ORIGINAL RESEARCH
published: 09 June 2020
doi: 10.3389/fpsyg.2020.01057
Attentional Bias and Training in
Individuals With High Dental Anxiety
Jedidiah Siev 1* , Evelyn Behar 2 and Meghan R. Fortune 3
1
Department of Psychology, Swarthmore College, Swarthmore, PA, United States, 2 Department of Psychology, Hunter
College, The City University of New York, New York, NY, United States, 3 Institute for Health Research and Policy, University
of Illinois at Chicago, Chicago, IL, United States
Edited by:
Bahar Güntekin,
Istanbul Medipol University, Turkey
Reviewed by:
Michael Grady Wheaton,
Columbia University, United States
Omer Horovitz,
Tel-Hai College, Israel
Anita Deak,
University of Pécs, Hungary
*Correspondence:
Jedidiah Siev
jsiev1@swarthmore.edu
Specialty section:
This article was submitted to
Psychopathology,
a section of the journal
Frontiers in Psychology
Received: 16 January 2020
Accepted: 27 April 2020
Published: 09 June 2020
Citation:
Siev J, Behar E and Fortune MR
(2020) Attentional Bias and Training
in Individuals With High Dental
Anxiety. Front. Psychol. 11:1057.
doi: 10.3389/fpsyg.2020.01057
Dental anxiety is common and associated with negative outcomes. According to
information-processing models, anxiety is maintained by maladaptive patterns of
processing threatening information. Furthermore, attention training interventions can
reduce anxiety in one session. Fifty-three individuals with high levels of dental anxiety
completed a Posner reaction-time task. Participants were randomized to attention
training or control using a dot-probe task, and then attentional bias was remeasured
using another Posner task. Participants then completed a script-driven imaginal
exposure task. Results indicated that individuals high in dental anxiety exhibit threatrelevant attentional bias. There was mixed evidence about the efficacy of attention
training. On the one hand, training did not eliminate attentional bias and training
condition did not predict distress during the imagery task. On the other hand, cue
dependency scores in the control group were higher for dental than neutral cues, but did
not differ in the training group. In addition, cue dependency scores for both dental and
neutral cues predicted subjective anxiety in anticipation of the imagery task. The mixed
results of training are considered in terms of the possibility that it enhanced attentional
control, rather than reducing bias.
Keywords: dental anxiety, attentional bias, attention training, information processing, imagery
INTRODUCTION
Dental anxiety is experienced by approximately 10–20% of adults, with 2.4–3.7% of the population
meeting diagnostic criteria for dental phobia (Stinson et al., 2007; Oosterink et al., 2009). Anxiety
about dental procedures is a predictor of avoidance of seeking dental care (e.g., Pavi et al., 1995),
with more than two-thirds of individuals with high dental anxiety reporting that they avoid or delay
dental visits (e.g., Armfield and Ketting, 2015). Such avoidance can ultimately lead to the onset or
exacerbation of poor oral health (e.g., Doerr et al., 1998). According to the Centers for Disease
Control and Prevention, 23% of adults between the ages of 20 and 64 have untreated dental caries
(National Center for Health Statistics, 2011), and dental fears are the reason an estimated 23 million
adults in the United States avoid dental care (Dionne et al., 1998). Therefore, not surprisingly,
dental anxiety is associated with higher rates of dental caries and dental neglect (e.g., Poulton
et al., 1998), as well as higher rates of periodontal disorders (Luca et al., 2014). Indeed, 75% of
dentists identify anxiety as an important cause of inadequate dental care in the population (O’Shea
et al., 1984). Moreover, poor oral health is associated with other health outcomes including systemic
diseases, stroke, heart disease, poor nutrition, and impaired social activities (U. S. Department of
Health and Human Services, 2000; Dye et al., 2007).
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anxiety exhibit attentional bias toward threat. In two studies of
individuals with high dental anxiety, participants were slower to
name words depicting dental anxiety-related threat relative to
those with low dental anxiety (Muris et al., 1995), and compared
to neutral words (Johnsen et al., 2003). In another study of
individuals with diagnosed dental phobia, Ries (1997) found that
relative to demographically matched non-anxious participants,
participants with a dental phobia diagnosis were slower to
name both standardized and idiographic words depicting dental
threat. Such attentional biases indicate an interference of the
attentional system such that cognitive resources are allocated to
feared stimuli1 . Notably, because the emotional Stroop paradigm
requires participants to state the color of a word when that word
itself depicts dental fears, it is possible that increased negative
affect (NA; as opposed to increased attention) is responsible for
longer reaction times (MacLeod et al., 1986). In contrast, other
tests of attention allocation (e.g., probe detection paradigms)
require participants to detect non-emotional stimuli that replace
either threat-related or neutral stimuli, and thus reduce the risk of
reaction times varying with emotional state. To complicate things
further, there is recent evidence that individuals with dental
phobia and a history of traumatic experiences associated with
dental treatment evince patterns of neural activation that differ
from individuals with other anxiety disorders when exposed to
threat-relevant pictures (Alexopoulos et al., 2019).
