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Low Level Constraints on Dynamic Contour Path
Integration
Sophie Hall*, Patrick Bourke, Kun Guo
School of Psychology, University of Lincoln, Lincoln, United Kingdom
Abstract
Contour integration is a fundamental visual process. The constraints on integrating discrete contour elements and the
associated neural mechanisms have typically been investigated using static contour paths. However, in our dynamic natural
environment objects and scenes vary over space and time. With the aim of investigating the parameters affecting
spatiotemporal contour path integration, we measured human contrast detection performance of a briefly presented foveal
target embedded in dynamic collinear stimulus sequences (comprising five short ‘predictor’ bars appearing consecutively
towards the fovea, followed by the ‘target’ bar) in four experiments. The data showed that participants’ target detection
performance was relatively unchanged when individual contour elements were separated by up to 2u spatial gap or 200 ms
temporal gap. Randomising the luminance contrast or colour of the predictors, on the other hand, had similar detrimental
effect on grouping dynamic contour path and subsequent target detection performance. Randomising the orientation of
the predictors reduced target detection performance greater than introducing misalignment relative to the contour path.
The results suggest that the visual system integrates dynamic path elements to bias target detection even when the
continuity of path is disrupted in terms of spatial (2u ), temporal (200 ms), colour (over 10 colours) and luminance (225% to
25%) information. We discuss how the findings can be largely reconciled within the functioning of V1 horizontal
connections.
Citation: Hall S, Bourke P, Guo K (2014) Low Level Constraints on Dynamic Contour Path Integration. PLoS ONE 9(6): e98268. doi:10.1371/journal.pone.0098268
Editor: Elkan Akyu
¨
rek, Unive rsity of Groningen, Netherlands
Received February 19, 2014; Accepted April 30, 2014; Published June 16, 2014
Copyright: ß 2014 Hall et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: shall@lincoln.ac.uk
Introduction
A fundamental process of human visual perception is contour
integration, whereby discrete contour elements are integrated into
coherent global (whole) shapes. This contour integration serves
important visual functions, such as boundary identification and
figure-ground segregation [1]. For static visual stimuli, contour
integration is a well-established research topic in visual psycho-
physics and neuroscience. However, we know relatively less about
how spatially and temporally separated dynamic features are
processed in contour integration tasks.
The well-documented literature on the spatial constraints of
contour integration has demonstrated the crucial role of relative
spacing, angle and axial offset between static neighbouring
contour elements in the grouping process [2–5]. Increasing the
spacing between adjacent features reduces contour detectability.
Observers’ performance drops to chance levels when the contour
spacing is beyond a critical range, about 2u between collinear line
segments [6] or 10l between collinear Gabor patches [5,7].
Introducing misalignment or orientation jitter relative to the
contour path also effectively decreases contour detectability [2,5].
Observers even show difficulty in grouping two line segments
when they are misaligned by as little as 19 [8]. Variability in the
colour and luminance of the contour elements can further affect
integration performance [9]. Our visual system is biased to group
elements of the same colour [10] and also shows better detection
performance to luminance-defined (achromatic) contours [11–13].
These stimulus parameters (e.g., spatial, temporal, alignment and
luminance features) influence neuronal contextual modulation in
primary visual cortex (area V1) [14,15]. Neurophysiologically, it
has been proposed that V1 neurons play a fundamental role in
contour integration, possibly via intrinsic long-range horizontal
connections that link neurons with similar orientation preferences
but non-overlapping receptive fields (RFs) [3,16] and/or feedback
projections from higher visual areas that process more sophisti-
cated information (such as colour) or information from more
extensive portions of the visual field by virtue of their large RFs
[12,17].
Less is known about the temporal constraints of contour
integration. The majority of studies investigating this topic have
focussed on the importance of temporal synchrony [1,8,18–20]. In
typical collinear flanker-target-flanker design, the flanker facilita-
tion has the maximum effect when the target precedes the flanker
by 20–80 ms and has no effect when the target-flank separation is
longer than 150 ms [20–22]. Studies have also shown that global
contour integration does not demonstrate strong dependency on
the temporal frequency of Gabor patches [19] suggesting it is a
rapid process that is likely to involve fast horizontal connections in
V1 [6,23].
In the natural visual world, objects and scenes around us often
occur and move in statistically predictable ways to create a stream
of visual inputs which are spatially and temporally coherent
[24,25], such as the trajectory of a car moving on the motorway.
Through evolution and development, our visual system should be
able to effectively group relevant information across space and
time, and exploit this spatiotemporal regularity when processing
PLOS ONE | www.plosone.org 1 June 2014 | Volume 9 | Issue 6 | e98268
current visual inputs. This hypothesis has been tested by a few
empirical studies using simplified dynamic visual stimuli to mimic
natural spatiotemporal regularity [26,27]. In our previous studies
we presented human observers with a dynamic stimulus sequence
comprising four collinear short bars (predictors) appearing
consecutively towards the fovea followed by a target bar at
fixation (see Fig. 1 for an example). Our paradigm combines the
principles of two well-established psychophysical paradigms: the
flanker facilitation task [3] and the contour integration task [2].
