Exp Brain Res (2013) 227:477–486
DOI 10.1007/s00221-013-3524-2
RESEARCH ARTICLE
The organization of digit contact timing during grasping
L. F. Schettino · A. Pallottie · C. Borland · S. Nessa ·
A. Nawroj · Y.‑C. Yu Received: 25 February 2013 / Accepted: 9 April 2013 / Published online: 30 April 2013
© Springer-Verlag Berlin Heidelberg 2013
Abstract While the process of hand preshaping during
grasping has been studied for over a decade, there is relatively little information regarding the organization of digit
contact timing (DCT). This dearth of information may be
due to the assumption that DCT while grasping exhibits
few regularities or to the difficulty in obtaining information through traditional movement recording techniques. In
this study, we employed a novel technique to determine the
time of digit contacts with the target object at a high precision rate in normal healthy participants. Our results indicate that, under our task conditions, subjects tend to employ
a radial to ulnar pattern of DCT which may be modulated
by the shape of the target object. Moreover, a number of
parameters, such as the total contact time, the frequency of
first contacts by the thumb and index fingers and the number of simultaneous contacts, are affected by the relative
complexity of the target object. Our data support the notion
that a great deal of information about the object’s physical
features is obtained during the early moments of the grasp.
Keywords Grasping · Digits · Motor control · Hand
Introduction
Until relatively recently, the study of prehension has
involved mostly the analysis of thumb and index control.
L. F. Schettino (*) · A. Pallottie · C. Borland · S. Nessa Department of Psychology and Neuroscience Program, Lafayette
College, 309 Oechsle Hall, Easton, PA 18042, USA
e-mail: schettil@lafayette.edu
A. Nawroj · Y.-C. Yu Department of Electrical and Computer Engineering, Lafayette
College, Easton, PA 18042, USA
In the last 15 years, however, studies have begun to look
at the pattern of whole-hand grasping. Research on hand
preshaping during the grasp has illuminated the role that
object parameters such as shape play on finger position and
placement as well as the effects of sensory constraints on
handshape (Santello and Soechting 1998; Schettino et al.
2003). Furthermore, the coordination and deployment of
digit forces following object purchase has been extensively
studied (Zatsiorsky and Latash 2008).
Interestingly, the process of finger contact during object
acquisition, which bridges hand preshaping and object purchase, has garnered little attention. A study by Reilmann
and colleagues focusing on the initiation of fingertip forces
during grasping provided some early evidence of the patterns observed during this process (Reilmann et al. 2001).
Their results indicated that human adult subjects produce
stereotyped movements that show a strong within-subject
consistency but also a considerable amount of betweensubject variability. In their conclusions, the authors suggested that the necessary adjustments that subjects undergo
while grasping objects of different shapes, sizes and orientations result in a lack of uniform contact patterns across
subjects. Unfortunately, their experimental setup did not
produce evidence to that effect as subjects were constrained
in their digit placement.
More recently, a work by Wong and Whishaw (2004)
looked at the precision grasps of individuals of different
ages while grasping beads of different sizes. Their results
suggested, once again, the existence of pronounced individual differences between subjects’ purchase patterns which
the authors suggested may be attributable to central factors, such as learned control of individual digits. While this
study provided some interesting ethological observations,
the open nature of the tasks involved resulted in a large
number of individual grasping behaviors which complicate
13
478
between-subject comparisons. Furthermore, the size of the
target objects, the largest of which was 16 mm in diameter,
impeded the use of more than a couple of digits during the
grasp.
There may be a few reasons why digit contact timing
(DCT) has not attracted more attention from researchers.
One of these reasons is precisely the notion that the pattern
of DCT may be the direct result of subject-specific motor
programs which would obscure the existence of more elementary patterns should they exist.
On the other hand, it is generally (and reasonably)
assumed that the order in which the digits come into contact with the target object is directly related to hand preshaping. That is, as the hand conforms to the shape of the
target object, the digits are brought increasingly closer to
the object surface until contact occurs.
It is well established that hand preshaping exhibits similar patterns across subjects when grasping objects of similar shapes (Santello and Soechting 1998; Schettino et al.
2003). While it is not yet clear whether grasping objects of
different shapes results in different DCT patterns, it seems
reasonable to think that common, discernible patterns may
be observed under such conditions which could provide
valuable information regarding the selection and implementation of finger coordination processes.
A second possibility for the dearth of studies on DCT is
that given the large number of degrees of freedom allowed
by the human hand, there could be a similarly large number of grasping and DCT patterns even when taking into
account the system’s anatomical and functional constraints
(Schieber and Santello 2004). This would seem to suggest
that DCT could occur in an almost haphazard fashion. If
this were the case, then DCT patterns should, given enough
instances, tend to produce an equal number of cases of each
possible digit contact permutation.