Importantly, no studies to date have examined the potential
efficacy of attention training for dental anxiety among individuals
high in dental anxiety (cf. Horovitz et al., 2016, who excluded
such individuals). This gap in the scientific literature is
underscored by the possibility that a brief computerized
intervention could be especially helpful for people with high
dental anxiety to increase their willingness to complete dental
procedures they would otherwise avoid. Although a few
studies have demonstrated that brief cognitive and behavioral
interventions can be helpful in reducing dental fear (e.g.,
Jerremalm et al., 1986; Getka and Glass, 1992; de Jongh et al.,
1995a; Haukebø et al., 2008; Gordon et al., 2013), and that
such interventions are associated with lower relapse rates relative
to benzodiazepine treatment (Thom et al., 2000), dentists are
generally unable to provide these brief interventions (e.g.,
Gujjar et al., 2019). Dentists are unlikely to have the training,
resources, and interest in implementing cognitive and behavioral
interventions, and thus instead frequently offer patients sedation
(e.g., benzodiazepine, nitrous oxide; Woodmansey, 2005).
Dentists who do want their patients to utilize psychosocial
interventions must appeal to liaison psychiatry and psychology
services to provide such interventions (Feinmann and Harrison,
1997; Woodmansey, 2005). Even so, successful implementation
of cognitive and behavioral interventions is predicated on
numerous conditions, including patient willingness to seek
adjunctive treatment, patient motivation to treat symptoms
to remission (as opposed to temporarily managing distress
Considering the high prevalence of dental anxiety and the
associated public health cost, there is a paucity of experimental
research on the psychological mechanisms associated with dental
anxiety and its treatment. This is especially striking when
considering that anxiety in general is among the best understood
and most effectively treated domains of psychopathology. With
regard to research on the mechanisms that might cause or
maintain dental anxiety, the majority of investigations have
relied on self-report questionnaires to examine cross-sectional
relationships between dental anxiety and variables of interest,
with markedly fewer studies employing laboratory and/or
experimental methods.
According to information processing theories, anxiety is
maintained by maladaptive patterns of processing threatening
information, such as selective attention toward threat and
difficulty disengaging from threat (e.g., Mogg et al., 1991).
That is, anxious individuals pay too much attention to the
things they fear, and they experience difficulty redirecting their
attention away from those things. Basic research in the broader
anxiety literature has demonstrated that individuals with anxiety
disorders or sub-syndromal anxiety symptoms evidence elevated
vigilance toward threatening information (for reviews, see BarHaim et al., 2007; Mogg and Bradley, 2016). One early study
further demonstrated that training individuals to attend to
threatening stimuli leads to enhanced emotional reactivity during
a subsequent stressful task, providing crucial evidence that
attentional bias toward threat is causally related to emotional
disturbance (MacLeod et al., 2002).
Studies documenting attentional bias toward threat among
anxious individuals have led to the development of cognitive
bias modification interventions that train attention away from
threat with the goal of reducing emotional distress. A series of
studies demonstrate the efficacy of these approaches in reducing
attentional threat biases in a wide array of conditions, including
generalized anxiety disorder (Amir et al., 2009a), social anxiety
disorder (e.g., Amir et al., 2008; Schmidt et al., 2009; Amir et al.,
2009b), and subclinical obsessive-compulsive disorder (OCD;
Najmi and Amir, 2010). Extant studies examining attentional
bias retraining procedures suffer from two important limitations.
First, the majority of these investigations have utilized the
same procedure for assessing attention as they did for training
attention, which might mean that participants simply became
more skilled at the task (Amir et al., 2009b). Second, although
many investigations have examined the efficacy of these attention
retraining paradigms for reducing attentional threat, fewer
have investigated whether these reductions in attentional bias
generalize to outcomes other than cognitive bias itself. Najmi
and Amir (2010) found that after only one session of attention
training, anxious individuals were more willing to approach
feared stimuli even when they did not experience reduced anxiety
while doing so, but the majority of studies do not include
behavioral approach tasks in order to examine generalizability of
findings to non-cognitive outcomes.
In the dental anxiety literature, several investigations have
likewise sought to examine the role of attentional bias in
dental fears. Three of these investigations have utilized the
Stroop procedure to examine whether individuals with dental
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1
de Jongh et al. (1995b) found that trait dental anxiety was unrelated to the length
of time participants directed their gaze at dental threat-related pictures relative to
neutral pictures. However, the participants were unselected vis-à-vis dental anxiety
and the task consisted of prolonged exposure to pictures, rather than paradigms
that detect attention allocation at early stages of information processing.
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Attentional Bias in Dental Anxiety
t-tests indicated that the experimental groups did not differ in
self-reported symptoms of depression (p = 0.11, d = 0.45), anxiety
(p = 0.35, d = 0.26), or stress (p = 0.52, d = 0.18).
sufficiently to complete a dental procedure), availability of
resources, and cost of care being non-prohibitive or deterring.
Therefore, there is a great need for portable, cost-effective, brief
interventions to help patients with dental anxiety manage their
anxiety in anticipation of a procedure, particularly interventions
that could realistically be implemented in dental clinics without
involving other specialists. Such an intervention could ultimately
lead to a notable public health benefit. Indeed, there is some,
albeit limited, evidence that brief computerized (e.g., Heaton
et al., 2013) or other technology-based interventions can be
helpful in reducing dental anxiety (e.g., Gujjar et al., 2019).
