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Eyes as windows to the soul: Gazing behavior is related to personality


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Gazing is a fundamental human behavior with important cognitive, affective, motivational, and social underpinnings that is likely to have produced individual differences linking it to major personality traits. If traits play a substantial role in gazing, they should predict eye movement parameters above and beyond stimuli without meaningful and topical information. The current eye-tracking study (N = 242) demonstrated with linear mixed models that personality (Big Five, Behavioral Inhibition System/Behavioral Activation System) predicts number of fixations, mean fixation duration, and dwelling time in two different abstract animations. Specifically, neuroticism, extraversion, openness, and the Behavioral Activation System were related to eye movement parameters. Prospective research in studying links between dispositions and gazing is discussed.
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Eyes as windows to the soul: Gazing behavior is related to personality
John F. Rauthmann
, Christian T. Seubert
, Pierre Sachse, Marco R. Furtner
Leopold-Franzens University of Innsbruck, Austria
article info
Article history:
Available online 28 December 2011
Eye movements
Big Five
Gazing is a fundamental human behavior with important cognitive, affective, motivational, and social
underpinnings that is likely to have produced individual differences linking it to major personality traits.
If traits play a substantial role in gazing, they should predict eye movement parameters above and
beyond stimuli without meaningful and topical information. The current eye-tracking study (N= 242)
demonstrated with linear mixed models that personality (Big Five, Behavioral Inhibition System/Behav-
ioral Activation System) predicts number of fixations, mean fixation duration, and dwelling time in two
different abstract animations. Specifically, neuroticism, extraversion, openness, and the Behavioral Acti-
vation System were related to eye movement parameters. Prospective research in studying links between
dispositions and gazing is discussed.
Ó2011 Elsevier Inc. All rights reserved.
1. Introduction
According to a popular proverb, ‘‘eyes are the window of/to the
And, indeed, people have long pondered whether there is
something in our eyes indicative of character. For example, someone
often socializing would naturally be called ‘‘extraverted,’’ but could
we also deduce extraversion from how someone gazes? Behaviors
are core features of traits, personality manifests at different levels
of behavior, and gazing is an essential human behavior. The current
work examines associations among traits and eye movements to
gain new insights on whether and how dispositional variables man-
ifest in oculomotor behavior.
1.1. Links between personality and gazing
Gazing behavior can (and should) be linked to personality for
several reasons. First, individual differences manifest on molar
and molecular levels of behavior (Furr, 2009). Fleeson and Noftle
(2008a) point out that there is only ‘‘very little knowledge about
how personality is present in behavior and about what behaviors
are relevant to personality,’’ which is ‘‘partly because of the diffi-
culty in specifying the level at which behavior should be studied’’
(pp. 1668/1679). This problem in identifying personality-relevant
behaviors is also crucial for other psychological disciplines such
as social and experimental psychology which tend to consider
possibly important individual differences only to a minor degree.
Attention should also be given to individual differences in micro-
behaviors such as eye movements.
Second, gazing serves important social, motivational, cognitive,
and regulatory functions. Eye contact is essential in our daily lives:
it is related to personality (Libby & Yaklevich, 1973) and we make
inferences about people based on their eye contact (Brooks,
Church, & Fraser, 1986). Gazing is used to track others’ behaviors
(Matsumoto, Shibata, Seiji, Mori, & Shioe, 2010) and to communi-
cate. It is also linked to motives and motivation (e.g., Furtner,
Martini, & Sachse, 2011; Terburg, Hooiveld, Aarts, Kenemans, &
van Honk, 2011), wishes, and preferences as it is directed toward
goal-consistent and averted from goal-inconsistent stimuli to
achieve and maintain good mood (Balcetis & Dunning, 2006;
Isaacowitz, 2006). Eye movements can also be used for
perceptual-cognitive performance tasks (e.g., Haley, 1971; Iacono
& Lykken, 1979) because they are indicative of early visual
attention processes guided by preconscious mechanisms (Terburg
et al., 2011;Wilkowski, Robinson, Gordon, & Troop-Gordon,
2007). Moreover, precise eye-hand coordination is an important
feat in humans (Furtner & Sachse, 2008).
Third, stimulus material substantially influences eye movement
parameters such as fixation rates (Land, 2007), but participants
nonetheless tend to show stable eye movement patterns (across
different stimuli/occasions) and differ in those from others (Etaugh,
1972; Etaugh & Rose, 1973; Furtner, 2006). This implies an under-
lying neurobiological system influencing oculomotor (re-)activity,
which is linked to personality (Canli, 2006).
Fourth, eye movements have already been linked to personality.
Kaspar and König (2011) found that interindividual differences in
personality and motivation influence attention processes in gazing,
and personality psychopathology has also been related to eye
0092-6566/$ - see front matter Ó2011 Elsevier Inc. All rights reserved.
Corresponding author. Address: Department of Psychology, Leopold-Franzens
University of Innsbruck, Innrain 52, A-6020 Innsbruck, Austria.
E-mail address: (J.F. Rauthmann).
These authors have equally contributed to this work and thus share the first
It can be traced back to the Latin proverbial phrase ‘‘oculus animi index’’ (engl.
the eye is the soul’s window/mirror).
Journal of Research in Personality 46 (2012) 147–156
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movements (Ceballos & Bauer, 2004; Iacono & Lykken, 1979;
Siever, 1982; Siever et al., 1990; Thaker, Cassady, Adami, Moran,
& Ross, 1996). Also, lateral eye movements (averting the gaze while
thinking; Day, 1970; Etaugh, 1972; Etaugh & Rose, 1973) and
attentional preferences in selective attention during early visual
processing have been linked to circumscribed personality dimen-
sions (optimism: Isaacowitz, 2005; trait anger: Wilkowski et al.,
In summary, previous research suggests that personality influ-
ences visual information processing, social gazing, and where we
gaze when making sense of (ambiguous) pictures. However, there
are limitations to this research. First, not all studies were able to
use sophisticated eye-tracking methodology, which provides
objective and quantifiable behavioral data. Second, rather small
sample sizes were used which present difficulties in detecting
small effects. Effect sizes in trait – eye movement relations ought
to be in the range of r= |.05 .30|, which can be deemed common
and realistic when broad, self-rated traits are related to narrowly
defined, objective behavioral data on a micro-level (Vazire &
Carlson, 2010). To demonstrate such effects, usually larger sample
sizes (N> 150) are needed. Third, many studies do not focus on
sub-clinical traits, and if they do, only on very specific ones (cf.
Isaacowitz, 2005; Terburg et al., 2011; Wilkowski et al., 2007)
which does not allow a bigger picture on trait – eye movement
relations. Lastly and most importantly, it remains unexplored
whether dispositional variables are also related to the ‘‘how’’ of
gazing (e.g., more fixations), not just the ‘‘where’’ (e.g., fixating a
car). Personality effects in the absence of meaningful stimuli with
semantic or topical information would provide compelling
evidence that traits are linked to gazing. The current study is a first
endeavor in demonstrating such effects.
1.2. The Big Five and gazing
The broad Big Five traits (neuroticism, extraversion/surgency,
openness/intellect/fantasy/culture, agreeableness, conscientious-
ness) were used for several reasons. First, the structural and
descriptive five factor model of personality is a widely acknowl-
edged, integrative taxonomy of most human individual differences
categories that are important, meaningful, and consequential
(Costa & McCrae, 1992; John & Srivastava, 1999).
