Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

Modeling the dynamics of observational behaviors
base on observers’ personality traits using hidden
Markov Models
Kuangzhe Xu∗ , Noriko Nagata∗ , Toshihiko Matsuka†∗ Kwansei Gakuin University, Hyoko, Japan

E-mail: kyoutetsu.jo@kwansei.ac.jp Tel/Fax: +81-79-565-7874† Chiba University, Chiba, Japan
E-mail: matsuka@chiba-u.jp Tel/Fax: +81-43-290-3578

Abstract—Previous studies indicated that how people lookat others’ faces depends on observers’ characteristics. A vast
majority of these studies used aggregated data, treating gaze data
in a static manner and often neglected dynamics of observational
behaviors. The present study, on the other hand, examined therelationship between observers’ personality traits and observational behaviors (i.e., which areas of face were looked at) using
time-series eye movement data. In particular, we collected gazedata using a smile judgments task, asking participants to judge
whether smiley faces were genuine or not. Observational behaviors were analyzed using a hidden Markov model. The results
showed that participants with different patterns of personality
traits exhibited different transition patterns. In addition, there
were also signiﬁcant interactions between personality traits andthe number of hidden states each participant had. Furthermore,
there were signiﬁcant relationships between participants’ personality traits and the result of judgments.

I. I NTRODUCTION
How people look at the faces of others attracted muchinterest, and a wide variety of research has been conducted.
Among them, studies that used observer’s gaze provided quantitatively sound and interesting results. For example, a study
that examined cultural differences in observational behaviors
while looking at others’ faces found that Westerners tended
to focus on the eyes and mouth, while Easterners tended tolook mainly at the nose [1]. Another study examining theeffects of gender, task, and stimulus types on gaze during
face observation showed that observational behaviors differeddepending on gender and (difﬁculty and type of) tasks, andthese effects were independent of the type of stimuli [2].
According to one study, individual participants had their own
scan paths when looking at the faces of others [3], and adifferent study that recorded observational behaviors over a
period of 18 months conﬁrmed that this type of behavior was
stable with almost no signiﬁcant deviations [4]. Furthermore,in a study that examined the relationship between observers’
gaze and personality traits, observers with different personality
traits exhibited different observational behaviors, and thesebehaviors were so robust, showing similar behaviors even if
their gazes were manipulated [5].
The above studies used the gaze data in an aggregated manner so that their analyses were based on static observational

978-988-14768-9-0/21/$31.00 ©2021 APSIPA

behaviors. Simply using the frequencies of attention alone may
not be sufﬁcient. In response to this potential problem, someresearchers began using raw gaze data and analyzed dynamics
of observational behavior using, for example, hidden Markov
models [6][7][8]. For example, the results of a study thatexamined cultural differences found three patterns (center ofthe face, left eye leaning and right eye leaning) common toboth Westerners and Easterners [6]. This result suggested thateye movement patterns may be essentially similar independentof cultures. Another empirical result showed that observational
behavior was also inﬂuenced by the age of the observer [7].
Older people tended to focus more on the center of the facethan younger people whose gaze shifted more frequently.
Furthermore, differences in gaze shifts were also observed
when judging whether or not the other person was lying or
not [8].
As mentioned above, dynamic analysis of the observers’
observational behaviors may provide much richer information
than static analysis. However, a majority of studies still analyze
gaze data in a static manner, and thus a little is known
about the dynamic relationship between observational behavior
and observer characteristics. In particular, no single study
examined the inﬂuence of observer personality traits on the
dynamics of eye movements. While previous studies havefound robust relationships between observational behavior
and personality traits using aggregated (static) gaze data, it
is unclear whether this relationship also exists in raw eye
movement data. In order to clarify this question, we conduct asmile judgment experiment to examine relationships between
observers’ personality traits and observational behavior using
raw eye movement data.
A smile is a means of expressing emotion for humans
and is also a means of communicating with others. There
are several types of smiles, and researchers have tried toclassify them depending on their functions and purposes[9][10][11][12][13][14]. One type of smile that is often used topromote smoother inter-personal communication in real life isa “presented” smile (i.e., non-Duchenne smile). The ability
to discriminate Duchenne (i.e., genuine smile) from nonDuchenne smiles particularly has attracted scientiﬁc interests