The flanker facilitation task typically requires the participant to
make a brightness judgement, or target present/absent judgement
on a central line segment that is flanked by two lines on either side
of the target. Reports show that optimally positioned (co-linear)
flankers increase target detection in comparison to target alone
presentation and orthogonally positioned flankers [3]. The
contour integration task often requires participants to make a 2-
interval forced choice decision (target present/absent) on whether
arrays of oriented elements (e.g., Gabor patches) contain a set of
elements which are aligned to form a path. Reports show that
observers can detect paths with relatively large element spacing,
but reducing the alignment of the path elements significantly
reduces path detection [2]. Our paradigm involves both contour
integration and target detection or judgement tasks. However,
whereas these classical paradigms requires the observer to detect a
static central target or static curved contour path from distractor
elements, our task needs the observer to integrate the dynamically
presented, straight contour path elements in the absence of
distractors.
Using this paradigm our studies show that observers’ orientation
judgment of the target bar was biased towards the orientation of
the predictors [24]. This bias was much stronger for the predictors
presented in a highly ordered and predictable sequence than in a
randomised order. Participants also needed less contrast and
showed quicker reaction times to detect the foveal target
embedded in this predictable spatiotemporal stimulus structure,
than in a randomised predictor-target sequence or presented in
isolation without any predictors [25]. Clearly, these spatially and
temporally separated collinear predictor bars were efficiently
integrated as a coherent spatiotemporal contour path. Recordings
of single-neuron responses in rhesus monkeys [28] and event-
related potentials (ERPs) in humans [29–31] suggest that V1
neurons may be involved in this dynamic contour path integration,
but this still remains unclear.
In the current set of studies, we aimed to further investigate the
parameters under which the visual system could group dynamic
contour elements to modulate performance in a target detection
task. Human contrast detection performance of a briefly presented
foveal target bar embedded in a dynamic contour path (typically
comprising six short collinear bars appearing consecutively
towards the fovea) was examined. The spatial and temporal
interval between neighbouring bars, the colour, luminance,
orientation and alignment of individual bars in the dynamic
sequence were systematically manipulated in four separate
experiments. We report each experiment separately in the results
section, and include a brief rationale and method section before
reporting the results in individual experiment. Our findings
Figure 1. A demonstration of the stimuli conditions. Non-scaled demonstration of the stimuli conditions used in the experiments.
doi:10.1371/journal.pone.0098268.g001
Low Level Constraints on Dynamic Contour Path Integration
PLOS ONE | www.plosone.org 2 June 2014 | Volume 9 | Issue 6 | e98268
illustrate that dynamic contour path integration shows little
sensitivity to disruption in spatial, temporal, colour, luminance
and alignment information, but is perhaps more influenced by
orientation cues.
Methods
The methods outlined in this section describe the general
protocol and design employed across the four experiments; the
individual experimental details are outlined in their respective
sections in the results section.
Ethics Statement
Informed written consent was obtained from each participant,
and all procedures complied with British Psychological Society
‘‘Code of Ethics and Conduct’’, and with the World Medical
Association Helsinki Declaration as revised in October 2008. The
ethical committee in the School of Psychology, University of
Lincoln approved the study.
Participants
In total 16 participants (including two authors), aged between
18- and 43-years (22 years 66, Mean 6 SD), took part in this
study. There were 9 females. All participants had normal or
corrected-to-normal visual acuity.
Design
Visual stimuli were presented through a ViSaGe Graphics
system (Cambridge Research Systems, UK) and displayed on a
non-interlaced gamma-corrected colour monitor (100 Hz frame
rate, 40 cd/m
2
background luminance, 10246768 pixels resolu-
tion, Mitsubishi Diamond Pro 2070SB). At a viewing distance of
57 cm the monitor subtended a visual angle of 40630u. The visual
stimuli comprised six short bars (1u length, 0.1u width) appearing
successively towards the fovea following a collinear path (predic-
tor-target sequence, see Fig. 1 for examples), so that they created
an apparent motion stream towards the presentation of the target
bar. Unless specified in individual experiments, the first five
‘predictor’ bars with 15% contrast were presented in the right
peripheral visual field (the centre of the furthest predictor bar was
5u away from the fovea). The sixth ‘target’ bar was presented 1u
below a small red fixation point (FP, 0.2u diameter, 10 cd/m
2
)in
varying contrast (0%, 0.25%, 0.5%, 0.75%, 1%, 1.25%, 1.5%,
1.75%, 2%, 2.5%, 15%). Each bar was presented for 200 ms.