Second, the Big Five have a biological basis (Angleitner &
Ostendorf, 1994; Buss & Plomin, 1975; DeYoung & Gray, 2009).
Indeed, each of Cloninger’s temperament and character factors
(Cloninger, Svrakic, & Przybeck, 1993) is substantially covered by
the Big Five traits due to considerable construct overlap (de Fruyt,
van de Wiele, & van Heeringen, 2000). Extraversion (linked to positive
affect and activation) and Neuroticism (linked to negative affect and
affective intensity) are considered strongly biologically determined
(MacDonald, 1995; Rothbart & Derryberry, 1981; Yik & Russell,
2001). Agreeableness and conscientiousness may be traced back to
effortful control, a super-ordinate regulatory system (Jensen-
Campbell et al., 2002). The biological basis and associations with
affect and activation may link the Big Five traits to eye movements.
Third, there is already empirical evidence that some of the Big
Five can be linked to gazing. Openness has been linked to eye fix-
ation points (Matsumoto et al., 2010), and extraversion to perfor-
mance on anti-saccade tasks (Nguyen, Mattingley, & Abel, 2008)
and spontaneous eye movements such as blinking rates (Franks,
1963). However, no study so far has linked the entire Big Five traits
at once to eye movements.
1.3. BIS/BAS and gazing
The Behavioral Inhibition System (BIS), regulating sensitivity to
punishment and avoidance behavior, and the Behavioral Ap-
proach/Activation System (BAS), regulating sensitivity to reward
and approach behavior (Carver & White, 1994; Corr, 2008; Gray,
1987, 1990, 1991), can also be tied to eye movements. The BIS/
BAS is rooted in Grays’s Reward Sensitivity Theory (1982, 1991),
which has been revised in the meanwhile. The BIS can be seen as
a basis for anxiety (Gray & McNaughton, 2000) and the BAS for re-
ward sensitivity/approach and impulsivity (Gray, 1991). First, BIS/
BAS are considered neurobiological underpinnings of affective and
motivational systems (Cloninger et al., 1993; Gray, 1991). Second,
BIS/BAS can be linked to psychopathology (Scholten, van Honk,
Aleman, & Kahn, 2006), which is known to influence oculomotor
behavior (e.g., Iacono & Lykken, 1979). Third, BIS and BAS can be
linked to the Big Five: consistent findings for relationships with
neuroticism (for BIS) and extraversion (for BAS) are found (Keiser
& Ross, 2011; Smits & Boeck, 2006). If the Big Five are associated
with eye movements, then BIS and BAS should also be associated
given their conceptualization in literature and empirical relations
with the Big Five.
1.4. Eye-tracking methodology
Because not all readers may be familiar with eye-tracking meth-
odology, we provide brief information on eye movement analyses.
For more detailed introductions, see, for example, Duchowsky
(2007). Visual perception relies on sequences of information input
via complex patterns of eye, head, and body movements (Furtner
& Sachse, 2008). High-resolution visual information input occurs
only at so-called fixation points which are fixated with the fovea
(the central point of highest visual resolution in the retina) (Posner,
1980, 1995). Saccades lie between fixations and entail a passive
suppression of visual information processing during which visual
perception is strongly reduced for about 50–80 ms. Eye movements
can be recorded and analyzed with two basic types of eye-trackers:
remote or table-mounted and head-mounted systems. Remote sys-
tems are usually affixed to a table with video cameras and infrared
lights (Furtner, Rauthmann, & Sachse, 2009; Goldberg & Wichansky,
2003; Jacob & Karn, 2003), whereas head-mounted systems are
worn on the head (Duchowsky, 2007). Data recording is usually car-
ried out with the pupil center corneal reflection method (Ohno,
Mukawa, & Yoshikawa, 2002) where the eyes’ position and direc-
tion of movement is related to a vector (visual axis) spanning from
the corneal reflection (Purkinje reflection), captured with infrared
light, to the center of the pupil. There are many different eye move-
ment parameters (e.g., saccade duration, saccadic velocity, saccadic
acceleration, saccadic amplitude, smooth pursuits, fixation dura-
tion, number of fixations, gaze duration, dwelling time; see Joos,
Rötting, & Velichkovsky, 2003), but a meta-analysis has shown that
the three most widely used eye movement parameters are number
of fixations, mean fixation duration, and dwelling time (Jacob &
Karn, 2003; see also Joos et al., 2003), which all refer to fixations
and can be sampled with the pupil center corneal reflection method
(Ohno & Mukawa, 2004). In the current study, we also utilize a ta-
ble-mounted eye-tracker with pupil center corneal reflection meth-
od to capture eye movement parameters.
2. The current study
The current work aims at showing that dispositional variables
are linked – even in the absence of any semantic or topical
It should be noted that most so-called ‘‘temperamental’’ factors are now also
referred to as ‘‘personality.’’ In this study, we do not delve into discussion about
(possible) distinctions between terms of ‘‘personality’’, ‘‘temperament’’, and ‘‘charac-
ter.’’ Rather, we refer to any dispositional variable that describes moderately stable,
enduring characteristics of a person with considerably broad bandwidth in the
perceptual-co gnitive, affective, motivatio nal, behavioral, and social domain as
148 J.F. Rauthmann et al. / Journal of Research in Personality 46 (2012) 147–156
stimulus information – to three most commonly used indices of
gazing behavior (number of fixations, mean fixation duration,
dwelling time; see Duchowsky, 2007; Jacob & Karn, 2003) across
two very different non-meaningful, abstract stimuli to control for
possible co-effects of topical and semantic sense, content and con-
text saliency, memory, lifestyle, preferences, and interests. First,
we investigated main effects of personality on gazing, distinct from
any matching to the type of stimulus involved. This provides a very
rigorous examination of whether dispositional variables are mani-
fest in gazing behavior. It was hypothesized that the Big Five and
BIS/BAS would predict eye movement parameters above and
beyond variation of stimulus material which would make a strong
case for the influence of traits on gazing behavior. Second, we also
explored interaction effects of condition by personality traits.
Effects can be specified in presence, magnitude, and direction.
Given empirical literature, presence of effects was expected at least
for extraversion (e.g., Franks, 1963; Nguyen et al., 2008) and open-
ness (e.g., Matsumoto et al., 2010), but also for neuroticism and
BIS/BAS given their strong affective-motivational conceptualiza-
tions. Nonetheless, we explore presence of effects for the entire
Big Five. Magnitude of effects was expected to be no higher than
|.35|, which is a typical finding when relating broad trait domains
to narrowly defined, unaggregated behavioral criteria (e.g., Back,
Schmukle, & Egloff, 2009; Vazire & Carlson, 2010). Specific direc-
tions of effects were not a priori hypothesized and are thus ex-
plored here for the first time as it could not be extrapolated from
existing literature which traits should be positively and which neg-
atively associated with which eye movement parameters. The cur-
rent study thus expands existing literature in establishing novel
findings on the presence, magnitude, and direction of trait effects
on commonly used eye movement parameters.
3. Method
3.1. Participants
Two hundred and forty-two students, 172 women and 70 men,
with a median age of 22 years (range: 18–46 years) and normal or
corrected-to-normal vision (glasses and contact lenses) were
examined and earned credit points for participating. They were
not required to fill out written consent forms, but we obtained ver-
bal consent after explaining the steps of the study and what would
be expected.
