J Neurophysiol 115: 674 – 684, 2016.
First published November 25, 2015; doi:10.1152/jn.00707.2015.
CALL FOR PAPERS
Neurophysiology of Tactile Perception: A Tribute to Steven Hsiao
Velocity of motion across the skin influences perception of tactile location
Elizabeth H. L. Nguyen,1 Janet L. Taylor,2,3 Jack Brooks,2,3 and Tatjana Seizova-Cajic1
1
Faculty of Health Sciences, University of Sydney, Sydney, Australia; 2Neuroscience Research Australia, Sydney, Australia;
and 3University of New South Wales, Sydney, Australia
Submitted 17 July 2015; accepted in final form 19 November 2015
somatosensory space; completion; motion interpolation; position
shift; local sign
A NUMBER OF ILLUSIONS reveal interdependence between perceptions of space and time. Temporal aspects of the stimulus may
affect perception of its spatial properties, and vice versa. A
well-known tactile illusion of the first kind is sensory saltation
(cutaneous rabbit; Geldard and Sherrick 1972), also shown to
occur in vision (Geldard 1976) and audition (Getzmann 2009).
In the “utterly reduced rabbit” version of the illusion (Geldard
1975; Trojan et al. 2010), two spatially separated taps delivered
at a temporal interval of 100 –200 ms are perceived as much
closer together than they really are. Normally, illusory motion
is also perceived as if the same moving object caused both taps
on the skin (for review and detailed investigation of the quality
of apparent motion in touch, see Cholewiak 1999; Cholewiak
and Collins 2000). Continuous motion is subject to a similar
spatial distortion: brushed regions of the forearm seem increas-
Address for reprint requests and other correspondence: T. Seizova-Cajic,
Faculty of Health Sciences, 75 East St., Lidcombe 2141 NSW, Australia
(e-mail: [email protected]).
674
ingly short as the stimulus velocity increases beyond 15 cm/s
(Whitsel et al. 1986). At 100 cm/s, the perceived path of the
moving brush is approximately half that at 10 cm/s (Fig. 4,
Whitsel et al. 1986). This dependence of perceived distance on
time in perception of motion suggests there are limits on the
“allowed” velocities, i.e., that very high speeds will not be
perceived. Instead, a bias in the perception of distance traveled
(or time) will occur to reduce perceived speed. Such a lowvelocity prior was proposed for both tactile and visual motion
(see Goldreich 2007; Stocker and Simoncelli 2006; Weiss et al.
2002).
The utterly reduced rabbit consisting of two taps is an utterly
impoverished stimulus and cannot offer many clues about the
way we process natural motion stimuli. The original form of
the rabbit is slightly richer: multiple taps are applied to one
skin site, followed by a tap at another. The resulting sensation
is that of multiple taps distributed along a motion trajectory, as
if a rabbit is hopping along the skin (Geldard and Sherrick
1972). The roughly uniform spatial distribution of the perceived taps mirrors the equal temporal intervals between them.
Perceived spatial intervals also mirror time intervals in paradigms not involving motion (the tau effect, Helson and King
1931). A reverse bias also exists, i.e., the judgment of temporal
intervals is strongly influenced by the spatial separation between successive stimuli (the kappa effect, Cohen et al. 1953).
The cutaneous rabbit, tau, and kappa effects all suggest an
underlying bias toward perceiving that objects move at constant velocity, an idea considered in the older psychological
literature (see Collyer 1977; Jones and Huang 1982). Jones and
Huang propose that “the functional relations between distance,
duration and velocity provide a natural context for distance and
duration judgments” (p. 128).
A related idea underpins our approach. We think that motion
across the receptor surface, being the most ubiquitous form of
natural stimulation, is a powerful organizing principle for
spatial maps, i.e., the neural representations of the skin’s
surface that underpin our spatial abilities (see Hiramoto and
Cline 2014; Seizova-Cajic and Taylor 2014; Wiemer et al.
2000). Statistical regularities of physical motion are therefore a
“natural context” not only for relational judgments but also for
perception of the location of individual stimuli.
Thus the aim of the present study was to test whether a
constant-velocity constraint, or perceptual tendency to minimize deviations from constant velocity, influences localization
of tactile stimuli. Specifically, we tested the idea that when a
position on the skin is arrived at through constant-velocity
0022-3077/16 Copyright © 2016 the American Physiological Society
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
Nguyen EH, Taylor JL, Brooks J, Seizova-Cajic T. Velocity of
motion across the skin influences perception of tactile location. J
Neurophysiol 115: 674 – 684, 2016. First published November 25,
2015; doi:10.1152/jn.00707.2015.—We investigated the influence of
motion context on tactile localization, using a paradigm similar to the
cutaneous rabbit or sensory saltation (Geldard FA, Sherrick CE.
Science 178: 178 –179, 1972). In one of its forms, the rabbit stimulus
consists of a tap in one location quickly followed by another tap
elsewhere, creating the illusion that the two taps are near each other.
Instead of taps, we used position of a halted brush and instead of
distance judgment, localization responses. The brush moved across
the skin of the left forearm, creating a clear motion signal before and
after a rabbitlike leap of 10 cm (at 100 cm/s). Three before-and-after
velocities (7.5, 15, or 30 cm/s) were used. Participants (n ⫽ 13)
pointed with their right arm at the felt location of the brush when it
halted either 1 cm before or after the leap. These stops were 12 cm
apart, but distances computed from localization responses were only
5.4, 6.5, and 7.5 cm for the three velocities, respectively (F[2,11] ⫽
15.19, P ⫽ 0.001). Thus the leap resulted in compressive position
shift, as described previously for sensory saltation, but modulated by
motion velocity before the leap: the slower the motion, the greater the
shift opposite to motion direction. No gap in stimulation was perceived. We propose that velocity extrapolation causes the position
shift: extrapolated motion does not have enough time to bridge the
real spatial gap and thus assigns a closer location to the skin on the
opposite side of the gap.
MOTION INFLUENCES PERCEIVED TACTILE POSITION
motion, its perceived location is susceptible to a constantvelocity bias. This has not been formally tested, as past
research has utilized mostly simple stimuli from which it
would be difficult to extrapolate velocity.
The stimulus used to test our proposal— called the Abridging stimulus—places a discontinuous motion signal, similar to
the cutaneous rabbit, in the context of continuous motion. It
was used for the first time in our previous study (Seizova-Cajic
and Taylor 2014). A brush moved up and down the forearm,
from a location near the wrist to a location near the elbow,
providing a reliable estimate of object velocity. We introduced
uncertainty into the motion signal by taking the brush quickly
(at 100 cm/s) across an artificial tactile scotoma in the middle
of the forearm (an area that remained unstimulated while we
brushed the surrounding parts of the skin; see Fig. 1). This
stimulation results in compressive mislocalization of positions
bordering the scotoma, abridging the space (Seizova-Cajic and
Taylor 2014). The segment of the Abridging stimulus that
crosses the scotoma provides a signal similar to the cutaneous
rabbit. A touch on one side of the scotoma is quickly followed
by a touch on the other side, and these positions are felt to be
closer together than they really are. However, an important
difference in comparison to the classical, discrete rabbit stimulus is the motion context.