Typically there was no spatial and temporal gap (or spacing)
between adjacent bars. The bars were flashed in turn, in a position
immediately adjacent (end-to-end) and in a time immediately
preceding the next bar at successive positions. The location,
orientation, luminance and colour of individual predictor bar were
manipulated independently in different predictor-target sequences
(the detailed manipulation of stimulus structure is described below
for the individual experiments). Regardless of experimental
manipulation of the predictor-target sequences, the horizontal
target bar (1u length, 0.1u width) was identical and always
presented 1u below the FP.
To familiarise the participants with the task a training session
(normally 20 trials) was given before the formal test. During the
experiments, the participants sat in a quiet, darkened room, and
viewed the display binocularly with the support of a chin-rest. In
each experiment four predictor-target sequences and 11 target
contrasts were presented in a random order; so that neither the
sequence nor the target contrast were predictable based upon the
stimulus previously viewed. The trial was started by a 350 Hz
warning tone lasting 150 ms followed by a delay of 1000 ms. A
predictor-target sequence was then presented. Across four
experiments we used ten conditions (target alone, predictable,
100 ms predictor gap, 200 ms predictor gap, 1u predictor gap, 2u
predictor gap, random colour, random luminance, random
orientation and misalignment; details are presented in the
respective experimental sections). In each experiment four of
these conditions were presented. The target alone and predictable
sequence were displayed in all of the experiments. In the target
alone condition no predictors were presented, only the target bar.
In the predictable sequence five collinear predictors appeared
successively towards the fovea in highly predictable spatial and
temporal order, followed by the target; there was no spatial and
temporal gap between adjacent bars (i.e., predictor 1Rpredictor
2Rpredictor 3Rpredictor 4Rpredictor 5Rtarget). Two other
conditions were selected based upon the aim of the experiment.
The participants were instructed to maintain fixation of the FP
throughout the trial, and to indicate, by pressing the ‘enter’ key on
a computer keyboard as quick as possible, when they were
reasonably confident that the target had been presented below the
FP within this predictor-target sequence (target present/absent
detection). No feedback was given. The inter-trial interval was set
to 1500 ms. A minimum of 20 trials were presented for each target
contrast, for each predictor-target sequence. During the experi-
ments the observers were encouraged to have a short break if it
was necessary.
The participants’ detection performance (percentage of target
detection judgment) was measured as a function of target contrast.
Catch trials (0% and 15% target contrast) were used to correct for
guessing target detection. Across the participants and predictor-
target sequences, the mean hit rate for the presence of 15% target
contrast was 99.6%62.2, and the mean false alarm rate for the
presence of 0% target contrast was 4.3%66.8. Analysis was
conducted on the data (detection rate) calculated after a bias
correction. The detection rate for target presence with a tested
contrast was calculated as (observed hit rate – false alarm rate)/(1-
false alarm rate)6100 [32]. The normalised detection rates were
plotted against the target contrasts and fitted with logistic
psychometric functions (Fig. 2, 3, 4 and 5). Pilot testing showed
that analysis Point of Subjective Equality (PSE) values did not
accurately capture observer’s sensitivity to the different parameter
manipulations. By including data from all contrast points we can
gain more information from the data than we can if we restrict our
analysis to PSE values. Furthermore, in using the psychometric
fitting we recognise that the fit to the data is not always ideal;
therefore, it is more reliable to base the statistics on actual
observed data as opposed to predicted fitted data. Therefore,
target detection performance in different predictor-target condi-
tions was analysed using repeated measures analyses of variance
(ANOVA), with condition (stimulus sequence) and contrast (0–
2.5%) entered as the within subjects factor in the initial analysis.
Tukeys error adjustments were applied in pairwise comparisons
and these are used to report the effects of condition across
contrasts. Across all analyses conducted there was a significant
main effect of contrast (Fs$67.70, ps ,.001, g
p
2
$93) illustrating
better target detection performance the higher the target contrast
(as would be expected). We do not report this main effect but
instead focus on the main effect of condition and the interaction
between condition and contrast. All interactions condition6con-
trast effects were analysed by comparing the effect of condition
separately at each contrast point to avoid multiple post-hoc testing
Low Level Constraints on Dynamic Contour Path Integration
PLOS ONE | www.plosone.org 3 June 2014 | Volume 9 | Issue 6 | e98268
Experiments and Results
Experiment 1: manipulation of spatial gap between
predictors
With similar stimulus arrangements, Hall et al. [25] demon-
strated that our detection of low-contrast targets depends heavily
on the context of the predictor path. In comparison with the target
alone sequence, participants showed increased detection rate and
shortened reaction time in response to the target embedded in a
predictable collinear predictor-target sequence (Fig. 1 in [25]).
Such enhanced target detection performance could not be fully
accounted for by response bias and uncertainty reduction (e.g.,
predictor presentation can reduce spatial and/or temporal
uncertainty about target presentation), suggesting our visual
system takes the regularity of the spatiotemporal contour path
into account when interpreting incoming target information
[24,25]. In other words, spatially and temporally separated
predictor information can be effectively integrated to facilitate
the detection of the target.
