Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-02-11T05:40:49.821Z Has data issue: false hasContentIssue false

Attention allocation in posttraumatic stress disorder: an eye-tracking study

Published online by Cambridge University Press:  26 February 2021

Amit Lazarov*
Affiliation:
School of Psychological, Tel-Aviv University, Tel-Aviv, Israel Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
Benjamin Suarez-Jimenez
Affiliation:
Department of Neuroscience, The Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, NY, USA
Xi Zhu
Affiliation:
Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, NY, USA
Daniel S. Pine
Affiliation:
Section on Developmental Affective Neuroscience, National Institute of Mental Health, Bethesda, MD, USA
Yair Bar-Haim
Affiliation:
School of Psychological Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
Yuval Neria
Affiliation:
Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute, New York, NY, USA
*
Author for correspondence: Amit Lazarov, E-mail: amitl76@yahoo.com
Rights & Permissions [Opens in a new window]

Abstract

Background

Eye-tracking-based attentional research implicates sustained attention to threat in posttraumatic stress disorder (PTSD). However, most of this research employed small stimuli set-sizes, small samples that did not include both trauma-exposed healthy participants and non-trauma-exposed participants, and generally failed to report the reliability of used tasks and attention indices. Here, using an established eye-tracking paradigm, we explore attention processes to different negatively-valenced cues in PTSD while addressing these limitations.

Methods

PTSD patients (n = 37), trauma-exposed healthy controls (TEHC; n = 34), and healthy controls (HC; n = 30) freely viewed three blocks of 30 different matrices of faces, each presented for 6 s. Each block consisted of matrices depicting eight negatively-valenced faces (anger, fear, or sadness) and eight neutral faces. Gaze patterns on negative and neural areas of interest were compared. Internal consistency and test-retest reliability were evaluated for the entire sample and within groups.

Results

The two trauma-exposed groups dwelled longer on negatively-valenced faces over neutral faces, while HC participants showed the opposite pattern. This attentional bias was more prominent in the PTSD than the TEHC group. Similar results emerged for first-fixation dwell time, but with no differences between the two trauma-exposed groups. No group differences emerged for first-fixation latency or location. Internal consistency and 1-week test-retest reliability were adequate, across and within groups.

Conclusions

Sustained attention on negatively-valenced stimuli emerges as a potential target for therapeutic intervention in PTSD designed to divert attention away from negatively-valenced stimuli and toward neutral ones.

Type
Original Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Posttraumatic stress disorder (PTSD) manifests as prolonged and maladaptive responding to traumatic events (American Psychiatric Association, 2013). Cognitive models implicate biased attentional processing of threat-related information in the disorder, suggesting that the attentional system of patients with PTSD may be distinctively sensitive to or biased in favor of such stimuli (Brewin & Holmes, Reference Brewin and Holmes2003; Buckley, Blanchard, & Neill, Reference Buckley, Blanchard and Neill2000; Chemtob, Roitblat, Hamada, Carlson, & Twentyman, Reference Chemtob, Roitblat, Hamada, Carlson and Twentyman1988; Ehlers & Clark, Reference Ehlers and Clark2000; Foa, Steketee, & Rothbaum, Reference Foa, Steketee and Rothbaum1989; Litz & Keane, Reference Litz and Keane1989). Eye-tracking methodology has been increasingly used to explore attention patterns in PTSD, with results consistently implicating increased sustained attention on threat in participants with PTSD, with little-to-no support emerging for enhanced threat detection or attentional avoidance (for a review, see Lazarov et al., Reference Lazarov, Suarez-Jimenez, Tamman, Falzon, Zhu, Edmondson and Neria2019; more recently, Mekawi et al., Reference Mekawi, Murphy, Munoz, Briscione, Tone, Norrholm and Powers2020; Powers et al., Reference Powers, Fani, Murphy, Briscione, Bradley, Tone and Jovanovic2019). Such findings point to sustained attention on threat as a potential target for cognitive bias modification interventions in PTSD (Gober, Lazarov, & Bar-Haim, Reference Gober, Lazarov and Bar-Haim2020; Lazarov et al., Reference Lazarov, Suarez-Jimenez, Tamman, Falzon, Zhu, Edmondson and Neria2019).

A more in-depth examination of findings from extant eye-tracking research (for a systematic review, see Lazarov et al., Reference Lazarov, Suarez-Jimenez, Tamman, Falzon, Zhu, Edmondson and Neria2019) shows that increased sustained attention on threat has been consistently shown when comparing PTSD participants with healthy participants with no trauma exposure (Armstrong, Bilsky, Zhao, & Olatunji, Reference Armstrong, Bilsky, Zhao and Olatunji2013; Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Matlow, Reference Matlow2013; Thomas, Goegan, Newman, Arndt, & Sears, Reference Thomas, Goegan, Newman, Arndt and Sears2013), with similar results emerging when comparing PTSD participants to trauma-exposed healthy participants (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Kimble, Fleming, Bandy, Kim, & Zambetti, Reference Kimble, Fleming, Bandy, Kim and Zambetti2010; Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Powers et al., Reference Powers, Fani, Murphy, Briscione, Bradley, Tone and Jovanovic2019). Comparing trauma-exposed healthy participants with healthy participants who did not experience a traumatic event, aiming to clarify the effects of trauma-exposure per-se on attention allocation, shows elevated threat-related sustained attention in the trauma-exposed group (Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Thomas et al., Reference Thomas, Goegan, Newman, Arndt and Sears2013; cf. see Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013). Taken together, it has been recently suggested that trauma exposure in itself may be sufficient to bias attention toward trauma-relevant stimuli, manifesting in sustained attention on threat cues, with PTSD symptomology further amplifying this bias (Lazarov et al., Reference Lazarov, Suarez-Jimenez, Tamman, Falzon, Zhu, Edmondson and Neria2019).

Despite these promising findings, extant eye-tracking research in PTSD carries some key limitations slowing the progress in converting the understandings of attention biases into novel intervention targets. First, all studies to date used small stimulus set-sizes. More complex, ecologically-valid visual displays are needed to enhance generalizability (Armstrong & Olatunji, Reference Armstrong and Olatunji2012; Richards, Benson, Donnelly, & Hadwin, Reference Richards, Benson, Donnelly and Hadwin2014). Second, most studies used small sample sizes, limiting power and generalizability. Third, the reliability of eye-tracking-derived indices in PTSD has yet to be examined, which is a vital step in increasing confidence in obtained findings (Lilienfeld & Strother, Reference Lilienfeld and Strother2020). Finally, only a few studies incorporated both trauma-exposed and non-trauma-exposed healthy participants as control participants within the same study to tease apart the effects of trauma exposure from those of clinical symptoms (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Thomas et al., Reference Thomas, Goegan, Newman, Arndt and Sears2013).