In the present study, our critical manipulation was to vary
the velocity of the brush before and after it crossed the
scotoma. The question of interest was whether the speed
outside the scotoma would modulate the amount of compression. Because a slow velocity would move the brush a shorter
distance than a fast velocity in the brief time taken to cross the
scotoma, we hypothesized that a constant-velocity constraint
on perception would lead to greater compression in the context
of slower velocity. If speed outside the scotoma did not
modulate compression, then this would provide evidence
against a constant-velocity constraint. On the other hand, the
presence of any compression would be consistent with a
low-velocity constraint. Note that the two constraints are not
mutually exclusive.
METHODS
Overview
Three different velocities were used on three different days to brush
the skin of the forearm from the wrist to the elbow and back, across
a 10-cm tactile occluder (scotoma) in the middle of the forearm. The
outcome of greatest interest was perceived terminal position of the
brush after it crossed the occluder and briefly halted. Blindfolded
Baseline
Baseline
C
subtract response
I
Computed
|B-D|
distance
D
D
after
the leap
Brush location and velocity
D
D
B
Slow
Medium
Fast
B
Brush Velocity
Di st al d i r ect i on
D E
GAP
Space
A B
3.6 cm
3.6 cm
brushed
areas
12 cm
110
0 ccm
m
brushed
areas
D
from response
B
III
Pr oxi mal d i r ect i on
VIII
VII
A B
VI
C
IV V
Space
before
the leap
D E
II
Time
Direction of approach
B
B
Time
[1. Baseline Pre 2. Short 3. Conditioning 4. Long 5. Baseline Post] x 3 Velocities (3 separate days)
E
Presentation Order
order
Fig. 1. Design and procedure. A: apparatus: blindfold (I), graphics tablet (II), graphics tablet stylus used for pointing response (III), proximal brush (IV), distal
brush (V), occluder (VI), sleeve (VII), brush carrier (VIII). B: baseline condition. Experimenter manually brushed locations A–E with brush identical to that used
in all other conditions but using lateral motion (orthogonal to long axis of forearm). C: two directions of brush approach to targets D and B, Proximal motion
and Distal motion. Arrows indicate brush motion in each sweep. Computed B–D distance is calculated using localization responses to B and D separately for
when the brush is moving proximally vs. distally to take into account whether the brush stopped before or after the leap in that sweep. D: space-time diagrams
showing skin areas touched by the brushes (gray strips) and velocity of the brush motion across the skin (broken lines) as well as across the occluder (black dashed
line). Space-time diagram on left shows position of the brushes and brush carrier over time. Black rectangles depict the brush that is in contact with the skin at
that time point, while gray rectangles depict the brush that is moving along the occluder (and hence not felt). At most, only 1 brush is ever in contact with the
skin at any time. Space-time diagram on right depicts velocity of the brush that is felt on the skin (black brush in left diagram) for the 3 conditions. Note that
the slower the velocity on the skin, the greater the acceleration when the brush crosses the gap. E: order of presentation during a single session. Each velocity
was tested in a separate session, on a separate day.
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
Apparatus
A
675
676
MOTION INFLUENCES PERCEIVED TACTILE POSITION
participants also reported on the sensations felt on the forearm during
repetitive back-and-forth brushing.
Participants
The study was approved by the University of Sydney ethics
committee. Participants provided their written informed consent to
participate in the study, according to the procedure approved by the
committee. Thirteen participants (6 women, 7 men; age 21– 48 yr)
each attended on 3 days to complete the experiment. Ten subjects
were naive and were paid for their participation.
Apparatus and Setup
Design
The experiment had a 3 (velocity) ⫻ 2 (duration of exposure to
brushing, hereafter simply “exposure”) ⫻ 2 (direction of approach)
design. The dependent variables were localization by pointing and
phenomenological report. In the report, participants indicated where
on the arm they felt the brushing and how intense it was.
The main independent variable was velocity on either side of the
occluder (Slow: 7.5 cm/s, Medium: 15 cm/s, or Fast: 30 cm/s). The
Baseline
Basic localization ability was determined at the beginning (“Baseline pretest”) and end (“Baseline posttest”) of each session. For each
trial, the experimenter brushed the participant’s arm at one of five
target locations (A–E) with the direction of brush motion orthogonal
to the long axis of the forearm (Fig. 1B; in other conditions, the brush
moved along the long axis of the forearm). Participants responded by
pointing to the location with the stylus. Each location was brushed six
times in a random order with the same constraint as the other
conditions (all targets were presented twice before any were presented
again) for a total of 30 trials.
Procedure
Participants attended on 3 days. On each day, they completed four
runs comprising Baseline pretest, Short, Long, and Baseline posttest
in that order. In a single session, the brushing on either side of the
occluder used in the Short and Long runs were of the same velocity.
The duration of each session differed for the Slow, Medium, and Fast
conditions, taking ⬃75 min, 60 min, and 45 min, respectively.
Participants provided phenomenological reports after Short and Long
runs or at the end of the session. They were asked to indicate where
on the arm they felt the brushing and how intense it was by drawing
on outlines of an arm. If they felt changes over time, they made
multiple drawings. Other comments regarding the sensations they had
noticed during the brushing were also encouraged.
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
Participants were seated with their left forearm pronated and
supported on an armrest placed perpendicular to the seat back (see
Fig. 1A). This arm was hidden from view by a 12 ⫻ 18-in. Wacom
graphics tablet sitting vertically upright between the participant’s
arms, parallel to the length of the forearm. The participant held a
graphics tablet stylus in the right hand. During all stimulation, participants were blindfolded and wore headphones playing white noise.
The left forearm was fitted with a leather sleeve that had two
rectangular 6 ⫻ 5-cm windows over the dorsum of the forearm. These
were separated by a 10-cm metal-covered occluder, which was centered on the forearm. Thus when the dorsum of the forearm was
brushed from one end to the other there were two ⬃5-cm areas of
stimulation separated by a 10-cm gap (artificial tactile scotoma). Two
brushes (1 cm thick and 4 cm wide) were mounted on a single carrier,
which was driven parallel to the forearm by a computer-controlled
stepper motor (Excitron Au Controllercoder model Au57-40M). The
distance between the brushes was set to achieve a 100-ms traverse
time over the occluder and was thus different in the three different
speed conditions. Only one of the brushes was in contact with the skin
at any given time (see Fig. 1A, C, and D). When the carrier moved
distally, the proximal brush swept the skin from the elbow toward the
occluder as the distal brush moved along the occluder. The proximal
brush then moved onto the occluder, where for ⬃100 ms both brushes
were located. This was followed by the distal brush stepping off the
occluder and moving along the skin until it stopped near the wrist
(while the proximal brush moved along the occluder). This constituted
a single sweep. When followed with a sweep in the other direction
(wrist to elbow) this constituted an up-and-down sweep.
The across-the-occluder time is approximate because 1-cm-thick
brushes were manually adjusted to the appropriate height for each
participant, resulting in small variations in the degree to which the
hairs splayed. The two brushes were also manually adjusted to the
appropriate separation for each speed, and their position could vary by
1 or 2 mm.
There were four targets at which the brush could stop, labeled A, B,
D, and E starting from a proximal location (near the elbow; see Fig.