In this study, we examined two primary questions. First, we
sought to assess whether individuals with dental anxiety evidence
an attentional bias toward dental anxiety-relevant stimuli.
Second, we examined the efficacy of a single-session attentional
bias training procedure for reducing both attentional bias and
self-reported distress during a behavioral (imaginal exposure)
task, using different paradigms to measure and train attention.
Diagnostic Interviews
Mini International Neuropsychiatric
Interview—Version 5.0 (MINI; Sheehan et al., 1998)
The MINI is a structured diagnostic interview based on the
diagnostic criteria in the DSM-IV. It was developed to meet
the need for a short but accurate structured interview for
clinical and epidemiological studies, and assesses the presence
of depression and dysthymia, manic and hypomanic episodes,
anxiety disorders, alcohol and substance abuse/dependence,
psychotic disorders, anorexia, bulimia, and antisocial personality
disorder. The MINI also allows for diagnostic assessments
made by interviewers with limited training. When comparing
diagnoses using the MINI to those with the Structured Clinical
Interview for DSM-III-R (SCID; Spitzer et al., 1990), the degree
of concordance evidences a range according to the method of
comparison and disorder, with kappa agreements ranging from
0.43 to 0.90, sensitivity values ranging from 0.45 to 0.96, and
specificity values ranging from 0.86 to 1.00 (Sheehan et al., 1998).
MATERIALS AND METHODS
Participants
Anxiety Disorders Interview Schedule—Specific
Phobia Module (ADIS; Brown et al., 1994)
Participants were recruited through various methods and
sources, including (1) advertisements posted at the Departments
of Psychology and Schools of Dentistry at Nova Southeastern
University and the University of Illinois at Chicago (UIC); (2)
advertisements sent to private dental clinics in South Florida and
Chicago; and (3) e-mails sent to all university students, faculty,
and staff at the UIC. Advertisements specified the need for
individuals who were afraid of going to the dentist, and instructed
participants to contact study personnel for a telephone screen.
Telephone screening procedures entailed verbal administration
of the Modified Dental Anxiety Scale (MDAS; Humphris et al.,
1995); screening questions regarding psychiatric symptoms,
substance use, and suicidality; questions regarding current or past
psychological or psychiatric treatment; and demographic items.
A total of 58 individuals with MDAS scores of 19 or higher
were invited to participate and consented. Of these individuals,
53 participants completed the study and were compensated
$50. One participant’s MDAS score had dropped to 18 at
the time of her participation but was nevertheless included.
Participants were excluded if they had a psychotic disorder,
bipolar disorder, current substance abuse or dependence,
homicidality, or suicidality. They were also excluded if there was
evidence of intellectual disability, dementia, brain damage, or
other cognitive impairment. Four participants who consented
were found ineligible due to current substance dependence,
and another one withdrew. Table 1 presents demographic and
symptom information for all participants2 . Independent samples
The ADIS is a semistructured interview for the assessment of
anxiety disorders, depressive disorders, and substance disorders.
Due to the MINI’s relatively weak concordance with SCID
diagnoses of specific phobia, we also sought to assess dental
phobia using a more comprehensive interview, the ADIS. Due
to an administrative error because of which requisite clinical
severity ratings were not assigned at one site, formal diagnoses
were not recorded; however, interviewers assigned 0 (none) to
8 (very severe) ratings of both interference and distress related
to dental fear; 96% of the participants received an interference
or distress score of at least 4, indicating that they likely met the
criteria for current dental phobia.
Self-Report Measures
Modified Dental Anxiety Scale (MDAS; Humphris
et al., 1995)
The MDAS is a five-item measure of dental anxiety designed
to assess anticipatory anxiety associated with an upcoming
dental visit, fear of dental cleaning and drilling, and receiving
a local anesthetic injection. Total scores range from 5 to 25.
The MDAS demonstrates good internal consistency (Humphris
et al., 2000) and retest reliability (Humphris et al., 1995). It
also demonstrates good convergent validity with scales of dental
avoidance (Humphris et al., 2000). A cutoff score of 19 maximizes
sensitivity and specificity in detecting cases and non-cases of
dental phobia (Humphris et al., 1995; King and Humphris, 2010).
2
Despite random assignment, the experimental groups differed in racial
composition. However, there is no theory or evidence linking race to the
outcomes of interest, and race was not associated with any measure of response
latency pre-training or post-training. It is also conceptually problematic to
covary a demographic variable that differs between groups because doing
so removes variance associated with the experimental manipulation itself,
thereby distorting the independent variable of interest (viz. experimental
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condition; Miller and Chapman, 2001). More generally, others have argued that
in randomized trials one should not covary baseline differences on the basis of
whether they are significant (Moher et al., 2010), and even that it is inappropriate
to conduct significance tests of such variables following randomization altogether
(e.g., Begg, 1990; Pocock et al., 2002; Moher et al., 2010). We therefore did not
covary race in any analyses.
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TABLE 1 | Demographic and clinical composition by group.
