rigid motion studies in whole-part task
TRANSCRIPT
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Abstract
It is more naturalistic for us to view faces in the real world in rigid-motion or non-rigid
motion ways instead of static all the time. The aim of this research is to study if rigid-facial
motion influences featural processing or it is processed in a holistic manner using WFE. 24
males and 24 females with age range of 19-35 years have participated in this study. The result
showed there was a non-significant difference between rigid-facial motion group and multi-
static group. There was a non-significant interaction between the groups stimulus and the
type of trials (whole-part trials). However whole-based trials scores were significantly better
than part-based trials. There was also a significant score differences in recognizing internal
facial features.
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In the real world, we are going to meet thousands of people in our entire life. It has
always been peculiar to the researchers of how human recognizes and remember the faces
they have encountered in their life and how human are able to differentiate one face different
from the other. It is commonly agreed that faces are processed differently compared to
objects (Boutet and Faubert, 2006; Piepers and Robbins, 2012; Tanaka and Farah, 1993) and
it was known in neural encoding, faces is a default special class in primate recognition system
(Gauthier and Logothetis, 2000; Farah, Wilson, Drain and Tanaka, 1998). Objects can be
easily identified in part-based manner than faces in non-rigid motion stimuli (Tanaka and
Farah, 1993) even compared to dogs whereby dog experts showed no face-like processing for
dogs (Robbins and McKone, 2007).
Lee et al (2011 as cited in Xiao, Quinn, Ge and Lee, 2012) stated there are three types
of information which are important for face recognition including featural information
(comprises of isolated parts of the face features i.e. nose, eyes and mouth), configural
information (spatial differences between the isolated face features) and holistic information
(both featural and configural processing) whereby in Piepers and Robbins (2012) review;
faces tend to be viewed as a whole/gestalt. Holistic processing could be seen in past
researches using different approaches including face-inversion effect (FIE), composite face
effect (CFE) and whole part effect (WFE) (Boutet and Faubert, 2006; Konar, 2012; Maurer,
Grand and Mondloch, 2002; Riesenhuber, Jarudi, Gilad and Sinha, 2004; Wang, Li, Fan,
Tian, and Liu, 2012). However, most of these researches only use static images as the stimuli.
More importantly, faces we see in the real world are dynamic and not static matters
and that include faces moving in rigid motion (eg. nodding and the turning of the head) or
elastic motion which involved alteration of the face shape such as smiling or talking
(Knappmeyer, Thornton and Bulthoff , 2003). Lander, Christie and Bruce
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(1999); Lander and Bruce (2000), Lander and Chuang (2005) and Pike, Kemp, Towell and
Philips (1997) researches using moving faces as stimuli are easier for face recognition.
Otsuka et al (2009) also founded the same effect in young infants and Guellai, Coulon, and
Streri (2011) study showed face with concurrent occurrence of speech sound, rigid and non-
rigid movementsincreases interactive faces recognition at birth. Furthermore, OToole,
Roark and Abdi (2002) stated rigid motion faces has aided in building up a 3D representation
of a face.
Hill and Johnson (2001) and Lander et al. (1999) studies showed participants were
able to recognize faces in motion even when the faces are inverted. Motion was founded to
lower FIE and act as a cue to face recognition in individual with prosopagnosia according to
study by Longmore and Tree (2013). These researches have indirectly showed that facial
motion might influence featural processing. Mckone (2010) stated FIE cannot inform us if
there are qualitative differences concerning face and object processing. Hence, previous
research using FIE could not directly quantity holistic processing.
Young, Hellawal and Hay (1987) first coined the CFE technique in measuring face
perception whereby they founded participants scored significantly lower when two familiar
faces (one top half image and another bottom half image) were to be aligned than misaligned
as this is to show that holistic processing has been interfered when both top and bottom
halves images were aligned to form a new face. Interestingly, Xiao et al. (2012) study using
CFE method founded rigid facial motion influences featural but not holistic processing but
such phenomena was not found in multi-static images stimuli and similar result was founded
in Xiao et al. (2013) elastic motion and face recognition research. McKone (2008) suggested
moving faces produces weaker effects on holistic processing than static faces and thus it tend
to depend on featural processing to for recognizing faces due to the alteration within its
second-order configuration (spacing between two features).