1B). Separation between the two proximal targets, A and B, was ⬃3.6
cm, with the same separation for distal targets D and E. Targets B and
D were 12.0 cm apart. In Baseline conditions, an additional target (C)
was located halfway between B and D. This location was underneath
the occluder in all other conditions. The purpose of using this
additional target in Baseline trials only was to suggest to participants
that stimulation could occur anywhere along the forearm.
three velocity conditions were presented in separate sessions on
separate days, counterbalanced across participants. The gap in stimulation was 100 ms in all three conditions. We also manipulated
exposure (Short and Long) and direction of approach (Proximal or
Distal).
In each session the participants completed a “Short” run followed
by a “Long” run of brushing. In Short the brushes made one or two
up-and-down sweeps before stopping at each target location. Between
Short and Long runs the brush made 100 nonstop up-and-down
sweeps. In Long there were six or seven up-and-down sweeps between stops. The order of runs was fixed to test for any immediate
illusion (observable in Short) and any cumulative effects (observable
during Long).
The targets near the occluder (B and D) could be approached from
two directions. We refer to the approach from the wrist toward the
elbow as the Proximal direction and the approach from the elbow
toward the wrist as the Distal direction. Targets A and E were at end
points of brushing motion and could only be approached from one
direction (A with proximal motion and E with distal motion).
In each run the brush stopped 24 times at the locations on either
side of the occluder (B and D). Half of the 24 stops were approached
during Proximal motion, while the other half were during Distal
motion. Locations A and E were stopped at only six times each, and
their main purpose was to suggest to the participant that the brush
might stop at any location along the arm. Each time the brush stopped,
a computer-generated sound alerted the participant to point to the
current location of the brush, which remained stationary on the skin
for the duration of the pointing response. Pointing comprised moving
the right hand holding the stylus from the rest position (at the right of
the table) to touch the stylus to the graphics tablet, as if pointing at the
site of the brush on the skin. After a period of 4 s from the moment
it stopped, the brush resumed moving. In each velocity condition,
participants completed a total of 60 pointing trials per Short and Long
runs or 120 trials overall [48 trials each for targets B and D (2
directions ⫻ 2 exposures ⫻ 12 repeats) and 12 trials each for targets
A and E (2 exposures ⫻ 6 repeats)]. Targets were presented randomly
with the constraint that all targets were presented twice before any
could be presented again.
MOTION INFLUENCES PERCEIVED TACTILE POSITION
Data Analysis
same
same
separately, the mean response to B was subtracted from the mean
response to D. This computation accounted for the potential dependence of perceived distance across the skin on velocity (Whitsel et al.
1986), due to which the perceived starting point of the brush crossing
the occluder would not be equal for different velocities. We were only
interested in localization of the brush after it crossed the occluder and
how it depended on the outside velocity (not in how velocity influenced perceived extent of the brushed skin).
A three-way repeated-measures ANOVA (velocity ⫻ exposure ⫻
direction of approach) was used to compare computed B–D distance
across conditions and a two-way repeated-measures ANOVA (velocity ⫻ pre/post) to compare computed B–D distance for the Baseline
data collected at the beginning and end of each of the three sessions.
Error bars in Figs. 3 and 4 were computed with the method suitable
for repeated-measures designs that removes irrelevant overall intersubject variability (see Cousineau 2005). Here, the removed component of variance was the tendency for individuals to reach closer or
further overall when pointing. The error bars thus reflect the intersubject variability in the response pattern, i.e., in the differences between
conditions, which is of interest to us. Confidence intervals (CIs)
reported in text were computed with BCa bootstrap procedure in
SPSS.
RESULTS
Phenomenological Report
Although 10 cm of the skin was never brushed, participants
indicated that the brushing stimulus felt continuous along the
forearm for at least part of the time in 35 of 39 responses across
all three velocities. Results broken down by velocity show that
6, 8, and 7 of 13 participants (46%, 62%, and 54%) experienced full motion completion throughout the brushing session
in Slow, Medium, and Fast conditions, respectively. Median
(IQR) scores for the three velocities, shown in Fig. 2, were 3.5
(3.0 – 4.0), 4.0 (3.5– 4.0), and 4.0 (3.0 – 4.0). Friedman’s
ANOVA shows that the difference between the velocities was
not significant (␹2[2] ⫽ 3.36, P ⫽ 0.186).
Pointing Responses
A timeline of pointing responses within each session is
shown in Fig. 3. For this illustration, each participant’s responses were binned such that their first three pointing responses to a target within a run were averaged to create the first
bin. The next bin contained the next three pointing responses to
(4) Full completion
throughout
(3) Full completion
part of the time
Fig. 2. Summary of phenomenological reports indicating
where participants felt the brushing. Left: examples of participants’ responses corresponding to the categories on the
y-axis. The 3 arms represent any change that was experienced
over time and were not all used by the participants whose
percept was stable. Right: box-and-whisker plot summarizes
the reports. Thick lines represent the medians, and each box
includes the middle 50% of data points; whiskers indicate the
lower end of range except for an outlier, indicated with a dot.
same
same
(2) No completion
but gap decreased
(1) No completion
- gap unchanged
Slow
Time
Medium
Fast
Brush velocity
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
Phenomenological report. Participants indicated where along the
arm they had felt brushing, and their reports were classified in the
following categories indicating the degree of motion completion
across the gap, i.e., closing of the gap: 1) two separate areas of
brushing (no completion) with no changes over time; 2) two separate
areas of brushing (no completion) but gap decreased during the
session; 3) no gap in brushed area (completion) for part of the time;
4) no gap in brushed area (completion) for the whole time.
If participants reported different percepts in Short and Long runs in
a single session, the corresponding ratings were averaged so that each
participant contributed three ratings in total, one for each velocity.
Categories 1– 4 thus indicate increasing degree of completion and
were analyzed with Friedman’s ANOVA to determine whether motion completion was affected by velocity.
Pointing responses. Raw data were localization responses to targets A–E expressed as x coordinates of the graphics tablet in millimeters, with 0 at the proximal corner of the tablet. Target A was near
the elbow crease and target E near the wrist. Responses of individual
participants were checked for outliers, using as the criterion the
variability of their 6 (in Baseline) or 12 (other conditions) responses
to a single target. Variability expressed as the standard deviation (SD)
was computed for each individual and each target. The mean and SD
were then computed for the set of those values (across all participants
for any given combination of velocity and exposure), and limits for
deviation from the mean were established with the 2.5 SD criterion. If
an individual SD for any target exceeded this limit, then the participant’s responses to that target were investigated for outliers and up to
two responses were removed. Approximately 0.9% of pointing trials
were excluded on this basis, with relatively even spread across
experimental conditions.
We computed position uncertainty for our brush stimulus as a
median variable error, i.e., one-half of the interquartile range (IQR)
for a given set of responses: 12 responses of the same participant to
the same target (B or D) in the same experimental condition (e.g.,
target B, Distal motion, Short exposure) or 6 responses for Baseline
conditions. A four-way ANOVA was conducted to compare the
brushing conditions, with factors 1) location (B, D), 2) velocity (Slow,
Medium, Fast), 3) brush localization Before vs. After it crossed the
gap, and 4) exposure (Short, Long).