Age M (SD)
Gender n (%) women
Attention training (n = 27)
Control (n = 26)
Entire sample (N = 53)
39.41 (15.09)
40.54 (14.96)
39.96 (14.89)
23 (85.2%)
19 (73.1%)
42 (79.2%)
Race
Caucasian n (%)
11 (40.7%)
20 (76.9%)
31 (58.5%)
Black or African-American n (%)
9 (33.3%)
2 (7.7%)
11 (20.8%)
Asian or Asian-American n (%)
6 (22.2%)
4 (15.4%)
10 (18.9%)
Other n (%)
1 (3.7%)
0 (0%)
1 (1.9%)
4 (14.8%)
6 (23.1%)
10 (18.9%)
MDAS M (SD)
22.15 (1.49)
21.73 (1.61)
21.94 (1.55)
ADIS Interference M (SD)
5.04 (1.63)
4.83 (2.16)
4.94 (1.88)
ADIS Distress M (SD)
5.48 (1.31)
5.46 (1.48)
5.47 (1.38)
7.02 (7.06)
Ethnicity n (%) Hispanic/Latino
DASS-21 Depression M (SD)
5.48 (6.51)
8.62 (7.37)
DASS-21 Anxiety M (SD)
7.07 (6.39)
9.01 (8.55)
8.03 (7.52)
DASS-21 Stress M (SD)
12.67 (11.23)
14.46 (8.70)
13.55 (10.01)
MDAS, Modified Dental Anxiety Scale; ADIS, Anxiety Disorders Interview Schedule; and DASS-21, Depression Anxiety Stress Scales-21.
Picture System (IAPS; Lang et al., 2008; pictures 2279, 3280,
and 9582) and others from Google Images. Neutral images
depicted individuals working with tools or other similar technical
implements, chosen to mirror the dental images; they were also
selected from Google Images. The final set of images was selected
from a larger pool of images following pilot testing using the
valence and arousal dimensions of the Self-Assessment Manikin
(SAM; Bradley and Lang, 1994), which are scored 1 to 9, with
a middle score of 5 indicating neutrality. Neutral images were
selected to be the most neutral in terms of valence. In contrast,
dental images were selected to be the most extreme in order to
(a) generate more variance in participants’ responses to them
and (b) test whether attention training away from threat would
be effective even when the threat image is more negative or
more arousing than the neutral image. Mean valence scores from
the pilot data were 3.67 (SD = 0.62) for dental pictures and
5.09 (SD = 0.38) for neutral pictures, indicating that the neutral
pictures were rated as almost exactly neutral. Mean arousal scores
from the pilot data were 5.73 (SD = 1.05) for dental pictures and
4.18 (SD = 1.30) for neutral pictures. Images are available from
the first author.
We do not report α in the present sample because all participants
had a minimum score of 19 on the MDAS, leading to a severely
restricted range (19–24). This has been demonstrated to produce
biased and sometimes even negative α values (Fife et al., 2012).
Depression Anxiety Stress Scales (DASS-21;
Lovibond and Lovibond, 1995)
The DASS-21 consists of three seven-item scales that measure
symptoms of depression, anxiety, and stress. Evidence indicates
that the DASS-21 distinguishes well between these three features
(Antony et al., 1998). It evidences good internal consistency
(Lovibond and Lovibond, 1995), as well as concurrent validity
(Antony et al., 1998). The DASS-21 was included to ensure that
levels of depression, anxiety, and stress did not differ between the
two experimental conditions. Internal consistency in the current
sample was acceptable (depression α = 0.84; anxiety α = 0.75; and
stress α = 0.88).
Positive and Negative Affect Schedule (PANAS;
Watson et al., 1988)
The PANAS is a 20-item measure that assesses positive affect
(PA) and NA. The Moment version of the PANAS involves
rating affect in the present moment on a 1 (very slightly or not
at all) to 5 (extremely) scale. The PANAS-Moment has good
internal consistency and favorable convergent, discriminant, and
predictive validities (Watson et al., 1988). Rather than creating
scores for analysis, the PANAS was used to identify emotions for
use in the imagery scripts.
Assessment of Attentional Bias
To assess attentional bias toward dental stimuli, we used the
Posner paradigm (Posner, 1980; Fox et al., 2001; Yiend and
Mathews, 2001), which has been used to measure attentional
bias in previous studies of anxiety disorders (e.g., Amir et al.,
2003). Participants were instructed to fixate on a cross at a central
point on a computer screen (1,000 ms), and then a threatening
or neutral image appeared (600 ms) in a rectangular box to the
left or right of the fixation point. In order to avoid masking
the target probe, the picture then disappeared for 50 ms, after
which a probe (an asterisk) appeared in one of the two boxes.