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Several researches assumed WFE to be an alternative key to measure holistic
processing. Konar (2012) experiment was adopted from Boutet and Faubert (2006) research
which is analogous to Tanaka and Farah (1993) whole-part task, whereby participants were
instructed to learn the full face and then respond to full-face trial or part-face trial in terms of
accuracy in face recognition. In the full-face trial, participants were shown one learned face
and one foil face. The foil face will has one internal features differed from the learned face
(i.e. eyes, nose and mouth). In part-face trial, internal features of the faces (both learned and
foil) were presented including only either two pairs of eyes, a pair of mouth and a pair of
nose. In their researches, participants scored significantly better in full-face trial than part-
face trial and this showned faces are processed holistically when features were presented in
the whole-face context than in isolation. It was suggested, a new face has been formed when
a new feature is added/altered within the learned face, making it easier to tell the two faces
apart (Goffaux and Rossion, 2005; Piepers and Robbins, 2012). Moreover, performance was
thought to be significantly better in full-fased trial than part-face trial because full faces were
shown in learning phase (Tulving and Thomson, 1973).
Furthermore, Joseph and Tanaka (2002) and Liu et al. (2013) identified the
importance of eyes and mouths in face recognition compared to nose with eyes on the lead
using whole-face effect trials. It was suggested eyes and mouth are essential for emotion and
speech processing in social context (Baron-Cohen, Wheelwright and Jolliffe, 1997).
For this research, we would be interested in looking into how rigid facial motion will
be processed. Since, Xiao et al. (2012) research founded featural processing is on the lead
using CFE, here we will be using WFE to measure the face processing on rigid facial motion
adopting Konar (2012) and Xiao et al. (2012) methodologies combined.
There will be five hypotheses in this experiment including (1) There will be
significant differences between rigid-facial motion and multi-static groups in whole-part task;
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(2) participants will score significantly higher in whole-based (full-face) trial than part-based
(part-face) trial; (3) there will be significant main effect for the type of internal facial features
being measured. Participants will score significantly better in eyes and mouth trials than nose
trial but with higher accuracy in eyes compared to mouth trials. (4) There will be an
interaction effect between groups (rigid-facial motion and multi-static) and type of trials
(whole-part trials) whereby there will be significant differences between rigid-facial motion
and multi-static groups in part-based trials.
Method
Design
The methodological design is a mixed design with one between-subject (groups
stimulus) two within-subjects (type of facial features and type of trials).
This experiment consists of two independent variables; one within-subject variable
and dependent variables. The first independent variable is the groups stimulusthat consists
of two levels (rigid-facial motion group and multi-static group). Participants in rigid-facial
motion group will have rigid-facial motion as stimuli while participants in multi-static group
will have multi-static face images as stimuli. The second independent variable is the type of
trials including whole-based trial and part-based trial. Participants in both groups will have to
complete the whole-part task having full-face and part-face trials. The third independent
variable is the facial features that will be altered from the learned face and will be used as a
foil face in whole and part trials. (e.g. alteration of eyes, nose and mouth). Operational
definition for dependent variables is face recognition accuracy in whole-part task in terms of
the number of correctness in percentage.
Participants
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Participants (N=48, 24 males and 24 females, M = 20.8542 years, SD = 2.5179, age
range: 19-35) were recruited from universities in Malaysia through convenient samplings. All
participants were informed consent for this study.
Materials
30 models (15 males and 15 females) were obtained from FEI Face Database
(Brazilian face database). Each model has 10 images from multiple angles (0 to 180) and a
full-frontal view (90) of themselves and all images were grey-scale colored with the size of
640 x 480 pixels. The models expression was neutral in all images taken.10 images from
each model were used to create familiarization stimulus as rigid facial motion stimuli.
Method in creating the familiarization stimulus was similar of Xiao et al. (2012) methodology
whereby one profile view image shown only once and the other nine non-fronts view images
twice, which summed up to 19 images in each familiarization. The picture sequence would be
1-2-3-4-5-6-7-8-9-10-9-8-7-6-5-4-3-2-1 forming facial turning motion from 0 to 180. The
duration of each image was 80ms with no interval on the following images which gave the
overall presenting time of 19 images x 80ms = 1520ms.
For multi-static stimuli, Xiao et al. (2012) experiment 3 methodology was adapted.