Of most interest to us were responses to the brush halted at targets
B and D, adjacent to the occluder (thus no statistical analysis is
presented for targets A and E, for which we collected a quarter of the
responses as for B and D). Distal motion took the brush across target
B to target D, while Proximal motion took it across target D to target
B. Thus, to compute our final measure (referred to as the “computed
B–D distance”) for each individual participant for each direction
677
678
MOTION INFLUENCES PERCEIVED TACTILE POSITION
Fast
E
250
cation (mm)
(mm
Location
D E
GAP
A B
Medium
Slow
300
D (before gap)
D (after gap)
200
C (Baseline)
150
B (after gap)
100
B (before gap)
A
50
1
2
1
B pre
2
3
4
1
Short
2
3
Long
1
4
2
B post
1
2
B pre
1
2
3
4
Short
1
2
3
Long
4
1
2
B post
1
2
1
B pre
2
3
Short
4
1
2
3
Long
4
1
2
B post
Exposure
that target location, and so on. Figure 3 shows that immediately
after Baseline there is a large compressive effect: separation
between the blue lines (Distal motion), as well as between
the green lines (Proximal motion), is smaller than B–D
separation in Baseline pretest at all three velocities. Shift of
proximal targets (A and B) toward the opposite side is very
pronounced for all velocities. Further analysis was carried
out on the computed distances (“blue” B–D distance and
“green” B–D distance) calculated for each velocity, exposure, and direction of approach. The actual distance between
targets B and D was 12 cm, including the 10-cm gap plus 2
cm of brushed skin, 1 cm on each side of the occluder. Note
that a smaller computed B–D distance means greater mislocalization of positions B and D.
A critical finding was that computed distance increased with
the velocity of the brush. The results are shown in Fig. 4. The
means were 53.9, 65.5, and 75.1 mm in the Slow, Medium, and
Computed B-D distance (mm)
A
B
B-D
120
Individual data
Direction of approach
Distal
100
80
Proximal
Slow (7.5)
Medium (15)
Fast (30)
60
x
40
20
x
x
0
Slow(7.5)
Medium (15)
Fast(30)
-1
Brush velocity (cm s )
Pre Post
Baseline
Fig. 4. B–D distance computed from pointing responses. Bars represent mean
distance calculated by subtracting localization responses for target D from
target B. A: computed distances as a function of brush velocity. Light bars
represent distances during Distal brushing and dark bars during Proximal
brushing. Actual B–D distance was 12 cm. Dark broken line indicates B–D
distance predicted from constant velocity hypothesis. Error bars represent
within-subject 95% CIs. Inset: individual data with each line indicating mean
computed distances averaged over Distal and Proximal brushing for each
subject. B: computed distances in Baseline conditions.
Fast conditions, respectively. The mean difference (95% CI)
between Medium and Slow was 11.6 mm (4.4 –20.7) and
between Fast and Medium was 9.7 mm (1.6 –17.1). These
differences are comparable to the differences of 7.5 and 15 mm
predicted from constant velocity assumption, although they do not
increase with velocity. (The prediction was derived as follows: at
Slow velocity of 75 mm/s distance traveled in 100 ms would be
7.5 mm, at Medium velocity of 150 mm/s it would be 15 mm, and
at Fast velocity of 300 mm/s it would be 30 mm. Each value is
corrected by adding a constant of 20 mm to account for the
accurate perception of extent of brush motion when traveling
across the skin between the occluder and targets B and D. Thus,
should localization be based on extrapolated motion alone, the
predicted values for the computed distance would be 27.5, 35.0,
and 50.0 mm for Slow, Medium, and Fast velocities, respectively.) Perceived B–D distances, however, are greater than the
predicted values (thick dashed line in Fig. 4).
A three-way repeated-measures ANOVA (velocity ⫻ exposure ⫻ direction of approach) showed a significant main effect
of velocity (F[2,11] ⫽ 13.841, P ⬍ 0.001). Repeated contrast
analysis compared Slow to Medium velocity conditions, finding a significant difference (F[1,12] ⫽ 6.164, P ⫽ 0.029, r ⫽
0.58), and Medium to Fast, also significant (F[1,12] ⫽ 7.528,
P ⫽ 0.018, r ⫽ 0.62). The main effect of exposure was not
significant (F[1,12] ⫽ 2.422, P ⫽ 0.146). However, one
interaction term involving exposure and velocity approached
significance (F[1,12] ⫽ 4.653, P ⫽ 0.052): the computed B–D
distance in Fast decreased from 80.5 mm in Short to 69.8 mm
in Long, while for Medium velocity it barely changed (66.0 vs.
65.0 mm). The main effect for direction of approach was
significant (F[1,12] ⫽ 6.327, P ⫽ 0.027, r ⫽ 0.59), with 6.3
mm smaller computed distance in Distal than Proximal approach (95% CI: 2.4 –10.3). There were no statistically significant interactions involving direction.
Baseline was analyzed to determine whether brushing resulted in a change in perceived locations in posttest depending
on velocity. Computed B–D distance in Baseline posttest
across all velocities was 99.9 mm, much smaller than the 119.9
mm in the pretest. The mean differences for Slow, Medium,
and Fast were 15.6 mm (95% CI: 1.9 –28.7), 25.5 mm (15.9 –
35.2), and 18.9 mm (8.8 –30.5), respectively. A 3 (velocity) ⫻
2 (pretest, posttest) ANOVA showed that the difference be-
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
Fig. 3. Binned localization responses showing changes with exposure to brushing over time for each of the 3 brush velocities. Each bin contains 3 pointing
responses, and they are shown in the order of presentation. Semitransparent yellow strip indicates the width of the area under the occluder. Lines represent
responses to a different target, and bands indicate within-subject 95% confidence intervals (CIs) (Cousineau 2005). Red line represents responses to target C,
only used in the Baseline conditions. For targets B and D, the line splits after the Baseline pretest to show mean localization when the brush motion was Distal
(blue lines) vs. Proximal (green lines). B pre, Baseline pretest; B post, Baseline posttest.
MOTION INFLUENCES PERCEIVED TACTILE POSITION
tween pretest and posttest was highly significant (F[1,12] ⫽
34.45, P ⬍ 0.0001), but there was no interaction with velocity
(F[2,24] ⫽ 0.640, P ⫽ 0.536).
As can be seen in Fig. 3, distribution of responses shifted
distally after Baseline pretest. We quantified this shift for responses B and D using their center (average), which was positioned at 153.4 ⫾ 9.2 mm (mean ⫾ SE) in Baseline pretest.
One-way ANOVA showed no significant difference in the
amount of shift between the three velocity conditions (F[2,24] ⫽
1.276, P ⫽ 0.297). However, their combined 28.5 ⫾ 12.8-mm
displacement from Baseline pretest was statistically significant
(t[12] ⫽ 2.228, P ⫽ 0.046). In Baseline posttest the center of B
and D responses shifted back to 141.0 ⫾ 9.8 mm.
Position Uncertainty
DISCUSSION
Our results strongly support the hypothesis that perceived
terminal position of a moving object is susceptible to a constant-velocity bias. Here, the velocity of the brush motion
before the gap modulated its perceived position after the gap,
such that slower before-the-gap motion resulted in a shorter
apparent leap than faster motion. In other words, how far the
cutaneous rabbit jumps depends on how fast it is running
before making the leap. Velocity modulation came on top of a
large inward position shift of the two targets. The third main
finding is that the 10-cm physical gap in stimulation was
perceptually completed, such that in most cases motion felt
continuous. The latter two findings, the Abridging effect and
motion completion, confirm the results from our previous study
(Seizova-Cajic and Taylor 2014).