The current study aimed to address the above-outlined limitations by adapting a well-established free-viewing task assessing attention allocation (Lazarov, Abend, & Bar-Haim, Reference Lazarov, Abend and Bar-Haim2016; Lazarov, Ben-Zion, Shamai, Pine, & Bar-Haim, Reference Lazarov, Ben-Zion, Shamai, Pine and Bar-Haim2018) to PTSD, hoping to identify a relevant and reliable target for intervention. Hence, here, patients with PTSD, trauma-exposed healthy control (TEHC), and healthy control (HC) participants freely viewed visual displays comprised of 16 faces, half negatively-valenced and half neutral, while their gaze was continuously recorded. Negatively-valenced faces included angry, fearful, and sad expressions, three key emotions implicated in the clinical presentation of PTSD (American Psychiatric Association, 2013), and found to be PTSD-relevant in extant attentional research (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Badura-Brack et al., Reference Badura-Brack, Naim, Ryan, Levy, Abend, Khanna and Bar-Haim2015; Beevers, Lee, Wells, Ellis, & Telch, Reference Beevers, Lee, Wells, Ellis and Telch2011; Lee & Lee, Reference Lee and Lee2014; Mekawi et al., Reference Mekawi, Murphy, Munoz, Briscione, Tone, Norrholm and Powers2020; Powers et al., Reference Powers, Fani, Murphy, Briscione, Bradley, Tone and Jovanovic2019). Internal consistency and 1-week test-retest reliability were also evaluated, for the entire sample and within groups.

Based on extant research in PTSD described above, we predicted that patients with PTSD would dwell longer on negatively-valenced faces over neutral faces, relative to both HC and TEHC participants. In addition, based on prior studies comparing TEHC and HC participants, it was further hypothesized that TEHCs would dwell longer on negatively-valenced faces compared with HCs. As previous attentional studies in PTSD have utilized different negatively-valenced faces (Lazarov et al., Reference Lazarov, Suarez-Jimenez, Tamman, Falzon, Zhu, Edmondson and Neria2019), we also explored possible differences between groups with regard to the three chosen negatively-valenced emotions. Finally, in accord with customary practices in eye-tracking research (Armstrong & Olatunji, Reference Armstrong and Olatunji2012; Chen & Clarke, Reference Chen and Clarke2017; Suslow, Hußlack, Kersting, & Bodenschatz, Reference Suslow, Hußlack, Kersting and Bodenschatz2020), we analyzed first-fixation variables, namely, first-fixation location, latency, and dwell time.

Methods

Participants

Participants were recruited via online advertisement, local media, and community postings; 37 with clinically diagnosed PTSD, 34 TEHCs, and 30 HCs with no trauma exposure, matched on age, sex, and race. Demographic and psychopathological characteristics by group are presented in Table 1, and described more fully along with group differences analyses in the Supplementary Material. All participants in the two trauma-exposed groups met DSM-5 criterion A for a traumatic event of an interpersonal nature (Forbes et al., Reference Forbes, Lockwood, Phelps, Wade, Creamer, Bryant and O'Donnell2014; Kelley, Weathers, McDevitt-Murphy, Eakin, & Flood, Reference Kelley, Weathers, McDevitt-Murphy, Eakin and Flood2009; Kessler & Üstün, Reference Kessler and Üstün2004), determined using the Life Events Checklist for DSM-5 (LEC-5; Weathers et al. Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013b). We chose to include only participants who experienced an interpersonal traumatic event to maximize the relevance of facial stimuli as trauma-relevant cues. Indeed, prior research has shown the emotional effect of viewing negative facial expressions in PTSD (Armony, Corbo, Clement, & Brunei, Reference Armony, Corbo, Clement and Brunei2005; Rauch et al., Reference Rauch, Whalen, Shin, McInerney, Macklin, Lasko and Pitman2000), also more specifically in those with a history of an interpersonal trauma (Fonzo et al., Reference Fonzo, Simmons, Thorp, Norman, Paulus and Stein2010; Garrett et al., Reference Garrett, Carrion, Kletter, Karchemskiy, Weems and Reiss2012; Lee & Lee, Reference Lee and Lee2014). In addition to a primary diagnosis of PTSD, patients also scored ⩾25 on the Clinician-Administered PTSD Scale-5 (CAPS-5; Weathers et al. Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013a, Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013b). TEHCs had no current/past diagnosis of PTSD coupled with a CAPS-5 score <10. HCs had no current/past diagnosis of any psychiatric disorder. See Supplementary Material for detailed inclusion/exclusion criteria.

Table 1. Demographic and psychopathological characteristics by group

PTSD, posttraumatic stress disorder; TEHC, trauma-exposed healthy control; HC, healthy control; CAPS, Clinician-Administered PTSD Scale; HAM-D, Hamilton Rating Scale for Depression; HAM-A, Hamilton Anxiety Rating Scale.

Asterisks indicate significant differences between groups. Different superscripts indicate significant pair-wise differences between groups.

The study adhered with the ethical guidelines of the Declaration of Helsinki and was approved by the New York State Psychiatric Institute (NYSPI) Institutional Review Board. After receiving explanations about the study, participants provided written informed consent. Participants were compensated $70 for participation.

Measures

All participants were assessed for primary and co-morbid psychiatric diagnoses using the Structured Clinical Interview for DSM-5 (SCID-5; First, Williams, Karg, & Spitzer, Reference First, Williams, Karg and Spitzer2015), a well-validated interview for DSM-5 diagnoses, conducted by an independent clinical assessor, a Ph.D.-level psychologist trained to 85% reliability with a senior clinician on all used measures. All participants were also administered the clinician-rated Hamilton Rating Scale for Depression (HAM-D; Hamilton, Reference Hamilton1960) and Hamilton Anxiety Rating Scale (HAM-A; Hamilton, Reference Hamilton1959). Finally, PTSD and TEHC participants underwent a full assessment of trauma exposure using the LEC-5 (Weathers et al., Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013a, Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013b). CAPS-5 (Weathers et al., Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013a, Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013b) was administered to PTSD and TEHC participants to determine the severity of posttraumatic symptoms in reference to the traumatic event identified by each participant on the LEC-5 as bothering them the most. See Supplementary Material for a full description of used measures.

Free viewing eye-tracking task

Gaze patterns were assessed using an established eye-tracking task with acceptable psychometric properties in both depression and anxiety (Chong & Meyer, Reference Chong and Meyer2020; Klawohn et al., Reference Klawohn, Bruchnak, Burani, Meyer, Lazarov, Bar-Haim and Hajcak2020; Lazarov et al., Reference Lazarov, Abend and Bar-Haim2016; Lazarov et al., Reference Lazarov, Ben-Zion, Shamai, Pine and Bar-Haim2018; Lazarov, Pine, & Bar-Haim, Reference Lazarov, Pine and Bar-Haim2017) adapted for the current study. The task was designed and executed using the Experiment Builder software (version 2.1.140; SR Research Ltd., Mississauga, Ontario, Canada).

The free viewing eye-tracking task comprised of three separate blocks delivered in a counterbalanced manner across participants in each group, each focusing on a different negative emotion-neutral contrast with theoretical relevance for PTSD. One block consisted of angry and neutral facial expressions, one of fearful and neutral expressions, and one of sad and neutral expressions. For each block, color photographs of eight male and eight female actors, each contributing an emotional and a neutral facial expression (for a total of 32 pictures; 16 male and 16 female), were taken from the Karolinska Directed Emotional Faces database (KDEF; Lundqvist, Flykt, & Öhman, Reference Lundqvist, Flykt and Öhman1998), with each actor appearing in only one of the blocks. We selected faces in which teeth were not exposed, or were barely visible, to reduce the effects of lower-level factors on gaze patterns (Lazarov et al., Reference Lazarov, Abend and Bar-Haim2016). Each block consisted of 30 different 4-by-4 matrices, with each matrix consisting of eight negative emotional (angry, fearful, or sad) and eight neutral facial expressions. Each individual face extended 225-by-225 pixels, including a 10-pixel white margin frame, for an overall size of 900-by-900 pixels (see Fig. 1 for a matrix example of each block). Each single face appeared randomly at any position on the matrix while ensuring that: (a) each actor appeared only once in a matrix; (b) each matrix contained eight male and eight female faces; and (c) half the faces were emotional and half were neutral, a ratio that was also kept for the four inner faces of the matrix. Each single facial expression had the same appearance prevalence within the block, that is, each facial expression appeared exactly 15 times per block.

