Society for the Neural Control of Movement meeting 2019

1. Contact Info: simone.toma@asu.edu, marco.santello@asu.edu
Active perception through muscle synergies: a force perception study
Neural Control of
Movement Laboratory
Simone Toma1,2, Marco Santello²
1IRCCS, Fondazione Santa Lucia, Rome, IT
²School of Biological and Health Systems Engineering, Arizona State University, Arizona, USA
PROTOCOL ANALYSIS
MODELING RESULTS VALIDATION
Trial
ExternalForces(N)
‘YES’ answer
0
30
15
Trial Time
EMG
30%
External Forces (N)
p(‘YES’)
PSE
Slope
‘NO’ answer
Muscle Synergy Model
(MSM)
𝑌𝑖𝑘
∗
= 𝛽0 + 𝛽1 𝐹𝑖 + 𝜀𝑖𝑘 + 𝜇 𝑘
Yik
∗
Synergy1
Most Contributive Model
(MCM)
𝑃𝑜𝑜𝑙𝐸𝑀𝐺𝑖 = 𝑛𝑜𝑟𝑚𝑀𝐴𝑉𝑘 · 𝑤 𝑘
8
𝑖=1
PoolEMGi(a.u.)
Trials
External forces (N)
p(activationincrease)
External forces (N)
decrease
increase
Muscles
ActivationStrength
1 2 3 4 5 67 8
1 2 3 4 5 67 8
1 2 3 4 5 67 8
Force stimuli (N)
ActivationCoefficients
0 15 30
0 15 30
0 15 30
increase
decrease
External Forces
Do you perceive an upward
force acting on your arm?
External forces (N)
Synergy curve
p(yes)Synergy2
Synergy3
Individual Synergy curve Overall Synergy curve
PoolEMG curve
p(yes)
0 3015
0
1
p(activationincrease)
Overall
Individual
MSM MCM Full
model
No-MSM
R²
0.2
0.4
0.6
0.8
1
0
Δ PSE Δ Slope
0
3.5
-3.5
External forces (N)
0
0.5
1
Coefficientof
sharingmuscles
Subjects
VAF: 92%
Muscles
1
2
4
3
5
6
8
7
Brachioradialis
Biceps brachii
Triceps brachii
Trapezius Mid
Latissimus
Deltoid Ant
Deltoid Post
Trapezius Up
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0.5
0
0.5
1
0
0.5
1
0 3015 0 3015
Participants’ Local and Global
Variance Accounted For (VAF)
1
2
3
4
5
6
8
7
0
90
100
3 5 5 4 7 6 3 6 7
Number of synergies
6
GlobalVAF(%)Muscles
1 23 4 5 6 87
Activationstrength
1 23 4 5 6 87
Muscles
0
1
0.5
Local VAF 75%
psychometric-muscular
**
p < 0.01
R² > 0.6
*
**
RATIONALE AND APPROACH
Despite longstanding evidence suggesting a relation between action and perception, the mechanisms underlying their
integration are still unclear. It has been proposed that active perception would rely on the coupling between
movements and patterns of movement-generated sensory inflow1,2. We tested the hypothesis that perceptual
inference might be mediated by the way the brain coordinates multiple muscles activity during action perception. We
addressed this question by asking participants to judge the presence of an upward force (yes/no answer) applied on
their forearm while maintaining the arm in a quasi-isometric posture against the force stimulus. Participants’
probabilities of detection as a function of force (psychometric curves) was described by Probit fitting.
We developed two probabilistic models accounting for the modulation of the muscle activity involved in the isometric task,
i.e., muscle synergy and most contributive model (MSM and MCM, respectively). We hypothesized that the MSM -
describing the modulation of a subset of muscle synergies - would account for higher perceptual variance than the MCM
describing task-related muscles activity through flexible recruitment of individual muscles. We further validated our
hypothesis by showing that MCM and MSM highest perceptual variance accounted for relied on different groups of muscle.
Finally, we found that the MSM perceptual description did not depend on the model ability to reconstruct a large portion of
the original EMG dataset.
DISCUSSION
The present study provides evidence that a subset of motor synergies involved in an isometric task may underlie
active perception of force. Specifically, a model based on the modulation of elbow and shoulder muscle synergies,
MSM, explained approximately 70% of participants’ perceptual variance of force stimulus detection. We also
demonstrated that a model that assumes a linear combination of individual muscles activity, i.e., MCM, described
perceptual behavior significantly worse than the MSM. Our data revealed that the MSM’s ability to explain perceptual
variance did not depend on the ability of the model to reconstruct a larger portion of the original EMG dataset.
Moreover, as hypothesized MCM and MSM described perception by means of different muscle combinations, the
latter model being composed of a subset of muscle synergies involved in the motor task.
Our findings are in agreement with previous work suggesting that muscle synergies may facilitate the coordination of
activity of multiple muscles, hence the integration of efferent and afferent muscle signals3 underlying force perception.
The higher perceptual variance explained by the MSM suggests the involvement of central mechanisms combining task-
related sensorimotor signals that are most relevant for a perceptual decision (active sampling). Our results also support
and extend the proposition that the transformation of sensorimotor signals into perceptual decisions is influenced by the
strategy employed by the brain to deal with effort4, costs5 and, as shown here, a redundant musculoskeletal system. In
conclusion, our findings suggest that patterns of activity of multiple muscles organized in synergies may simplify the
sampling of relevant motor-sensory inputs6,7 underlying the action-perception loop.
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6. Bicchi, A., Gabiccini, M., Santello, M. Modelling natural and artificial hands with synergies. Phil Trans R Soc. 366, 3153-61, https://doi.org/10.1098/rstb.2011.0152 (2011).
7. Santello M, Bianchi M, Gabiccini M, Ricciardi E, Salvietti G, Prattichizzo D, Ernst M, Moscatelli A, Jorntell H, Kappers A, Kyriakopoulos K, Albu Schaeffer A, Castellini C, Bicchi A. Hand synergies:
Integration of robotics and neuroscience for understanding the control of biological and artificial hands. Physics of Life Reviews 17, 1-23 https://doi.org/10.1016/j.plrev.2016.02.001 (2016).
EMGEMG
VAF: 99%
VAF: 99%
1 2 3 4 5 6 871 2 3 4 5 6 87
VAF: 95%