Findings secondary to our main aims included a change in
Baseline localization after brushing, with 20-mm inward shift
of points on either side of the gap (Fig. 4B). This effect was not
modulated by velocity. Previously we found a similar compression not only when the gap was stitched in time but also
when the brush had a long traverse time over the gap (see Fig.
4A, Seizova-Cajic and Taylor 2014) and thus cannot interpret
it as the carryover (learned) effect of compressive mislocaliza-
tion. It is not clear what causes it. There was also an effect of
direction: distal brush motion resulted in a smaller apparent
leap (greater mislocalization) than proximal. Our estimates of
position uncertainty were similar for proximal and distal locations (B and D), so the result is unlikely to be due to this
potential factor. Our final finding that has no obvious explanation is that the distribution of responses to the brushing
stimuli shifted distally soon after the brushing began (relative
to the responses in Baseline pretest). It is possible that some
aspect of the setup that differed between Baseline and brushing
runs caused distal attention shift, which in turn affected localization. Manipulation of attention caused position shift of a
sensory saltation stimulus pair in a study by Kilgard and
Merzenich (1995). Their stimulus location and parameters
were similar to ours, and the mean difference between the
perceived locations under proximal and distal instructions was
3.1 ⫾ 0.5 cm, comparable to our 2.8 ⫾ 1.3 cm mean distal shift
for the center of the B and D pair.
Our main results can be summarized as 1) motion interpolation (by which we mean filling in of any spatiotemporal
discontinuity with motion signal from the surround) and 2)
velocity-dependent compressive mislocalization (position
shift). We discuss each in turn, examining their potential
mechanisms and interrelationship.
Motion Interpolation
Our motion stimulus, a brush, paused briefly at times when
the localization response was required but otherwise moved up
and down along the forearm. The brushed areas that provided
a clear motion signal and speed estimate therefore flanked the
gap, creating an artificial scotoma and conditions for motion
interpolation when the object was “hidden” by the occluder.
Participants anecdotally reported that they could not always
clearly feel the position of the halted brush, which might have
reinforced a motion tracking strategy.
Vision research has shown that small spatiotemporal interruptions in a motion path (e.g., 13 ms, 0.16° presented in
peripheral vision) go unnoticed if sufficient time has passed
between the motion onset and the interruption, as this allows
for the motion percept to stabilize (Kanai et al. 2007). Smooth
motion interpolation can also be achieved through attentive
tracking of moving objects in the presence of an ambiguous
motion signal, crossing much larger gaps (Shiori et al. 2000).
These and similar findings (e.g., Chun and Cavanagh 1997)
suggest that, once established, the motion percept plays a role
in the “retention of spatiotemporal continuity of an object” (p.
944, Kanai et al. 2007), i.e., an object constancy assumption.
Consistent with this idea, as well as the gestalt principles of
grouping by motion (Todorovic 2011; Wertheimer 1923) and
completion (Pessoa et al. 1998), most of our participants
reported perceiving a single object moving up and down their
forearm with no gaps in stimulation, although in reality there
were two brushes straddling a very large (10 cm) gap. Anecdotal reports and lighter brushing in the middle of the forearm
reported in drawings suggest that other stimulus features, such
as the bristliness of the brush, were not necessarily interpolated, only the motion. We also know that there are time limits
on completion—when the temporal gap in our previous study
was ⬃660 ms, completion normally did not occur (SeizovaCajic and Taylor 2014).
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
In Baseline pretest, position uncertainty measured as median
variable error was on average 9.0 (⫾0.6 SE) mm for target B,
not significantly different from 11.3 (⫾1.1) mm for target D
(t[37] ⫽ 1.733, P ⫽ 0.09). Brushing conditions produced more
uncertainty, with 14.4 mm average across both targets (meaning that ⬃50% of all responses to any single target would have
fallen within a 30-mm range). The four-way ANOVA showed
no significant difference between proximal target B (14.7 ⫾
0.8 mm) and distal target D (14.2 ⫾ 0.8 mm) (F[1,12] ⫽ 0.17,
P ⫽ 0.69). Position error decreased with velocity and was 15.5,
14.8, and 13.0 mm for Slow, Medium, and Fast, respectively
[linear trend approached significance (F[1,12] ⫽ 4.04, P ⫽
0.07)]. Exposure to brushing increased the error from 13.3
(⫾0.4) in Short to 15.6 (⫾0.4) in Long brushing condition
(F[1,12] ⫽ 4.76, P ⫽ 0.05). The most pronounced and highly
significant difference was between the variable error for location targeted before the brush crossed the gap (12.8 ⫾ 0.8 mm)
vs. after it crossed the gap (16.0 ⫾ 0.9 mm), regardless of
whether the location was B or D (F[1,12] ⫽ 15.55, P ⫽ 0.002).
None of the two-way or three-way interactions was statistically
significant.
679
MOTION INFLUENCES PERCEIVED TACTILE POSITION
It is important to note that phenomenological experiences of
motion continuity can occur in response to very different
spatiotemporal patterns. Two such patterns are shown in Fig. 5,
A and B. Figure 5A mimics a real-world experience of occlusion: it shows constant-velocity motion with a spatiotemporal
gap that allows (the perceived) motion to continue unchanged
throughout the gap until the physical stimulus reappears. Figure 5B shows our Abridging stimulus, with a highly incongruent spatiotemporal gap (also illustrated in Fig. 1D, right): any
object undergoing interpolated motion would need to suddenly
accelerate in order to connect to the position where the physical
stimulus reappears.
Velocity-Dependent Compressive Mislocalization
(Position Shift)
A
Fig. 5. Space-time diagrams of constant-velocity motion with a gap in the motion path
(A) and our Abridging motion stimulus (B)
and a spatial diagram summarizing our results (C). Black fields in A and B correspond
to stimulation and gray fields to the constantvelocity motion extrapolation, which in A
allows the motion to smoothly bridge the
gap. In B, however, the spatial extent implied
by the extrapolated motion is left hanging,
and the real motion continues elsewhere.
Note that abridging of space would allow
smooth interpolation. Large gray rectangle in
C represents the occluder. See text for more
details.
B
Δt
gap in space
Time
Time
ce
pa
n s ime
i
p
t
ga nd
a
Space
Space
C
Δt
Total extent of back-and-forth brush motion
7.5 cm s-1
15 cm s-1
30 cm s -1
Starting
position
of the brush
Localization response
predicted from motion
extrapolation lasting 100 ms
Actual
localization
response
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Position of the brush
during localization
response
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
Position shift of the two targets bordering the scotoma
brought them 54 mm closer to each other relative to their
119-mm distance in Baseline pretest (averaged across all 3
velocities). This combined position shift exceeds the combined
position uncertainty of the two targets, which we estimated to
be ⬃30 mm. Illusory position shifts in the presence of motion
have been richly documented and debated in the vision literature (see reviews by Eagleman and Sejnowski 2007; Whitney
2002), but in most cases they are in the direction of motion:
motion stimuli appear to be displaced forward compared with
briefly presented stationary stimuli (flash-lag effect, Nijhawan
1994) or when their last location is reproduced from memory
(representational momentum; for review see Hubbard 2005);
stationary stimuli enclosing areas filled with motion seem to be
displaced in the direction of motion (Ramachandran and Anstis
1983), as do stationary objects placed in the vicinity of motion
(Whitney and Cavanagh 2000). This forward bias is opposite to
the compressive illusory shift we report, which is backward,
toward the motion origin.