Fig. 1. An example of a single matrix for (a) the angry-neutral block; (b) the fear-neutral block; and (c) the sad-neutral block. In each block, the eight emotional faces comprise the angry/fearful/sad area of interest (AOI) and the eight neutral faces comprise the neutral AOI.

Each trial began with a centrally-presented fixation-cross necessitating a 1000 ms fixation for the next display to appear. Then the matrix appeared for 6000 ms, followed by an inter-trial-interval of 2000 ms. Participants were instructed to look freely at the matrix until it disappeared. A 2 min break was introduced between blocks to reduce fatigue. Each block was preceded by a five-point eye-tracking calibration followed by a five-point validation procedure. The task/block did not ensue unless a visual deviation below 0.5° was achieved for each point on both the X and Y axes.

Eye-tracking measures

Eye-tracking data were processed using EyeLink Data Viewer software (version 3.1.246; SR Research Ltd.). Fixations were defined as at least 100 ms of stable fixation within 1-degree visual angle. For each presented matrix, we defined two areas of interest (AOIs), one including the eight negatively-valenced faces (angry, fearful, or sad; the negative AOI) and one including the eight neutral faces (the neutral AOI). Total dwell time per AOI was calculated by averaging the total dwell time on each AOI across the 30 matrices of the block. First-fixation latency was calculated by averaging the latency to first fixations, in milliseconds, for each AOI. First-fixation location was measured by counting the number of times the first fixation was in each AOI. First-fixation dwell time was computed by averaging first-fixation duration, in milliseconds, for each AOI. Finally, for complementary correlational analyses (see Data analysis), we followed previous research (Lazarov et al., Reference Lazarov, Abend and Bar-Haim2016, Reference Lazarov, Pine and Bar-Haim2017, Reference Lazarov, Ben-Zion, Shamai, Pine and Bar-Haim2018) and quantified percent dwell time on the negatively-valenced AOI (DT%) in each block as the total dwell time on the negatively-valenced AOI out of the total dwell time on both AOIs.

Apparatus

Eye movements were recorded using a remote high-speed Eyelink 1000+ eye tracker (SR Research Ltd.), with a sampling rate of 500 Hz. Operating distance to the eye-tracking monitor was 60–65 cm. The stimuli were presented on a 24-inch monitor with a 1920 × 1080 pixel screen resolution.

Procedure

Participants were tested individually at the Anxiety Disorders Clinic, NYSPI. They were told that they are going to participate in an eye-tracking study examining gaze patterns. After providing informed consent, participants were seated in front of the eye-tracking monitor and told that during the experiment, they would be presented with different matrices of faces, appearing one after the other. They were also told that before the appearance of each matrix, a fixation cross will appear at the center of the screen, on which they should fixate to make the matrix itself appear. Participants were instructed to look freely at each matrix until it disappeared. Upon completion of the task, participants were scheduled to take part in a second session, held approximately 1 week later (M days = 7.35, s.d. = 4.44), which was identical to session 1 using new matrices from the same set of actors. Four PTSD participants and one TEHC failed to attend session 2.

Data analysis

Eye-tracking data

We powered our study to detect a group-by-AOI interaction using a two-tailed α = 0.05, with 0.85 power, and an effect size of η 2p = 0.12, an effect size estimate derived from previous studies using the same task and study design in other disorders (Lazarov et al., Reference Lazarov, Abend and Bar-Haim2016, Reference Lazarov, Ben-Zion, Shamai, Pine and Bar-Haim2018), as well as in a previous eye-tracking study in PTSD exploring similar groups (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013). This resulted in a required sample of 87 participants, for a minimum of 29 participants per group. We decided to recruit a minimum of 30 per group as a precaution. Power analysis was performed using G*Power 3.1.9.4 (Faul, Erdfelder, Lang, & Buchner, Reference Faul, Erdfelder, Lang and Buchner2007).

One-way analyses of variance (ANOVAs) compared between-group descriptive characteristics, including HAM-D and HAM-A scores, with χ2 tests used to compare groups on gender and ethnicity ratios. An independent sample t test was also used to compare the trauma-exposed groups on CAPS-5 scores. Follow-up analyses for significant one-way ANOVAs included independent sample t tests and χ2 tests for gender ratio and ethnicity.

We examined group differences on the eye-tracking measures by performing a three-by-two-by-three mixed-model ANOVAs with group (PTSD, TEHC, HC) as a between-subjects factor and AOI (threat, neutral) and negative emotion (anger, fear, sad) as within-subject factors. Because the three groups differed in the number of years of education, this variable was introduced as a covariate in all analyses. Complementary correlational analyses examined the possible association between scores on different psychopathology measures (i.e. CAPS-5, HAM-D, and HAM-A) and attention allocation, quantified as percent dwell time on the negatively-valenced AOI (DT%; see measures above). For CAPS-5 analyses, only PTSD and TEHC participants were included.

Reliability was assessed for three variants of the total dwell time measure: total dwell time on negatively-valenced faces, total dwell time on neutral faces, and percent dwell time on negatively-valenced faces (DT%). Internal consistency was examined for the entire sample and separately by group, using Cronbach's α while treating each trial (i.e. each matrix) as a single item. Test-retest reliability was computed using Pearson correlations.

Statistical analyses were conducted using SPSS (IBM; version 25.0) and were two-sided, using α of 0.05. Effect sizes are reported using η 2p values for ANOVAs and Cohen's d for mean comparisons. Bonferroni correction was applied to multiple comparisons.

Results

Sustained attention (total dwell time)

The omnibus ANOVA of group × block × AOI was not significant, F (2,97) = 0.41, p = 0.66. However, a significant group × AOI emerged, F (2,97) = 16.83, p < 0.001, η 2p = 0.26, indicating differential dwell time patterns of the three groups for the negatively-valenced and the neutral AOIs. We therefore collapsed across blocks for the remaining analyses by computing mean total dwell time for a negative-valence AOI (total dwell time on anger, fear, and sad faces) and a neutral AOI (mean total dwell time on neutral faces from all three blocks; see Fig. 2).

Fig. 2. Mean averaged total dwell time (in seconds) by area of interest (AOI) and group. Higher values indicate higher mean average dwell time. Error bars denote standard error of the mean. HC, healthy controls; PTSD, posttraumatic stress disorder; TEHC, trauma-exposed healthy control.

