Psychology
2013. Vol.4, No.12, 998-1003
Published Online December 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.412144
Open Access
998
Dependence of Manual Grasping on the Behavioral Context: A
Comparison between Arms and between Age Groups
Otmar Bock, Benjamin Baak
Institute of Physiology and Anatomy,German Sport University, Cologne, Germany
Email: bock@dshs-koeln.de
Received September 12th, 2013; revised October 16th, 2013; accepted November 15th, 2013
Copyright © 2013 Otmar Bock, Benjamin Baak. This is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited. In accordance of the Creative Commons Attribution License all
Copyrights © 2013 are reserved for SCIRP and the owner of the intellectual property Otmar Bock, Benjamin
Baak. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
We evaluated the kinematics and dynamics of grasping in a typical laboratory situation (L) and in a more
everyday-like situation (E), using right-handed subjects. Performance was compared when young subjects
used their right versus left arm, and when young versus old subjects used their left arm. As in our previ-
ous work, multiple differences emerged between parameter values in the two contexts, L and E. These
context differences were, however, more pronounced for the left rather than for the right arm of young
subjects, and more pronounced for the left arm of young rather than older subjects. We propose an expla-
nation based on the differential involvement of the dorsal and ventral cortical processing stream in L and
in E: The differential involvement would be accentuated for the left arm of young, but not for the left arm
of older subjects.
Keywords: Motor Control; Prehension; Context-Dependence; Sensorimotor Integration
Introduction
It has been suggested before that findings on motor perfor-
mance, yielded in the laboratory, may not necessarily apply in
everyday life (Chaytor & Schmitter-Edgecombe, 2003; In-
gram & Wolpert, 2011). We have recently scrutinized this view
by asking subjects to grasp and move a lever either in a typical
laboratory context (L)—grasping was instructed, externally
triggered, repetitive, and served no ultimate purpose—or in a
more everyday-like context (E)—grasping was not explicitly
instructed, self-initiated, embedded in complex behavior and
had the ecologically valid purpose to earn money (Bock &
Hagemann, 2010). Even though the mechanical constraints
were identical in both contexts, movement kinematics and dy-
namics widely differed. These differences could not be reduced
to a single underlying cause since factor analysis yielded multi-
ple orthogonal factors (Bock & Züll, 2013). This led us to con-
clude that grasping is controlled by multiple functional modules
which are differently sensitive to context.
Further research revealed that context-sensitivity can’t be
reduced to differences between L and E regarding movement
speed, attention focusing or task complexity, since manipula-
tions of those differences didn’t consistently change con-
text-sensitivity (Steinberg & Bock, 2013c). However, we ob-
served a consistent effect of personality traits: context-sensitiv-
ity was accentuated in subjects who prefer slow, attentive and
prudent processing (Steinberg & Bock, 2013b). We proposed
that this processing style is characteristic for the ventral rather
than the dorsal occipito-frontal stream in the human cortex
(Goodale & Milner, 1992; Milner & Goodale, 1993). while the
dorsal stream engages in quick automated reactions, the ventral
stream specializes in slow and attention-demanding behavior
(Buxbaum, Johnson-Frey, & Bartlett-Williams, 2005; Daprati
& Sirigu, 2006; Rossetti & Pisella, 2002). Our interpretation
thus links context-sensitivity to different cortical processing
streams.