359

APSIPA-ASC 2021

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japanamong various types of smiles. Duchenne smiles and nonDuchenne smiles are known to be triggered by different muscle
movements, but the results of the previous study showed that it
is very difﬁcult to distinguish them. Although several previous
studies conﬁrmed the difﬁculty of distinguishing a Duchenne
from a non-Duchenne smile, a little was discussed why it
was difﬁcult. In addition to investigating relationships between
observers’ personality traits and observational behavior using
raw eye movement data, the present research also investigate
why it is so difﬁcult to distinguish a Duchenne from a nonDuchenne smile.

pictures as long as they would like. When participants madea judgment, then the picture disappeared, and the next session
started. After completing the judgment task, participants wereasked to complete the Japanese version of Ten Item Personality
Inventory (TIPI) to collect their ﬁve personality traits, namely
Agreeableness, Conscientiousness, Extraversion, Neuroticism,
and Openness to experience [15].
The present experiment with human participants was approved by the Internal Review Board of the Faculty of Letters,
Chiba University.

II. E XPERIMENTFirst, we divided the data recorded by the eye tracker into
increments of 10 Hz. This was done to ensure sufﬁciently
long time-series data while imputing missing measurements.
A Gaussian ﬁlter (sd = 10) was then applied to the segmented
data, and relative attention weight information was added.
Next, we applied individualized facial area masks (pre-madefor each stimulus) to the facial images to extract data corresponding to each facial area. The facial area mask includes theeyes, nose, mouth, corners of the eyes, corners of the mouth,and cheeks. We used these areas because they were said tobe important in determining and/or distinguishing smiles inprevious studies. The area with the highest relative attention
weight (relative to the size of the areas) was considered tobe the representative behavior at this time. Figure 1 shows
examples of the preprocessed data, showing two participants’observational behaviors to the nose for three different facial
stimuli.

A. Participants
Twenty-three students from Chiba University participatedin the experiment. Among them, there were 12 male and 11
female participants. Their mean age was 21.35 (sd = 1.229).
B. Materials
Twenty actors (13 males and seven females) provided pictures of both Duchenne and non-Duchenne smiles. Each actor
was asked to come to a photo shooting session with at least oneof his or her friends. For each actor, we ﬁrst asked him or her
to show a non-Duchenne smile and took a picture of the actor.
We then asked the actor to have a chat with his or her friend(s).
All actors were instructed to look into a camera throughout
their chatting. While chatting, we took some pictures of theactor when he or she exhibited smiles or laughs. After taking
these pictures, we showed them to the actor to conﬁrm whether(or which pictures of) his or her smiles were Duchenne. We
randomly selected one self-conﬁrmed Duchenne smile picture
for each actor. All pictures were taken from the front. The
brightness of the pictures was corrected with Photoshop.

E. Data preprocessing

C. Apparatus
We used Tobii T120 eye-tracker to present stimuli andcollect data on eye movements. The entire experiment wascontrolled by PsychoPy. The distance between the monitor andparticipant’s heads was ﬁxed at 65cm, and the visual angle wasset at 13-degree to imitate a real interpersonal communication
scene. We used a jaw stand to keep the heads of participantsfrom moving.
D. Procedure
Before starting the experiment, we calibrated the eye-tracker
for each participant. After calibration, the experiment wasstarted according to each participant’s timing. There was a
total of 40 sessions (20 Duchenne and 20 non-Duchenne
smiles) in the experiment. Each session began with a taskstatement asking participants to judge a picture they wereabout to see would be a Duchenne or non-Duchenne smile.
When the participant pushed the space-key, a ﬁxation marker(i.e., ”+”) was presented at the center of the monitor for 500
milliseconds, followed by a randomly selected smile picture(either Duchenne or non-Duchenne smile). The observational
duration was not ﬁxed, and thus participants could look at theFig. 1. An example of the preprocessed data, showing two participants’observational behaviors for three different facial stimuli. N: nose; M: mouth;
MS: corners of the mouth; EY: eyes; ET: corners of the eyes; CH: cheek;
Oth: other; Inc: incorrect; Cor: correct.