Follow-up analyses comparing the PTSD and HC groups indicated a significant group-by-AOI interaction, F (1,64) = 21.15, p < 0.001, η 2p = 0.25. Follow-up independent t tests per AOI revealed that the PTSD group (M = 2403 ms, s.d. = 399) spent significantly more time fixating on the negatively-valenced AOI compared with the HC group (M = 1961 ms, s.d. = 635), t(65) = 3.48, p = 0.002, Cohen's d = 0.83, and significantly less time fixating on the neutral AOI (M = 1952 ms, s.d. = 341) compared with the HC group (M = 2665 ms, s.d. = 819), t(65) = 4.81, p < 0.001, Cohen's d = 1.14. A significant group-by-AOI interaction also emerged when exploring the PTSD and TEHC groups, F (1,68) = 10.83, p = 0.002, η 2p = 0.14, with the PTSD group spending significantly more time fixating on the negatively-valenced AOI compared with the TEHC group (M = 2189, s.d. = 373), t(69) = 2.33, p = 0.04, Cohen's d = 0.55. No group differences were noted for the neutral AOI (TEHC; M = 2006, s.d. = 304), t(69) = 0.70, p = 0.49. Finally, comparing the TEHC and HC groups also yielded a significant group-by-AOI interaction, F (1,61) = 12.84, p = 0.002, η 2p = 0.17. Follow-up t tests revealed that the HC group spent significantly more time fixating on the neutral AOI compared with the TEHC group, t(62) = 4.36, p < 0.001, Cohen's d = 1.07, with no group differences for the negatively-valenced AOI, t(62) = 1.78, p = 0.16. Exploratory within-block analyses and results can be found in the online Supplementary Material and Fig. S1.

Examining within-group differences between the two AOIs using paired-samples t tests indicated a significant difference for the PTSD group, t(36) = 5.03, p < 0.001, Cohen's d = 1.21, and the TEHC group, t(33) = 3.87, p < 0.001, Cohen's d = 0.54, favoring the negatively-valenced AOI. Conversely, for the HC group, a significant difference also emerged but favoring the neutral AOI, t(29) = 2.69, p = 0.02, Cohen's d = 0.96.

Analyzing data from session 2 yielded a similar results pattern to that observed in session 1, revealing a significant group-by-AOI interaction, F (2,92) = 15.49, p < 0.001, η 2p = 0.25. Detailed follow-up analyses, corresponding to those reported for session 1, are reported in the Supplementary Material.

First-fixation measures

For first-fixation latency, a non-significant group × block × AOI interaction, F (2,97) = 1.09, p = 0.34, emerged, with no other significant findings. Similar null results were obtained for first-fixation location, F (2,97) = 1.05, p = 0.35. For first-fixation dwell time, while the omnibus group × block × AOI was not significant, F (2,97) = 0.71, p = 0.49, a significant group × AOI emerged, F (2,97) = 6.06, p = 0.009, η 2p = 0.11. Hence, we once again collapsed across blocks for the remaining of our analyses (see Fig. 3). Results of within-block exploratory analyses can be found in the Supplementary Material and Fig. S2.

Fig. 3. Averaged first-fixation dwell time (in milliseconds) by area of interest (AOI) and group. Higher values indicate higher average dwell time. Error bars denote standard error of the mean. HC, healthy controls; PTSD, posttraumatic stress disorder; TEHC, trauma-exposed healthy control.

For first-fixation dwell time, comparing the PTSD and HC groups indicated a significant group-by-AOI interaction, F (1,64) = 9.89, p = 0.009, η 2p = 0.13, which was also evident when comparing the TEHC and HC groups, F (1,61) = 5.02, p = 0.04, η 2p = 0.08. However, unlike total dwell time (see above), comparing the PTSD and TEHC groups did not reveal a significant group-by-AOI interaction, F (1,68) = 1.41, p = 0.24. Detailed follow-up analyses, including descriptive statistics, for first-fixation dwell time are reported in the online Supplementary Material.

Analyzing data from session 2 showed similar results, namely, a non-significant omnibus group × block × AOI interaction, F (2,92) = 0.55, p = 0.58, but a significant group × AOI interaction for first-fixation dwell time only, F (2,92) = 4.92, p = 0.03, η 2p = 0.10. Detailed follow-up analyses, corresponding to those reported for session 1, are described in the Supplementary Material.

Correlation analyses

Percentage of total dwell time spent on the negatively-valenced AOI (DT%) was positively correlated with CAPS-5 scores, r(71) = 0.29, p = 0.04, and with the HAM-D scores, r(101) = 0.34, p < 0.001, but not with HAM-A scores, r(101) = 0.17, p = 0.24. No correlations emerged for first-fixation mean dwell time. Results of within-block exploratory analyses are described in the Supplementary Material.

Internal consistency and test-retest reliability

Internal consistency and test-retest reliability for total dwell time on each AOI, and for DT%, were high for the full sample and within groups. See Table 2 for detailed results, including test-retest for first-fixation dwell time.

Table 2. Internal consistency and test-retest reliability

DT = dwell time; DT% = the percentage of total dwell time on negative-valenced faces out of total dwell time spent on both types of faces.

Discussion

The present study compared the gaze patterns of patients with clinically diagnosed PTSD, trauma-exposed healthy participants, and healthy participants with no trauma exposure when viewing different negatively-valenced and neutral cues. Our main finding differentiates groups' attention allocation patterns, as reflected in sustained attention, with PTSD participants found to dwell longer on negatively-valenced stimuli compared with both control groups. This increased dwell time on negatively-valenced stimuli in PTSD corroborates and extends prior eye-tracking studies in the field (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Kimble et al., Reference Kimble, Fleming, Bandy, Kim and Zambetti2010; Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Mekawi et al., Reference Mekawi, Murphy, Munoz, Briscione, Tone, Norrholm and Powers2020; Powers et al., Reference Powers, Fani, Murphy, Briscione, Bradley, Tone and Jovanovic2019), and has been previously linked to several possible theoretical explanations. First, increased dwell time may reflect an attentional component of trauma-related rumination – the repetitive and perseverative thinking about trauma-related issues (Echiverri, Jaeger, Chen, Moore, & Zoellner, Reference Echiverri, Jaeger, Chen, Moore and Zoellner2011; Ehring, Frank, & Ehlers, Reference Ehring, Frank and Ehlers2008; Michael, Halligan, Clark, & Ehlers, Reference Michael, Halligan, Clark and Ehlers2007). Second, it may be seen as the attentional manifestation of harm-preventing heightened monitoring related to hypervigilance symptoms (American Psychiatric Association, 2013; Kimble, Fleming, & Bennion, Reference Kimble, Fleming and Bennion2013). Alternatively, elevated dwelling on negatively-valenced stimuli may reflect deficient attention control (i.e. the capacity to execute voluntary and effortful goal-directed attentional deployment), which has been shown to moderate the association between posttraumatic symptoms and attention biases (Bardeen & Daniel, Reference Bardeen and Daniel2017; Bardeen & Orcutt, Reference Bardeen and Orcutt2011; Bardeen, Daniel, Gordon, Hinnant, & Weathers, Reference Bardeen, Daniel, Gordon, Hinnant and Weathers2020; Bardeen, Tull, Daniel, Evenden, & Stevens, Reference Bardeen, Tull, Daniel, Evenden and Stevens2016). Thus, reduced ability to disengage and shift attention away from negatively-valenced stimuli at will may reflect a particular case of reduced attention control in PTSD. While the present study did not assess attention control, future studies could explore this possibility as it pertains to the present task.