The spatial and temporal parameters of contour integration are
typically investigated by focussing on the role of relatively local
mechanisms, such as flanker facilitation (as opposed to a more
global integration process). For instance, the well-studied phe-
nomenon of flanker facilitation or collinear facilitation has
demonstrated that our contrast sensitivity to a low-contrast Gabor
target is enhanced when presented in the context of spatially
separated collinear flankers (flanker-target-flanker) [24,33]. Studies
regarding the spatial and temporal determinants of such flanker
facilitation have further revealed that the amount of facilitation
varies with the spatial and/or temporal gaps between the target
and flankers. Spatially, facilitation is the greatest when the spatial
gap between the target and the flanker is about 3l or 4l.
Increasing or decreasing the spatial gap from this optimal distance
leads to a significant reduction in the facilitation effect [33–35].
The effects of temporal gaps are not so cohesive. The majority of
evidence suggests a short range determinacy on temporal
integration, with flanker facilitation having maximum effect when
the target precedes the flanker by 20–80 ms, and no effect when
the target-flank separation is longer than 150 ms [21,22].
However, it has also been reported that memory processes can be
evoked in visual integration, with contrast detection performance
facilitated when collinear flanker and target are separated up to
16 seconds [36]. Although there is considerable debate about the
neural mechanisms underlying the flanker facilitation, the long-
range horizontal connections in primary visual cortex seem to play
a dominant role [16,37].
To extend our knowledge of the spatial and temporal
determinants of visual integration to dynamic contours (as opposed
to flanker-target-flanker integration), we manipulated the spatial
(experiment 1) and temporal (experiment 2) intervals between the
presentations of adjacent predictor (contour) bars. We predicted
that if the visual system could integrate contour information over
different spatial and temporal intervals then the information
provided by the predictor bars (the contour) would be used to bias
Figure 2. Target detection performance across spatial gaps.
Target detection rate as the function of target contrast. The target was
embedded in predictable predictor-target sequence, but the spatial gap
(0u,1u,2u) between the adjacent bars was systematically varied. Error
bars represent the standard error of mean.
doi:10.1371/journal.pone.0098268.g002
Figure 3. Target detection performance across temporal gaps.
Target detection rate as the function of target contrast. The target was
embedded in predictable predictor-target sequence, but spatial gap
(0 ms, 100 ms, 200 ms) between the adjacent bars was systematically
varied. Error bars represent the standard error of mean.
doi:10.1371/journal.pone.0098268.g003
Figure 4. Target detection performance with randomised
colour and luminance of the contour elements. Target detection
rate as the function of target contrast. The target was embedded in
predictable predictor-target sequence, but the colour and contrast of
the adjacent bars was systematically varied (see methods). Error bars
represent the standard error of mean.
doi:10.1371/journal.pone.0098268.g004
Low Level Constraints on Dynamic Contour Path Integration
PLOS ONE | www.plosone.org 4 June 2014 | Volume 9 | Issue 6 | e98268
target detection performance (i.e., target detection performance
would be comparable across different spatial and temporal
intervals between the bars). If dynamic contour integration is
disrupted by spatial and temporal gaps the visual system will be
less efficient at using the contour information to predict the target
appearance and therefore target detection rates will decrease with
greater spatial and/or temporal gaps.
Method. To examine to what degree spatially separating
individual bars in the predictor-target sequence affect target
detection performance, the stimulus structure was manipulated in
four conditions: (1) Predictable sequence: see main methods, (2) 1u
spatial gap: the dynamic stimulus structure (including the number of
the bars, the length and width of the bars) was the same to that in
the predictable sequence, only the spatial gap between adjacent
bars was increased to 1u; (3) 2u spatial gap: the same as in condition
2, only the spatial gap between the adjacent bars was increased to
2u; (4) Target alone sequence: see main methods. Five volunteers
participated in this experiment.
Results and Discussion. A 4 (stimulus sequences)610
(target contrast levels) ANOVA revealed that compared to target
alone sequence, predictable sequence significantly increased target
detection rate (F(3, 12) = 20.80, p,.001, g
p
2
= .84; Fig. 2) even
with the large 2u spatial gaps (pairwise comparisons: all ps,.02). A
significant condition6contrast interaction (F(27, 108) = 7.21, p,
.001, g
p
2
= .64) was analysed further. The results showed that the
enhancement in contrast detection performance was most evident
when the target contrast was varied 1–2.5% (Fs$6.07, ps,.05).
Unlike those results reported in flanker facilitation, the distance of
spatial interval had negligible effects on the amount of facilitation
in target detection performance. That is, increasing spatial gap
between the predictors had limited effect of decreasing detection
rate to target detection. Specifically, compared to the predictor-
target sequence with larger spatial gaps ($1u), only the detection
rate for 0.75% contrast target was higher in the predictable
sequence without spatial gap (F = 6.70, p,.01). For lower target
contrasts (0.25–0.5%) no significant effects of condition were
observed (Fs#5.01, ps$.05). For higher contrast targets ($1%),
the detection rate was indistinguishable among stimulus sequences
with different spatial gaps (all ps..05). This indicates that dynamic
global contour path integration operates over relatively large
spatial intervals.