The formation and the sequence of the models images were same as rigid facial motion
stimuli however in multi-static stimuli, each images consisted 400ms interval between each
images of the model. The total duration for this multi-static stimulus would be 19 pictures x
80ms + 18 interval x 400ms = 8729ms. It was suggested apparent motion was removed with
400ms intervals thus even if images have the same displaying order as the rigid facial motion
stimuli, the images seen would be considered static images (Xiao et al., 2012). All rigid facial
motion and multi-static stimuli were compiled and made into video format (.mpg) using
Windows Movie Maker. Please refer to appendix 1.
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Konar (2012) whole-part task was adapted as testing stimulus. For the full-face trial,
30 modelsfull-front images were processed in terms of alteration either of the eyes, nose or
mouth using Gimp (GNU image manipulation program). The foil face will only has one
internal feature being altered using the other model feature. In 15 male models, 5 model eyes
were replaced with another models eyes whereas 5 other models have their nose being
replaced and another 5 models mouth were replaced. This alteration has also been done for
the 15 female models. For the part-face trial, 30 models original full-front images and the
foil images internal features would be cropped and labeled as part-face stimuli. Please refer to
appendix 2.
The final compilation of all the stimulus were completed using Psychopy program
whereby the accuracy of the matching task (whole-part task) would be recorded and save in
excel sheet with only two-options (forced-choice) given to respond to the whole-part tasks
being shown.
Procedure
The experiment was run in the Nottingham Malaysia Campus as well as other
universities and participants were recruited through convenient samplings. The participants
were assigned to either rigid-facial motion group or multi-static group. Participants were
instructed to complete the whole-part task as quickly as possible in order to prevent
overthinking which may affect the result. The Psychopy program was set with a total of 60
whole-part matching tasks in each group. Each group will have 30 full-face trials and 30 part-
face trials in which participants will have to complete the same models full -face and part-
face trials. Before running the actual test, the participants were given a pilot test to ensure
they comprehend the idea of the research. The participants responded the whole-part tasks
through forced choices (only 2 options given) by keying in left or right key on the
keyboard. The accuracy of whole-part tasks were documented and tabulated.
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whole-based trial and part-based trial and the third independent variable is type of internal
facial features encompasses three levels: the eyes, nose and mouth.
Levenes Test for part-based trials with its features and whole-based trial with mouth
feature were assumed, p > .05 whereas whole-based trial with eyes and nose features were
not assumed, p < .05 (Refer to appendix 3).
A 2 (groups stimulus: rigid-facial motion vs. multi-static) x 2 (type of trials: whole-
based vs part-based) x 3 (type of features: eyes vs. nose vs. mouth) repeated measures
ANOVA showed a non-significant main effect for groups stimulus, F(1,46) = 2.802, p > .05.
Participants from rigid-facial motion group did not score significantly better than participants
from multi-static group in whole-part task. This result has failed to support our first
hypothesis stating there will be a significant difference between rigid-facial motion group and
multi-static group in scoring whole-part task.
There was a significant main effect of type of trials, F(1,46) = 25.675, p< .001
(=0.025). Participants from both groups stimulus scored significantly better in whole-based
trial (M = 71.04, SD = 9.73) than part-based trial (M = 60.49, SD = 9.94). Second hypothesis
was supported.
There was a significant main effect of accuracy in different features, F(2, 92) =
18.752, p < .001. Participants from both conditions scored significantly different in
recognizing the eyes (M = 72.50, SD = 11.299), nose (M = 59.69, SD = 9.86) and mouth (M
= 65.21, SD = 11.53). A post hoc was conducted and it showed that participants from both
conditions scored significantly better in recognizing the eyes compared to nose (p = .010) and
mouth (p = .002). Theres a non-significant difference in recognizing the mouth or nose (p >
.05). *Refer to Appendix 6. The third hypothesis was partially supported as the eyes have the
highest accuracy scores than both nose and mouth features. Accuracy in recognizing mouth
was supposedly better than nose however result showed the otherwise.
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There was no significant interaction between types of trials and participants in both
groups, F(1, 46) = .023, p> .05. In Fig. 1, participants from both conditions did not score
significantly different in both trials even though rigid-facial motion groups participants
seemed to score better in both trials than multi-static group. Fourth hypothesis was not
supported whereby participants in rigid-facial motion group supposedly score significantly
better than multi-static group in part-based trial.