3.2. Procedure
Participants were welcomed to our lab and told that they would
participate in a study examining ‘‘personality and information pro-
cessing.’’ After informing participants by explaining the procedures
of the study (eye-tracking and filling out questionnaires), we ob-
tained verbal consent of the participants. Then, participants were
seated in front of the table-mounted eye-tracking system which
was calibrated to be approximately 23 in. away from participants’
faces. After successful calibration of the eye-tracking system to
participants’ eye movement patterns, participants were instructed
to watch the PC screen and try not move (to avoid decalibration of
the table-mounted eye-tracker), and stimulus presentation began.
The two animations were shown in randomized order and with a
filler task
screen in between (Fig. 1): each trial commenced with
a centrally presented fixation cross (10 s), followed by either Anima-
tion Red or Blue (60 s), another fixation cross (10 s), and then either
Animation Blue or Red (60 s). Illumination was kept constant in the
laboratory setting across all participants. Participants were subse-
quently administered the personality questionnaires. Time to first
number of fixations, mean fixation duration, dwelling time,
and the Big Five were sampled; for a sub-sample of n= 89 BIS/BAS
scales were additionally sampled.
3.3. Apparatus: table-mounted eye-tracker
A Pentium IV computer with a graphics card NVIDIA GeForce 4
MX 4000 was used, displaying animations on a 17-in. computer
monitor (View Sonic VG700b) with a display refresh rate of
75 Hz in Windows Media Player (fullscreen). Eye movements were
recorded with a frequency of 2 60 Hz by two binocular cameras
positioned beneath the computer display and with 0.4°accuracy.
NYAN 2.0 software from Interactive Minds Dresden (IMD) was
used for the table-mounted Eyegaze Analysis System from LC Tech-
nologies Inc., which allowed registering, recording, and analyzing
participants’ fixations (minimum duration: 100 ms). The four eye
movement parameters time to first fixation
, absolute number of
fixations, mean fixation duration (in ms), and dwelling time (total
time of all fixation durations in ms) can be directly sampled (the
software did not allow for the direct sampling of any saccadic vari-
ables, though) with the pupil center corneal reflection method.
Two observation monitors allowed watching both eyes separately
through input from the two binocular cameras beneath the moni-
tor while in the process of eye-tracking in order to correct the sit-
ting posture of participants to recalibrate during recording if
necessary (Fig. 2). Calibrations regarding fixations were accepted
if fixation accuracy showed an average drifting error of maximally
0.25 in. or smaller. The average drifting error was 0.12 in.
3.4. Stimulus material: Animation Red versus Animation Blue
Two different animations were selected to be in maximum con-
trast to each other on several dimensions (e.g., color, forms, move-
ments, velocity; cf. Furtner, 2006). Their specific characteristics are
summarized in Table 1. The animations were programmed by pro-
gramers of the University of Zurich in C++ without external soft-
ware. They rely on cellular automata comprised of a number of
different species (cells) that change their state over time according
to their current state and the state of neighboring species. The spe-
cies were assigned different colors, and cellular automata were
programmed to run some thousand steps.
Animation Red had fast and jerky movements across the whole
screen, a mix of different colors (red: 0x7D2926; yellow:
0xE2D468; blue: 0x4B88C9; black: 0x12110F),
and edgy forms
(Fig. 3, first row). It had several sections that differed in position,
velocity, movements, and color of object patterns (01–08 s: patterns
edging towards the observer; 08–16 s: blinking patterns edging fast
towards the observer; 16–34 s: fast vertical movements of patterns
from left to right; 34–45.4 s: patterns edging towards the observer
with changes in yellow and red of objects; 45.4–60 s: vertically posi-
tioned objects steadily increasing in size). It was to provoke more
fixations with shorter fixation durations and dwelling time. Anima-
tion Blue had slow and smooth movements of white dots swirling
around in the middle of the screen on a blue background (dark blue:
According to Sundar et al. (2001), fast movements (such as in Animation Red)
induce higher levels of physiological activation in perceivers. This, again, may entail a
higher number of fixations even in subsequent, slower animations. Thus, it is
necessary to implement filler tasks (for example, a fixation cross for about 10 s).
Time to first fixation was also sampled (for n= 104 participants ), but apart from
finding no effects of traits on this eye movement parameter, this parameter makes no
sense in stimuli with non-topical information. If used at all, it should be used in
pictures with topical information. However, it has also been proposed to eliminate the
first 1–3 fixations even within pictures (e.g., Joos et al., 2003), thus rendering time to
first fixation almost completely irrelevant. As a consequence, we therefore do not
report any findings on time to first fixation.
Color codes are reported in the hexadecimal (base 16) number system widely
used in programming languages. It can also be easily converted into the RGB metric.
J.F. Rauthmann et al. / Journal of Research in Personality 46 (2012) 147–156 149
0x2E3A90; blue: 0x3A46AC; light blue: 0x9FABF2)
(Fig. 3, second
row). It was to provoke fewer fixations with longer fixation dura-
tions and dwelling time. Quick movements of objects, as in Anima-
tion Red, are known to elicit activation (Mikunda, 2002), whereas
slow and small movements, as in Animation Blue, tend to elicit deac-
tivation (Schwender, 2001). This allowed us to devise two anima-
tions with diametric effects on observers (Sundar, Kalyanaraman,
Martin, & Wagner, 2001).
3.5. Measures
3.5.1. Big Five
The Big Five traits (neuroticism, extraversion, openness, agree-
ableness, conscientiousness) were assessed with the NEO-FFI (ori-
ginal: Costa & McCrae, 1992; German version: Borkenau &
Ostendorf, 1993) with 60 items (12 items per scale) on a five-point
Likert-type scale (0 strongly disagree –4strongly agree) and means
across items were computed. Item language was German.
3.5.2. BIS/BAS
BIS (sensitivity and reactions to the anticipation of punishment:
e.g., ‘‘If I think something unpleasant is going to happen, I usually
get pretty ‘worked up’’’), BAS Drive (persistent pursuit of desired
Fig. 1. Illustration of the sequential presentation of stimuli within the study.
Fig. 2. Table-mounted eye-tracking system.
Table 1
Synopsis of characteristics of Animation Red and Blue.
Dimension Animation Red Animation Blue
Colors Red, yellow, blue, orange, black Blue, white
Brightness, intensity Bright and intense color Darker and ‘‘soft’’ colors
Saturation Different saturation levels Monochromic planes with consistent saturation
Objects, forms Polygons and complex patterns that constantly changed Circles and dots that did not change
Texture Abrasive surface Soft, smooth surface
Perspective, penetration Differences in size and perspectives of moving objects Almost no differences in size between objects
Position of objects Vertical movements from right to left Horizontal, centered positions
Movements Quick, erratic, jerky Slow, orbital, smooth
Animations Red and Blue can be seen in Figs. 1 and 2.
150 J.F. Rauthmann et al. / Journal of Research in Personality 46 (2012) 147–156
goals: e.g., ‘‘I go out of my way to get things I want’’), BAS Reward
Responsiveness (positive responses to and anticipation of rewards:
e.g., ‘‘When I get something I want, I feel excited and energized’’),
and BAS Fun-Seeking (desire for novel rewards and their approach-
ing in the spur of the moment: e.g., ‘‘I crave excitement and new
sensations’’) were assessed with the BIS/BAS Scale (original: Carver
& White, 1994; German version: Strobel, Beauducel, Debener, &
Brocke, 2001), comprising 20 items to be rated on a four-point
Likert-type scale (0 strongly disagree –3strongly agree). Means
across items were computed to form the scales. Item language
was German.