The present study investigates whether context-sensitivity,
observed previously for the dominant arm, holds equally for the
non-dominant arm. It is well established that movement per-
formance is not the same for both arms: the non-dominant arm
controls intersegmental torques less well (Sainburg & Kalaka-
nis, 2000), which shows poorer performance on tasks requiring
high precision (Gonzalez, Ganel, & Goodale, 2006; Wing, Tur-
ton, & Fraser, 1986; Woodworth, 1899), but better performance
than the dominant arm on tasks requiring high speed (Annett,
Annett, Hudson, & Turner, 1979; Carson, Chua, Goodman, By-
blow, & Elliott, 1995; Elliott et al., 1993). It has been conclud-
ed that the non-dominant arm is optimized for controlling limb
posture, and the dominant arm for regulating limb trajectory
(Sainburg, 2004). Such a specialization might reflect the pre-
ferred arm use in bimanual activities: objects are typically held
and stabilized by the non-dominant, and manipulated by the
dominant hand (Grosskopf & Kuhtz-Buschbeck, 2006; Trevar-
then, 2010). Arm specialization has also been linked to differ-
ences in the underlying control principles, feedback control for
the non-dominant versus preplanning for the dominant arm (Sain-
burg & Kalakanis, 2000). Given these profound differences
between arms, we reasoned that context-sensitivity might also
be different. Specifically, we formulated two alternative hypo-
theses: according to the first, the non-dominant arm depends
more heavily on sensory feedback and therefore should be less
susceptible to extraneous influences such as behavioral context.
O. BOCK, B. BAAK
According to the second, the non-dominant arm is not well-
practiced in manipulation tasks such as grasping and therefore
should be more susceptible to extraneous influences such as
behavioral context.
We have shown before that context-sensitivity of grasping
changes in old age; some parameters become more and other
less context-sensitive, with no substantiable net change across
all parameters (Bock & Steinberg, 2012). Again, these data
have been yielded in the dominant arm. Since handedness is
less pronounced in old age (Kalisch et al., 2006), we expected
that any increase or decrease of context-sensitivity observed in
the left arm of young subjects should be smaller in the left arm
of seniors. To find out, the present study includes data from the
non-dominant arm of elderly participants.
Methods
Participants
Forty-eight young (24.3 ± 3.9 years) and thirty older subjects
(71.8 ± 7.4 years) participated. All were right-handed, free of
musculoskeletal impairments, diseases of the nervous system and
visual deficits except for corrected vision by self-report, and
lived independently in the community. None of them had par-
ticipated in research on grasping or cognition within the last 12
months. An ethical approval for this study was given by the ins-
titutional review board of the German Sport University Colog-
ne, and all subjects signed an informed consent statement be-
fore participating. Half of the young subjects were tested using
their dominant (right) arm. The other half of the young and all
older subjects were tested using their non-dominant (left) arm.
Task and Procedure
Experimental hardware and procedures were as in our previ-
ous studies (Bock & Beurskens, 2010). Subjects sat at a table
facing a 17 computer screen 67 cm ahead. A cylindrical lever of
4 cm length and 1.5 cm diameter was positioned 35 cm away
from the front edge and 16 cm above the surface of the table 10
cm to the right of the screen or, for left arm testing, 10 cm to
the left. The lever was covered by a hood from three sides, to
ensure that subjects could only grasp it with the precision grip
(thumb and index finger). The lever could slide 3.5 cm towards
the subjects’ body midline along a rail (see Figure 1), where it
met a mechanical stop. A displacement sensor (Burster® 8740)
registered the lever’s position and a 6 df force transducer (ATI®
Nano 17) registered the forces applied to the lever, both with a
sampling rate of 250 Hz. A joystick was mounted 41 cm in
front of the screen with its tip 12 cm above the table’s surface,
such that its distance from the lever was 32 cm horizontally and
4 cm vertically. Six reflecting markers of 6 mm diameter were
placed on thumb and index finger of the subjects’ grasping
hand with double-sided adhesive tape, and two Vicon® MX-
F20 3D high resolutions infrared cameras (sampling rate: 250
Hz, 1680 × 1280 pixels) registered their positions.
In a laboratory task (L), the joystick was locked in its central
position and subjects touched its tip with thumb and index fin-
ger. At randomly varying intervals of 2 - 6 s, a green dot was
displayed on the screen accompanied by a beep, prompting
subjects to release the joystick and grasp the lever, to slide it
towards them and back again, and then to return the hand to the
joystick. In an everyday-like task (E), the joystick was un-
locked and subjects were asked to play a computer game of
C1
C2
S
J
L
Figure 1.