Fig 1. Rigid-facial Motion and Multi-static groups did not score significantly different in both
whole-part trials.
There was a significant interaction between accuracy in recognizing the type of
features in both trials, F(1.678, 77.199) = 6.929, p < .001. Both trials score were significantly
different whereby whole-based trial indeed has better scores than part-based trial. Refer to
Appendix 7.
There was a significant interaction for the groups stimulus, the type of trials and
accuracy in different features in the recognition task, F(1.678, 77.199) = 3.679, p = .037
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Fig. 2 Participants from both condition (Rigid-facial Motion and Multi-static) accuracy scores
in both whole-part trials and their individual scores on each internal feature (eyes, nose and
mouth).
The three-way interaction indicated whether type of trials x accuracy scores in
different features interaction is the same or different in both rigid-facial motion and static
groups. A further analysis was conducted using two one-way ANOVA to see the differences
of groups stimulus in both trials.
The first one-way ANOVA showed there was a non-significant difference between
two groups on accuracy in recognizing internal features for whole-based trial, F(2,92) =
1.479, p >.05.
The second one-way ANOVA analysis showed there was a significant differences
between two groups on accuracy in recognizing internal features for part-based trial, F(2,92)
= 3.951, p = 0.023. It was shown rigid-facial motion group scored significantly higher (M =
62.5, SE = .027) for mouth features accuracy than multi-static group (M = 50, SE = .027).
Refer to Appendix 9.
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Fig. 3 Participants accuracy scores in part-based trial on recognizing the internal features.
Discussion
The aim of this research was to examine how rigid-facial motion was being processed
in terms of featural and holistic processing. According to the result, the first hypothesis was
not supported indicating there were no significant differences between participants in rigid-
facial motion group and multi-static group. We thought rigid-facial motion group will score
significantly higher due to their advantageous in viewing a motion thus should have higher
scoring in part-based trial and brings up the overall accuracy for whole-part task. According
to Hill and Johnson (2001), Lander (1999) and Xiao et al. (2012) suggested recognizing faces
in motion were due to featural processing. Another possible reason would be the amount of
time exposed to the familiarization stimuli, whereby participants in rigid-facial motion
stimulus have shorter time exposure to the stimuli compared to the multi-static group in
which multi-static group has extra 400ms intervals between each images. Even though it was
suggested when the stimulus onset asynchrony (SOA) is more than 400ms, the visual
attention cue will be inhibited (Klein, 2000 as cited in Xiao et al. 2012).
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The second hypothesis was supported showing there was a significant difference
between whole-based trial and part based trial. As expected, the result showed the mean
accuracy in whole-based trial was significantly higher than part-based which was equal with
other researchers result (Boutet and Faubert, 2006; Konar, 2012; Tanaka and Farah, 1993).
As it confirmed Tulving and Thomson (1973) statement that the full-face learned trial has aid
participants to score significantly better in full-face trial than part-face trial. This indicates the
presence of holistic processing.
The third hypothesis was supported partially showing there was a significant
difference of accuracy in recognizing the eyes, nose and mouth. As indicated from past
researches (Joseph and Tanaka, 2002; Liu, 2013), showed eyes as the main importance in
social context. According to Baron-Cohen et al. (1997), basic and complex emotions are
easier to be recognized in the eyes than the mouth part. However, basic emotion could be
recognized in the mouth as well. Even though in our research, there were no significant
differences between accuracy for the nose and mouth, it still showed in the graph accuracy
for mouth is slightly higher. Accuracy for recognizing the nose part was the lowest due to
nose is considered the salient and informative inner feature and it act as a reference point for
the other inner features whereby the eyes and the mouth could be optimally processed (Liu et
al. 2013).
The fourth hypothesis was not supported whereby it was hypothesized participants in
rigid-facial motion group will score significantly better in part-based trial than the multi-static
group in order to propose featural processing. Even though the graph showed rigid-facial
motion group scored slightly better however, the differences are too small to be significant.
This may due to the error in stimuli manipulation and research being conducted in
unconducive environment.
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Participants in both groups did not score significantly different in whole-based trials
for recognizing the internal features. Similar to Xiao et al. (2012) results suggested the multi-
static images which were shown without the frontal view are sufficient enough to form a full
frontal view representation and there were no significant differences between two groups in
their CFE. This again supported holistic processing.