3.6. Statistical analyses
To estimate what dispositional variables predict above and be-
yond condition variation (Animation Red vs. Blue), linear mixed
modeling (LMM) was employed. LMM allows modeling of hierar-
chical data, such as measurements (conditions) nested within per-
sons. When data is collected at different levels, this case can be
addressed with classical regression techniques by either treating
the data as unrelated to within-group processes (effectively ignor-
ing this information: complete pooling) or by computing separate
models for each group (restoring group-specific information, but
lacking an aggregate model: no pooling). LMM, in contrast, incor-
porates data collected at different levels into one comprehensive
model, while also yielding reasonable estimates for each level. This
is accomplished by balancing the information obtained from pro-
cesses within and between levels (partial pooling; Snijders &
Bosker, 1999). In the baseline model, eye movement parameters
(number of fixations, mean fixation duration, dwelling time) were
predicted from effect-coded condition. The comparison model,
then, included dispositional variables (Big Five; BIS/BAS scales) at
level 2 (individual level) while controlling for condition (Animation
Red vs. Blue) at level 1 (measurements level). Thus, the ‘‘pure’’ ef-
fect of personality traits on eye movement parameters, controlling
for stimulus variation as well as cross-level personality stimulus
interactions, can be obtained. Intercepts and/or slopes were
allowed to vary randomly across individuals. Equations for the
models can be found in Table 2. Model comparisons by means of
Log-Likelihood (LR) indicate whether dispositional variables can
significantly reduce prediction error (measured in R
;Luke, 2004;
Snijders & Bosker, 1999) over and above condition. All LMM anal-
yses were computed in GNU R (R Development Core Team, 2011)
with the nlme package (Pinheiro & Bates, 2000).
4. Results
4.1. Descriptive statistics
Descriptive statistics for eye movement parameters (M,SD) and
traits (M,SD,
) can be found in Table 3. Intercorrelations (bivariate
zero-order Pearson correlations) among all variables can be found
in Table 4. Findings on the bivariate correlation level may still con-
tain spurious effects, likely enhancing type I errors. Thus, LMM was
used to further analyze the data because a multi-level approach is
conceptually superior regarding our data structure.
Compared to random intercept-only models, random slopes and
intercept models provided superior fit for number of fixations [Big
Five models: LR(2) = 80.11, p< .001; BIS/BAS models: LR(2) = 6.20,
p= .045] and mean fixation duration [Big Five models: LR(2) =
214.91, p< .001; BIS/BAS models: LR(2) = 22.06, p< .001], but they
did not for dwelling time [Big Five models: LR(2) = 1.29, p= .526;
BIS/BAS models: LR(2) = 0.16, p= .921]. Consequently, the more par-
simonious random intercept-only model was deemed more appro-
priate for dwelling time. As there is no a priori theory to guide us
whether random intercept-only or random slopes and intercept
models are conceptually superior for trait – eye movements links,
we opted to use the method that fit the data better in each case.
4.2. Condition main effects
As expected and frequently found in eye-tracking studies, we
found main effects for condition on eye movement parameters
Fig. 3. Stimulus material: Animation Red (upper row) and Animation Blue (lower row). (For interpretation of the references to colour in this figure legend, the reader is
referred to the web version of this article.)
Table 2
Prototypical equations used in linear mixed model analyses.
Random intercept-only model
Level 1 Y
Level 2 b
Random slopes and intercept model
Level 1 Y
Level 2 b
Y= dependent variable (number of fixations, mean fixation duration, dwelling
N= neuroticism, E= extraversion, O= openness, A= agreeableness, C=
Subscripts denominate measurement occasions iand individuals j.
Main effects are obtained by modeling level 1 intercept (b
) as a function of level 2
dispositional variables, whereas cross-level interactions are obtained by modeling
slope (b
) of condition as a function of level 2 dispositional variables.
Models for BIS/BAS variables are constructed accordingly by substituting Big Five
measures for BIS/BAS measures.
The pattern of LMM findings was additionally compared to the pattern of findings
obtained from generalized linear models and ordinary least squares regressions.
Effects were robust and very similar across these methods.
J.F. Rauthmann et al. / Journal of Research in Personality 46 (2012) 147–156 151
(Big Five models:
=.38, p< .001 for number of fixations;
= .40,
p< .001 for mean fixation duration;
=.12, p= .058; BIS/BAS
=.33, p< .005 for number of fixations;
= .23, p< .06
for mean fixation duration;
=.02, p= .811). Differences between
Animation Red and Blue were in line with the gazing behavior the
animations should evoke (see descriptive statistics of eye move-
ment parameters broken down for animation in Table 3): Anima-
tion Red produced more fixations, smaller fixation durations, and
less dwelling time than Animation Blue. Further, the relatively high
correlations between eye movement parameters in different condi-
tions (see Table 4) indicate consistency in gazing behavior.
4.3. Personality main effects
LMM findings for Big Five traits in level 2 can be found in Ta-
ble 5. Big Five variables were able to significantly reduce prediction
error above and beyond condition for number of fixations
= .004, LR(10) = 32.18, p< .001], for mean fixation duration
= .04, LR(10) = 37.12, p< .001], and dwelling time [R
= .03,
LR(10) = 43.07, p< .001]. Level 2 main effects were only found for
neuroticism, extraversion, and openness. Specifically, neuroticism
manifested marginally significantly longer mean fixation durations
= .11, p= .051) and dwelling time (
= .12, p= .071), extraversion
a marginally significantly shorter dwelling time (
=.12, p= .10),
and openness significantly longer mean fixation durations (
= .11,
p= .047) and dwelling time (
= .13, p= .046). From all Big Five
traits, openness emerged as the only trait with significant
(p< .05) effects on eye movement parameters.
LMM findings for BIS/BAS traits in level 2 can be found in Ta-
ble 6. BIS/BAS variables were able to significantly reduce prediction
error above and beyond condition for number of fixations [R
= .07,
LR(8) = 16.24, p= .039], but not for dwelling time [R
= .05,
LR(8) = 11.31, p= .185] and mean fixation duration [R
= .04,
LR(8) = 11.93, p= .154]. Level 2 main effects were only found for
all BAS scales. Specifically, BAS Drive manifested significantly less
number of fixations (
=.31, p= .001) and shorter dwelling
time (
=.29, p= .01), BAS Reward Responsiveness a marginally
significantly longer mean fixation duration (
= .20, p= .081), and
BAS Fun-Seeking a marginally significantly shorter mean fixation
duration (
=.18, p= .075). From all BIS/BAS traits, BAS Drive
emerged as the only trait with significant (p< .05) effects on eye
movement parameters.
4.4. Condition personality interaction effects
As can be seen in Table 5 under ‘‘cross-level interactions,’’ only
neuroticism and extraversion from the Big Five manifested signif-
icant interactions with condition. Specifically, neuroticism mani-
fested under Condition Red significantly less number of fixations
=.12, p= .001) and shorter dwelling time (
=.08, p< .001),
and extraversion a significantly higher number of fixations
= .08, p= .030) and shorter dwelling time (
=.05, p= .008).
As can be seen in Table 6 under ‘‘cross-level interactions,’’ only
BAS Fun-Seeking manifested significant interactions with condi-
tion. Specifically, it manifested under Condition Red a significantly
shorter mean fixation duration (
=.10, p= .024).