A closer examination of present results shows that both trauma-exposed groups (PTSD, TEHC) demonstrated an attention allocation pattern favoring negatively-valenced stimuli over neutral stimuli, with a greater bias noted in the PTSD group than in the TEHC group. Conversely, the HC group showed the opposite pattern, favoring neutral stimuli. Considered concurrently, these results suggest that traumatic exposure is sufficient to induce lasting alterations to one's attentional system such that negatively-valenced stimuli become prioritized over neutral stimuli (as reflected in the differences between the TEHC and HC groups), and that these alterations are even more pronounced in PTSD (as reflected in the differences between PTSD and TEHC participants). These results are in line with previous eye-tracking studies showing differences between HC and TEHC participants (Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Matlow, Reference Matlow2013; Thomas et al., Reference Thomas, Goegan, Newman, Arndt and Sears2013) and between PTSD and TEHC participants (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Kimble et al., Reference Kimble, Fleming, Bandy, Kim and Zambetti2010; Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Powers et al., Reference Powers, Fani, Murphy, Briscione, Bradley, Tone and Jovanovic2019) on sustained attention on threat. Moreover, the suggested effects of traumatic exposure on one's attentional system echo the results of numerous fear-conditioning studies showing that learned fear associations are sufficient to capture and hold attention even if one tries to resist, enhancing the sensory processing of fear-conditioned stimuli (Mulckhuyse, Crombez, & Van der Stigchel, Reference Mulckhuyse, Crombez and Van der Stigchel2013; Nissens, Failing, & Theeuwes, Reference Nissens, Failing and Theeuwes2017; Preciado, Munneke, & Theeuwes, Reference Preciado, Munneke and Theeuwes2017; Schmidt, Belopolsky, & Theeuwes, Reference Schmidt, Belopolsky and Theeuwes2015). Taken together, the present results suggest that increased sustained attention to negatively-valenced stimuli may be conceptualized as a consequence of exposure to a traumatic event, and as a correlate of PTSD symptomology. On a more speculatively note, present results may also implicate sustained attention as an etiological contributor to PTSD following trauma exposure. While the present study cannot fully differentiate these possibilities, future studies could build on current results and methodology to more clearly address them by, for example, employing longitudinal study designs using the present task (Beevers et al., Reference Beevers, Lee, Wells, Ellis and Telch2011; Wald et al., Reference Wald, Shechner, Bitton, Holoshitz, Charney, Muller and Bar-Haim2011, Reference Wald, Degnan, Gorodetsky, Charney, Fox, Fruchter and Bar-Haim2013).

Akin to previous eye-tracking studies in PTSD, no group differences emerged for first location or latency, lending no support for facilitated detection of negatively-valenced stimuli (for a review, see Lazarov et al., Reference Lazarov, Suarez-Jimenez, Tamman, Falzon, Zhu, Edmondson and Neria2019). Yet, the present study was the first to also examine first-fixation dwell time, reflecting difficulty in initial attention disengagement from negatively-valenced stimuli, once detected, with findings showing a group-by-AOI interaction similar to that observed for sustained attention. Thus, the observed initial difficulty to disengage attention from negatively-valenced stimuli may be viewed as a ‘gate-way’ leading to sustained attention on these stimuli. Interestingly, however, unlike total dwell time, here PTSD and TEHC participants did not differ. Considering both sets of results (i.e. total dwell time and first-fixation dwell time; Figs 2 and 3, respectively) gives rise to the tentative possibility that, from an attentional standpoint, the resilience characterizing TEHC participants might be related to what transpires after the first encounter with a negatively-valenced stimulus. Put differently, while both groups demonstrate the same biased attention allocation pattern once a negatively-valenced stimulus is detected (as compared with HC participants), as ‘contact’ persists, only TEHC participants manage to break away and ‘stay afloat’ attention-wise, while patients with PTSD become increasingly biased, eventually differing from TEHC participants on sustained attention.

The present study did not find differences between the three negatively-valenced emotional stimuli, suggesting a general bias toward negatively-valenced stimuli, with no specificity of discrete emotions. If one is to consider the three negative emotions (anger, fear, sadness) tested here as PTSD-relevant, this finding is not surprising as it replicates substantial prior research in PTSD (for a review, see Lazarov et al., Reference Lazarov, Suarez-Jimenez, Tamman, Falzon, Zhu, Edmondson and Neria2019; also Mekawi et al., Reference Mekawi, Murphy, Munoz, Briscione, Tone, Norrholm and Powers2020; Powers et al., Reference Powers, Fani, Murphy, Briscione, Bradley, Tone and Jovanovic2019). This is also in line with the phenomenology of PTSD per DSM-5 implicating all three emotions in the disorder (American Psychiatric Association, 2013). Conversely, if one is to regard fearful and/or sad faces as general negative stimuli, rather than trauma-specific ones, then lack of differences in sustained attention per emotion could reflect over-generalization from an attentional standpoint (Lee & Lee, Reference Lee and Lee2014). For fear faces, this conundrum is not a new one, as some researchers have considered fearful faces as trauma-relevant stimuli (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013), whereas others as general threat stimuli not specifically trauma-related (Lee & Lee, Reference Lee and Lee2014). While research examining the generalization of threat-related attentional bias is scarce, preliminary evidence does imply that it might be more prominent when comparing PTSD to HC participants than to TEHC individuals (Kimble et al., Reference Kimble, Fleming, Bandy, Kim and Zambetti2010; Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Thomas et al., Reference Thomas, Goegan, Newman, Arndt and Sears2013). Our exploratory within-block analyses (see Supplementary Material) supports the conceptualization of fear faces as general negative stimuli, as this was the only block on which PTSD and TEHC participants did not differ on sustained attention. Still, this should be taken with caution as these were exploratory post-hoc analyses.

The present study is first to examine the psychometric properties of the task used to explore attention patterns in PTSD. For our main outcome measure, sustained attention, results show acceptable test-retest reliability and internal consistency, across participants and within groups, which are echoed by the highly similar results emerging in session 2 (i.e. the re-test session). Current results are in line with research showing sound psychometric properties of eye-tracking attentional indices (In-Albon & Schneider, Reference In-Albon, Schneider, Hadwin and Field2010; Sears, Quigley, Fernandez, Newman, & Dobson, Reference Sears, Quigley, Fernandez, Newman and Dobson2019; Skinner et al., Reference Skinner, Hubscher, Moseley, Lee, Wand, Traeger and McAuley2017), especially for those computed over long presentation duration (i.e. sustained attention), and less so for early stage-indices reflecting vigilance (Skinner et al., Reference Skinner, Hubscher, Moseley, Lee, Wand, Traeger and McAuley2017; Waechter, Nelson, Wright, Hyatt, & Oakman, Reference Waechter, Nelson, Wright, Hyatt and Oakman2014; Wermes, Lincoln, & Helbig-Lang, Reference Wermes, Lincoln and Helbig-Lang2017). This latter proposition is resonated by the null findings of first-fixation latency and location, and by the reduced psychometric properties of first-fixation dwell time. Finally, the task's sound psychometrics replicate previous research employing different versions of the task in social anxiety disorder (Lazarov et al., Reference Lazarov, Abend and Bar-Haim2016), depression (Klawohn et al., Reference Klawohn, Bruchnak, Burani, Meyer, Lazarov, Bar-Haim and Hajcak2020; Lazarov et al., Reference Lazarov, Ben-Zion, Shamai, Pine and Bar-Haim2018), problematic drinking behavior (Soleymani, Ivanov, Mathot, & de Jong, Reference Soleymani, Ivanov, Mathot and de Jong2020), and pediatric anxiety (Abend et al., Reference Abend, Bajaj, Matsumoto, Yetter, Harrewijn, Cardinale and Pine2020; Chong & Meyer, Reference Chong and Meyer2020). Importantly, reported psychometrics are striking compared with reaction-time-based attention indices, which show poorer reliability (Rodebaugh et al., Reference Rodebaugh, Scullin, Langer, Dixon, Huppert, Bernstein and Lenze2016; Waechter et al., Reference Waechter, Nelson, Wright, Hyatt and Oakman2014).