So why did position shifts occur in our study? As indicated
above, the large spatial and negligible temporal gap in our
stimulus would require a single moving object to speed up
dramatically (see Fig. 1D and Fig. 5B). We have previously
proposed that the observed compressive mislocalization, or
perceptual shortening of the gap, in these circumstances is
driven by perception that the stimulus is delivered by a single
brush (consistent with an object constancy assumption), combined with a bias against unusual perceived accelerations
(Seizova-Cajic and Taylor 2014). However, unequivocal evidence that motion is at least partly responsible for the position
shifts was lacking until now. The fact that localization error is
correlated with the surround velocity removes any doubt.
Nevertheless, we do not rule out minor contributions from
other sources, such as the enlargement of receptive fields
devoid of input when they are surrounded by active neurons,
shown in vision to result in small position shifts (Kapadia et al.
1994; Pettet and Gilbert 1992).
Pure extrapolation of motion velocity predicts a greater
compressive mislocalization than observed, and thus cannot be
its sole explanation (compare thin dashed arrows in Fig. 5C to
actual localization responses). It is likely that the position
signal from the halted brush also affected localization. A
formal model of length compression developed by Goldreich
(2007) and Goldreich and Tong (2013) for briefly presented tap
stimuli—including the cutaneous rabbit— combines a current
position signal (“measured” position) with a constraint (prior)
to predict perception of intertap distance. Our results are
consistent with this general approach. These authors also
implement the idea that subsequent positions of a moving
object influence perception of its previous positions (“postdictive” inference, Eagleman and Sejnowski 2000) in their model.
Applied to our paradigm, it means that the perceived brush
position before the gap (which we measured with the brush
halted in position) would feel different in retrospect, after the
brush crossed the gap; it would be drawn toward subsequent
brush positions, as shown in cutaneous rabbit studies. In other
words, the postdictive element of the illusion would likely
result in an even smaller measurement of gap size. However,
the continuous brush motion as well as perceptual completion
gap
in
time
680
MOTION INFLUENCES PERCEIVED TACTILE POSITION
Mechanisms Responsible for Position Shift
Percept
Global motion
Local motion
Position
Neural
model
Stimulus
Space
Fig. 6. Abridging motion stimulus, percept, and its possible neural basis.
Stimulus is shown as a space-time diagram and as a brush path along the
forearm. Neural model consists of 3 hierarchically ordered layers: sensory
neurons with small receptive fields able to signal stimulus position on the skin
(I); local motion detectors that signal direction, speed, and extent (II); and
global motion units (III). Percept: motion completion and velocity-dependent
compressive mislocalization.
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
Our findings suggest that both motion and local position
signals contributed to the assignment of terminal location of
the moving brush. They support the idea that spatiotemporal
mechanisms are used and rule out some of the alternative
theories proposed in vision that focus on separable temporal or
spatial mechanisms (for review see Whitney 2002). One is the
temporal integration (or temporal pooling) hypothesis, according to which the position of a moving target is integrated over
a fixed time window (Lappe and Krekelberg 1998). It predicts
greater bias toward the origin of motion for the faster-moving
objects in the present study— opposite to our findings. Another
proposition is that a spatial bias in perception of moving
objects compensates for neural latency (see Whitney 2002).
Such a bias could also be velocity dependent, but again in the
direction of motion, opposite to our findings.
Our spatiotemporal account needs to explain the origin of
the observed bias against sudden accelerations. Its possible
origin is experience with moving objects accumulated through
evolutionary and personal experience. How accumulated
knowledge is built into perception has been a subject of much
theorizing and variously labeled unconscious inference, perceptual constraints, expectations, biases, priors, etc. (see
Kubovy et al. 2013). As mentioned in the introduction, two
plausible constraints have been proposed for perception of
moving objects that come into contact with our skin: a lowspeed constraint, according to which objects move at relatively
low speeds, and a constant-velocity or low-acceleration constraint, according to which they rarely undergo sudden changes
in speed. Either of these constraints would influence perception
more when the motion signal is less clear, but only the latter
(low acceleration) constraint can take advantage of motion
context in the surround of the area with the poor motion signal.
A clear motion signal in the surround allows velocity to be
interpolated into areas where the signal is less clear (as described in vision by Kanai et al. 2007; Nowlan and Sejnowski
1995). Note that an untested notion of constant-acceleration
constraint might be the most general and most adequate formulation of constraints on our motion perception, given that
statistical regularities of physical motion also include accelerations and decelerations, which could also be interpolated.
A low-acceleration prior in touch was formalized in a
Bayesian model by Goldreich and Tong (2013) and pitted
against their low-speed prior to determine which of the two
better predicts Helson and King’s (1931) tau effect and
Geldard’s (1982) multitap rabbit sequence. The low-speed
prior won (see their Figs. 10 –12). With sufficiently dense
sampling, tap stimuli modeled by Goldreich and Tong approach our continuous brushing stimulus. If a low-speed constraint were superior in predicting the above experimental
results, could it also explain ours? The answer is negative: a
low-speed prior predicts greater contraction for higher beforethe-leap speed (see APPENDIX for details; predictions based on
the low-speed prior were computed with the Leaping Lagomorphs application, Goldreich and Tong 2013), a pattern
opposite to both our results and predictions of the low-accel-
eration constraint. It is of interest to consider why the lowacceleration constraint did not predict the tau and multitap
rabbit patterns as well as the low-speed constraint, as described
above. We think the reason is the absence of a clear velocity
signal in those patterns, which did not allow the low-acceleration constraint to be given a fair test.
We thus propose that motion, i.e., velocity interpolation,
causes position shift. What neural mechanisms might be responsible? Motion provides context for perception of spatial
position, and two prime candidates for context-sensitive neural
mechanisms are feedback from higher-level motion neurons
with large receptive fields to the neurons with more accurate
representation of spatial position, and lateral connections between neurons at various levels (Bouecke et al. 2011; Khuu et
al. 2010; Muckli et al. 2005; Neumann 2003; Nishida and
Johnston 1999). The basic architecture of such a neural model,
and how it would process our stimulus, is shown in Fig. 6. Its
feedback and horizontal connections impose contextual “meaning” on the individual neurons through selective excitation and
inhibition. Note that neurons represented as gray filled circles
receive only feedback and horizontal signals when the Abridging stimulus is applied, but this is sufficient, without direct
sensory input, to account for perception of motion in the
nonstimulated area. The model also needs to explain the
position shift. We propose that motion neurons, in addition to
direction and speed, also signal motion extent and bias perceived absolute position of the local neurons via feedback
connections. Support for this idea comes from the Kilgard and
Merzenich (1995) study mentioned above, which shows a
relative independence of the perceived separation between two
Time
in the present paradigm make individual target positions indistinguishable. This makes it difficult to assess the degree of
postdictive inference.
681
682
MOTION INFLUENCES PERCEIVED TACTILE POSITION
APPENDIX
Predictions based on a low-speed prior (Goldreich 2007; Goldreich
and Tong 2013) were computed with Goldreich and Tong’s (2013)
Leaping Lagomorphs application (http://psych.mcmaster.ca/goldreichlab/LL/Leaping_Lagomorphs.html). Percepts for the three brushing conditions were simulated to estimate expected gap sizes for the three
brushing conditions. Densely spaced taps were used to approximately
simulate our continuous motion stimulus (see Table A1 for parameter
values: Sigma-s in the table represents spatial uncertainty, and sigma-v is
dispersion around the zero-speed expectation; smaller values of sigma-v
indicate stronger low-speed expectation).