5. Discussion
5.1. Interpretation of findings
Differences in eye movement parameters between Animation
Red and Blue were observed (which is line with the gazing behavior
they should evoke, as outlined by Furtner, 2006), but with high cor-
relations between the animations. This may suggest relatively sta-
ble eye movement patterns despite variability caused by stimulus
variation and is in line with behavioral rank-order consistency
(Fleeson & Noftle, 2008a, 2008b): people can exhibit substantial
variation in behavior across different occasions (intraindividual
perspective), but still remain ‘‘consistent’’ when compared to others
(interindividual perspective). For example, Person 1 generally
exhibits more fixations than Person 2 – regardless the animation
– and thus the rank order (Person 1 > Person 2 in fixations) remains
consistent although both persons may vary intraindividually in fix-
ations between animations. This may imply the influence of person-
ality (i.e., an underlying system generating observed consistency).
Neuroticism manifested a tendency towards longer fixation
duration and dwelling time and, specifically, less fixations and
shorter dwelling time in Animation Red. As a domain of affective
intensity (MacDonald, 1995), affect regulation (McAdams, 1992;
van Lieshout, 2000), pondering and rumination (Costa & McCrae,
1992), and negative affect (John & Srivastava, 1999;Yik & Russell,
2001) it might be tied to longer processing of stimuli to determine
their (potentially negative or aversive) value. Interestingly, BIS did
not show any relations to eye movement parameters despite its
convergence with neuroticism. Although BIS and neuroticism are
linked, BIS may represent a somewhat rotated version of neuroti-
cism and capture distinct aspects that seem not related to eye
movement parameters. In any case, this dissociation between BIS
and neuroticism in associations with eye movement parameters
should be replicated in further studies.
Extraversion manifested shorter dwelling time and, specifically,
in Animation Red higher number of fixations and less dwelling
time. As a domain of external orientation and outgoingness (Costa
& McCrae, 1992); liveliness, energy, and activation (Buss & Plomin,
1975; MacDonald, 1995); stimulation, sensation, excitement, or
activity seeking (Costa & McCrae, 1992; McCrae & John, 1992)it
is linked to more frequent or rapid behaviors (Brebner, 1985) and
higher interest in external stimuli which could manifest in more
fixations (with less fixation durations and overall dwelling time).
Openness to new experiences manifested longer mean fixation
durations and dwelling time. Individuals scoring more highly on
Table 3
Descriptive statistics for eye movement parameters and trait scales.
Variables MSD
Eye movements over both animations
Number of fixations 155.31 56.93
Mean fixation duration 241.92 137.35
Dwelling time 35,714.63 17,088.15
Eye movements in Animation Red
Number of fixations 177.01 64.19
Mean fixation duration 187.37 76.82
Dwelling time 33678.12 16786.75
Eye movements in Animation Blue
Number of fixations 133.61 37.82
Mean fixation duration 296.48 161.03
Dwelling time 37751.15 17178.39
Personality (Big Five)
Neuroticism 1.72 0.65 .83
Extraversion 2.55 0.60 .81
Openness 2.82 0.50 .78
Agreeableness 2.73 0.46 .77
Conscientiousness 2.66 0.61 .79
Temperament (BIS/BAS)
BIS 1.76 0.51 .81
BAS Drive 1.95 0.56 .75
BAS Reward Responsiveness 2.17 0.46 .76
BAS Fun-Seeking 2.04 0.58 .75
N= 242.
Eye movement parameters mean fixation duration and dwelling time are given in
ms. Number of fixations reflect absolute numbers of fixations.
n= 89.
152 J.F. Rauthmann et al. / Journal of Research in Personality 46 (2012) 147–156
openness may actively seek information by processing also non-
meaningful stimuli longer and more deeply. Longer fixations could
be indicative of early perceptional ‘‘deep processing’’ mechanisms
that underlie openness as open individuals have been found to be
‘‘deep,’’ thoughtful, intellectual, culturally interested, and creative
(Buss, 1996; John & Srivastava, 1999; van Lieshout, 2000). This is
in line with Matsumoto et al.’s (2010) findings that individuals
high in openness increased eye fixation points when watching
gestures of other people, possibly to obtain more information.
Indeed, longer mean fixation durations are associated with more
attention, personal relevance (of stimuli), higher motivation, and
deeper processing (Furtner et al., 2011).
Agreeableness and conscientiousness were the only two
traits from the Big Five not to show effects on eye movement
parameters. Agreeableness is a genuinely social-interpersonal
domain (Costa & McCrae, 1992), and our animations contained
Table 4
Intercorrelations among all variables.
Variables Eye movement parameters Big Five BIS/BAS
Across both animations
Eye movement parameters
NF –
MFD .24
DT .42
Big Five
N .05 .14
E.04 .16
O .00 .08
.07 .06 –
A .04 .13
.10 –
C.04 .06 .09
.11 –
BIS .08 .02 .03 .52
.01 .07 .15 .04 –
BAS-D .28
.05 .11 .05 .07 .34
.12 –
BAS-RR .01 .09 .06 .13 .15 .28
.07 .00 .46
BAS-FS .01 .18
.12 .05 .31
.10 .07 .14 .29
Animation Blue
Eye movement parameters
NF –
MFD .31
DT .23
Big Five
E .17 .24
O .03 .08 .13
A .11
C .04 .09 .08
BIS .13 .06 .03
BAS-D .27
.14 .26
BAS-RR .05 .12 .06
BAS-FS .07 .23
Animation Red
Eye movement parameters
NF –
MFD .11
DT .70
Big Five
N .17
.07 .16
O.01 .12
A .01 .16
C.09 .05 .09
BIS .06 .02 .03
BAS-D .31
.17 .29
BAS-RR .03 .06 .06
BAS-FS .07 .12 .11
Correlations are bivariate zero-order Pearson product-moment correlation coefficients.
NF = number of fixations, MFD = mean fixation duration (in ms), DW = dwelling time (in ms);
N = neuroticism, E = extraversion, O = openness (to new experiences), A = agreeableness, C = conscientiousness;
BIS = Behavioral Inhibition System, BAS-D = Behavioral Activation System Drive, BAS-RR = Behavioral Activation System Reward Responsiveness, BAS-FS = Behavioral
Activation System – Fun-Seeking.
N= 484.
n= 178.
N= 242.
n= 89.
p< .001.
p< .01.
p< .05.
p< .10.
J.F. Rauthmann et al. / Journal of Research in Personality 46 (2012) 147–156 153
no social information. It is likely that agreeable persons show char-
acteristic gazing patterns when confronted with social stimuli.
Conscientiousness refers to perseverant impulse control despite
hindrances (Denissen & Penke, 2008) which per se does not imply
an association with any eye movement parameter. The non-effects
of agreeableness and conscientiousness need to be replicated,
though, in order to estimate whether or not they are also manifest
in oculomotorics.
BAS, which can be seen as a somewhat rotated version of extra-
version in a factor space, also manifested significant relations with
eye movement parameters. BAS Drive manifested less fixations and
shorter dwelling time. This indicates that individuals high in BAS
Drive show more and longer saccades in gazing and that they have
their gaze ‘‘swirl’’ around more often and wider (without narrow
fixations). It could be possible that the perseverant goal-pursuit
of appetitive stimuli in BAS Drive is accompanied by a gazing style
that resembles visual scanning or searching (which is manifested
in fewer fixations and shorter dwelling time). BAS Reward Respon-
siveness manifested a tendency towards longer mean fixation
duration. It is related to reward responsiveness and dependence
(Carver & White, 1994), and this could manifest in oculomotor out-
put if reward responsive individuals engage into longer gazing to
extract more potentially positive and rewarding stimuli (even in
abstract material with no topical information). On the other hand,
BAS Fun-Seeking interestingly manifested a tendency towards a
shorter mean fixation duration, and particularly so in interaction
with Animation Red.