F. Analysis
1) Estimation of hidden states: We constructed Hidden
Markov Model (HMM) to model relationships (transition)
among observational behavior as well as relationships between
observational behaviors and smile judgments using hidden360

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

states. The HMM is a model that estimates the transition
probability due to hidden states. An example of HMM for
this study is shown in Fig. 2 The formulation of the model isgiven in Eq. (1) and (2).

3) Effects of hidden states and personality traits on thetransition probabilities: We constructed linear regression tomodel the relationships between the transition probabilities
from hidden state to observational behavior and the numbersof the hidden state as well as their interactions. The model
was formulated as in Eq. (4), where βs indicate the ﬁxed
effect of personality traits and the number of hidden states.
The objective variable y indicates the transition probabilities
from hidden states to observed areas (plus the result of
judgment). In particular, y was the maximum value of thetransition probabilities from the hidden states to each facial
area and judgment, representing the hidden states that are
most associated with observed behaviors (observed areas andjudgment).
y ∼ N ormal(α +11∑βp Xp , σ)

(4)

p=1

Xp = K + Agr + Con + Ext + N eu + Ope + K × Agr+
K × Con + K × Ext + K × N eu + K × Ope
Fig. 2. Example of the Hidden Markov model.

The state model included time T and ﬁrst-order Markov
chain zt ∈ 1..., K whose transition probabilities were given byp(zt |zt−1 ). The observation model was governed by p(yt |zt ),where yt indicate the observational behaviors (the area thatparticipants looked at) and the result of judgment (i.e., either
correct or incorrect) at time t. The corresponding joint probability distribution was given as follows:p(z1:T , y1:T ) = [p(z1 )

T
∏
t=2

p(z1 |zt−1 )][T∏p(yt |zt )]

(1)

t=1

p(yt |zt = k, θ) ∼ Categorical(yt |θk )(2)

The maximum number of hidden states was set at 10 (K =10).
2) Relationship between the number of hidden states andpersonality traits: As shown in Fig. 3, the distribution of
hidden states is best explained by a categorical distribution,so we constructed multinomial logistic regression to model
the relationships between the number of hidden states andpersonality traits. The model was formulated as in Eq. (3),where βs represent the ﬁxed effect and k for the numbers of
the hidden states. Given that multinomial logistic regression
requires the base or reference category, we used “two hidden
states” as the reference category. This means that βs in themodel represent how likely or unlikely other (non-reference
categories) numbers of hidden states are presented in a particular participant-stimuli pair while judging smile is genuine ornot.
k ∼ Categorical(α + βa Agr + βc Con + βe Ext+
βn N eu + βo Ope) (3)(5)

We used Rstan [16][17][18][19] for Bayesian parameter
estimations. The uniform prior was used for ﬁxed effects,
and weakly informative prior (gamma with α = 10, β = 10).
We used Rstan’s default settings for MCMC sampling. For
each model, there were four chains, each of which had 1000
warmup steps, 2000 iterations and thin factor being one. Thus
there were a total of 4000 MCMC samples for each model.
To verify whether MCMC samplings had converged, we
checked R̂ values. R̂ values for all coefﬁcients were less than
1.1, which is a typically used criterion, and we considered that
our MCMC sampling had converged.
We used the Highest Density Interval (HDI) as a method
to determine the “signiﬁcance” of the estimation results. If
the 95% HDI does not contain 0, we consider the estimation
result to be “signiﬁcant.” The HDI indicates which points ofa distribution are most credible. Thus, the HDI speciﬁes aninterval that spans most of the distribution such that every
point inside the interval has higher credibility than any point
outside the interval.
III. R ESULTS
The overall judgment accuracy was 37.5%, and only oneout of 23 subjects performed better than the chance level,
conﬁrming previous research that it is very difﬁcult to distinguish Duchenne and non-Duchenne smiles with a single sourceof visual information. Even though the judgment accuracy
was low, we examined the relationships between participants’personality traits, observational behaviors, and the results ofjudgments using the raw eye movement data.
A. The optimal number of hidden states
In order to verify the optimal number of hidden states foreach participant-stimuli pair, we used the WAICs for models
containing a different number of hidden states. Fig. 3 shows
the optimal number (i.e., best WAICS) of the hidden states361

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japanamong 685 (we removed the low sampling-rates data sets)time series.

to 2 hidden states. On the other hand, participants with highlevels of neuroticism have smaller numbers of hidden states(e.g., nine and 10 being negatively signiﬁcant and eight being
marginally signiﬁcant) compared to two hidden states.