Schematic representation of the experimental set-up with joystick (J),
lever (L), screen (S) and cameras (C1 & C2).
chasing spiders on the screen with a joystick-driven cursor. A
reward of .02 € was displayed near the right edge of the screen
for each spider hit. Each game level terminated after 10 s, and
subjects then had to collect their reward by moving the joystick
to the center, grasping the lever, moving it towards them and
back, and then returning their hand to the joystick. To keep the
game motivating, speed and complexity of spider movement
increased after every fifth level. No instructions were given on
how to grasp the lever in task E, and subjects were not told that
the purpose of their participation was to collect data on grasp-
ing. In accordance with our earlier study (Bock & Steinberg,
2012), spider speed was 30% lower in older subjects than in
young ones.
To exclude carryover effects, each subject was engaged in
only one of the tasks. Thus, 12 young subjects were tested in L
and 12 in E using their left arm, 12 young subjects were tested
in L and 12 in E using their right arm, and 15 older subjects
were tested in L and 15 in E using their left arm. Each subject
had 3 - 5 practice trials with the pertinent task and hand, to
ensure that procedures were understood, and data were then
collected for 20 grasping responses per subject. Note that both
tasks used the same objects (joystick and lever) in the same
location, and required the same hand and lever movements;
they only differed with respect to their context: grasping was
instructed, repetitive and served no ultimate purpose in L, but
was uninstructed, part of complex behavior and had financial
gain as purpose in E.
Data Analysis
Registered data were reduced by an interactive computer al-
gorithm to 20 parameters representing the means of kinematic
and dynamic landmarks across trials, and 20 parameters repre-
senting the pertinent coefficients of variation. The additional
parameter “Peaks” can’t be parsed into a mean and a CV. A
definition of all parameters is provided in Table 1. Each pa-
rameter was submitted to a two-way analysis of variance
(ANOVA). Left and right arm performance of young subjects
was compared with the between-factors Arm (left, right) and
Open Access 999
O. BOCK, B. BAAK
Open Access
1000
Table 1.
Parameter definitions and the pertinent ANOVA outcomes*.
ANOVA
left/right
ANOVA
young/old
Acronym Definition Task Arm
Task *
Arm Task Age
Task *
Age
TT (s) Time from movement onset to lever contact (transport time) Mean
CV
70.7***
1.2
2.8
2.5
2.1
4.6*
91.7***
4.7*
10.6***
4.5*
0.8
0.0
Vmax (cm/s) Peak tangential hand velocity Mean
CV
28.1***
0.0
8.9**
0.8
1.0
3.7
15.2***
4.1*
3.1
3.8
11.8**
3.5
Skew-T Ratio of deceleration time (Vmax to lever contact) and TT Mean
CV
29.9***
8.3**
0.0
0.0
3.3
0.1
7.8**
16.4***
1.6
3.0
1.0
1.6
Detour-V (cm) Peak vertical distance of hand from a straight path Mean
CV
27.1***
7.1*
7.8**
0.2
1.4
0.0
22.4***
0.9
1.8
3.0*
0.4
1.6
Transport component
Detour-H (cm) Peak horizontal distance of hand from a straight path Mean
CV
13.8***
7.1*
11.1**
1.1
6.3*
3.4
22.0***
0.0
5.4*
0.6