However, it has been observed that in spite of the relatively high dimensionality of the space of possible movements afforded by the human hand, most of the variability
across subjects and tasks can be captured by a small number of basic postures (Ingram et al. 2008). Also, Reilmann
et al. (2001) indicated that subjects, on average, contacted
the apparatus first with the thumb, the index or the little finger, suggesting a broad but defined pattern for DCT. The
authors went on to suggest that a possible function for such
patterning could include the prevention of object destabilization. Other possible functions for particular DCTs
could be obtaining feedback related to implicit assumptions regarding object weight and the location of the center
of mass, as well as preparing the hand for efficient object
transport.
Perhaps the strongest critique regarding DCT is that,
during a grasp, object lift is the result of fingertip forces that
occur in the 250 ms subsequent to digit contact (Reilmann
13
Exp Brain Res (2013) 227:477–486
et al. 2001). This means that by the onset of object lift, all
the necessary digit forces are in place, and the object has
been successfully grasped. This would suggest that DCT is
not relevant for object lift and that whatever rules govern it,
their functional importance is limited to the lapse between
contact and force development.
While it is possible that the functional relevance of DCT
may be limited to the early moments of object acquisition,
describing the patterns that guide it could allow us to determine the underlying patterns that may result from basic
coordinative processes determined by central and peripheral anatomical and functional organization of the prehension system. Furthermore, the coordination of DCT may
provide us with critical information for the determination
of what represents a successful grasp. This information
could prove useful when assessing progress in patient rehabilitation and in the design of artificial systems.
The precise timing of object-digit contact has been difficult to study using traditional motion tracking techniques.
In this study, we employed a novel technique to determine
the DCT at a high sampling rate in a naturalistic grasping task, allowing for a fine-grained determination of the
moment in which each one of the digits comes into contact
with the target object. Subjects grasped objects of different shapes with minimal constraints on the choice of finger placement under two visual feedback conditions. Our
results suggest that a number of timing parameters are
affected by target object shape and its relative complexity.
Furthermore, our results offer evidence for the organization
patterns of finger control while grasping.
Methods
Subjects
Fifteen young adults (ages 19–22, 8 women, 7 men) participated in the study. All subjects were informed about
the nature of the study and signed consent forms approved
by the IRB of Lafayette College. All subjects were righthanded and without history of any neurological disorder or
impairment.
Apparatus
Our digit contact system consists of a glove with metal fingertips that close an electrical circuit as the digits contact
a target object holding a fixed voltage. Digit contacts with
the objects were recorded individually at a sampling rate of
1,000 Hz to a data acquisition system (National Instrument
PCI-3036E) through five A/D channels. The fingertips of a
black cotton glove were wrapped in copper foil tape (5 mm
wide). Small ovals of thin copper flashing were glued to the
479
Exp Brain Res (2013) 227:477–486
back of each glove finger and were used to make contact
between the foil tape and the wires conducting the signal
to the computer, which were soldered to the flashing. The
portion of the copper foil (including the sides) and flashing that does not make contact with the object was covered
with black vinyl paint. Wires were connected to a comparator circuit to improve the quality of the voltage signals.
The comparator circuit consists of five independently
operating comparators implemented by using two LM324
quadruple operational amplifier chips. When the voltage
signal at a fingertip goes beyond the preset threshold of 2 V
in the comparator, the output voltage of the comparator to
the data acquisition system is 5 V. Otherwise, the comparator output is 0 V. The threshold voltage was determined
empirically to overcome the environmental noise yet preserve the initial contact voltage bounces produced by circuit closure of digit contact with the object.
Data capture was implemented with QuaRC (Quanser,
inc.) and controlled through a Simulink model (The Mathworks, Inc.).
commonly encountered regular shape. Both the SQ and the
OB objects are grasped with a prismatic grasp (Cutkosky
and Howe 1990) though the OB object requires a rotation
of the wrist and the placement of digits at two different
distances from the thumb. Finally, the CX object normally
elicits a circular grasp (Schettino et al. 2003) including the
abduction of the fingers. The index and middle fingers generally make contact at the top angle of the right-hand side
of the object and the ring and little finger contact the bottom angle.
In order to hold the voltage provided by the system, each
object had a thin electrical cable (24 gauge) attached to
the back using self-adhesive copper foil tape. The contact
areas of the objects (left and right faces) were covered with
lightly wrinkled aluminum foil which ensured the presence
of the voltage and increased friction during the grasp. The
objects’ front faces were painted with a light-green fluorescent paint (Seal-Krete) in order to remain visible in complete darkness.