The present study is not without limitations. First, we did not include positive-valenced emotional stimuli (e.g. happy faces), and hence cannot determine whether the observed enhanced sustained attention is specific to negatively-valenced emotions or alternatively, to all emotions (i.e. the emotionality hypothesis). While no previous eye-tracking study has supported the emotionality hypothesis in PTSD (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Bardeen et al., Reference Bardeen, Daniel, Gordon, Hinnant and Weathers2020; Lee & Lee, Reference Lee and Lee2012, Reference Lee and Lee2014; Thomas et al., Reference Thomas, Goegan, Newman, Arndt and Sears2013), future research may wish to also incorporate happy-neutral matrices. Second, the present study examined attention allocation patterns to negatively-valenced faces as done in prior eye-tracking research in PTSD (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Lee & Lee, Reference Lee and Lee2012; Mekawi et al., Reference Mekawi, Murphy, Munoz, Briscione, Tone, Norrholm and Powers2020; Powers et al., Reference Powers, Fani, Murphy, Briscione, Bradley, Tone and Jovanovic2019). However, other studies have used more trauma-specific stimuli such as pictures (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Bryant, Harvey, Gordon, & Barry, Reference Bryant, Harvey, Gordon and Barry1995; Felmingham, Rennie, Manor, & Bryant, Reference Felmingham, Rennie, Manor and Bryant2011; Kimble et al., Reference Kimble, Fleming, Bandy, Kim and Zambetti2010; Lee & Lee, Reference Lee and Lee2012; Matlow, Reference Matlow2013). Still, given that we only included participants for whom DSM-5 criterion A was of an interpersonal nature, we think that faces may be highly relevant stimuli (Armstrong et al., Reference Armstrong, Bilsky, Zhao and Olatunji2013; Fonzo et al., Reference Fonzo, Simmons, Thorp, Norman, Paulus and Stein2010; Garrett et al.,Reference Garrett, Carrion, Kletter, Karchemskiy, Weems and Reiss2012; Lee & Lee, Reference Lee and Lee2014). Third, as face stimuli were chosen from the KDEF database, only White actors were used in the task, which may have had different effects on White and Black participants (Dickter & Bartholow, Reference Dickter and Bartholow2007). Future research should rectify this shortcoming by using more racially diverse face stimuli. Fourth, the present study ascertained interpersonal trauma exposure using the LEC-5 and assessed PTSD symptoms related to the traumatic experience indicated by each participant as most distressing (Weathers et al., Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013a, Reference Weathers, Blake, Schnurr, Kaloupek, Marx and Keane2013b). However, we did not more fully code data regarding the number of traumatic experiences endorsed by each participant, either of an interpersonal nature or across other non-interpersonal traumatic events (e.g. natural disaster). As previous research on trauma exposure has shown that multiple exposure to traumatic events is associated with higher levels of symptoms and distress, also specifically for interpersonal trauma (Green et al., Reference Green, Goodman, Krupnick, Corcoran, Petty, Stockton and Stern2000), and in light of the emergent positive association between CAPS-5 scores and DT% in the present study, it is possible that current findings were affected by participants' number of traumatic experiences. Future research should address this interesting possibility by, for example, comparing the attention allocation of single-trauma v. multiple-trauma groups using the present task. Finally, due to safety concerns, severe depression and suicidality were exclusionary in the present study, potentially reducing the generalizability of findings, especially as co-morbid depression in PTSD is high (Brady, Killeen, Brewerton, & Lucerini, Reference Brady, Killeen, Brewerton and Lucerini2000). Future studies could apply less stringent inclusion/exclusion criteria.

Notwithstanding the above-mentioned limitations, current results show a clear and stable aberration in the attention allocation patterns of patients with PTSD when faced with negatively-valenced stimuli, implicating heightened sustained attention on such stimuli. Thus, sustained attention may serve as a potential target for intervention, possibly through the development of PTSD-adapted gaze-contingent treatments (Lazarov et al., Reference Lazarov, Pine and Bar-Haim2017; Shamai-Leshem, Lazarov, Pine, & Bar-Haim, Reference Shamai-Leshem, Lazarov, Pine and Bar-Haim2020). A randomized controlled trial in patients with PTSD could determine the therapeutic value of modifying one's attention away from negatively-valenced stimuli and toward neutral ones. This could be done using either a combination of all included negative emotions or by targeting a specific emotion such as anger.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291721000581

Financial support

This work was supported by the Israel Science Foundation, Grant number 374/20, Irving Institute for Clinical and Translational Research, Imaging Pilot Award, Grant number UR002611, and the National Institute of Mental Health T32-MH020004 (Amit Lazarov); the National Institute of Mental Health K01MH118428 (Benjamin Suarez-Jimenez). The funding agencies had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