Experiment 2: manipulation of temporal gap between
predictors
Method. To examine to what degree temporally separating
individual bars in the predictor-target sequence affect target
detection performance, the stimulus structure was manipulated in
four stimulus conditions: (1) Predictable sequence; (2) 100 ms temporal
gap: the dynamic stimulus structure was similar as in the
predictable condition, only the temporal gap between adjacent
bars was increased to 100 ms; (3) 200 ms temporal gap: the same as
in condition 2, only the temporal gap between the adjacent bars
was increased to 200 ms; (4) Target alone sequence. Five volunteers
participated in this experiment.
Results and Discussion. A 4 (stimulus sequences)610
(target contrast levels) ANOVA revealed that compared to target
alone sequence, predictable sequence significantly increased target
detection rate (F(3, 12) = 23.45, p,.001, g
p
2
= .85; Fig. 3) even
with the long 200 ms temporal intervals (pairwise comparisons: all
ps,.02). Varying temporal interval between the presentation of
adjacent bars (offset-onset delay as 0 ms, 100 ms, and 200 ms), on
the other hand, had no clear effect on the target detection
performance (all ps..05). A significant condition6contrast inter-
action (F(27, 108) = 4.45, p,.001, g
p
2
= .53) showed that the
enhancement of the predictable sequence (with varying temporal
intervals) was most noticeable between target contrasts 1–2.5%
(Fs$5.42, ps,.05). At contrasts below this (0.25–0.75%) no
significant effects were observed (Fs#2.82, ps$.05). Across the
tested target contrasts detection rates were indistinguishable when
the temporal interval was varied between 0 and 200 ms (ps..05).
Overall, experiments 1 and 2 revealed a robust facilitation effect
of dynamic contour path on target detection. Disrupting this
contour integration by increasing spatial interval up to 2u or
temporal interval up to 200 ms between adjacent contour
elements had very limited detrimental effect on target detection
performance, suggesting that our visual system can integrate
spatially or temporally separated events into a coherent represen-
tation when these events change according to a predictable
temporal structure (pattern of changes over time). These results
contribute to the debate in the literature on the spatial and
temporal determinants of contour integration by supporting
studies which suggest integration can occur over large distances
[36] and we extend this to show this is true for dynamic global
integration mechanisms as well as flanker-target-flanker integra-
tion mechanisms.
Experiment 3: manipulation of predictor’s luminance and
colour
Horizontal connections in area V1 tend to connect neurons
sharing the same orientation and colour preferences [38,39], and
V1 neurons which are sensitive to chromaticity show less
sensitivity to orientation [40]. These neurophysiological studies
have indicated that contour elements with the same luminance
contrast or colour would be easier to integrate. Psychophysical
studies have observed that we are biased to group static elements
of the same colour [10]. Detection performance is slightly better to
luminance-defined (achromatic) than colour-defined (chromatic)
contours, with evidence suggesting that higher cortical areas are
involved in processing colour-defined contours compared to
luminance defined contours [11–13]. For instance, the integration
of achromatic contour elements is approximately 100 ms faster
than for chromatic contour elements [11]. Additionally, we can
Figure 5. Target detection performance with random orienta-
tion and random alignment of the contour elements. Target
detection rate as the function of target contrast. The target was
embedded in predictable predictor-target sequence, but the alignment
and orientation between adjacent bars was systematically varied (see
methods). Error bars represent the standard error of mean.
doi:10.1371/journal.pone.0098268.g005
Low Level Constraints on Dynamic Contour Path Integration
PLOS ONE | www.plosone.org 5 June 2014 | Volume 9 | Issue 6 | e98268
integrate achromatic contours efficiently over 4.6u element
spacing, but this declines to 3.6u for blue-yellow contours and
2.9u for red-green contours [12]. However, it has also been
reported that collinear facilitation for static targets is similar for
chromatic and achromatic stimuli [41] and that the processing of
both is likely to be sub-served by neurons in V1 and V2 [42]. As
such there remains confusion as to the capabilities of early visual
neurons in integrating chromatic and achromatic stimuli. To our
knowledge, the effects of chromaticity have yet to be investigated
with dynamic contour integration.