Participants in rigid-facial motion scored significantly better in part-based trial in
overall accuracy scores compared to multi-static group, one-way ANOVA was conducted for
further analysis it showed that participants in rigid-facial group has only scored significantly
better in mouth-part task only. Perhaps, this is due to the errors in the stimuli manipulation or
participants accurately identify the mouth-part by chance. Otherwise, featural processing
might leads to higher accuracy in recognizing the mouth-part in rigid-motion stimulus.
Even though the conclusion of whether rigid-facial motion influences featural
processing was inconclusive using WPE unlike CFE, it was shown that like any other whole-
part task researches, faces tend to be recognized in holistic form whereby face parts are easier
to familiarize in a face gestalt than in seclusion. Piepers and Robbins (2012) stated rigid
motion may assist in holistic processing and process the face more effectively as a whole due
to supplementary Gestalt consortium principles specific to moving stimuli. For instance,
facial features in images seen in motion shared the common fate when they move in same
direction at the same pace.
Further work need to be carried out to confirm how rigid motion reveals information
in holistic and featural processing. Perhaps in future research, external features in the whole-
face trail should be removed to see if external features of the face has tremendously affect the
result being collected in this research. It was noted in Boutet and Faubert (2006) research
external features does not affect the WPE however the review of several researches in Konar
(2012) research, external features was a factor affecting WPE.
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Appendix 1
Fig. 1 The upper image showed the sequence of complete rigid-facial motion stimulus while
the bottom was the multi-static images with 400ms intervals. The first profile and last profile
view of the model is equal as a standardization format of the motion.
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Appendix 2
Fig. 2 showed the differences between whole-based trial and part-based trial in which
participants have to complete in whole-part task.
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Appendix 4
Levene's Test of Equality of Error Variancesa
F df1 df2 Sig.
Weyes 5.855 1 46 .020
Wnose 4.060 1 46 .050
Wmouth .223 1 46 .639
Peyes .575 1 46 .452
Pnose .431 1 46 .515
Pmouth .358 1 46 .553
Tests the null hypothesis that the error variance of
the dependent variable is equal across groups.
a. Design: Intercept + Group
Within Subjects Design: Types + Features +
Types * Features
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Appendix 5
Pairwise Comparisons
Measure: Correctness
(I)
FacialFeatures
(J)
FacialFeatures
Mean
Difference (I-
J)
Std. Error Sig. 95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
Eyes
Nose .106 .034 .010 .021 .192
Mouth .119 .033 .002 .038 .200
Nose
Eyes -.106 .034 .010 -.192 -.021
Mouth .013 .026 1.000 -.053 .078
Mouth
Eyes -.119 .033 .002 -.200 -.038
Nose -.013 .026 1.000 -.078 .053
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Bonferroni.
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Appendix 6
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Appendix 7
Accuracy in recognizing internal features in both trials
Measure: Accuracy for both trials.
Types Features Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
Whole-Based
Trial
Eyes .765 .019 .726 .803
Nose .606 .025 .556 .657
Mouth .756 .023 .709 .803
Part-Based Trial
Eyes .681 .026 .629 .733
Nose .575 .024 .527 .623
Mouth .562 .019 .525 .600
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Appendix 8
Accuracy for recognizing internal features in both trials for both groups
Measure: Accuracy
Group Types Features Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
Motion
Rigid-Facial
Motion
Eyes .817 .027 .762 .872
Nose .612 .035 .541 .684
Mouth .762 .033 .696 .829
Multi-Static
Eyes .671 .036 .597 .744
Nose .558 .034 .490 .626
Mouth .625 .027 .571 .679
Static
Rigid-Facial
Motion
Eyes .713 .027 .658 .767
Nose .600 .035 .529 .671
Mouth .750 .033 .683 .817
Multi-Static
Eyes .692 .036 .618 .765
Nose .592 .034 .524 .660
Mouth .500 .027 .446 .554
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Appendix 9
Interaction between Groups stimulus and Facial Features
Measure: Accuracy
Group FacialFeatures Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
Motion
Eyes .671 .036 .597 .744
Nose .558 .034 .490 .626
Mouth .625 .027 .571 .679
Static
Eyes .692 .036 .618 .765
Nose .592 .034 .524 .660
Mouth .500 .027 .446 .554