To estimate the leap distances for the three velocity conditions,
two separate simulations were performed within each condition
(see Fig. A1)— one to find the perceived position of location B
before the brush moved over the gap (Fig. A1, left: Bbefore) and
another to simulate the response to location D after the brush had
just traversed the gap (Fig. A1, center: Dafter). The perceived
locations of B and D were then used to compute a gap size (Fig.
A1, right). This simulated how computed B–D gap size was
derived in our experiment (subtracting the response to the location
before the leap from the response to the location after the leap).
The observer’s spatial uncertainties (sigma-s) for each stimulation
location were taken from our position uncertainty results for B and D
in Baseline pretest (see Position Uncertainty).
Predictions based on these parameters gave computed B–D distances of 10.58 cm for Slow, 10.01 cm for Medium, and 9.85 cm for
Fast, demonstrating a trend of greater compression at faster velocities
(opposite to that found in our study). We also ran the simulations
using position uncertainty values of B and D during brushing conditions (when there is greater uncertainty), which resulted in the same
(but stronger) trend as that reported here.
Conclusions
We answer in the affirmative to Whitney (2002), who asked
whether tactile motion information influences the apparent
position of a tactile target (p. 215). The velocity of surround
motion biases perceived position of skin patches bordering an
artificial scotoma in the direction opposite to the direction of
motion, unlike previously described motion-induced position
shifts. Our artificial tactile scotoma simulates loss of input
from an area of the receptor surface but is only temporary.
Permanent scotomas create a need for lasting, plastic change
of the sensory apparatus. Motion interpolation or completion can thus be viewed as only the beginning of a more
lasting plastic change that will occur if the conditions of
stimulation last (Pessoa and De Weerd 2003). Cortical
plasticity studies (Merzenich and Jenkins 1993) show that
such plastic changes occur in sensory neurons. A possible
neural mechanism that involves motion neurons signaling
extent would support this function of motion.
ACKNOWLEDGMENTS
We thank David Menardo for constructing the apparatus.
GRANTS
This work was supported by Australian Research Council Discovery Project
DP110104691 to T. Seizova-Cajic. J. L. Taylor was supported by a fellowship
from the National Health and Medical Research Council of Australia.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: E.H.L.N., J.L.T., and T.S.-C. conception and design
of research; E.H.L.N. and J.B. performed experiments; E.H.L.N. and T.S.-C.
analyzed data; E.H.L.N., J.L.T., J.B., and T.S.-C. interpreted results of experiments; E.H.L.N. and T.S.-C. prepared figures; E.H.L.N. and T.S.-C. drafted
Table A1. Stimulus input parameters for Leaping Lagomorphs simulation
Slow (7.5 cm/s)
Bbefore
No. of stimulus locations
Spacing of taps (see Fig. A1)
No. of taps at each location
ISI, s
ISI over gap, s
Sigma-s, cm
Sigma-v, cm/s
Medium (15 cm/s)
Dafter
Bbefore
Dafter
Fast (30 cm/s)
Bbefore
Dafter
8
13
8
13
8
13
Taps across brushed skin were 0.5 cm apart (except for one pair, which was 0.6 cm apart), and taps across the gap, 10 cm apart
1
1
1
1
1
1
0.067
0.067
0.033
0.033
0.017
0.017
N/A
0.1
N/A
0.1
N/A
0.1
0.90
1.13
0.90
1.13
0.90
1.13
10
10
10
10
10
10
ISI, interstimulus interval.
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
stimuli delivered in very quick succession and their perceived
absolute position (see their Fig. 1c). It suggests that the
mechanism signaling separation or extent is partially separable
from that signaling position. We believe that our proposed
neural model is also consistent with the Bayesian model by
Goldreich and Tong (2013; see their Fig. 8 and related discussion) who explain the same (Kilgard and Merzenich 1995)
finding as the change in uncertainty about position of taps
under the influence of attention. Their model postulates a
low-speed prior, which modulates perceived position and perceived separation, distorting the original position signal (“measured” position). Implementation of a low-speed prior would
presumably involve higher-order tactile motion neurons, which
would distort the position signal and determine perception of
extent, as we propose (for comparison, Stocker and Simoncelli
2006 propose that the population response of neurons in visual
area MT reflects the influence of the visual low-velocity prior).
Applied to our stimulus, the model described above would
“hook” the end point of extrapolated motion (the rightmost
gray square in Fig. 5B) to the next available signal about
stimulus position (black square on the other side of the gap).
The instantaneous effect of this would be a compromise between a position signal from the end point of a motion vector
and a local position signal, resulting in the velocity-dependent
stimulus mislocalization (backward position shift). Should a
similar stimulation pattern be repeated many times, mimicking
surgical extraction of a patch of skin or a tactile “scotoma,”
longer-term changes might occur such as a change in the neural
signature (local sign) in the local position neuron.
MOTION INFLUENCES PERCEIVED TACTILE POSITION
683
stimulated
perceived
perceived location of D after the gap
perceived location of B before the gap
15.6
Dafter
14
Dafter
4
Bbefore
2
0
10
computed
B-D distance
(Dafter - Bbefore)
8
6
4
Bbefore
2
0
0
0.1
0.2
0.3
0.4 0.48
0
time (s)
Bbefore
Apparatus
0.2
0.4
0.6
0.85
time (s)
Apparatus
B-D distance
Dafter
Apparatus
Fig. A1. Example B–D distance estimation using a low-speed prior simulation (Leaping Lagomorphs application). Parameter values used are for the Slow
condition. Arm illustrations are on left of left and center panels: tap locations are depicted as black dots, arrows indicate direction of apparent motion, and shaded
gray strips show stimulated skin. Space-time diagrams are on right in left and center panels: black lines represent stimulated points, while gray lines represent
the perceived locations corresponding to each stimulated point. Bbefore: panel depicts simulation of movement in the distal direction to location B, before the brush
has traversed the gap. The perceived location of B (Bbefore; last stimulated point) is the value of interest, shown by the dashed arrow. Dafter: panel depicts
simulation of movement toward location D, after the brush has traversed the gap. Perceived location of D (Dafter; last stimulated point) is extracted (dashed arrow).
Right: Bbefore is subtracted from Dafter to generate a computed B–D distance.
manuscript; E.H.L.N., J.L.T., J.B., and T.S.-C. edited and revised manuscript;
E.H.L.N., J.L.T., J.B., and T.S.-C. approved final version of manuscript.
REFERENCES
Bouecke J, Tlapale E, Kornprobst P, Neumann H. Neural mechanisms of
motion detection, integration, and segregation: from biology to artificial
image processing systems. EURASIP J Adv Signal Process 1: 781561, 2011.
Cholewiak RW. The perception of tactile distance: influences of body site,
space, and time. Perception 28: 851– 875, 1999.
Cholewiak RW, Collins AA. The generation of vibrotactile patterns on a
linear array: influences of body site, time, and presentation mode. Percept
Psychophys 62: 1220 –1235, 2000.
Chun MM, Cavanagh P. Seeing two as one: linking apparent motion and
repetition blindness. Psychol Sci 8: 74 –79, 1997.