10.6**
0.3
GT (s) Time during which finger aperture changes (grasp time) Mean
CV
79.5***
4.5*
3.2
2.5
2.9
4.5*
90.0***
13.6***
7.6**
5.9*
0.1
1.8
PGA (cm) Peak 3D distance from thumb to index finger (peak grip aperture)Mean
CV
6.1*
1.8
3.0
0.2
2.0
1.5
14.1***
0.0
1.1
3.5
0.4
0.0
Peaks Proportion of multi-peaked aperture profiles 17.2*** 9.4** 2.0 12.3*** 4.9* 4.8*
t(PGA) (s) Interval movement onset to PGA Mean
CV
102.3***
2.9
2.1
6.2*
2.1
0.5
98.1***
11.3**
4.9*
4.9*
0.1
3.9
t(FGA) (s) Interval PGA to lever contact (final grip aperture) Mean
CV
3.4
0.2
3.8
1.6
2.8
4.1*
8.3**
0.6
6.3*
5.9*
0.0
1.0
Skew-G Ratio of t(FGA) and GT Mean
CV
89.3***
1.0
1.3
1.1
0.0
1.7
40.9***
5.5*
0.0
4.0
2.1
0.0
incli-start (˚) Hand inclination with respect to horizontal at movement onsetMean
CV
0.0
1.7
0.5
1.4
1.7
0.0
2.9
0.1
2.2
1.5
0.3
0.7
incli-100 (˚) Hand inclination after 100 ms Mean
CV
0.9
0.4
0.0
0.0
2.4
0.2
7.7**
0.4
0.1
1.9
0.5
0.3
incli-PGA (˚) Hand inclination at time of PGA Mean
CV
4.3*
5.3*
1.5
1.2
4.7*
2.9
16.3***
10.9**
6.9*
0.3
0.8
1.3
Grasp component
incli-end (˚) Hand inclination at lever contact Mean
CV
7.8**
7.2*
10.7**
2.9
3.9
6.7*
16.4***
13.0***
9.5**
0.1
1.8
1.1
Sync-start Interval onset of finger opening and of hand transport Mean
CV
27.7***
0.0
1.7
7.0*
1.6
11.2**
27.2***
3.8
1.1
0.1
0.3
0.5
Coupling
Sync-peak Interval t(PGA) and t(Vmax) Mean
CV
79.8***
0.4
1.7
8.9**
3.9
0.5
61.5***
2.7
3.9
1.9
0.1
5.7*
RT-lever (s) Interval lever contact and onset of lever motion (reaction time)Mean
CV
11.1**
1.3
110.8***
3.1
0.7
1.8
8.9**
0.2
7.9**
0.8
2.2
0.4
F-100 (N) Force compressing the lever 100 ms after lever contact Mean
CV
25.6***
7.2**
3.2
0.0
0.0
8.4**
3.5
0.4
1.3
4.3*
8.7**
17.4***
TQ-100 (N/mm) 3D lever torque 100 ms after lever contact Mean
CV
25.2***
9.6**
3.4
2.6
0.0
16.7***
35.6***
7.2**
1.0
19.9***
5.7*
15.0***
Lever manipulation
LT (s) Interval onset and end of lever motion (lever time) Mean
CV
12.7***
0.1
2.2
5.8*
1.5
2.2
12.2***
0.1
0.6
2.8
3.3
2.1
Note: *Numbers are F-values with 1.44 degrees of freedom. *,** and ***Represent p < 0.05, p < 0.01 and p < 0.001, respectively. For each parameter, ANOVA for the means
is presented above ANOVA for the CVs.
Task (L, E). Left arm performance of young and older subjects
was compared with the between-factors Age (young, older) and
Task. Since the main effects of Task have already been ana-
lysed in several earlier publications, the present work focuses
on the other effects.
Each parameter with a significant effect of Arm * Task was
transformed by
rh RHLHLH
rh rhLHLH
rh RH 1hLH
rhRHLH1h
1EL E
or LeLE
or LEIE
or LELe
 
 
 
 
(1)
where lrh, erh, llh and elh are parameter values of individual sub-
jects participating with their right hand in task L or E, or with
their left hand in task L or E, respectively, while LRH, ERH, LLH
and ELH are the corresponding group means. Thus, large
scores represent a stronger task-dependence of the right com-
pared to the left hand.
Significant effects of Age * Task were transformed accord-
ingly. We then submitted the scores to factor analyses with
varimax rotation, using the inclusion criterion F = 1.
