Rigid motion studies in Whole-Part Task

8/13/2019 Rigid motion studies in Whole-Part Task

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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


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


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


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

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Rigid motion studies in Whole-Part Task