Participants indicated “as quickly as possible” where the asterisk
appeared. The probe remained visible for 3,000 ms for trials on
which a participant did not respond, and the intertrial interval
varied from 500 to 1,500 ms. In valid trials (2/3 of the trials), the
Experimental Tasks
Stimuli
A total of 24 dental images and 24 neutral images were selected
for use in the attention tasks, so that a different set of 8 dental
and 8 neutral images was used in each of the three tasks. Dental
images were pictures of dental procedures and apparatuses, in
most cases including tools in use by a dental professional on
a patient. Several were chosen from the International Affective
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down what he/she could imagine seeing, touching, smelling,
and hearing in that situation. The experimenter then showed
the participant a copy of the PANAS, and asked him/her to
identify the five adjectives (from the 20 listed on the PANAS)
that he/she would feel in the situation, and then to place the
adjectives in order from 1 to 5 (1 = most prominent feeling,
5 = least prominent feeling). Next, the experimenter showed
the participant the 20 physical symptoms of a panic attack
that appear in the ADIS interview and asked the participant to
identify the five panic symptoms that he/she would feel in the
situation, and then to place the symptoms in order from 1 to
5 (1 = most prominent feeling, 5 = least prominent feeling). The
experimenter used the participant’s ideographically identified
situation (and accompanying sensory details), accompanying
affective experiences, and panic symptoms to construct a
narrative imagery script according to a standardized template
provided by Lang et al. (1983), as follows:
probe appeared in the location of the cue; in invalid trials (1/6), it
appeared on the other side of the cue; in other trials (1/6), there
was no cue. Relative facilitation to detect probes that follow in the
location of threatening images, as well as relative delays to detect
probes that appear in the position opposite threatening images,
indicate attentional bias.
After eight practice trials, participants completed 192 trials
in each of two Posner task administrations (pre- and postexperimental manipulation). In each administration, different
sets of eight dental pictures and eight neutral pictures were
presented 10 times each, and 32 trials were uncued (in which
case the interval between the onset of the trial and presentation of
the probe was 1,600 ms). Although not included in the analyses,
uncued trials are typically presented to avoid a fixed cue interval
(e.g., Amir et al., 2003).
Attentional Bias Modification
Attentional bias training was modeled after the probe detection
paradigm (MacLeod et al., 1986), as well as after prior
investigations that sought to train attention away from anxietyrelevant cues (e.g., Najmi and Amir, 2010). In this probe detection
paradigm, participants viewed a fixation cross for 500 ms. Two
pictures then appeared for 500 ms, one above the other, after
which a probe (the letter “E” or “F”) appeared in the location
of one of the pictures. Participants were instructed to indicate
“as quickly as possible” whether the letter was an “E” or an “F.”
Shorter response latencies imply that a participant’s attention was
already engaged at the location of the probe. Therefore, decreased
latency to identify the probe when it followed the location of
threat, relative to neutral images, is evidence for attentional bias
toward threat. However, in the present study, attentional bias was
assessed using the Posner paradigm, and this task was used only
as an experimental manipulation.
In the attention training condition, the location of the probe
was contingent upon the location of the threat such that it
always followed the location of a neutral image, thereby training
participants to direct their attention away from threat in threat–
neutral pairs. In the control condition, the location of the probe
appeared equally in the previous location of the threat and neutral
images. Researchers have demonstrated the remedial effects of
attention training using the probe detection paradigm as the
training mechanism (e.g., Schmidt et al., 2009; Amir et al., 2009a;
Najmi and Amir, 2010).
After 10 practice trials with neutral pictures, participants
completed 192 trials in which a dental picture was always paired
with a neutral picture. There were eight dental pictures and eight
neutral pictures, and each picture appeared 24 times in total. The
pictures were different from those used in the Posner tasks, and
the intertrial interval was 1,000 ms.
You are (Situation). You notice that you are feeling (PANAS
5) and (PANAS 4). As you look around, you see (Sight Item).
You smell (Smell Item). You hear (Hearing Item). As you
are noticing all of the sights, smells, and sounds you are
experiencing, you notice that you are also feeling (PANAS
3), (PANAS 2), and (PANAS 1). You notice that (Panic 5),
(Panic 4), and (Panic 3). You also notice that you (Panic 2)
and (Panic 1). For the next several minutes, please continue
to imagine, as intensely and as vividly as possible, that you
are (Situation).
For the imaginal exposure exercise, the experimenter asked the
participant to close his/her eyes and listen to the experimenter
read the idiographic narrative script while imagining the
situation, emotions, and sensations as vividly and intensely
as possible. Participants were asked to provide 0–100 distress
(SUDS) ratings at five time points: just prior to the reading,
immediately after the reading, and after 1, 2, and 3 min of
continued imagery. Imaginal simulations of anxiety-provoking
situations have been shown to elicit subjective and physiological
responses (e.g., McDonagh-Coyle et al., 2001), which decrease
following exposure therapy (Wangelin and Tuerk, 2015). Such
simulations of dental anxiety-relevant cues have also been
shown to decrease anxiety prior to and following dental visits
(Armitage and Reidy, 2012).
Procedure
The study was conducted in compliance with the Institutional
Review Boards at both sites. Upon arriving at the laboratory,
participants provided written informed consent and then
completed the clinician interviews. Next, the experimenter
guided participants through the completion of forms used to
create and tailor a narrative that was relevant to their idiographic
dental fears for the script-driven imagery. Participants then
completed a battery of self-report measures while the
experimenter constructed the imagery scenes based on the
situational, physiological, cognitive, and affective stimuli
provided by each participant. Following completion of the
questionnaires, participants completed the three computerized
Construction of Script-Driven Imagery
We utilized procedures for constructing narrative scripts of fearrelevant situations for imagery inductions (Lang, 1979; Lang
et al., 1983). The experimenter asked the participant to complete
an imagery construction form, which entailed identifying a
situation that would be one of the participant’s greatest fears
in a dental procedure. The participant was also asked to write
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Attentional Bias in Dental Anxiety
attention tasks, after which the experimenter guided them
through the imaginal exposure task in which they vividly
imagined personally feared dental situations as per the scripts
constructed and read by the experimenter.
as a function of picture type, t(51) = 0.33, p = 0.74, d = 0.05, and
CI95% [-0.23, 0.32].