Procedure
Objects
A set of three objects was used in the study. All objects
were approximately the same size. The objects were constructed of solid wood and measured 9.5 cm in height and
2.8 cm in thickness (average weight 80 g). The width and
shapes of the objects varied as follows: a “square” object
(SQ) of rectangular prism shape (width was 5 cm). An
“oblique” object (OB) whose width tapered from the base
upwards in two steps. A “convex” object (CX) exhibiting
a triangular protrusion extending from the half point of its
width to a central point 3.5 cm from it (Fig. 1). During the
experiment, both the oblique and the convex objects were
presented with the straight edge on the left side.
The object shapes can be thought of as a continuum in
complexity as a function of geometrical regularity (which
facilitates finger placement and the localization of the
center of mass of the object) and similarity to commonly
grasped objects (which presumably elicit the selection
of well-learned motor programs). The SQ object has a
Fig. 1 Object shapes used in the study from left: a CX, convex;
b OB, oblique; c SQ, square
Each trial started with the subject’s hand in a preset initial position (hand naturally pronated on the table at
20 cm from object). During the experiment, objects were
presented in blocks of ten trials on a black presentation
platform consisting of a 6.2 cm wide board at a height of
6 cm. Presentation of object blocks was randomized across
conditions.
In all conditions, subjects were given sufficient time
(>2 s) to view adequately the objects before trial initiation.
The subjects were instructed to maintain the initial position
until they heard a tone signaling the start of the trial. Once
the tone sounded, the subjects were to reach to and grasp
the object at a comfortable speed. A successful grasp was
defined as the thumb being positioned on the left vertical
surface opposing the other four fingers in a precision grip.
Subjects were instructed to lift the object 10 cm vertically
after grasping and to put it back on the presentation platform. Total reaches per experiment were 60 (3 shapes × 2
conditions × 10 trials).
Subjects practiced grasping the objects before the experiment for 5–10 trials. Trials in which the object was not
grasped as indicated were replaced.
Object vision condition: The grasping procedure was
essentially the same as in the full vision condition. The
main differences were as follows: the overhead lights were
turned off, and the experimental room fully darkened, so
that only the fluorescent objects were visible. This manipulation eliminated visual feedback of the subject’s moving
arm during the experiment. After each trial, the lights were
switched on for approximately 1–2 s in order to prevent
dark adaptation.
13
480
Exp Brain Res (2013) 227:477–486
Data analysis
The data were loaded into MATLAB (The Mathworks,
Inc.), and custom software was employed to calculate the
relative contact timing between the five digits for each trial.
Visual inspection of finger contacts per trial was employed
to eliminate bad trials (grasps in which the 5 digits did not
make full and continuous contact with the object).
Measurements
Total contact time (TCT) was defined as the latency
between first and last digit contact.
Digit contact order (DCO) was defined as the sequence
of contacts by the digits. The digits can touch the object
at any of 5 different consecutive times (first through fifth),
which results in 120 (5!) possible permutations. The digits
were numbered as follows: 1: thumb, 2: index, 3: middle, 4:
ring, 5: little.
Inter-digit latencies (IDL) are the four latencies occurring between digit contacts: first-second, second-third,
third-fourth and fourth-fifth.
Statistical analyses
Repeated-measures ANOVAs were run to determine the
existence of statistically significant differences between
experimental conditions. Post hoc analyses with Bonferroni
correction were employed to run pair-wise comparisons.
Chi-square tests were used to determine the relative frequency of occurrences where sample sizes and distribution
did not permit the use of parametric methods.
Mann–Whitney U tests were conducted to look for statistical differences between the distributions of trial counts
per DCO permutations.
Statistical significance was set at p < 0.05.
Fig. 2 Total contact time per object shape and visual condition. The
time between the first and last digit contacts increases as a function of
object complexity and visual constraints (OV, object vision; FV, full
vision). Error bars represent ±1 SEM
Table 1 Final trial counts per condition and object shape
CX
OB
SQ
Totals per
condition
Full vision
Object vision
127
128
131
116
125
129
383
373
Totals per
shape
255
247
254
756
The minimum average number of trials per subject was 8.4 per visual
condition/object shape with a standard deviation of 1.5. Total number
of trials analyzed was 756
2003). These results indicate that besides the typical
deceleration profile of the wrist observed during a reachto-grasp movement, at least part of the increased movement time is due to the time required to place the digits on
the object. Also, the results suggest that TCT increases as
a function of object complexity (see “Methods”) (Fig. 2;
Table 1).