Method. The stimulus structure was manipulated in four
sequences: (1) Predictable sequence; (2) Random luminance sequence: the
dynamic stimulus structure was similar as in the predictable
sequence, but the individual grey predictor’s luminance contrast
was randomly varied between 225% and 25% with 5% step (0%
contrast was excluded); (3) Random colour sequence: the dynamic
stimulus structure was similar as in the predictable sequence, the
individual predictor’s contrast was kept as 15%, but its’ colour was
randomly varied between 10 different colours (CIE 1931 colour
space, x1 = 0.284, y1 = 0.597; x2 = 0.383, y2 = 0.513; x3 = 0.47,
y3 = 0.449; x4 = 0.289, y4 = 0.213; x5 = 0.237, y5 = 0.223;
x6 = 0.195, y6 = 0.234; x7 = 0.311, y7 = 0.353; x8 = 0.373,
y8 = 0.424; x9 = 0.329, y9 = 0.292; x10 = 0.311, y10 = 0.328); (4)
Target alone sequence. Six volunteers participated in this experiment.
Results and Discussion. A 4 (stimulus sequences)610
(target contrast levels) ANOVA showed that randomising element
colour and luminance significantly affected target detect perfor-
mance (F(3, 15) = 19.86, p,.001, g
p
2
= .80; Fig. 4). Randomising
colour and luminance impaired target detection performance, with
better detection rates in predictable sequence compared to
random colour, luminance and target alone conditions (ps,.05).
However, randomising colour and luminance did not eliminate
contour integration, with better detection rates in these two
conditions compared to target alone (ps,.01). Detection rates were
comparable when colour and luminance were randomised
(p = .45). A significant condition6contrast interaction was further
analysed (F(27, 135) = 4.68, p,.001, g
p
2
= .48). The enhancement
of the predictable sequence compared to all other sequences was
most evident at low target contrasts 0.5–1.25% (Fs $ 8.16, ps,.01),
but not at 0.25% and higher contrasts (1.5–2.5%) (ps$.05). In
comparison to target alone, detection performance was better in
predictable, random colour and random luminance sequences
between contrasts 0.75–2.5% (Fs$5.10, ps,.05) and was not
significant at very low target contrasts (0.25–0.5%) (Fs#3.01,
ps$.05).
The results suggest that favourable conditions for dynamic
contour path integration occur when the contour elements
(predictors) are of the same colour and luminance. This is
consistent with the previous findings that we tend to group contour
elements of the same colour [10]. Given that randomising colour
and luminance did not show significant differences in target
detection performance, our results support evidence which
suggests that static contour integration for chromatic and
luminance elements is similar [41] and this extends to dynamic
global contour integration.
Experiment 4: manipul ation of predictor’s orientation
and alignment
Earlier investigations with static [2,43] and dynamic [25] stimuli
have shown that contour elements which share the same
orientation are easier to integrate than elements which are
randomly orientated, and elements which are aligned are easier
to integrate than those that are misaligned [8]. Neurophysiological
studies have also suggested that elements need to be precisely
aligned for simple V1 cells to effectively integrate contour signals
[44,45]. However, there is little experimental work to directly
compare the influence of orientation and alignment, particularly in
dynamic contour integration.
Method. To examine the effects of orientation and alignment
in contour path integration, the stimulus structure was manipu-
lated in four sequences: (1) Predictable sequence; (2) Random orientation
sequence: predictors with random orientation (0–180u in steps of
22.5u) appeared successively towards the fovea, followed by the
target; (3) Misalignment sequence: five horizontal predictors with
randomised vertical position (in the range of 61u in relation to the
target position) appeared successively towards the fovea, followed
by the target; (4) Target alone sequence. It was predicted that if V1
orientation selective neurons play a key role in dynamic contour
integration then disrupting continuity in orientation and alignment
between the path elements should reduce contour path integration
and therefore target detection. Five volunteers participated in this
experiment.
Results and Discussion. A 4 (stimulus sequences)610
(target contrast levels) ANOVA illustrated that disrupting the
orientation and alignment of the predictor-sequence impaired
target detection (F(3, 12) = 15.03, p,.001, g
p
2
= .79; Fig. 5).
Specifically, the target detection performance was better in the
predictable sequence compared to all other conditions (ps,.05).
The misalignment condition produced better detection rates than
target alone condition (p,.05), whereas the random orientation
condition did not (p = .23). However, target detection performance
was not significantly different in random orientation and
misalignment condition (p = .21), therefore it should only be
tentatively suggested that randomising orientation impairs spatio-
temporal contour integration greater than misalignment. A
significant condition6contrast interaction (F(27, 108) = 4.71, p,
.001, g
p
2
= .54) demonstrated that between 0.5–1.75% target
contrast performance was better in the predictable sequence
compared to all other conditions (Fs $7.25, ps,.01), but these
effects were not significant between predictable sequence and
random orientation or misalignment sequence at higher contrasts
(2–2.5%) (ps..05). At contrasts 0.75–1.75% target detection was
better in misalignment compared to target alone condition (Fs$
9.66, ps,.01), but not at lower (0.25–0.5%) and higher (2–2.5%)
contrasts (p..05).