Results
The right part of Table 1 summarizes the ANOVA outcome
for young subjects using the left or right arm in L or E.
As in our previous work (Bock & Steinberg, 2012; Bock &
Züll, 2013; Steinberg & Bock, 2013a; Steinberg & Bock,
O. BOCK, B. BAAK
2013b), the effect of Task was significant for a number of pa-
rameters. The effect of Arm was significant for several pa-
rameters as well, and that of Task * Arm was significant for
nine parameters. The latter were transformed into scores, and
were then reduced by factor analysis to three orthogonal factors,
explaining 64.5% of total variance (see Table 2).
To obtain a global measure of Task * Arm effects, we nor-
malized each parameter p with Task * Arm significance by
RH
PPL
(2)
and then calculated the rms value of p’ across parameters.
The outcome is depicted in Figure 2(a): the nine parameters
with Task * Arm significance were task-independent for the
right, but task-dependent for the left hand.
We then replicated the same procedure for factor rather than
parameter values, yielding the outcomes in Figures 2(b)-(d):
the same pattern described above also emerged for each factor.
The right part of Table 1 summarizes the ANOVA outcome
for young and older subjects using the left arm. Again, a num-
ber of parameters showed significant effects of Task and/or
Age, and eight parameters yielded significant effects of Task *
Age. The latter were reduced by factor analysis to three or-
thogonal factors explaining 69.29% of total variance (see Table
3). The right part of Figure 2 illustrates that parameters with
significant Task * Age interactions were task-dependent in
young but not in older subjects, and that this is reflected by all
three factors, although to a varying degree.
Discussion
The purpose of our study was to evaluate the role of context
when grasping with the non-dominant arm. The outcome con-
firms once more that the kinematics and dynamics of grasping
are context-sensitive (effects of Task in Table 1), and docu-
ments differences for grasping with the left versus right arm
(effects of Arm in Table 1). The arms differed with respect to
speed and accuracy, as expected from literature (see Introduc-
tion), but they also differed with respect to path shape (de-
tour-H and detour-V) and final hand posture (incli-end). The
latter findings can’t be explained by biomechanical constraints
since the task was exactly mirror-symmetrical for the two arms,
and rather support the existence of different control principles
for the two arms (Grosskopf & Kuhtz-Buschbeck, 2006; Sain-
burg & Kalakanis, 2000; Trevarthen, 2010). Most importantly
for the purposes of our study, context-sensitivity was not the
same for both arms (effects of Task * Arm in Table 1). Spe-
cifically, nine parameters showing no context-sensitivity for the
right arm did show such sensitivity for the left arm; this result
emerged when all nine parameters were considered together,
and also when each grasping factor was considered separately.
From this we conclude that context played a larger role for the
left arm, as stipulated by our second hypothesis, and not a
smaller role, as stipulated by the first hypothesis (see Introduc-
tion). We therefore discard the view that the non-dominant arm
is less influenced by context because of its stronger reliance on
sensory feedback, and rather adopt the alternative view that it is
more influenced by context because of its low experience with
manipulation tasks such as grasping. Obviously, further work
will be needed to substantiate this view. One possible approach
could be to compare right- and left-handed subjects in our para-
digm. Since righthanders strongly prefer to grasp with their
right arm while lefthanders exhibit no arm preference (Gon-
Figure 2.
Root mean square values across parameters with significant Task * Arm
effects (a, a’) and for each constituent factor (b-d, b’-d’). Graphs at the
left illustrate the differences between right and left hand in young sub-
jects, and those to the right the differences between the left hand of
young and older subjects. Symbols represent averages across subjects,
and error bars the pertinent interindividual standard deviations.
Table 2.
Outcome of factor analysis for parameters with significant Task * Arm
effects*.