Post-training
Post-training mean response latencies were examined using a 2
(Group: Training/Control) × 2 (Cue: Valid/Invalid) × 2 (Picture:
Dental/Neutral) ANOVA with repeated measures on the last two
factors. There was a main effect of Cue such that participants
were slower to detect invalidly cued than validly cued probes,
F(1, 49) = 52.89, p < 0.001, ηp 2 = 0.52, and CI90% [0.35,0.63].
In addition, there was still a Cue × Picture interaction indicating
attentional bias regardless of condition, and the effect posttraining was significant, F(1, 49) = 6.99, p = 0.01, ηp 2 = 0.13,
and CI90% [0.02,0.27]. Again, analyses of simple effects indicated
that participants were quicker to detect validly cued probes that
followed dental pictures than those that followed neutral pictures,
t(50) = 3.61, p = 0.001, d = 0.51, and CI95% [0.21, 0.80], but the
reaction time for invalidly cued probes did not differ as a function
of picture type, t(50) = -1.10, p = 0.28, d = -0.15, and CI95% [-0.43,
0.12]. Hence, training did not eliminate attentional bias. No other
effects in the ANOVA were significant.
Another metric commonly used to examine cue dependency
is the validity effect, calculated as the difference between
response latencies for invalidly cued trials and validly cued trials
(Compton, 2000). The validity effect indicates the degree to which
participants are influenced by cue location. Rather than simply
comparing response latencies separately for validly and invalidly
cued trials (as in the simple effects analyses), comparing groups
on the validity effect tests potential differences between slowness
on invalidly cued trials relative to quickness on validly cued trials.
Validity effect data are presented in Figure 1. Within-group
differences in validity effect scores as a function of picture type
were examined via paired samples t-tests, and group differences
in validity effect scores were examined via independent samples
t-tests. In the attention training group, validity effect scores
did not differ as a function of picture type, indicating that the
influence of cue location on RT did not depend on picture
content, t(24) = 1.60, p = 0.12, d = 0.32, and CI95% [-0.09, 0.72].
In contrast, the control group had higher validity effect scores
(i.e., was more influenced by cue location) for dental than neutral
pictures, t(25) = 2.16, p = 0.04, d = 0.42, and CI95% [0.02, 0.82].
Similarly, group comparisons revealed a trend indicating that the
attention training group had lower validity effect scores (i.e., was
less influenced by cue location) than did the control group for
dental pictures, t(49) = 1.87, p = 0.067, d = 0.52, and CI95% [0.04, 1.08], but not for neutral pictures, t(49) = 1.31, p = 0.197,
d = 0.37, and CI95% [-0.19, 0.92].
RESULTS
Data Reduction
Reaction time data were included in analyses for trials on which
responses were (a) accurate, (b) within 2 SD of that participant’s
mean response latency (Reese et al., 2010), and (c) between 50 and
1,200 ms (Amir et al., 2003). In addition, participant-level mean
reaction time data that were determined to be outliers relative to
the overall means for that type of trial across the sample were
converted in accordance with the Windsor method to one unit
(10 ms) above the next most extreme value. This resulted in
Windsorized data from two participants on a total of four trial
types (e.g., validly cued dental pictures at pre-training). Mean
response latencies at pre-training and post-training are provided
in Table 2.
Pre-training
Pre-training mean response latencies were examined using a 2
(Cue: Valid/Invalid) × 2 (Picture: Dental/Neutral) ANOVA with
repeated measures on both factors. There was a main effect of Cue
such that participants were slower to detect invalidly cued than
validly cued probes, F(1, 51) = 46.13, p < 0.001, ηp 2 = 0.48, and
CI90% [0.30,0.59]. There was also a trend indicating that this effect
was modified by a Cue × Picture interaction, F(1, 51) = 3.70,
p = 0.06, ηp 2 = 0.07, and CI90% [0,0.20]. Analyses of simple
effects indicated that participants were quicker to detect validly
cued probes that followed dental pictures than those that followed
neutral pictures, t(51) = 2.97, p = 0.005, d = 0.41, and CI95% [0.13,
0.69], but the reaction time for invalidly cued probes did not differ
TABLE 2 | Response Latencies [M (SD) in Milliseconds].