References

Abend, R., Bajaj, M. A., Matsumoto, C., Yetter, M., Harrewijn, A., Cardinale, E. M., … Pine, D. S. (2020). Converging multi-modal evidence for implicit threat-related bias in pediatric anxiety disorders. Journal of Abnormal Child Psychology, 49, 227240. doi: 10.1007/s10802-020-00712-w.Google ScholarPubMed
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Publishing.Google Scholar
Armony, J. L., Corbo, V., Clement, M.-H., & Brunei, A. (2005). Amygdala response in patients with acute PTSD to masked and unmasked emotional facial expressions. The American Journal of Psychiatry, 162(10), 19611963. doi: 10.1176/appi.ajp.162.10.1961.CrossRefGoogle ScholarPubMed
Armstrong, T., Bilsky, S. A., Zhao, M., & Olatunji, B. O. (2013). Dwelling on potential threat cues: An eye movement marker for combat-related PTSD. Depression and Anxiety, 30(5), 497502. doi: 10.1002/da.22115.CrossRefGoogle ScholarPubMed
Armstrong, T., & Olatunji, B. O. (2012). Eye tracking of attention in the affective disorders: A meta-analytic review and synthesis. Clinical Psychology Review, 32(8), 704723. doi: 10.1016/j.cpr.2012.09.004.CrossRefGoogle ScholarPubMed
Badura-Brack, A. S., Naim, R., Ryan, T. J., Levy, O., Abend, R., Khanna, M. M., … Bar-Haim, Y. (2015). Effect of attention training on attention bias variability and PTSD symptoms: Randomized controlled trials in Israeli and U.S. combat veterans. American Journal Psychiatry, 172(12), 12331241. doi: 10.1176/appi.ajp.2015.14121578.CrossRefGoogle ScholarPubMed
Bardeen, J. R., & Daniel, T. A. (2017). A longitudinal examination of the role of attentional control in the relationship between posttraumatic stress and threat-related attentional bias: An eye-tracking study. Behaviour Research and Therapy, 99, 6777. doi: 10.1016/j.brat.2017.09.003.CrossRefGoogle ScholarPubMed
Bardeen, J. R., Daniel, T. A., Gordon, R. D., Hinnant, J. B., & Weathers, F. W. (2020). Individual differences in attentional control explain the differential expression of threat-related attentional bias among those with posttraumatic stress symptomatology and predict symptom maintenance up to one year later. Behaviour Research and Therapy, 133, 103709. doi: 10.1016/j.brat.2020.103709.CrossRefGoogle ScholarPubMed
Bardeen, J. R., & Orcutt, H. K. (2011). Attentional control as a moderator of the relationship between posttraumatic stress symptoms and attentional threat bias. Journal of Anxiety Disorders, 25(8), 10081018. doi: 10.1016/j.janxdis.2011.06.009.CrossRefGoogle ScholarPubMed
Bardeen, J. R., Tull, M. T., Daniel, T. A., Evenden, J., & Stevens, E. N. (2016). A preliminary investigation of the time course of attention bias variability in posttraumatic stress disorder: The moderating role of attentional control. Behaviour Change, 33(2), 94111. doi: 10.1017/bec.2016.5.CrossRefGoogle Scholar
Beevers, C. G., Lee, H. J., Wells, T. T., Ellis, A. J., & Telch, M. J. (2011). Association of predeployment gaze bias for emotion stimuli with later symptoms of PTSD and depression in soldiers deployed in Iraq. American Journal of Psychiatry, 168(7), 735741. doi: 10.1176/appi.ajp.2011.10091309.CrossRefGoogle ScholarPubMed
Brady, K. T., Killeen, T. K., Brewerton, T., & Lucerini, S. (2000). Comorbidity of psychiatric disorders and posttraumatic stress disorder. The Journal of Clinical Psychiatry, 61(Suppl 7), 2232.Google ScholarPubMed
Brewin, C. R., & Holmes, E. A. (2003). Psychological theories of posttraumatic stress disorder. Clinical Psychology Review, 23(3), 339376. doi: 10.1016/S0272-7358(03)00033-3.CrossRefGoogle ScholarPubMed
Bryant, R. A., Harvey, A. G., Gordon, E., & Barry, R. J. (1995). Eye movement and electrodermal responses to threat stimuli in post-traumatic stress disorder. International Journal of Psychophysiology, 20(3), 209213. doi: 10.1016/0167-8760(95)00036-4.CrossRefGoogle ScholarPubMed
Buckley, T. C., Blanchard, E. B., & Neill, W. T. (2000). Information processing and PTSD: A review of the empirical literature. Clinical Psychology Review, 20(8), 10411065. doi: 10.1016/S0272-7358(99)00030-6.CrossRefGoogle ScholarPubMed
Chemtob, C. M., Roitblat, H. L., Hamada, R. S., Carlson, J. G., & Twentyman, C. T. (1988). A cognitive action theory of post-traumatic stress disorder. Journal of Anxiety Disorders, 2(3), 253275. doi: 10.1016/0887-6185(88)90006-0.CrossRefGoogle Scholar
Chen, N. T., & Clarke, P. J. (2017). Gaze-based assessments of vigilance and avoidance in social anxiety: A review. Current Psychiatry Reports, 19(9), 59. doi: 10.1007/s11920-017-0808-4.CrossRefGoogle ScholarPubMed
Chong, L. J., & Meyer, A. (2020). Psychometric properties of threat-related attentional bias in young children using eye-tracking. Developmental Psychobiology, 00, 112. doi: 10.1002/dev.22053.Google Scholar
Dickter, C. L., & Bartholow, B. D. (2007). Racial ingroup and outgroup attention biases revealed by event-related brain potentials. Social Cognitive and Affective Neuroscience, 2(3), 189198. doi: 10.1093/scan/nsm012.CrossRefGoogle ScholarPubMed
Echiverri, A. M., Jaeger, J. J., Chen, J. A., Moore, S. A., & Zoellner, L. A. (2011). ‘Dwelling in the past’: The role of rumination in the treatment of posttraumatic stress disorder. Cognitive and Behavioral Practice, 18(3), 338349. doi: 10.1016/j.cbpra.2010.05.008.CrossRefGoogle ScholarPubMed
Ehlers, A., & Clark, D. M. (2000). A cognitive model of posttraumatic stress disorder. Behaviour Research and Therapy, 38(4), 319345. doi: 10.1016/S0005-7967(99)00123-0.CrossRefGoogle ScholarPubMed
Ehring, T., Frank, S., & Ehlers, A. (2008). The role of rumination and reduced concreteness in the maintenance of posttraumatic stress disorder and depression following trauma. Cognitive Therapy and Research, 32(4), 488506. doi: 10.1007/s10608-006-9089-7.CrossRefGoogle ScholarPubMed
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175191. doi: 10.3758/bf03193146.CrossRefGoogle ScholarPubMed
Felmingham, K. L., Rennie, C., Manor, B., & Bryant, R. A. (2011). Eye tracking and physiological reactivity to threatening stimuli in posttraumatic stress disorder. Journal of Anxiety Disorders, 25(5), 668673. doi: 10.1016/j.janxdis.2011.02.010.CrossRefGoogle ScholarPubMed
First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L.. (2015). Structured clinical interview for DSM-5 – research version (SCID-5 for DSM-5, research version; SCID-5-RV). Arlington, VA: American Psychiatric Association.Google Scholar
Foa, E. B., Steketee, G., & Rothbaum, B. O. (1989). Behavioral/cognitive conceptualizations of post-traumatic stress disorder. Behavior Therapy, 20(2), 155176. doi: 10.1016/S0005-7894(89)80067-X.CrossRefGoogle Scholar
Fonzo, G. A., Simmons, A. N., Thorp, S. R., Norman, S. B., Paulus, M. P., & Stein, M. B. (2010). Exaggerated and disconnected insular-amygdalar blood oxygenation level-dependent response to threat-related emotional faces in women with intimate-partner violence posttraumatic stress disorder. Biological Psychiatry, 68(5), 433441. doi: 10.1016/j.biopsych.2010.04.028.CrossRefGoogle ScholarPubMed
Forbes, D., Lockwood, E., Phelps, A., Wade, D., Creamer, M., Bryant, R. A., … O'Donnell, M. (2014). Trauma at the hands of another: Distinguishing PTSD patterns following intimate and nonintimate interpersonal and noninterpersonal trauma in a nationally representative sample. The Journal of Clinical Psychiatry, 75(2), 147153. doi: 10.4088/JCP.13m08374.CrossRefGoogle Scholar
