lower whiskers size were calculated considering the values extracted from Q3 + 1.5 * (Q3-Q1) and Q3–1.5 * (Q3-Q1), respectively.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4816335&req=5

pone.0152552.g008: Threshold and slope ratios (muscle/behavior) of experimental data and two simulated models.Upper graph shows the ratio between the psychophysical threshold and the muscle-metric PSE from the empirical muscular model with 8 predictors (EXP), the simplest and reliable model selected with 3 predictors (BEST) and the model with just one predictors (LEAST). Data samples were obtained by merging 11 subjects (same as in Fig 4) PSEs from muscle and psycho -metric measures calculated across bootstrap procedure. Lower graph depicts the ratio between the slopes of the two metrics curves for the three muscular models mentioned above. Data samples were obtained by merging 9 subjects (same as in Fig 4) slopes muscle and psycho -metric measures calculated across bootstrap procedure. In each graphs gray lines indicate the medians of the ratio sample (50° percentile). Boxes represent data between the 25°, Q1, and 75°, Q3, percentile of the whole sample. Upper and lower whiskers size were calculated considering the values extracted from Q3 + 1.5 * (Q3-Q1) and Q3–1.5 * (Q3-Q1), respectively.

Mentions: In agreement with the neuro-metric literature, we quantified the similarity between the muscle-metric and the psycho-metric functions by calculating a ratio for both the absolute thresholds and the shape of the curves (i.e., slope) obtained by the two metrics, where the greater is the concordance between the two curves, the closer the ratio will be to unity. Fig 8 depicts PSE and slope ratios obtained by merging the thresholds and the slopes of 11 and 9 subjects (same subjects as in Fig 4), respectively. In both graphs, the three boxplots represent the absolute thresholds and the shape of the curves obtained by bootstrapping the psychometric and muscle-metric data from the real experiment (EXP, eight terms muscular model), the best nested muscular model (BEST, three terms nested model) and the least muscular model composed by only one muscle (LEAST). As it can be noticed in the upper graph, the PSEs extracted from both the experimental and the best nested muscular model show a similar good concordance with the psychophysical thresholds in terms of PSE ratio median (0.972 and 0.975 for EXP and BEST, respectively), inter-quantiles difference (0.39 and 0.38) as well as upper (1.89 and 1.86) and lower (0.31 and 0.35) bounds. Instead the LEAST muscular model provided a slight reduction in the PSE ratio median (0.92) and a relevant increase in both the inter-quantiles difference (0.67) and the upper lower bounds (2.48 and -0.2, respectively). Overall, the PSE ratios suggest that the muscle metric curves obtained from the experimental and the best nested model provided a reliable concordance with the psychometric curve. Moreover in the boxplots of all PSE ratios the higher distance of the median from the third quartile indicates a tendency of the muscles to provide an absolute thresholds higher than the psychophysical ones (i.e., positive ratio). Likewise the PSE ratio, EXP and BEST models yielded a very similar slope ratio distribution. Again, in this case the median of the ratio approximated unity (0.97 and 1 for EXP and BEST, respectively), but it presented an increase of the inter-quantile difference (1.41 and 1.43) and the upper (3.93 and 4.00) and lower bounds (-0.04 and 0.01). On the contrary, LEAST nested model presented lower dispersion in the distribution of its slope ratios but with a relevant reduction in the median (0.59). In sum, the differences found in the slope ratio distributions indicate again a better muscle-perception concordance between the muscle metric curves obtained with either all eight or the three, most relevant, muscles. However the high dispersion of the data indicates a relevant difference between the variability of the muscles versus the psycho-metric curves. In particular the consistent presence of above unity slope ratios suggests higher variability (i.e., lower precision) in the perceptual decisions rather than in muscular estimates.


Mapping Muscles Activation to Force Perception during Unloading.

Toma S, Lacquaniti F - PLoS ONE (2016)

Threshold and slope ratios (muscle/behavior) of experimental data and two simulated models.Upper graph shows the ratio between the psychophysical threshold and the muscle-metric PSE from the empirical muscular model with 8 predictors (EXP), the simplest and reliable model selected with 3 predictors (BEST) and the model with just one predictors (LEAST). Data samples were obtained by merging 11 subjects (same as in Fig 4) PSEs from muscle and psycho -metric measures calculated across bootstrap procedure. Lower graph depicts the ratio between the slopes of the two metrics curves for the three muscular models mentioned above. Data samples were obtained by merging 9 subjects (same as in Fig 4) slopes muscle and psycho -metric measures calculated across bootstrap procedure. In each graphs gray lines indicate the medians of the ratio sample (50° percentile). Boxes represent data between the 25°, Q1, and 75°, Q3, percentile of the whole sample. Upper and lower whiskers size were calculated considering the values extracted from Q3 + 1.5 * (Q3-Q1) and Q3–1.5 * (Q3-Q1), respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4816335&req=5