Attention training
Control
Entire sample
Pre-training
Dental images
Validly cued
415.94 (74.06)
388.73 (51.56)
402.86 (65.08)
Invalidly cued
449.37 (79.69)
440.41 (65.28)
445.06 (72.55)
Neutral images
Validly cued
421.66 (82.38)
399.07 (53.57)
410.80 (70.29)
Invalidly cued
448.76 (79.47)
438.59 (61.87)
443.87 (71.05)
Post-training
Imagery Task
Dental images
Validly cued
397.56 (84.91)
369.33 (63.79)
383.17 (75.49)
Invalidly cued
445.36 (70.84)
448.06 (72.31)
446.74 (70.89)
Training condition did not predict distress during the imagery
task. First, the training groups did not differ on SUDS
ratings immediately prior to commencing the imagery task,
t(51) = 0.91, p = 0.37, d = 0.25, and CI95% [-0.29, 0.79]. Second,
post-imagery SUDS ratings were examined via a 2 (Group:
Training/Control) × 4 (post-task SUDS ratings time points)
ANOVA with repeated measures on the last factor, covarying pretask SUDS. Mauchly’s test of sphericity was significant, p < 0.001,
Neutral images
Validly cued
406.21 (86.52)
376.02 (62.99)
390.82 (76.22)
Invalidly cued
446.43 (77.40)
437.48 (65.26)
441.87 (70.89)
53 participants completed the study. However, due to missing data, N = 52 for pretraining reaction time data, and N = 51 for post-training reaction time data (n = 25
and n = 26 in the training and control conditions, respectively).
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Attentional Bias in Dental Anxiety
so the Huynh–Feldt correction was applied. There were no
significant main effects of Group [F(1, 50) = 0.01, p = 0.93,
and ηp 2 < 0.001], or time [F(2.34, 117.03) = 0.37, p = 0.72,
and ηp 2 = 0.007], nor was the interaction significant [F(2.34,
117.03) = 0.11, p = 0.92, and ηp 2 = 0.002]. The average peak SUDS
rating was 76.64 (SD = 21.61) across the sample, and similar
within both groups [for the training group, 77.78 (SD = 19.34);
for the control group, 75.46 (SD = 24.08)]. This indicates that
the imagery task induced considerable anxiety and therefore
the reason training did not impact anxiety was not because
participants were insufficiently anxious.
Across the entire sample, validity effect scores for dental
pictures were correlated with distress immediately before
commencing the imagery task (r = 0.47, p = 0.001), but not
at any point after the imagery task (r’s < 0.20, p’s > 0.17).
However, the pattern was similar for validity effect scores for
neutral pictures, which likewise predicted distress immediately
before commencing the imagery task (r = 0.41, p = 0.003), but
not at any point thereafter (|r| ’s < 0.09, p’s > 0.56).
facilitated attention toward threat was seen as evident when
participants detected validly cued probes that follow threat
cues more quickly than those that follow neutral cues. In
contrast, difficulty disengaging from threat was inferred when
participants detected invalidly cued probes that follow threat
cues more slowly than those that follow neutral cues. On
the basis of this reasoning, the present study would seem to
indicate the presence of facilitated attention toward threat,
but not difficulty disengaging from threat, in dental anxiety.
More recently, however, researchers have identified limitations
in the ability of this paradigm to test the attentional bias
mechanism, and demonstrated that alternative processes may
even reverse the results (e.g., Mogg et al., 2008; Grafton and
MacLeod, 2014; Mogg and Bradley, 2016). Therefore, the present
results indicate attentional bias, but cannot elucidate the nature
of the mechanism.
The results of the attention training manipulation were mixed.
Contrary to expectations, training did not eliminate attentional
bias, and the patterns of cue dependency (i.e., validity effect)
scores for dental versus neutral trials appeared similar in the
two groups. However, cue dependency scores for participants
in the attention training group did not depend on picture type
(dental versus neutral), but cue dependency scores for those in
the control group were larger for dental than neutral picture cues.
Furthermore, training reduced overall cue dependency, which
may indicate that it was successful in training participants to
allocate their attention more effectively in general, whether or
not it specifically reduced threat-relevant bias. Although there
was no impact of training on anxiety after the imagery task,
the ability to allocate attention without being as affected by the
cue location—for both threat and neutral cues—predicted less
anticipatory anxiety before the task.
Taken together, it is possible that the effect of training was to
enhance attentional control, not to reduce bias. If participants
were more able to focus their attention on the fixation point,
this would result not only in smaller cue dependency scores
for both dental and neutral pictures, but also in smaller
differences between cue dependency scores for dental versus
neutral pictures. At the least, the impact of attention on anxiety
prior to the imagery task must result from overall attentional
control rather than something specific to threat content because
cue dependency scores for both dental and neutral pictures
predicted pre-imagery distress. If so, these results are consistent
with other studies that demonstrate that attention modification
procedures have similar effects whether participants are trained
to direct their attention away from, or toward, threat (e.g.,
Klumpp and Amir, 2010), implying that the critical element
is overall attentional control. In fact, attentional control has
been associated with improvements in anxiety outcomes even in
investigations that did not find evidence for the superiority of
attention training relative to control conditions (for a review, see
Mogg and Bradley, 2016).
Attention training did not impact subjective anxiety before
or following an imagery task. Importantly, Najmi and Amir
(2010) demonstrated that attentional bias modification can
influence behavioral outcomes without reducing subjective
distress. Specifically, they implemented an attention training
DISCUSSION
The aims of this investigation were to examine whether
individuals with dental anxiety have threat-relevant biased
attention and to test the impact of an experimental one-session
attention training intervention on attentional bias and subjective
anxiety during an imagery task. Several previous studies
demonstrate an attentional bias in individuals with dental anxiety
(Muris et al., 1995; Ries, 1997; Johnsen et al., 2003); however,
those studies used the Stroop paradigm, which confounds the
potential roles of attentional bias and NA in influencing reaction
times. In the present study, there was consistent evidence both
before and after the training manipulation of attentional bias
toward threat as measured using the Posner paradigm.