Simultaneous digit contacts
Results
Total contact time
A repeated-measures ANOVA revealed main effects of
both vision condition (F1,14 = 6.9, p = 0.02) and object
shape (F2,28 = 12.32, p < 0.0001) on TCT. There were
no significant interactions. Subsequent post hoc tests for
object shape indicated that TCT was significantly different for all object shapes (mean TCT in ms: SQ = 94.6,
OB = 122.7, CX = 146.6). Similarly, TCT was longer
for object vision (mean = 133.4 ms) than for full vision
(mean = 109.2 ms). We have reported longer movement
times from movement onset to object lift for the CX
object versus the SQ object previously (Schettino et al.
13
In spite of the high sampling rate of our system, 62/756
trials showed simultaneous digit contacts. We defined
a simultaneous digit contact as any two digits contacting the object with the same latency. Most of these
occurrences involved two digits (57/62). The remainder involved three digits. There were no cases in which
more than three digits contacted the target object
simultaneously.
The breakdown per object was CX (12 trials), OB (19
trials) and SQ (31 trials). A Chi-square test on the number of trials in which simultaneous contacts occurred per
object shape revealed a significant difference between the
frequencies X2 (2, N = 62) = 8.96, p < 0.02. This is interesting in that the more complex shapes resulted in fewer
simultaneous contacts.
Exp Brain Res (2013) 227:477–486
Digit contact order
There were 694 trials across object shapes and visual
conditions in which no simultaneous digit contacts were
observed. Trial counts per object shape were CX = 243,
OB = 228 and SQ = 223. All analyses below involving
digit contact order (DCO) were conducted on these trials.
Digits 2‑5
Figure 3 shows the total number of trials in which each
of the 120 possible digit permutations occurred. Analysis
of the eight tallest peaks in the graph indicated that they
corresponded to three digit sequences involving digits 2
through 5 with different translocations of the thumb (digit
1). First, a 2-3-4-5 radial to ulnar (RtoU) ordering including permutations: 2-3-1-4-5 (28 trials), 1-2-3-4-5 (25 trials), 2-1-3-4-5 (25 trials) and 2-3-4-1-5 (23 trials). Second,
the sequence 2-4-3-5 including permutations 2-4-3-1-5 (24
trials) and 2-1-4-3-5 (19 trials). Lastly, the sequence 2-45-3 including permutations 2-1-4-5-3 (16 trials) and 2-4-15-3 (15 trials). These data indicate that there are particular
digit sequences that are produced more commonly while
grasping.
Given the data described and the design of our objects,
which isolates the thumb from the fingers, we decided to
look at the thumb and digits 2-5 separately. In order to
find out if visual condition made a difference in DCO,
we compared the trial counts for each of the possible
24 permutations for digits 2-5 for the full vision versus the object vision conditions. A Mann–Whitney U test
showed no significant difference between the distributions
(z = −0.63, p = 0.5289). This indicates that under reduced
481
visual feedback conditions, our subjects did not modify
their DCO patterns relative to their performance under
full visual input. This result is parallel to the observation
that hand preshaping patterns in normal subjects are not
affected by decreased visual input (Schettino et al. 2003).
All subsequent analyses were conducted on the pooled data
from both conditions.
Figure 4 shows the permutation distributions for objects
CX, OB and SQ for digits 2-5. It is apparent that subjects
favored the use of the index finger as the first digit to touch
the objects (top six permutations). The first DCO permutation (2-3-4-5) had the most cases (114/694 trials) in all
object shapes indicating a strong tendency to use an RtoU
digit order sequence. In the case of the CX object, a 4-23-5 sequence is also observed that does not appear in the
OB and SQ objects, suggesting a shape-specific pattern.
Another interesting observation is that the CX object elicited the use of relatively fewer permutations than the other
two objects in spite of having a greater trial count.
In order to assess the strength of the tendency to use
RtoU DCOs, we calculated the timing relationship between
each pair of fingers. For this, we calculated the proportion
of trials per object shape in which each of digits 2-5 was
followed by the other digits. Figure 5 shows the resulting matrices. Each matrix is composed of an upper and a
lower triangular matrix where each element represents the
proportion of trials in which a digit (first digit) contacted
the object earlier than any other digit (second digit).The
main diagonal represents each digit’s relationship to itself
and is therefore equal to zero. For example, in the CX
object matrix, the top row represents the proportion of
trials in which digit 2 contacted the object before digit 2
(itself, therefore equal to zero), before digit 3 (about 70 %
Fig. 3 The distribution of DCO
patterns for all trials for each of
the 120 possible permutations
of 5 digits, starting with 1-2-34-5 and ending with 5-4-3-2-1.
Letters a-h mark the permutations with the highest trial
counts (defined in the inset). As
can be observed, there are clear
preferences for a reduced number of permutations containing
an even smaller number of fourdigit patterns. The dashed line
represents chance level
13
482
Exp Brain Res (2013) 227:477–486
Fig. 4 Trial distributions for
the three object shapes pooled
across visual conditions.