Acronym GF1 GF2 GF3
detour-H
Transport
component CV TT
incli-PGA 0.69
CV incli-end 0.66
CV GT 0.93
Grasping
component
CV t(FGA)
Coupling CV Sync-start 0.69
CV F-100 0.92
Lever
manipulation CV TQ-100 0.91
Expl. variance 0.26 0.24 0.15
Open Access 1001
O. BOCK, B. BAAK
Table 3.
Outcome of factor analysis for parameters with significant Task * Age
effects*.
Acronym GF1 GF2 GF3
detour-H 0.77
Transport component Vmax
Grasping component Peaks 0.78
Coupling CV Sync-peak
F-100 0.86
TQ-100 0.90
CV F-100 0.89
Lever manipulation
CV TQ-100 0.84
Expl. variance 0.25 0.24 0.20
Note: *Tables 2 & 3, Numbers are factor loadings, only values 0.6 are shown.
The bottom row indicates the fraction of total variance explained by the respective
factor. GF stands for grasping factor.
zalez, Whitwell, Morrissey, Ganel, & Goodale, 2007a), the
nondominant arm of lefthanders is experienced with manipula-
tion tasks and their Task * Arm effects should therefore be less
pronounced than in righthanders, if our second hypothesis is
indeed correct.
In our previous work, we have related context-sensitivity to
the existence of two occipito-frontal processing streams in the
human cortex: A dorsal stream is mainly concerned with fast
automated reactions, and a ventral stream dealing with slow,
attention-demanding behavior (Buxbaum et al., 2005; Goodale
& Milner, 1992; Rossetti & Pisella, 2002). Since both streams
are interconnected (Goodale & Westwood, 2004), processing of
a given sensorimotor action may not be exclusively confined to
one of the two streams, but may involve both of them in vary-
ing degrees. Given these facts, we posit that young subjects
using their right arm in L will preferentially engage the dorsal
stream, since L requires externally triggered, stereotyped be-
havior. In E, however, they will more strongly involve the ven-
tral stream since E requires complex, volitional behavior. This
view is illustrated in a simplified fashion by the top half of
Figure 3. The bottom half of that figure illustrates how the neu-
ral activation might change when young subjects use their left
arm. Since arm is specialized for postural rather than voli-
tional responses (see Introduction), its control circuitry might
be well suited for the automated responses in L, but might re-
quire particularly strong ventral activation for the volitional
responses in E. Accordingly, Figure 3 shows no difference
between left and right arm in L, but a shift towards the ventral
stream for the left arm in E. Note that as a consequence, context
sensitivity (i.e., the difference between L and E) is more pro-
nounced for the left than for the right arm, as observed experi-
mentally.
Our data further show that grasping performance is affected
by old age (effects of Age in Table 1). Elderly persons expect-
edly differ from younger ones regarding movement duration
and variability, but also regarding path shape and hand posture.
Most importantly for the purposes of our study, context-sen-
sitivity of the left arm differed between age groups (effects of
Task * Age in Table 1): eight parameters showing context dif-
ferences in young subjects showed smaller, null or even in-
versed context differences in the elderly, with the net effect
across all parameters being an absence of an appreciable con-
text-sensitivity. In other words, the increase of context-speci-
Figure 3.
Schematic representation of assumed sensorimotor processing
through the dorsal and the ventral stream in task L and E,
when young subjects use their right versus left arm.
ficity from the dominant to the non-dominant arm, as observed
in young subjects, was attenuated if not absent in the elderly.
This conforms to our expectation (see Introduction), according
to which less pronounced handedness in old age is paralleled by
less pronounced differences between the two arms regarding
context-sensitivity. Referring back to Figure 3, one could argue
that seniors grasping in E can’t increase the ventral contribution
when using their left rather than their right arm, and their per-
formance with the left arm therefore resembles that of young
subjects using their right arm.
Acknowledgements
We thank Thomas Kesnerus for software development, the
team of Hans-Martin Küsel-Feldker for hardware modifications
and Malte Kraul & Annica Brosel for Figure preparation. Be-
sides we wish to thank Fabian Steinberg for his support in de-
signing the experiment and procedures.
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