5.2. Merits and implications
The present findings link personality and individual differences
to eye movements and have theoretical and practical significance.
First, eye-tracking methodology has thus far rarely been applied to
the study of personality and individual differences. Refining para-
digms utilizing eye-tracking methodology seems worthwhile be-
cause selective attention and attentional deployment of
environmental cues in different traits could be studied systemati-
cally. Hypotheses about trait sensitivities regarding circumscribed
classes of environmental stimuli (Traits as Sensitivities Model:
Marshall & Brown, 2006; see also Denissen & Penke, 2008) could
be tested, for example, whether people scoring highly on neuroti-
cism would fixate socially threatening stimuli more (or less) than
other stimuli in a given situation or setting. Such research may also
go a long way in ultimately highlighting person environment
transactions. Second, we demonstrated that personality also man-
ifests in a micro-behavior neglected up to now in personality re-
search: eye movements. Elaborating on trait – eye movement
relationships could yield important findings on why, when, and
how personality and individual differences are behaviorally mani-
fest (and consistent). Thus, personality psychology can advance its
knowledge on behavior (Fleeson & Noftle, 2008a; Furr, 2009). Sec-
ond, we demonstrated that individual differences play a role in eye
movement parameters – above and beyond stimulus variation and
in non-meaningful stimuli. This is compelling evidence for person-
ality influences on gazing. Particularly cognitive and social psy-
chologists should seek to assess dispositional ‘‘covariates’’ in
experimental settings to estimate (or control for) their effects.
Moreover, we have also identified interaction effects, highlighting
the necessity to carefully consider possible trait x stimulus interac-
tions in cognitive-experimental paradigms. Third, it has always
been an ambitious goal to construct objective measures of person-
ality. The fact that personality is manifest also in abstract stimuli
without topical information opens up the door for new and excit-
ing avenues in objective personality assessment. Ultimately, per-
sonality and individual differences could also be objectively
assessed with eye-tracking methodology because how and where
we gaze may be related to personality and individual differences.
5.3. Limitations and prospects
There are some limitations that future in-depth research should
attend to in order to replicate, corroborate, and extend the findings
presented here. First, a coherent theory should be elaborated on
why and how exactly traits manifest in oculomotorics. Findings
on trait manifestations in gazing behavior in the sub-clinical do-
main remain scant thus far.
Second, a wider variety of stimuli (e.g., meaningful vs. non-
meaningful, social vs. non-social, simple vs. complex, etc.), traits
(e.g., needs, goals, interests, lifestyle, etc.), and eye movement
parameters (e.g., saccades and anti-saccades; smooth pursuit eye
movements; pupil dilation; time to first fixation; etc.) should be
employed. Also, affective tonality of stimuli as well as participants’
momentary mood should be sampled to gauge to what extent state
affect plays a role in gazing. Thus, complex state trait stimulus
interactions can be explored in future eye-tracking studies which
will help further tease apart the unique influence that traits may
Table 5
Prediction of eye movement parameters from Big Five variables in linear mixed models.
Dispositional variables Number of fixations
Mean fixation duration
Dwelling time
SE t p
SE t p
SE t p
Level 2 main effects
Neuroticism 0.03 0.05 0.59 .557 0.11 0.05 1.96 .051 0.12 0.06 1.81 .071
Extraversion 0.06 0.06 1.04 .299 0.10 0.06 1.62 .107 0.12 0.07 1.65 .100
Openness 0.01 0.05 0.15 .882 0.11 0.05 2.00 .047 0.13 0.06 2.01 .046
Agreeableness 0.08 0.06 1.38 .169 0.09 0.06 1.59 .114 0.06 0.07 0.84 .399
Conscientiousness 0.03 0.05 0.53 .593 0.02 0.06 0.28 .778 0.00 0.07 0.01 .992
Cross-level interactions
Condition Red neuroticism 0.12 0.03 3.44 .001 0.03 0.03 1.12 .264 0.08 0.02 4.71 .000
Condition Red extraversion 0.08 0.04 2.19 .030 0.12 0.03 3.79 .000 0.05 0.02 2.66 .008
Condition Red openness 0.01 0.03 0.40 .689 0.02 0.03 0.82 .414 0.02 0.02 1.31 .191
Condition Red agreeableness 0.02 0.03 0.66 .513 0.01 0.03 0.26 .797 0.02 0.02 1.43 .154
Condition Red conscientiousness 0.03 0.03 1.00 .320 0.00 0.03 0.08 .935 0.00 0.02 0.20 .839
N= 242.
Level 1 is condition (effect-coded) and Level 2 is Big Five factors. Cross-level interactions denote interactions among condition personality trait.
Standardized regression coefficients are given.
Eye movement parameters mean fixation duration and dwelling time were sampled in ms.
Findings with p< .10 are indicated bold.
Random slopes and intercept model.
Random intercept-only model.
154 J.F. Rauthmann et al. / Journal of Research in Personality 46 (2012) 147–156
exert on gazing behavior while also highlighting interaction
Third, social consequences of trait – eye movement links should
be investigated. For example, it can be examined whether people
perceive differences in others’ gazing patterns and how they make
sense of these in impression formation and personality judgments
(Bayliss & Tipper, 2006). Not only should the manifestation of traits
in gazing be studied (cue validity), but also whether gazing can
function as a social cue for forming impression of others (cue
In summary, empirical and systematic research on the links be-
tween personality and oculomotor behavior has been scarce so far,
but findings here lay important ground work and are promising
that further research will yield (a) theoretically interesting (e.g.,
consistency of gazing across different contexts and trait manifesta-
tions in eye movement parameters) and (b) practically applicable
findings (e.g., personality and motive assessment with eye-track-
ing technology) that are (c) interesting for personality, social, cog-
nitive, and applied psychologists (e.g., in media and marketing
6. Conclusion
The current study presented evidence in a large sample that
sub-clinical personality traits manifest in gazing behavior. Specifi-
cally, neuroticism, extraversion, openness, and BAS are associated
with common eye movement parameters such as number of fixa-
tions, mean fixation duration, and dwelling time. A future goal will
be to determine which eye movement parameters are indicative of
personality, which are not, and why this is the case to explore to
what extent eyes can be used as ‘‘windows to personality.’’
We thank our undergraduate research assistants Pia Dröber,
Christian Karlegger, and Katja Schneider for their help in acquiring
subjects and collecting the data. We also thank Jonas Bösch for pro-
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... [1] [2] [1] [3] PC [4] Berkovsky [5] Hoppe [6] ...
... fixation saccade [19] Rauthman [3] Rauthman [3] ...
... fixation saccade [19] Rauthman [3] Rauthman [3] ...
We applied semi-supervised Information Maximizing Generative Adversarial Networks (ss-InfoGAN) to visualize a relationship between a worker's personality traits and head and eye movements during office work. As a result of the experiment, we found a relationship between one personality trait called ``openness'' and sensor data on the head and eye movements. In this relationship, the period of head and eye movements increases as the degree of openness increases, which is consistent with the previous results of the laboratory experiment. Importantly, these relationships were not extracted by using simple signal processing methods, suggesting the effectiveness of our method.