These results suggests that dynamic contour integration is better
when the contour elements are spatially co-aligned and of the
same orientation. However, reducing the spatial alignment
between the predictor bars impaired target detection less than
randomising predictor orientation, suggesting that orientation is
more disruptive to contour integration than element alignment
when modulated in the space-time domain. Given V1 neurons’
sensitivity to orientation information [46], these results indicate
that horizontal connections in area V1 could be heavily involved
in dynamic contour path integration [6,28]. This suggests that
preferential connectivity between V1 columns may be more reliant
on similar orientation preferences rather than direct alignment.
Although apparent motion effects are typically reported in terms of
shortening neural response latency [47,48] it may contribute to
our findings here. That is, the higher cortical areas could be
involved in linking the contour elements based upon the motion
trajectory [49,50]. However, motion detectors are highly sensitive
to spatial frequency [51] and the optimal temporal frequency for
detecting coherent motion is between 59-24 Hz (17–42 ms) [52].
In experiment 1 and 2 we showed that dynamic contour path
integration was relatively stable across different spatial gaps up to
2u and temporal gaps up to 200 ms. This suggests that although
motion detectors are likely to play a role in processing the
Low Level Constraints on Dynamic Contour Path Integration
PLOS ONE | www.plosone.org 6 June 2014 | Volume 9 | Issue 6 | e98268
apparent motion produced by the stimuli, they are unlikely to be
the primary contributors to dynamic contour path integration
which biases target detection performance.
General Discussion
The constraints on contour integration have typically been
explored in static stimuli using a path detection task [2,43,53].
Given that we live in a dynamic visual world whereby visual inputs
from different spatial and temporal windows are highly correlated
[54,55], it is beneficial that we also explore spatiotemporal contour
integration to further define the parameters under which contour
integration occurs. Overall, we observed that the human visual
system effectively integrates spatially and temporally dispersed
contour path information to facilitate target detection. These
experimental findings are compatible with our previous psycho-
physical investigations [24,25] which have suggested that we
exploit prior knowledge of natural scene statistics (spatiotemporal
regularity in this case) to facilitate the processing of current visual
inputs [56–58]. For the rest of this discussion, we first compare our
data to previously reported results, which have predominately used
static stimuli and/or flanker-target-flanker designs, as opposed to
the dynamic path integration used in our design. We then consider
possible neural candidates for the basis of static and dynamic
contour path computations.
Our experimental findings from the manipulation of spatial or
temporal gap between predictors have suggested that dynamic
collinear contour path integration was still evident when individual
elements were separated by up to 2u spatial gap or 200 ms
temporal gap (Fig. 2 and 3). This is relatively consistent with those
observed in static contour integration, which suggest that collinear
bar elements can be successfully integrated when the spatial gap
does not exceed 2u [6]. Limited studies have reported the influence
of temporal gap between contour elements, but in contrast to our
findings reports suggest that flanker and target are only integrated
when the target is presented within a 150 ms of the onset of the
flanker [21]. Here we have demonstrated that integration of a
dynamic contour path occurs over greater temporal gap than the
integration of singular flanker-target stimuli, being robust at
200 ms intervals.
The present investigations are one of the first to directly
compare the influence of orientation and alignment in dynamic
contour integration tasks; this allows us to define more precisely
the parameters of alignment which are important to successful
contour integration [2,59,60]. In agreement with previous studies
using static stimuli we have shown that integrating dynamic
contour elements was more efficient (as evidenced by better target
detection performance in Fig. 5) when the elements shared the
same orientation and alignment [2,8,43]. We have also demon-
strated that observers were potentially more sensitive to orienta-
tion cues than misalignment. Target detection performance was
enhanced when the contour elements were of the same orientation
but misaligned in comparison to when the elements were aligned
but of different orientation. The reliance on orientation cues for
successful contour integration is compatible with the functioning of
V1 orientation-selective neurons [46]. Furthermore, dynamic
contour path integration was better for contour elements defined
by the same luminance contrast (predictable sequence in Fig. 4).
This is comparable with static contour integration, which is more
efficient for contours defined by achromatic elements [11–13].
Randomising elements’ colour and luminance showed similar
detrimental effect but did not abolish contour path integration,
such that the observer could still integrate the elements to bias
target detection performance. This suggests that the visual system
is able to link similarly oriented dynamic contour elements even
when luminance and colour cues are reduced.
These constraints on dynamic contour path integration are
suggestive of the neural mechanisms underlying dynamic contour
integration. Previous investigations using static stimuli have
strongly suggested that contour elements can be integrated as
early as area V1 through contextual interactions and intrinsic
horizontal connections [2,61]. For instance, V1 responses are
facilitated by collinear line segments [6,62] and closely correlate
with the perceptual saliency of the static contours [15]. Recent
extracellular recordings in rhesus monkeys have similarly reported
the involvement of V1 neurons in the processing of dynamic
contour path. Typically when the collinear predictors (extra-RF
stimuli) and target (RF stimulus) were arranged as a dynamic
predictable sequence, orientated towards and through to the
neuron’s RF, half of the recorded neurons responded to the
predictors presented outside their RFs at the time that there was
no visual stimulus presented inside the RFs [28]. However,
findings from human ERPs [29,30] and fMRI studies [63] have
indicated the crucial role of later processing stages and neural
generators beyond V1 in grouping dynamic contours. Our current
findings provide original evidence to suggest that (as in primates)
V1 is also critically involved in contour integration in the space-
time as well as space domain.