The bar graphs on the right
correspond to the trial counts
for each of the 24 possible
permutations of digits 2-5 per
object shape (CX, OB, SQ).
The left panel is a representation of Contact Order Timing
for each permutation starting at
the top with the RtoU pattern:
index (red), middle (green), ring
(orange) and little (yellow) and
ending with the UtoR pattern at
the bottom. A clear RtoU preference is observed for all object
shapes. Observe the relatively
few DCO patterns deployed
during CX grasps (color figure
online)
Fig. 5 Timing relationships between pairs of digits 2-5 during grasps
to the CX, OB and SQ objects, respectively, in terms of the percentage of trials in which each occurred. The upper triangular matrices
correspond to RtoU patterns while the lower triangular matrices
correspond to UtoR patters. The main diagonals represent each digit
versus itself and are therefore zero. The values of complementary
squares (e.g., 2 → 5 and 5 → 2) add to 100 %
of trials), before digit 4 (about 60 % of trials) and before
digit 5 (about 75 % of trials). Given that the upper triangular matrix represents the proportion of trials in sequence
2 → 3 → 4 → 5, it shows the proportions in an RtoU fashion. The lower triangular matrix has the opposite arrangement and therefore shows the data in an UtoR fashion. The
sum of each component of the upper triangular matrix (e.g.,
2 → 3) added to its corresponding component of the lower
triangular matrix (3 → 2) equals 100 %.
Figure 5a shows that for the most part, the CX object
resulted in a strong RtoU timing organization of digit
contacts for all fingers with the sole exception of the
relationship between digits 3 and 4 (3 → 4 ≈ 40 %,
4 → 3 ≈ 60 %). Interestingly, this relationship is not
observed in other object shapes and corresponds to the
shape-specific permutation pattern observed in Fig. 4 (permutation 4-2-3-5) but also to the second highest trial count
for the CX object (permutation 2-4-3-5).
Objects OB and SQ resulted in detectable yet weakening
RtoU DCO patterns. The type of permutations observed in
Fig. 4 for these objects also shows a number of trials initiated by a contact of the little finger including more than 10
trials per object in pure UtoR sequence (last permutation).
13
First contact
Reilmann et al. (2001) reported that their subjects tended
to use the thumb, index or little finger as their first contact
with their apparatus. Our data mostly support their findings but with some interesting differences between object
483
Exp Brain Res (2013) 227:477–486
require such careful digit placement, allowing for a more
pronounced use of the thumb early during the grasp.
Inter‑digit contact latencies
Fig. 6 Total trial counts per object shape in which each digit was first
to contact the object. The index finger made contact more often with
the objects with less familiar shapes
shapes (Fig. 4). Both the CX and the OB objects produced
a large number of trials in which the index finger made
contact with the object first. This was not the case for the
SQ shape in which the thumb had the most first contacts,
followed by the index (Fig. 6).
It is generally accepted that as digits come into contact
with the target object, information is gathered in the form
of haptic feedback, which is presumably contrasted with an
internal model of the action in order to correct for perturbations (Iberall and MacKenzie 1990).This, together with our
data, suggests that digit selection for first contact may have
a particular functional significance.
Thumb contact order
Our object shapes were originally chosen in part due to the
fact that using a precision grasp isolates the thumb from
the other four digits (Schettino et al. 2003). This may be
the reason for the apparent independence between the
thumb contact timing relative to the rest of the digits. We
decided to test the thumb contact order separately from that
of digits 2-5. A repeated-measures ANOVA on the number
of trials per contact timing of the thumb (first through fifth)
for each object shape (SQ, OB, CX) revealed a significant
main effect of object shape (F2,28 = 18.4, p < 0.001) and an
interaction of object shape X contact timing (F8,112 = 2.24,
p = 0.03). Post hoc tests indicated that the distribution of
thumb contact order trial counts for the SQ object (1 = 81,
2 = 36, 3 = 29, 4 = 36, 5 = 41) was different from that of
the CX object (1 = 39, 2 = 53, 3 = 69, 4 = 59, 5 = 23).
Our interpretation of these data is that subjects may have
directed their attention to the right-hand side of the CX
object in order to ensure an efficient grasp, thereby delaying the contact by the thumb which tended to be the third
digit to touch the object, generally behind the index and
ring fingers. The OB and particularly the SQ object did not
An interesting result of Reilmann et al. (2001) was that the
relative latencies between digits as they made contact with
the target object, regardless of the digits involved, exhibited a typical pattern. The authors reported that the latencies between the first and second as well as that between
the fourth and fifth digit contacts were longer than those of
the second-third and third-fourth latencies.