Fig. 3. The optimal number of hidden states among 685 time series.

Fig. 5. The 95% HDI of the effects of participants’ personality traits on thenumbers of hidden state.

The Fig. 4(a) shows the relationship between numbers of
hidden states and reaction time. The Fig. 4(b) shows therelationship between the numbers of observational behaviors’
type (the number of areas of interest actually looked at during
the task) and numbers of hidden states. They show that as thenumber of hidden states increased, the reaction time and thenumber of observed areas increased.

TABLE IP ERSONALITY AND HIDDEN STATES
State
4
7
9
10

Fig. 4. (a) Relationship between the numbers of hidden states and reaction
time. (b) relationship between the numbers of hidden states and the numbersof type of observational behaviors.

B. Relationship between hidden states and personality traits
Based on the results from the optimal number of hidden
states model, the relationship between personality traits andthe number of hidden states was examined. Fig. 5 shows the
95% HDI of the effects of participants’ personality traits on
the number of hidden states. Among them, the signiﬁcanteffects are shown in Table I. We considered an effect wassigniﬁcant if the 95% HDI interval did not contain zero.
Participants with low levels of conscientiousness tended tohave four hidden states compared to two hidden states. High
openness participants tended to have higher numbers of hidden
states (e.g., seven and 10 being positively signiﬁcant while six,eight, and nine states being marginally signiﬁcant) compared

Personality traits
Conscientiousness
Openness
Neuroticism
Neuroticism
Openness

Mean-0.6920.3191-0.2461-0.1650.182

95%HDI-1.387 ∽ -0.062
0.073 ∽ 0.579-0.460 ∽ -0.028-0.334 ∽ -0.001
0.046 ∽ 0.309

1) Effects of hidden states and personality traits on thetransition probabilities: Table II shows the results of thesigniﬁcant effects of the number of hidden states, personality
traits, and their interaction on observational behaviors (i.e.,observed areas and results of judgments). The results can besummarized as follows:
The higher the number of hidden states (K), the less likely
participants were to look at the nose or mouth.
The higher conscientious participants were, the less likely
they were to look at the nose. But, they tended to look at theeyes more. When the interaction between conscientiousness
and the number of hidden states was considered, the result
was reversed. The higher conscientiousness and the higher
numbers of hidden states, the less likely participants were tolook at the eyes. Likewise, participants with lesser levels ofconscientiousness with lower numbers of hidden states tended
to look at the eyes more (Fig. 6).
The higher extravert participants were, the less likely they
were to look at the mouth. But, they tended to look at
the cheek more. When the interaction between extraversion
and numbers of hidden states was considered, the result was
reversed. Participants with higher levels of extraversion andhigher numbers of hidden states tended to look at the mouth
but not at the cheek.

362

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, JapanThe higher neuroticism participants were, the less likely
they were to look at the eyes and nose. When interactions
with the number of hidden states were considered, the result
was reversed. Participants with a higher level of neuroticism
and the higher numbers of hidden states tended to look at theeyes and nose more often.
Participants with higher levels of openness tended not tolook at the mouth. But, they tended to look at the eyes. Wheninteraction with the number of hidden states was considered,
the result was reversed. Participants with a higher level of
openness and higher numbers of hidden states tended not tolook at the eyes, while those with a lesser level of openness
and smaller numbers of hidden states tended to look at theeyes.
The above results are summarized in Fig. 6. The rectangles
with ﬁlled background represent the main effects of participants’ personality traits. The rectangles with solid and dottedlines with white background show the interactions between
participants’ personality traits and the number of hidden
states (solid for positive and dotted for negative interaction).
The interaction between the number of hidden states andpersonality traits can be partition into four types of factorsthat inﬂuence the probability of transition from hidden states
to looking at different areas of the faces. They are: a highpersonality trait score with a high number of hidden states; alow personality trait score with a high number of hidden states;
high personality trait low score and a low number of hidden
states; and a low personality trait score with a low number ofhidden states.
TABLE IIP ERSONALITY AND HIDDEN STATES EFFECT ON PROABLITY
Face parts