Previously, researchers used the Posner task to differentiate
between facilitated attention toward, and difficulty disengaging
attention from, threat (e.g., Cisler and Koster, 2010). Specifically,
FIGURE 1 | Cue dependency scores (milliseconds) as a function of
experimental condition and cue type. Error bars represent 1 SE.
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Attentional Bias in Dental Anxiety
visual angle. Fifth, without a comparison group of individuals
low in dental anxiety, one cannot be certain that the attentional
bias toward dental cues relative to neutral cues is specific to
individuals high in dental anxiety.
In conclusion, this is the first study to demonstrate that high
dental anxiety is associated with threat-relevant attentional bias
using the Posner paradigm. The results of attention training
were mixed, but overall seem to indicate that training led to
enhanced attentional control; it is less clear whether it led to
decreased attentional bias toward threat. Attention training did
not reduce anxiety during an imagery task, although overall
attentional control was associated with less anticipatory anxiety
before the task. Considering that the literature is inconclusive
about whether the remedial effects of attention training require
that it reduces attentional bias (cf. MacLeod and Clarke, 2015;
Mogg and Bradley, 2016), and that attention training can increase
the willingness to approach feared situations without reducing
anxiety (Najmi and Amir, 2010), future research should examine
the effects of attention training on behavioral outcomes such as
completing a feared dental procedure.
protocol similar to the one used in the present study with
individuals who had subclinical symptoms of OCD focused on
contamination. They found that although attention training had
no impact on anxiety or OCD symptoms, participants who
received training completed more steps on a contamination
behavioral approach task. Therefore, it is possible that individuals
with dental anxiety may be more willing to visit the dentist or
complete dental procedures following attention training, even if
such events do not lead to less anxiety. Although the availability
of such a brief and portable intervention capable of promoting
healthy behavioral changes despite maintained anxiety would
offer a considerable public health benefit, the efficacy of such
an intervention for dental anxiety awaits empirical investigation.
In addition, testing the impact of attention training before an
actual dental procedure could raise the emotional salience of
the threat cues and generally raise the degree of environmental
threat, which may facilitate threat detection (e.g., Cornwell et al.,
2011), and impair cognitive flexibility related to attention (e.g.,
Cornwell et al., 2012).
One previous investigation examined attention training in a
dental clinic, where participants completed attention training,
control, or distraction tasks while awaiting an appointment
(Horovitz et al., 2016). The authors found that the attention
training condition was associated with maintained anxiety before
and after dental treatment, whereas the control and distraction
conditions were associated with decreased anxiety before and
after dental treatment, respectively. Of note, the researchers
specifically excluded participants with high levels of dental
anxiety, so these results may not be relevant to individuals
with clinically significant levels of dental anxiety, for whom an
intervention is more necessary and relevant. Moreover, cognitive
bias modification procedures may produce larger effects in
clinical samples (e.g., Hallion and Ruscio, 2011). Therefore, an
important next step is to test the efficacy of attention training with
highly dentally anxious participants in a dental setting, using a
behavioral approach outcome assessment.
There were several limitations to the present study. First, due
to an administrative error, formal diagnoses of dental phobia
were not available. However, the clinician ratings of interference
and distress were consistent with the likelihood that nearly
all participants met criteria for current dental phobia. Second,
although all participants exceeded the clinical cutoff score on
a measure of dental anxiety, most participants were referred
from university dental clinics. Presumably, individuals with the
most severe dental anxiety avoid going to the dentist altogether,
in which case the most severe patients would not have had
the opportunity to participate. It is important to include even
these most severe individuals in future investigations, particularly
because such individuals might benefit the most, and/or might
improve even in the absence of exposure-based interventions.
Third, in spite of random assignment, the experimental groups
differed in racial composition. As described, it is not appropriate
to covary a demographic variable in a randomized trial especially
without a theoretical or empirical reason to expect it to influence
outcomes of interest. Nevertheless, the group difference in racial
composition presents a conceivable alternative hypothesis or
confound. Fourth, participants were not seated at a standardized
distance from the computers, and therefore we cannot report
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DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Institutional Review Boards at Nova Southeastern
University and the University of Illinois at Chicago. The
participants provided their written informed consent to
participate in this study.
AUTHOR CONTRIBUTIONS
JS and EB designed the study, attained funding for it, supervised
data collection at the two sites, analyzed and interpreted the
data, and prepared the manuscript. MF collected the data and
contributed to the manuscript.
FUNDING
This work was supported by a President’s Faculty Research and
Development Grant from Nova Southeastern University.
ACKNOWLEDGMENTS
The authors thank Alex Jendrusina, Allyson Bontempo, Falguni
Patel, Giovanna Rivano de Gómez, Jamie Ginberg, Joe Slimowicz,
Shaina Fieldstone, and Victoria Schlaudt for their work as
research assistants; An Nguyen, Ana Karina Mascarenhas, Lizania
Montero, and Shadzi Jebraeili, for assistance with recruitment;
and Ryan Black for advice about analyses.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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June 2020 | Volume 11 | Article 1057