Cohen J, Hansel C, Sylvester J. A new phenomenon in time judgment.
Nature 172: 901, 1953.
Collyer CE. Discrimination of spatial and temporal intervals defined by three
light flashes: effects of spacing on temporal judgments and of timing on
spatial judgments. Percept Psychophys 21: 357–364, 1977.
Cousineau D. Confidence intervals in within-subject designs: a simpler solution to Loftus and Masson’s method. Tutor Quant Methods Psychol 1:
42– 45, 2005.
Eagleman DM, Sejnowski TJ. Motion integration and postdiction in visual
awareness. Science 287: 2036 –2038, 2000.
Eagleman DM, Sejnowski TJ. Motion signals bias localization judgments: a
unified explanation for the flash-lag, flash-drag, flash-jump, and Frohlich
illusions. J Vis 7: 3, 2007.
Fu YX, Shen Y, Dan Y. Motion-induced perceptual extrapolation of blurred
visual targets. J Neurosci 21: RC172, 2001.
Geldard FA. Sensory Saltation: Metastability in the Perceptual World. Hillsdale, NJ: Erlbaum, 1975.
Geldard FA. The saltatory effect in vision. Sens Processes 1: 77– 86, 1976.
Geldard FA. Saltation in somesthesis. Psychol Bull 92: 136 –175, 1982.
Geldard FA, Sherrick CE. The cutaneous “rabbit”: a perceptual illusion.
Science 178: 178 –179, 1972.
Getzmann S. Exploring auditory saltation using the “reduced-rabbit” paradigm. J Exp Psychol Hum Percept Perform 35: 289 –304, 2009.
Goldreich D. A Bayesian perceptual model replicates the cutaneous rabbit and
other tactile spatiotemporal illusions. PLoS One 2: e333, 2007.
Goldreich D, Tong J. Prediction, postdiction, and perceptual length contraction: a Bayesian low-speed prior captures the cutaneous rabbit and related
illusions. Front Psychol 4: 221, 2013.
Helson H, King SM. The tau effect: an example of psychological relativity. J
Exp Psychol 14: 202–217, 1931.
Hiramoto M, Cline HT. Optic flow instructs retinotopic map formation
through a spatial to temporal to spatial transformation of visual information.
Proc Natl Acad Sci USA 111: E5105–E5113, 2014.
Hubbard TL. Representational momentum and related displacements in
spatial memory: a review of the findings. Psychon Bull Rev 12: 822– 851,
2005.
Jones B, Huang YL. Space-time dependencies in psychophysical judgment of
extent and duration: algebraic models of the tau and kappa effects. Psychol
Bull 91: 128 –142, 1982.
Kanai R, Sheth BR, Shimojo S. Dynamical evolution of motion perception.
Vision Res 47: 937–945, 2007.
Kapadia M, Gilbert D, Westheimer G. A quantitative measure for short-term
cortical plasticity in human vision. J Neurosci 14: 451– 457, 1994.
Khuu SK, Kidd JC, Errington JA. The effect of motion adaptation on the
position of elements in the visual saltation illusion. J Vis 10: 19, 2010.
Kilgard MP, Merzenich M. Anticipated stimuli across skin. Nature 373: 663,
1995.
Kubovy M, Epstein W, Gepshtein S. Visual perception theoretical and
methodological foundations. In: Handbook of Psychology. Vol. 4: Experimental Psychology (2nd ed.), edited by Healy AF, Proctor RW. Hoboken,
NJ: Wiley, 2013.
Lappe M, Krekelberg B. The position of moving objects. Perception 27:
1437, 1998.
Merzenich M, Jenkins W. Reorganization of cortical representations of the
hand following alterations of skin inputs induced by nerve injury, skin island
transfers, and experience. J Hand Ther 6: 89 –104, 1993.
Muckli L, Kohler A, Kriegeskorte N, Singer W. Primary visual cortex
activity along the apparent-motion trace reflects illusory perception. PLoS
Biol 3: e265, 2005.
Neumann H. Completion phenomena in vision: a computational approach. In:
Filling In: From Perceptual Completion to Cortical Reorganization, edited
by Pessoa L, De Weerd P. Oxford, UK: Oxford Univ. Press, 2003.
Nijhawan R. Motion extrapolation in catching. Nature 370: 256 –257, 1994.
Nishida S, Johnston A. Influence of motion signals on the perceived position
of spatial pattern. Nature 397: 610 – 612, 1999.
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
posit ion (cm
position (cm)
10 cm
12
684
MOTION INFLUENCES PERCEIVED TACTILE POSITION
Nowlan S, Sejnowski TJ. A selection model for motion processing in area MT
of primates. J Neurosci 15: 1195–1214, 1995.
Pettet MW, Gilbert CD. Dynamic changes in receptive-field size in cat
primary visual cortex. Proc Natl Acad Sci USA 89: 8366 – 8370, 1992.
Pessoa L, Thompson E, Noë A. Finding out about filling-in: a guide to
perceptual completion for visual science and the philosophy of perception.
Behav Brain Sci 21: 723– 802, 1998.
Pessoa L, De Weerd P (Editors). Filling In: From Perceptual Completion to
Cortical Reorganization. Oxford, UK: Oxford Univ. Press, 2003.
Ramachandran VS, Anstis SM. Extrapolation of motion path in human
visual perception. Vision Res 23: 83– 85, 1983.
Seizova-Cajic T, Taylor JL. Somatosensory space abridged: rapid change in
tactile localization using a motion stimulus. PLoS One 9: e90892, 2014.
Shioiri S, Cavanagh P, Miyamoto T, Yaguchi H. Tracking the apparent
location of targets in interpolated motion. Vision Res 40: 1365–1376, 2000.
Stocker AA, Simoncelli EP. Noise characteristics and prior expectations in
human visual speed perception. Nat Neurosci 9: 578 –585, 2006.
Todorovic D. What is the origin of the Gestalt principles? Humanamente 17:
1–20, 2011.
Trojan J, Stolle AM, Carl AM, Kleinböhl D, Tan HZ, Hölzl R. Spatiotemporal integration in somatosensory perception: effects of sensory saltation
on pointing at perceived positions on the body surface. Front Psychol 1:
206, 2010.
Weiss Y, Simoncelli EP, Adelson EH. Motion illusions as optimal percepts.
Nat Neurosci 5: 598 – 604, 2002.
Wertheimer M. Principles of perceptual organization [1923]. In: Readings in
Perception, edited by Beardslee D, Wertheimer M. New York: van Nostrand, 1964.
Whitney D. The influence of visual motion on perceived position. Trends
Cogn Sci 6: 211–216, 2002.
Whitney D, Cavanagh P. Motion distorts visual space: shifting the perceived
position of remote stationary objects. Nat Neurosci 3: 954 –959, 2000.
Whitsel BL, Franzen O, Dreyer DA, Hollins M, Young M, Essick GK,
Wong C. Dependence of subjective traverse length on velocity of moving
tactile stimuli. Somatosens Res 3: 185–196, 1986.
Wiemer J, Spengler F, Joublin F, Stagge P, Wacquant S. Learning cortical
topography from spatiotemporal stimuli. Biol Cybern 82: 173– 87, 2000.
Downloaded from http://jn.physiology.org/ by 10.220.33.5 on June 18, 2017
J Neurophysiol • doi:10.1152/jn.00707.2015 • www.jn.org