To elaborate, the ability to integrate contour elements over a
range of spatial (up to 2u) and temporal (up to 200 ms) spacing is
harmonious with the characteristics of horizontal connections in
V1 [6,23]. Additionally, the importance of orientation information
compared to alignment information is compatible with V1
orientation selective neurons communicating predictive informa-
tion about the appearance of upcoming targets based up on their
motion trajectory. Evidence from V1 neuronal populations shows
that V1 responses increase in amplitude in the contour region
(region of co-oriented contour elements) and decrease in the
background region (region of randomly oriented elements),
indicating that V1 is actively involved in the perceptual grouping
of similarly oriented elements [61]. Computational models have
suggested that recurrent excitatory and inhibitory horizontal
connections in V1 could sub-serve this process, prioritising
targeting cells which are linked with similar orientation prefer-
ences [64].
It is also thought that V1 orientation selective neurons show
limited selectivity to chromaticity [40,42]. The performance data
in Fig. 4 fits well with this neurophysiological observation. The
target detection was better when the grey contour elements were of
the same luminance contrast, yet randomising colour and
luminance did not eliminate integration altogether, with better
performance in the random colour and luminance condition
compared to target alone. This illustrates that V1 neurons may be
communicating information to predict the target based upon
linking of contour elements. The strength of these connections is
likely to be stronger when connecting neural responses which
share similar orientation, colour and luminance preferences.
When colour and/or luminance preferences are not matched
the strength of the connections are reduced, such that target
detection was decreased, but not eliminated, such that target
detection was better than when the target is presented without the
contour information (target alone condition). This speculation is
supported by the earlier neurophysiological observation that a
population of V1 neurons (,30%) showed approximately equal
orientation selectivity to both chromatic and luminance gratings
[65,66] suggesting horizontal connections in V1 can still function
with color-defined orientated colour elements.
Low Level Constraints on Dynamic Contour Path Integration
PLOS ONE | www.plosone.org 7 June 2014 | Volume 9 | Issue 6 | e98268
On the other hand, the integration of coherent but spatially and
temporally separated visual signals is often subject to the influence
of top-down modulation (e.g., expectation and prediction), and has
traditionally been ascribed to the neural processes in higher
cortical areas, such as frontal and parietal cortex [26,67,68].
Indeed, some evidence suggests that contour integration responses
in monkey V1 is absent when the task is novel and when under
anaesthesia (passive viewing). With active perceptual learning V1
shows delayed contour integration responses, which are thought to
be the result of recurrent top-down processes [69]. Furthermore,
recordings of ERPs showed similar contour integration processes
in humans with dynamic sequences [30]. When dynamic contour
paths were passively viewed (no task), or when attention was taken
away from the path (by a secondary colour counting task), the
shortened N1 peak latency associated with viewing targets
embedded in dynamic predictor-target paths was abolished.
Because N1 peaks at relatively later stages of processing (i.e. post
200 ms), it was suggested that this component reflects the
involvement of top-down processes, and these processes are
imperative to successfully linking contour elements to bias target
detection [30]. However, a recent ERP study has shown that even
at very early stages of processing, at a time window associated with
V1 processing (,66 ms), contour integration of predictable path
elements (co-linear paths) shortens peak latencies of early ERP
components in comparison to the integration of less predictable
paths (co-circular paths) [31]. This suggests that V1 may play an
independent role in human contour integration. Although we
cannot differentiate the relative contribution of long-range
horizontal connections and feedback connections in dynamic
contour integration in this study, the similar constraints on the
integration of static and dynamic contour path suggests that V1
neurons are directly involved in the dynamic contour integration.
In conclusion, using dynamic contour elements the investiga-
tions reported here illustrate that human observers utilise contour
path information modulated in space and time to facilitate their
detection of low contrast targets. The spatial, temporal, colour/
luminance and alignment parameters under which this perfor-
mance contribute to the growing debate within the literature as to
the parameters under which contour integration is facilitated and
broadly supports with the functioning of V1 neural processes. It
seems that the visual system can integrate dynamic contour paths
to bias target detection even when the path is disrupted by spatial
and temporal intervals and breaks in alignment, colour and
luminance.
Acknowledgments
We would like to thank Stephan Bekk and Ruth Brown for helping with
data collection.
Author Contributions
Conceived and designed the experiments: KG. Performed the experiments:
SH. Analyzed the data: SH KG. Contributed reagents/materials/analysis
tools: SH KG PB. Wrote the paper: SH KG PB.
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