... They indicated that individuals' cognitive style can modulate only object-based attention, not space-based attention by altering the size of attentional scope. Notably, personality traits, which refer to the relatively enduring patterns of thoughts, feelings and behaviors that distinguish individuals from one another, were the core features in describing individuals' characteristics (e.g., Rauthmann, Seubert, Sachse, & Furtner, 2012;Roberts & Mroczek, 2008;Simon, Lee, & Stern, 2020 Hahn et al. (2015) have adopted Revised Eysenck Personality Questionnaire (EPQ-R), which was also revised by Eysenck and his colleagues in 1985, to investigate the individual difference in attentional performance by using the change detection task. The EPQ-R was aimed to improve the reliability of the psychoticism (P) scale of the original EPQ by adding some new P items and also selected 12 items from each of scales to constitute the short version. ...
... They found that different personality traits can modulate attention performance and argued that neuroticism and extraversion were associated with decreased and increased attentional control over visual field, respectively. Meanwhile, some studies have also indicated that extraversion and neuroticism were associated with positive and negative emotions, which can broaden and narrow attentional scope, respectively (e.g., Rauthmann et al., 2012;Yik & Russell, 2001). Therefore, the aim of the present study was to use the Chinese version of the Eysenck Personality Questionnaire (EPQ-C) to further explore whether and how personality traits (i.e., extraversion, neuroticism, etc.) modulate different modes of attentional selection and whether this moderating effect was due to ones' ability of attentional control or the size of attentional scope by adopting two-rectangle paradigm. ...
... Based on the attentional control theory, individuals with high attentional control have high ability to effectively inhibit taskirrelevant stimuli and flexibly allocate attention to current task in the visual field (e.g., Berggren & Derakshan, 2013;Eysenck & Derakshan, 2011;Eysenck, Derakshan, Santos, & Calvo, 2007), thus they can flexibly allocate their attention to the letter discrimination task and inhibit the processing of the objects in the two-rectangle paradigm, suggesting that high attentional control was associated with low object-based effect. Moreover, previous studies demonstrated that extraversion and neuroticism, can also modulate the size of attentional scope (e.g., Rauthmann et al., 2012;Yik & Russell, 2001), which was negatively associated with object-based effect. Accordingly, individuals' ability of attentional control and the size of attentional scope have different predict on the object-based attentional selection. ...
People have different personality traits, which are the core features to distinguish individuals from one another. Moreover, a growing number of studies have called to establish the framework of the personality and cognition such as attention, memory, etc. Moreover, attentional selection, including space- and object-based attention, is a fundamental human behavior with important cognitive function. However, it is still unclear whether and how personality traits modulate different types of attentional selection. Therefore, the aim of the present study was to explore the issues mentioned above by differentiating space- and object-based attention. The results showed that space-based effect was observed for both low- and high-extraversion groups and there was no significant difference between them; but the object-based effect was obtained for low-extraversion, but not for high-extraversion groups. The same pattern with neuroticism for space-based effect was found. However, the object-based effect was observed for high-neuroticism, but not for low-neuroticism groups. The results suggested that personality traits can modulate only object-based attention by way of altering attentional scope. The present study can not only explain the fundamental accounts underlying the instability of object-based attention and can further support sensory enhancement theory, but also can provide new evidence for adaptive personality-attention framework.
... In this realm, internal consistency and temporal stability of gaze events (e.g., saccades) have gained currency to define endophenotypes of specific disorders [136]; furthermore, a signature in terms of explainable parameters is likely to help interpreting distorted eye movements in psychiatric patients in terms of functional brain systems. Other examples can be easily gathered from recent developments in emotion [137][138][139] and personality research [72,[140][141][142][143]. ...
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A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.
... Optimists, for example, spend less time inspecting negative emotional stimuli than pessimists (Isaacowitz, 2005), and extraversion influences fixation time of people-based images (Moss et al., 2012(Moss et al., ), 2012. Individuals high in openness spend a longer time fixating and dwelling on locations when watching abstract animations (Rauthmann et al., 2012), and perceptually curious individuals inspect more of the regions in a naturalistic scene (Risko et al., 2012). More recently, Hoppe et al. (2018) tracked eye movements while participants ran an errand on a university campus. ...
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We study an individual’s propensity for rational thinking; the avoidance of cognitive biases (unconscious errors generated by our mental simplification methods) using a novel augmented reality (AR) platform. Specifically, we developed an odd-one-out (OOO) game-like task in AR designed to try to induce and assess confirmatory biases. Forty students completed the AR task in the laboratory, and the short form of the comprehensive assessment of rational thinking (CART) online via the Qualtrics platform. We demonstrate that behavioural markers (based on eye, hand and head movements) can be associated (linear regression) with the short CART score – more rational thinkers have slower head and hand movements and faster gaze movements in the second more ambiguous round of the OOO task. Furthermore, short CART scores can be associated with the change in behaviour between two rounds of the OOO task (one less and one more ambiguous) – hand-eye-head coordination patterns of the more rational thinkers are more consistent in the two rounds. Overall, we demonstrate the benefits of augmenting eye-tracking recordings with additional data modalities when trying to understand complicated behaviours.
... The common factor in all these uses is the need for an objective way to study the cognitive process involved in many situations. Eyes are indeed often considered as windows to the soul [6], and studying the gaze behavior can teach us a lot about the reasons of one's actions and decisions. ...
... For instance, Wilbers et al. (2015) showed that Extroversion was negatively correlated with the duration of fixation when people viewed a set of images, independently of the stimulus type, e.g., color, gist, or valence. Likewise, Rauthmann et al. (2012) found that individuals with a higher Openness to experience manifested longer fixation duration and dwelling times when viewing abstract animations without any semantic or topical stimulus information, while those with higher Extroversion manifested shorter dwelling times. In the area of recommender systems, recent work has studied how personality influences the way users perceive and process explanations (Millecamp et al. 2020(Millecamp et al. , 2021. ...
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Recent research in behavioral decision making demonstrates the advantages of using eye-tracking to surface insights into users’ underlying cognitive processes. Personality, according to psychology definition, accounts for individual differences in our enduring emotional, interpersonal, experiential, attitudinal, and motivational styles. In recommender systems (RS), it has been found that user personality is related to their preferences and behavior, which attracted an increasing attention to the ways to leverage personality into the recommendation process. However, accurate acquisition of a user’s personality is still a challenging issue. In this work, we investigate the possibility of automatically detecting personality from users’ eye movements when interacting with a recommendation interface. Specifically, we report an experiment that harnesses two recommendation interfaces to collect eye-movement data in several product domains and then utilize the data to predict the users’ Big-Five personality traits through various machine learning methods. The results show that AdaBoost combined with Gini index score-based feature selector predicts the traits most accurately, and interface- and domain-specific data allow to improve the accuracy of personality trait predictions. Our findings could inform personality-based RS by improving the process of indirect user personality acquisition.
... Optimists, for example, spend less time inspecting negative emotional stimuli than pessimists (Isaacowitz, 2005), extraversion influenced fixation time of people-based images (Mercer Moss et al., 2012). Individuals high in openness spend a longer time fixating and dwelling on locations when watching abstract animations (Rauthmann et al., 2012), and perceptually curious individuals inspect more of the regions in a naturalistic scene (Risko et al., 2012). More recently, Hoppe et al. (2018) tracked eye movements while participants ran an errand on a university campus. ...