Our results showed a similar pattern in all object shapes
(Fig. 7). A repeated-measures ANOVA with object shape
and inter-digit contact latency (ICL) as main factors produced no significant effects of object shape, but there was
a significant effect of ICL (F3,42 = 5.19, p = 0.004). The
interaction of shape X contact latency was not significant.
Post hoc tests revealed a significant difference between the
first-second latency and the second-third latency. Also, the
fourth-fifth digit contact latency was different from both
the second-third and third-fourth contacts. This indicates
that while grasping, subjects organize the timing of their
contacts in a particular pattern regardless of digits involved
or the shape of the object they are interacting with.
Discussion
Our study looked at the organization of digit contact patterns during reach-to-grasp movements directed to objects
of different shapes under two different visual feedback
conditions. Our results indicate that while some parameters
in the pattern of digit contacts are affected by a reduced
Fig. 7 Inter-digit latencies during the grasp per object shape. The
columns represent the mean latency between each pair of subsequent
contacts, regardless of digit identity. Error bars represent ±1 SEM
13
484
amount of visual input and by object shape (e.g., TCT),
other parameters are only affected by object shape (e.g.,
SDCs, DCO). Interestingly, there are also parameters that
are not affected by either modulation of visual input or target object shape (IDL).
Digit latencies
Our results show that the time elapsed between the first
and last digit contact (total contact time, TCT) is inversely
related to the amount of visual information available, but
directly related to the object complexity.
Interestingly, the latencies between digits conform to
an inverted U pattern where the second to third and third
to fourth digit contacts occur quicker than the first to second and fourth to fifth regardless of digit contact order.
While the longer fourth-fifth digit contact latency may be
explained by Reilmann and colleagues’ observation that
in a small percentage of their trials, subjects would initiate global load force before all digits made contact with the
apparatus (2001), suggesting that the long latency between
digit contacts 4 and 5 could be due to the low functional
importance of the last digit to reach the object, the difference between the 1-2 and the 2-3 ICLs could represent a
mechanism by which the hand may ensure a quick succession of digit contacts in order to maintain control of the target object.
According to our data, the selection of a given digit as
first contact with the object is shape-dependent. Early work
looking at haptic feedback has suggested that a grasp is
the first approach to obtaining initial information about an
object (Lederman and Klatzky 1987). Subjects, unknowingly, may obtain relevant information from an object such
as weight, texture and center of mass (CoM) location as
they grasp.
Recent work by Brouwer et al. (2009) has described
the tendency of subjects to look toward their index finger while grasping. This was particularly so when grasping objects with higher precision constraints, suggesting
that subjects deploy higher attentional processes when
interacting with objects that are either more complex
and/or with which they are less familiar. In our results,
the relative use of the index finger as first contact followed that of object complexity/familiarity, with higher
numbers occurring in the CX object, followed by the OB
and finally the SQ shape. While we did not track gaze
position, the longer TCTs and fewer simultaneous digit
contacts for the CX object grasps indicate that our subjects may have used an information-seeking strategy
while grasping this novel object shape. The frequency
of the thumb as first contact also suggests a difference in
the level of attentional focus between the CX and the SQ
object grasps.
13
Exp Brain Res (2013) 227:477–486
Digit contact order
Regarding digit contact order (DCO), our results indicate
that subjects’ DCO patterns cluster into a few sequences,
commonly favoring an RtoU sequence even in cases in
which shape-specific patterns are also present.
A possible way to interpret our data is through the virtual finger (VF) hypothesis proposed by Arbib et al. (1985).
Given the orientation and shapes of our objects, a small
number of digits would be sufficient to produce the forces
for a successful lift. For example, a successful lift of the SQ
object can be accomplished with only two digits (the thumb
plus one of the fingers in opposition). In the case of the OB
object, while it is possible to lift the object using two digits
(selecting either of the oblique “steps” as the location for
the opposing finger), a tripod grasp involving both “steps”
is more efficient. Finally, the CX object requires a tripod
grasp for a successful lift.
Since our subjects were required by the task to use all of
their digits, there was a level of redundancy which would
favor the “fusing” of real fingers into one or two VFs.
Therefore, the central nervous system (CNS) would need to
(1) determine which real fingers should form part of each
VF and (2) coordinate their behavior to produce a single
force in the appropriate direction and place (Arbib et al.
1985). Work carried out by Zatsiorsky et al. (1998), Martin
et al. (2009) has shown that during multi-finger tasks, a particular finger, taking the function of “master,” may enslave
adjacent fingers during force production. While their work
has not focused on more than a single VF, it is reasonable
to suppose that each VF may have its own distribution of a
master and one or more slave digits.