Eye

Nose

Mouth

Cheek

Parameters
Conscientiousnes
Neuroticism
Openness
K × Conscientiousnes
K × Neuroticism
K × Openness
K
Conscientiousnes
Neuroticism
K × Neuroticism
K
Extraversion
Openness
K × Extraversion
Extraversion
K × Extraversion

Mean
0.032
-0.0490.032-0.0030.005-0.003-0.050-0.030-0.0270.005-0.031-0.028-0.0260.003
0.034-0.003

95%HDI
0.007 ∽ 0.056-0.069 ∽ -0.029
0.014 ∽ -0.050-0.006 ∽ -0.001
0.002 ∽ 0.007-0.005 ∽ -0.001-0.084 ∽ -0.018-0.054 ∽ -0.005-0.047 ∽ -0.007
0.001 ∽ 0.007-0.061 ∽ -0.002-0.046 ∽ -0.011-0.043 ∽ -0.011
0.001 ∽ 0.005
0.009 ∽ 0.057-0.006 ∽ -0.001

Table III shows the signiﬁcant effects of the number ofhidden states and personality traits on the smile judgment task.
The results can be summarized as follows:
The probability of being correct was positively correlatedwith extraversion. That means the higher extravert participants
were, the more likely they were to correctly judge if the faceexhibited a genuine smile or not.
The probability of being correct was negatively correlatedwith neuroticism. That means the higher neuroticism partici-

Fig. 6. Hidden states and personality traits on the transfer probability ofobservational behavior.

pants were, the more likely they were to incorrectly judge if
the face exhibited a genuine smile or not.
TABLE IIITABLE T YPE S TYLES
Respond
Correct
Incorrect

Parameters
Extraversion
Neuroticism

Mean
0.009
-0.00995%HDI
0.001 ∽ 0.017-0.015 ∽ -0.003

In order to examine potentially important hidden states,
we extracted (A) the hidden states that were most strongly
associated, in terms of transition probabilities, with correct orincorrect judgments, and (B) the hidden states that were moststrongly associated with the hidden states identiﬁed in (A) for
each participant-stimulus pair. Our rationales are as follows:(A) the manifested behaviors (i.e., facial areas that were lookedat) that were associated with the hidden states that were also
most strongly associated with correct (incorrect) judgments
were the areas that were important for correct judgments (wellcall this type of hidden states as Judgment States or JSs); and(B) the manifested behaviors that were associated with thehidden states that were also associated with JSs were the areasthat were most likely to look at just before making judgments(we call this type of hidden states as Pre-judgment States or
PSs). For both JS and PS, there were two types, namely JS
that were associated with either correct or incorrect judgments,
and PS that were associated with either correct JS or incorrect
JS. We then extracted the transition probabilities from JSs andPSs to each facial area. We have done this for each optimal
number of states separately (Fig. 3).
Fig. 7a shows the distributions of transition probabilities
from JS to each area when judgments were correct and Fig. 7bfor incorrect judgments. Overall, the distributions of transitions
probabilities from JSs to each area for correct and incorrect
judgments were similar to each other within a given number ofhidden states. However, there were some differences as well.
When the number of hidden states was small, participants who363Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japanjudged correctly were more likely to look at the cheek thanthose who misjudged. Likewise, when the number of hidden
states was large, those who judged correctly were less likely tolook at areas outside AOI. On the other hand, when the numberof hidden states was two, misjudged participants tended to lookat the corners of the eyes and mouth than those who judged
correctly, indicating misjudged participants might have been
deceived by these particular areas.

Fig. 7. The distributions of the transition probabilities from “Judgment States”
to each facial area, (a) for correct, and (b) for incorrect judgments.