Full-text available
We study an individual’s propensity for rational thinking; the avoidance of cognitive biases (unconscious errors generated by our mental simplification methods) using a novel augmented reality (AR) platform. Specifically, we developed an odd-one-out game-like task in AR designed to try to induce and assess confirmatory biases. Forty students completed the AR task in the laboratory, and the short form of the comprehensive assessment of rational thinking (CART) online via the Qualtrics platform. We used distance correlation approaches and stepwise regression to identify objective markers (based on eye, hand and head movements) associated with the psychometric measures of propensity for rational thinking. We show that the proposed markers are associated with the short CART score – more rational thinkers have slower head and hand movements, faster gaze movements and more consistent hand-eye-head coordination patterns across conditions.
Immersion plays a crucial role in video watching, leading viewers to a positive experience, such as increased engagement and decreased fatigue. However, few studies measure immersion while watching videos, and questionnaires are typically used in the measurement of immersion for other applications. These methods may rely on the viewer's memory and cause biased results. Therefore, we propose an objective immersion detection model by leveraging people's gaze behavior while watching videos. In a lab study with 30 participants, an in-depth analysis is carried out on a number of gaze features and machine learning (ML) models to identify the immersion state. Several gaze features are highly indicative of immersion and ML models with these features are able to detect an immersion state of video watchers. Post-hoc interviews demonstrate that our approach is applicable to measure immersion in the middle of watching a video, where some practical issues are discussed as well.
Users’ personality traits can take an active role in affecting their behavior when they interact with a computer interface. However, in the area of recommender systems (RS), though personality-based RS has been extensively studied, most works focus on algorithm design, with little attention paid to studying whether and how the personality may influence users’ interaction with the recommendation interface. In this manuscript, we report the results of a user study (with 108 participants) that not only measured the influence of users’ personality traits on their perception and performance when using the recommendation interface but also employed an eye-tracker to in-depth reveal how personality may influence users’ eye-movement behavior. Moreover, being different from related work that has mainly been conducted in a single product domain, our user study was performed in three typical application domains (i.e., electronics like smartphones, entertainment like movies, and tourism like hotels). Our results show that mainly three personality traits, i.e., Openness to experience , Conscientiousness , and Agreeableness , significantly influence users’ perception and eye-movement behavior, but the exact influences vary across the domains. Finally, we provide a set of guidelines that might be constructive for designing a more effective recommendation interface based on user personality.
Personality analysis is widely used in occupational aptitude tests and entrance psychological tests. However, answering hundreds of questions at once seems to be a burden. Inspired by personality psychology, we propose a multimodal attention network with Category-based mean square error (CBMSE) for personality assessment. With this method, we can obtain information about one's behaviour from his or her daily videos, including his or her gaze distribution, speech features, and facial expression changes, to accurately determine personality traits. In particular, we propose a new approach to implementing an attention mechanism based on the facial Region of No Interest (RoNI), which can achieve higher accuracy and reduce the number of network parameters. Simultaneously, we use CBMSE, a loss function with a higher penalty for the fuzzy boundary in personality assessment, to help the network distinguish boundary data. After effective data fusion, this method achieves an average prediction accuracy of 92.07%, which is higher than any other state-of-the-art model on the dataset of the ChaLearn Looking at People challenge in association with ECCV 2016.
We report the development of a self-report questionnaire of the reinforcement sensitivity theory (RST) of personality for use with children. Focus groups were held with children to sample their experiences of situations modelled on components of three RST systems: fight-flight-freeze system (FFFS, related to fear), behavioural inhibition system (BIS, related to anxiety), and behavioural approach system (BAS, related to approach). The thematic responses formed the conceptual anchors to the development of test items that were examined using exploratory factor analysis in a sample of 288 9–13 year olds. After eliminating items that did not load on their designated factor, or substantially cross-loaded across factors, the original 48 items were reduced to 21 items: 7 items for each of the BIS, FFFS and BAS factors extracted from the data. The separation of the BIS and FFFS items across two factors is consistent with the revised model of RST. We offer this new questionnaire as a RST measure of fundamental motivation and emotion traits in children.
Considering the volume of work published separately in the two areas of personality and movement it is surprising that relatively few studies are concerned with individual differences in movement. In part this must be due to the fact that most personality theories are concerned with attitudes and verbal expressions of mental states rather than motor behaviour. But it is also the case that studies of movement tend to be concerned with specific movements and their central control mechanisms rather than the organism as a functioning whole. Even those studies which look at motor behaviour as an expression of the internal state of the individual tend to regard gestures and movements as a sort of library of communication possibilities revealing emotional states like anxiety, aggression or sexual interest, rather than as enduring characteristics of personality. This may be because, despite an early belief that expressive style reflected personality1, more recently it has been argued that it can be dangerous to assume expressive style means consistency in other behavioural areas2.
The study of the relationships of fixation sequences to the conduct of everyday activities had its origins in the 1950s but only started to flourish in the 1990s as head-mounted eye trackers became readily available. The main conclusions from a decade of study are presented. Results show that (1) eye movements are not driven by the intrinsic salience of objects, but by their relevance to the task in hand, (2) appropriate fixations typically lead manipulations by up to a second, and the eye often leaves the fixated object before manipulation is complete, and (3) many fixations have identifiable and often surprising roles in providing information for locating, guiding, and checking activities. The chapter concludes that in contrast to free viewing, the oculomotor system is under tight top-down control, and eye movements and actions are closely linked.
Gray (1981, 1982) holds that 2 general motivational systems underlie behavior and affect: a behavioral inhibition system (BIS) and a behavioral activation system (BAS). Self-report scales to assess dispositional BIS and BAS sensitivities were created. Scale development (Study 1) and convergent and discriminant validity in the form of correlations with alternative measures are reported (Study 2). In Study 3, a situation in which Ss anticipated a punishment was created. Controlling for initial nervousness, Ss high in BIS sensitivity (assessed earlier) were more nervous than those low. In Study 4, a situation in which Ss anticipated a reward was created. Controlling for initial happiness, Ss high in BAS sensitivity (Reward Responsiveness and Drive scales) were happier than those low. In each case the new scales predicted better than an alternative measure. Discussion is focused on conceptual implications.
First, a word about “neuropsychology.” This term has commonly been used in a quite restricted sense to delineate that part of psychology which is concerned with the study, in human beings, of the effects of known (even if often poorly known) structural damage to the brain. I use “neuropsychology,” in contrast, in a much wider sense, as in my book, The Neuropsychology of Anxiety (Gray, 1982a), to mean the study, quite generally, of the role played by the brain in behavioral and psychological function, whether in human or animal subjects, and whether there is structural damage to the brain or not. Since I also take it as axiomatic (and few would, I think, disagree with the axiom) that all behavioral and psychological function depends upon the activities of the brain, it follows that “neuropsychology” has a breadth which shadows that of “psychology” itself: if there is a psychology of hunger, intelligence, love, or learning French, then there is ipso facto a neuropsychology of the same.
Animated advertisements on the Web come in a variety of shapes, sizes, and colors; they also animate at different speeds. Although recent studies have shown animated ads to be more effective than still ads, the role played by the rate of motion in animated ads has been neglected. An experiment was designed to address this issue by focusing specifically on the physiological and psychological effects of animation speeds in Web ads. Hypotheses derived from motion effects, excitation transfer, limited capacity, and vividness effects theories were tested via a mixed-design experiment wherein participants (N = 47) were exposed to both slow-paced and fast-paced animated ads in one of two sequences (fast then slow, or slow then fast). Arousal was monitored during reception, while memory, conation, and impression formation were measured via a postexposure paper-and-pencil questionnaire. Results indicate that animation speed is a psychologically significant variable. Theoretical and practical implications are discussed.