pone.0152552.g008: Threshold and slope ratios (muscle/behavior) of experimental data and two simulated models.Upper graph shows the ratio between the psychophysical threshold and the muscle-metric PSE from the empirical muscular model with 8 predictors (EXP), the simplest and reliable model selected with 3 predictors (BEST) and the model with just one predictors (LEAST). Data samples were obtained by merging 11 subjects (same as in Fig 4) PSEs from muscle and psycho -metric measures calculated across bootstrap procedure. Lower graph depicts the ratio between the slopes of the two metrics curves for the three muscular models mentioned above. Data samples were obtained by merging 9 subjects (same as in Fig 4) slopes muscle and psycho -metric measures calculated across bootstrap procedure. In each graphs gray lines indicate the medians of the ratio sample (50° percentile). Boxes represent data between the 25°, Q1, and 75°, Q3, percentile of the whole sample. Upper and lower whiskers size were calculated considering the values extracted from Q3 + 1.5 * (Q3-Q1) and Q3–1.5 * (Q3-Q1), respectively.
Mentions: In agreement with the neuro-metric literature, we quantified the similarity between the muscle-metric and the psycho-metric functions by calculating a ratio for both the absolute thresholds and the shape of the curves (i.e., slope) obtained by the two metrics, where the greater is the concordance between the two curves, the closer the ratio will be to unity. Fig 8 depicts PSE and slope ratios obtained by merging the thresholds and the slopes of 11 and 9 subjects (same subjects as in Fig 4), respectively. In both graphs, the three boxplots represent the absolute thresholds and the shape of the curves obtained by bootstrapping the psychometric and muscle-metric data from the real experiment (EXP, eight terms muscular model), the best nested muscular model (BEST, three terms nested model) and the least muscular model composed by only one muscle (LEAST). As it can be noticed in the upper graph, the PSEs extracted from both the experimental and the best nested muscular model show a similar good concordance with the psychophysical thresholds in terms of PSE ratio median (0.972 and 0.975 for EXP and BEST, respectively), inter-quantiles difference (0.39 and 0.38) as well as upper (1.89 and 1.86) and lower (0.31 and 0.35) bounds. Instead the LEAST muscular model provided a slight reduction in the PSE ratio median (0.92) and a relevant increase in both the inter-quantiles difference (0.67) and the upper lower bounds (2.48 and -0.2, respectively). Overall, the PSE ratios suggest that the muscle metric curves obtained from the experimental and the best nested model provided a reliable concordance with the psychometric curve. Moreover in the boxplots of all PSE ratios the higher distance of the median from the third quartile indicates a tendency of the muscles to provide an absolute thresholds higher than the psychophysical ones (i.e., positive ratio). Likewise the PSE ratio, EXP and BEST models yielded a very similar slope ratio distribution. Again, in this case the median of the ratio approximated unity (0.97 and 1 for EXP and BEST, respectively), but it presented an increase of the inter-quantile difference (1.41 and 1.43) and the upper (3.93 and 4.00) and lower bounds (-0.04 and 0.01). On the contrary, LEAST nested model presented lower dispersion in the distribution of its slope ratios but with a relevant reduction in the median (0.59). In sum, the differences found in the slope ratio distributions indicate again a better muscle-perception concordance between the muscle metric curves obtained with either all eight or the three, most relevant, muscles. However the high dispersion of the data indicates a relevant difference between the variability of the muscles versus the psycho-metric curves. In particular the consistent presence of above unity slope ratios suggests higher variability (i.e., lower precision) in the perceptual decisions rather than in muscular estimates.

Bottom Line: In fact a global measure of the muscles considered was able to predict approximately 60% of the perceptual decisions total variance.Moreover the inter-subjects differences in psychophysical sensitivity showed high correlation with both participants' muscles sensitivity and participants' joint torques.Overall, our findings gave insights into both the role played by the corticospinal motor commands while performing a force detection task and the influence of the gravitational muscular torque on the estimation of vertical forces.

View Article: PubMed Central - PubMed

Affiliation: Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy.

ABSTRACT
It has been largely proved that while judging a force humans mainly rely on the motor commands produced to interact with that force (i.e., sense of effort). Despite of a large bulk of previous investigations interested in understanding the contributions of the descending and ascending signals in force perception, very few attempts have been made to link a measure of neural output (i.e., EMG) to the psychophysical performance. Indeed, the amount of correlation between EMG activity and perceptual decisions can be interpreted as an estimate of the contribution of central signals involved in the sensation of force. In this study we investigated this correlation by measuring the muscular activity of eight arm muscles while participants performed a quasi-isometric force detection task. Here we showed a method to quantitatively describe muscular activity ("muscle-metric function") that was directly comparable to the description of the participants' psychophysical decisions about the stimulus force. We observed that under our experimental conditions, muscle-metric absolute thresholds and the shape of the muscle-metric curves were closely related to those provided by the psychophysics. In fact a global measure of the muscles considered was able to predict approximately 60% of the perceptual decisions total variance. Moreover the inter-subjects differences in psychophysical sensitivity showed high correlation with both participants' muscles sensitivity and participants' joint torques. Overall, our findings gave insights into both the role played by the corticospinal motor commands while performing a force detection task and the influence of the gravitational muscular torque on the estimation of vertical forces.

Show MeSH
Related in: MedlinePlus