Fig. 8 shows the distributions of transition probabilities from
Pre-judgment States (PSs) to each facial area. Fig. 8a shows
the results for PS that were associated with Judgement States(JSs) that were associated with correct judgments. Fig. 8bshows the results for PS associated with incorrect JS. As inanalyses in JS, the overall distributions for both types of PSs
were similar to each other within a given number of hidden
states. In addition, the distributions for the correct JSs andcorresponding PSs were also similar to each other within agiven number of hidden states. We conducted Tukey’s range

test to compare transition probabilities (from hidden states toeach facial area) between (a) correct JSs and incorrect JSs, (b)correct JSs and PSs that were associated with correct JSs, and(c) incorrect JSs and PSs that were associated with incorrect
JS wherever applicable. However, there was no signiﬁcantdifference in those transition probabilities.

Fig. 8. The distributions of the transition probabilities from “Pre-judgment
States” or PS to each facial area. (a) Distribution of transition probabilities
for PS that were associated with the correct Judgement States, and (b) for theincorrect Judgment States.

IV. DISCUSSION AND CONCLUSIONSThe present study conducted a smile judgment experiment
to examine the relationships between observers’ eye movement
trajectories and personality traits. In our analyses, we, ﬁrst,
used hidden Markov models to detect the optimal numberof hidden states for each participants-stimulus image pair
independently. The resulted optimal numbers of hidden states
were then examined for their relationship with personality
traits using a multinomial logistic regression model. Finally,
we examined the effects of the number of hidden states and364

Proceedings, APSIPA Annual Summit and Conference 2021

14-17 December 2021, Tokyo, Japan

personality traits on the transition probabilities from the hidden
states to each area of the face, as well as the results ofjudgments.
The results showed that the optimal numbers of hidden
states were mostly concentrated on two and 10, and the
distributions from three to ten showed an upward trend. Notethat this study only used a maximum of ten hidden states, but
it was possible that there might have been more than ten states.
However, since our aims in the present study were not to ﬁnd
the optimal number of hidden states per se, we truncated thenumber of states that were more than ten states and counted
them as ten states.
One important ﬁnding of this study is that the interaction
between the observer’s personality traits and the numberof hidden states leads to different observational behaviors(transition probabilities from the hidden state to each areaof the face). Although each personality trait was signiﬁcantly
correlated with the observed behavior, the effects were allreversed when the effect of the number of hidden states wasadded. As the number of hidden states increased, participants
began to pay more attention to the areas on the face that we
did not originally focus on, or vice versa. The results provide
us some insights and better understandings about relationships
between personality traits and observational behaviors thatwere previously difﬁcult to interpret. For example, suppose aperson with a high level of neuroticism looks at the eyes frequently, contrary to the general behavior of this type of person
who tends not to look at the eyes. One possible explanationis that this neurotic person’s observational behavior of faces
might contain many hidden states. Based on this result, we
believe that personality traits can have both ”positive aspects”
and ”negative aspects” depending on the number of hidden
states that particular individuals have in their observational
behaviors.
Other signiﬁcant relationships were found between personality traits and the optimal number of hidden states. In particular, openness and neuroticism were found to have opposite
effects. This result is similar to previous studies based on
aggregated data and statical models of observational behaviors
and personality traits. In other words, the attributes of thepersonality traits themselves may be reﬂected in the hidden
state.
The effect of personality traits in the judgment results was
also found. People with a high level of extroversion weremore likely to correctly judge whether smiles were genuine ornot, while people with a high level of neuroticism were more
likely to judge incorrectly. Considering this result in terms ofobservational behavior characteristics, people who are highly
extroverted are more likely to judge correctly because theylook at the face extensively and have access to more materials
and information that lead to correct judgments. However,people with high levels of neuroticism with lower numbersof hidden states look at the face more intensively (looking at
a smaller number of areas), not having enough material andinformation, thus resulting in incorrect judgments.