Since the SQ object may be manipulated with only
two VFs, digits 2-5 would all belong to the same VF and
would be more or less interchangeable. The larger number
of simultaneous digit contacts for the SQ object plus the
wider spread of DCO patterns observed in the data support this idea. A second interesting observation is that the
relative use of the digits as first contact in the SQ object
corresponds quite strikingly to the measures of digit individuation reported by previous studies (Häger-Ross and
Scheiber 2000; Ingram et al. 2008). This suggests to us that
in the absence of strong task constraints, digit coordination (and VF determination and constitution) may be only
limited by the relative independence of the digits, which, in
turn, may be due to mechanical factors. This brings up the
intriguing possibility that the pattern of first contacts used
by our subjects for each object shape may provide clues as
to which fingers were commonly selected as VF masters by
our subjects.
Further support for this idea comes from the use of
digit 4 in grasps to the CX object. The CX object elicited
a large number of DCOs involving digit 4 as first contact.
485
Exp Brain Res (2013) 227:477–486
Also, digit 4 was employed earlier than digit 5 and 3 in
about 85 and 65 % of CX trials, respectively (Fig. 5a). This
behavior is very interesting taking into account the fact that
digit 4 is thought to be the least independent of the digits
(Ingram et al. 2008), which suggests that it would rarely be
selected as VF master unless the task requirements made it
necessary.
Neural bases
Earlier studies regarding DCO have noted that, while grasping, subjects employed highly similar intra-subject DCOs,
but that inter-subject variability was relatively high (Reilmann et al. 2001; Wong and Whishaw 2004). While these
results may have been partially due to task constraints,
particularly the fact that object shape was held constant in
both studies, it would also be expected that developmental
processes would tend to optimize an individual’s grasping
patterns (Wong and Whishaw 2004).
The RtoU digit sequence observed in our experiment
suggests the existence of a background pattern that the
CNS may employ as an early coordination program that
is sculpted by experience under the constraints of hand
geometry and mechanics. Evidence for this notion comes
from infant grasping studies (Lantz et al. 1996) which have
reported that children younger than 7 months of age show
an RtoU digit preference while grasping.
While early studies looking at digit fractionation in
non-human primates suggested that independent finger
movements and their development depended largely on
corticospinal (CS) pathways (Lawrence an Kuypers 1968a,
b; Lawrence and Hopkins 1976), more recent work has
shown that skilled forelimb movements are possible following CS tract lesions (Whishaw et al. 1998; Pettersson
et al. 2007). It has since been suggested that coordination
of the digits, hand preshaping and forearm orientation during a grasping motion may be a process that involves all
descending pathways working in concert (Iwaniuk and
Whishaw 2000).
Importantly, lesion studies have suggested that functional loss following CS pathway damage, may recover in
time, though a number of sensory deficits remain, including light touch, tactile placing and “the orienting response
to a surface following light contact” (Schwartzman 1978).
It has also been described that damage to sensory regions,
including somatosensory cortex and its afferents (the dorsal
column) results in a loss of individual finger motion (Asanuma and Arissian 1984). These findings underscore the
importance of continuous and immediate sensory feedback
provided by the digits during grasping.
Early work on hand preshaping conceived of the process as the fingers organizing themselves according to target object shape (Santello and Soechting 1998; Schettino
et al. 2003). Subsequent work has shown that the location of object CoM (Lukos et al. 2007) and task end goal
have a strong influence on preshaping (Sartori et al. 2011;
Craje et al. 2011). We propose that preshaping may not be
a control strategy to mold the fingers to the contours of the
object, but to ensure finger placement where useful feedback may be obtained. For example, Lukos et al. (2007)
reported the use of a “generalized” grasp when subjects
could not predict the CoM location of their target object.
We suggest that the functional significance of such “average” grasps, besides ensuring some form of force control,
as the authors suggest, may be a search for sensory feedback that may improve the efficiency of subsequent grasps
to the same type of object.
Based on our results, the neurological evidence and
previous studies on the hand preshaping deficits observed
in Parkinson’s disease (Schettino et al. 2004) and stroke
patients (Sangole and Levin 2009), we may predict that in
these groups abnormal DCT patterns will be observed. Particularly in the case of Parkinson’s disease, where sensory
integration is compromised, reduced fractionation would
result in shorter TCTs (in spite of longer movement times)
due to abnormal coordination. Therapeutic techniques
designed to facilitate the rehabilitation of not only motor
recovery but also tactile sensory recovery in stroke patients
could improve their rehabilitation prospects.
References
Arbib MA, Iberal T, Lyons D (1985) Coordinated control programs
for movements of the hand. Exp Brain Res Suppl. 10:111–129
Asanuma H, Arissian K (1984) Experiments on functional role of
peripheral input to motor cortex during voluntary movements in
the monkey. J Neurophysiol 52:212–227
Brouwer AM, Franz VH, Gegenfurtner KR (2009) Differences in
fixations between grasping and viewing objects. J Vis 9(1):18,
1–24
Craje C, Lukos JR, Ansuini C, Gordon AM, Santello M (2011) The
effects of task and content on digit placement on a bottle. Exp
Brain Res 212:119–124
Cutkosky MR, Howe RD (1990) Human grasp choice and robotic
grasp analysis. In: Venkataraman ST, Iberall T (eds) dextrous
robot hands pp. 5–31. Springer-Verlag, New York, NY
Häger-Ross C, Schieber MH (2000) Quantifying the independence
of human finger movements: comparisons of digits, hands and
movement frequencies. J Neurosci 20:8542–8550
Iberall T, MacKenzie CL (1990) Opposition space and human prehension. In: Venkataraman ST, Iberall T (eds) Dextrous robot hands.
Springer, New York, pp 32–54
Ingram JN, Kording KP, Howard IS, Wolpert DM (2008) The statistics of natural hand movements. Exp Brain Res 188:223–236
Iwaniuk A, Whishaw IQ (2000) On the origin of skilled forelimb
movements. Trends Neurosci 23:372–376
Lantz C, Melen K, Forssberg H (1996) Early infant grasping involves
radial fingers. Dev Med Child Neurol 38:668–674
Lawrence DG, Hopkins DA (1976) The development of motor control
in the rhesus monkey: evidence concerning the role of corticomotoneuronal connections. Brain 99:235–254
13
486
Lawrence DG, Kuypers HG (1968a) The functional organization of
the motor system in the monkey. I. The effects of bilateral pyramidal lesions. Brain 91:1–14
Lawrence DG, Kuypers HG (1968b) The functional organization of
the motor system in the monkey. II. The effects of lesions of the
descending brain-stem pathways. Brain 91:15–36
Lederman SJ, Klatzky RL (1987) Hand movements: a window into
haptic object recognition. Cogn Psychol 19:342–368
Lukos J, Ansuini C, Santello M (2007) Choice of contact points during multidigit grasping: effect of predictability of object center of
mass location. J Neurosci 27:3894–3903
Martin JR, Latash ML, Zatsiorsky VM (2009) Interaction of finger
enslaving and error compensation in multiple finger force production. Exp Brain Res 192:293–298
Pettersson LG, Alstermark B, Blagovechtchenski E, Isa T, Sasaski S
(2007) Skilled digit movements in feline and primate-recovery
after selective spinal cord lesions. Acta Physiol 189:141–154
Reilmann R, Gordon AM, Henningsen H (2001) Initiation and development of fingertip forces during whole-hand grasping. Exp
Brain Res 140:443–452
Sangole AP, Levin MF (2009) Palmar arch modulation in patients
with hemiparesis after a stroke. Exp Brain Res 199:59–70
Santello M, Soechting JF (1998) Gradual molding of the hand to
object contours. J Neurophysiol 79:1307–1320
Sartori L, Straulino E, Castiello U (2011) How objects are grasped:
the interplay between affordances and end-goals. PLoS One
6:e25203
13
Exp Brain Res (2013) 227:477–486
Schettino LF, Adamovich SV, Poizner H (2003) Effects of object
shape and visual feedback on hand configuration during grasping.
Exp Brain Res 151:158–166
Schettino LF, Rajaraman V, Jack D, Adamovich SV, Sage J, Poizner H
(2004) Deficits in the evolution of hand preshaping in Parkinson’s
disease. Neuropsychologia 42:82–94
Schieber MH, Santello M (2004) Hand function: peripheral and central constraints on performance. J Appl Physiol 96:2293–2300
Schwartzman RJ (1978) A behavioral analysis of complete unilateral
section of the pyramidal tract at the medullary level in Macaca
mulatta. Ann Neurol 4:234–244
Whishaw IQ, Gorny B, Sarna J (1998) Paw and limb use in skilled
and spontaneous reaching after pyramidal tract, red nucleus and
combined lesions in the rat: behavioral and anatomical dissociations. Behav Brain Res 93:167–183
Wong YJ, Whishaw IQ (2004) Precision grasps of children and young
and old adults: individual differences in digit contact strategy,
purchase pattern and digit posture. Behav Brain Res 154:113–123
Zatsiorsky VM, Latash ML (2008) Multifinger prehension: an overview. J Motor Behav 40:446–476
Zatsiorsky VM, Li ZM, Latash ML (1998) Coordinated force production in multi-finger tasks: finger interaction and neural network
modeling. Biol Cybern 79:139–150