365

R EFERENCES[1] Blais, C., Jack, R. E., Scheepers, C., Fiset, D., & Caldara, R. (2008).
Culture shapes how we look at faces. PloS one, 3(8), e3022. DOI :https://doi.org/10.1371/journal.pone.0003022[2] Armann, R., Bülthoff, I.(2009). Gaze behavior in facecomparison. The roles of sex, task, and symmetry. Attention,
Perception, & Psychophysics 71, 1107–1126 . DOI :https://doi.org/10.3758/APP.71.5.1107[3] Kanan C, Bseiso DN, Ray NA, Hsiao JH, Cottrell GW.(2015). Humans have idiosyncratic and task-speciﬁc scanpaths for judging faces.
Vision Res. 2015 Mar;108:67-76. doi: 10.1016/j.visres.2015.01.013.
Epub 2015 Jan 30. PMID: 25641371.
[4] Mehoudar, E., Arizpe, J., Baker, C. I., & Yovel, G. (2014). Faces
in the eye of the beholder: Unique and stable eye scanning patterns of individual observers. Journal of Vision, 14(7):6, 1–11,
http://www.journalofvision.org/content/14/7/6, doi:10.1167/14.7.6.
[5] K. Xu and T. Matsuka,(2018). Effects of observers’ characteristics on
impression formation on face,. Asia-Paciﬁc Signal and Information
Processing Association Annual Summit and Conference (APSIPA
ASC), Honolulu, HI, USA, 2018, pp. 1293-1298, doi: 10.23919/APSIPA.2018.8659469.
[6] Chuk, T., Crookes, K., Hayward, W. G., Chan, A. B., & Hsiao, J.
H. (2017). Hidden Markov model analysis reveals the advantage ofanalytic eye-movement patterns in face recognition across cultures.
Cognition, 169, 102-117.
[7] Chan CYH, Chan AB, Lee TMC, Hsiao JH.(2018). Eye-movement
patterns in face recognition are associated with cognitive decline inolder adults. Psychonomic Bulletin & Review. 2018;25: 2200–2207.
pmid:29313315[8] J. Zuo, T. Gedeon, Z. Qin. (2019). Your eyes say you’re lying: aneye movement pattern analysis for face familiarity and deceptive cognition. International Joint Conference on Neural Networks (IJCNN)(2019), pp. 1-8.
[9] Ekman, P., & Friesen, W. V. (1982). Felt, false, and miserable smiles.
Journal of nonverbal Behavior, 6, 238252.
[10] Surakka V, & Hietanen JK. (1998) . Facial and emotional reactions
to Duchenne and non-Duchenne smiles. International Journal of Psychophysiology, 29, 2333.
[11] Song, R., Over, H., & Carpenter, M. (2016). Young children discriminate genuine from fake smiles and expect people displaying genuine
smiles to be more prosocial. Evolution and Human Behavior, 37,
490501.
[12] Bryant G. A., & Aktipis, C.A. (2014). The animal nature of spontaneous human laughter. Evolution and Human Behavior 35, 327-335[13] Korb, S., With, S., Niedenthal, P., Kaiser, S., & Grandjean, D. (2014).
The perception and mimicry of facial movements predict judgmentsof smile authenticity. PLoS One. doi:10.1371/journal.pone.0099194[14] Hoque,M.E.,McDuff,D.J.,& Picard,R.W.(2012).Exploringtemporal
patterns in xifying frustrated and delighted smiles. IEEE Transactions
on Affective Computing, 3, 323334.
[15] Oshio, A. Abe, S. & Cutrone, P. (2012). Development, Reliability, andValidity of the Japanese Version of Ten Item Personality Inventory(TIPI- J). The Japanese Journal of Personality 21, 4052.
[16] Matsuura, K. (2016). Bayesian Statistical Modeling Using Stan and
R. Kyoritsu Shuppan Co., Ltd.
[17] Kruschke, J. K.(2014). Doing Bayesian data analysis: A tutorial
with R, JGAS, and Stan (2nd ed). Burlington, MA: Academic
Press/Elsevier.
[18] Luis,
D.,
Brian,P.,&
Michael,W.(2017).
A
Tutorial
on
HiddenMarkov
Modelsusing
Stan.
https://github.com/luisdamiano/stancon18[19] Stan Development Team (2020). RStan: the R interface to Stan. R
package version 2.17.3. http://mc-stan.org/.