Garrett, A. S., Carrion, V., Kletter, H., Karchemskiy, A., Weems, C. F., & Reiss, A. (2012). Brain activation to facial expressions in youth with PTSD symptoms. Depression and Anxiety, 29, 449459.CrossRefGoogle ScholarPubMed
Gober, C. D., Lazarov, A., & Bar-Haim, Y. (2020). From cognitive targets to symptom reduction: Overview of attention and interpretation bias modification research. Evidence-Based Mental Health. doi: 10.1136/ebmental-2020-300216.Google ScholarPubMed
Green, B. L., Goodman, L. A., Krupnick, J. L., Corcoran, C. B., Petty, R. M., Stockton, P., & Stern, N. M. (2000). Outcomes of single versus multiple trauma exposure in a screening sample. Journal of Traumatic Stress, 13(2), 271286. doi: 10.1023/A:1007758711939.CrossRefGoogle Scholar
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry, 23(1), 5662. doi: 10.1136/jnnp.23.1.56.CrossRefGoogle ScholarPubMed
Hamilton, M. A. X. (1959). The assessment of anxiety states by rating. British Journal of Medical Psychology, 32(1), 5055. doi: 10.1111/j.2044-8341.1959.tb00467.x.CrossRefGoogle ScholarPubMed
In-Albon, T., & Schneider, S. (2010). Using eye tracking methodology in children with anxiety disorders. In Hadwin, J. A. & Field, A. P. (Eds.), Information processing biases and anxiety: A developmental perspective (pp. 129149). Chichester, UK: John Wiley & Sons Ltd.CrossRefGoogle Scholar
Kelley, L. P., Weathers, F. W., McDevitt-Murphy, M. E., Eakin, D. E., & Flood, A. M. (2009). A comparison of PTSD symptom patterns in three types of civilian trauma. Journal of Traumatic Stress, 22(3), 227235. doi: 10.1002/jts.20406.CrossRefGoogle ScholarPubMed
Kessler, R. C., & Üstün, T. B. (2004). The World Mental Health (WMH) Survey Initiative version of the j (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research, 13(2), 93121. doi: 10.1002/mpr.168.CrossRefGoogle Scholar
Kimble, M. O., Fleming, K., Bandy, C., Kim, J., & Zambetti, A. (2010). Eye tracking and visual attention to threating stimuli in veterans of the Iraq War. Journal of Anxiety Disorders, 24(3), 293299. doi: 10.1016/j.janxdis.2009.12.006.CrossRefGoogle ScholarPubMed
Kimble, M. O., Fleming, K., & Bennion, K. A. (2013). Contributors to hypervigilance in a military and civilian sample. Journal of Interpersonal Violence, 28(8), 16721692. doi: 10.1177/0886260512468319.CrossRefGoogle Scholar
Klawohn, J., Bruchnak, A., Burani, K., Meyer, A., Lazarov, A., Bar-Haim, Y., & Hajcak, G. (2020). Aberrant attentional bias to sad faces in depression and the role of stressful life events: Evidence from an eye-tracking paradigm. Behaviour Research and Therapy, 135, 103762. doi: 10.1016/j.brat.2020.103762.CrossRefGoogle ScholarPubMed
Lazarov, A., Abend, R., & Bar-Haim, Y. (2016). Social anxiety is related to increased dwell time on socially threatening faces. Journal of Affective Disorders, 193, 282288. doi: 10.1016/j.jad.2016.01.007.CrossRefGoogle ScholarPubMed
Lazarov, A., Ben-Zion, Z., Shamai, D., Pine, D. S., & Bar-Haim, Y. (2018). Free viewing of sad and happy faces in depression: A potential target for attention bias modification. Journal of Affective Disorders, 238, 94100. doi: 10.1016/j.jad.2018.05.047.CrossRefGoogle ScholarPubMed
Lazarov, A., Pine, D. S., & Bar-Haim, Y. (2017). Gaze-contingent music reward therapy for social anxiety disorder: A randomized controlled trial. American Journal of Psychiatry, 174(7), 649656. doi: 10.1176/appi.ajp.2016.16080894.CrossRefGoogle ScholarPubMed
Lazarov, A., Suarez-Jimenez, B., Tamman, A., Falzon, L., Zhu, X., Edmondson, D. E., & Neria, Y. (2019). Attention to threat in posttraumatic stress disorder as indexed by eye-tracking indices: A systematic review. Psychological Medicine, 49(5), 705726. doi: 10.1017/S0033291718002313.CrossRefGoogle ScholarPubMed
Lee, J. H., & Lee, J. H. (2012). Attentional bias to violent images in survivors of dating violence. Cognition and Emotion, 26(6), 11241133. doi: 10.1080/02699931.2011.638906.CrossRefGoogle ScholarPubMed
Lee, J. H., & Lee, J. H. (2014). Attentional bias towards emotional facial expressions in survivors of dating violence. Cognition and Emotion, 28(6), 11271136. doi: 10.1080/02699931.2013.867834.CrossRefGoogle ScholarPubMed
Lilienfeld, S. O., & Strother, A. N. (2020). Psychological measurement and the replication crisis: Four sacred cows. Canadian Psychology/Psychologie Canadienne, 61(4), 281288. doi: 10.1037/cap0000236.CrossRefGoogle Scholar
Litz, B. T., & Keane, T. M. (1989). Information processing in anxiety disorders: Application to the understanding of post-traumatic stress disorder. Clinical Psychology Review, 9(2), 243257. doi: 10.1016/0272-7358(89)90030-5.CrossRefGoogle Scholar
Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska directed emotional faces (KDEF). CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet, 91(630), 2–2.Google Scholar
Matlow, R. B. (2013). Attentional processes associated with victimization history and posttraumatic symptomatology in women exposed to intimate partner violence. Doctoral dissertation, University of Denver.Google Scholar
Mekawi, Y., Murphy, L., Munoz, A., Briscione, M., Tone, E. B., Norrholm, S. D., … Powers, A. (2020). The role of negative affect in the association between attention bias to threat and posttraumatic stress: An eye-tracking study. Psychiatry Research, 284, 112674. doi: 10.1016/j.psychres.2019.112674.CrossRefGoogle ScholarPubMed
Michael, T., Halligan, S. L., Clark, D. M., & Ehlers, A. (2007). Rumination in posttraumatic stress disorder. Depression and Anxiety, 24(5), 307317. doi: 10.1002/da.20228.CrossRefGoogle ScholarPubMed
Mulckhuyse, M., Crombez, G., & Van der Stigchel, S. (2013). Conditioned fear modulates visual selection. Emotion (Washington, D.C.), 13(3), 529536. doi: 10.1037/a0031076.CrossRefGoogle ScholarPubMed
Nissens, T., Failing, M., & Theeuwes, J. (2017). People look at the object they fear: Oculomotor capture by stimuli that signal threat. Cognition & Emotion, 31(8), 17071714. doi: 10.1080/02699931.2016.1248905.CrossRefGoogle Scholar
Powers, A., Fani, N., Murphy, L., Briscione, M., Bradley, B., Tone, E. B., … Jovanovic, T. (2019). Attention bias toward threatening faces in women with PTSD: Eye tracking correlates by symptom cluster. European Journal of Psychotraumatology, 10, 1. doi: 10.1080/20008198.2019.1568133.CrossRefGoogle ScholarPubMed
Preciado, D., Munneke, J., & Theeuwes, J. (2017). Was that a threat? Attentional biases by signals of threat. Emotion (Washington, D.C.), 17(3), 478486. doi: 10.1037/emo0000246.CrossRefGoogle ScholarPubMed
Rauch, S. L., Whalen, P. J., Shin, L. M., McInerney, S. C., Macklin, M. L., Lasko, N. B., … Pitman, R. K. (2000). Exaggerated amygdala response to masked facial stimuli in posttraumatic stress disorder: A functional MRI study. Biological Psychiatry, 47(9), 769776. doi: 10.1016/S0006-3223(00)00828-3.CrossRefGoogle ScholarPubMed
Richards, H. J., Benson, V., Donnelly, N., & Hadwin, J. A. (2014). Exploring the function of selective attention and hypervigilance for threat in anxiety. Clinical Psychology Review, 34(1), 113. doi: 10.1016/j.cpr.2013.10.006.CrossRefGoogle ScholarPubMed
Rodebaugh, T. L., Scullin, R. B., Langer, J. K., Dixon, D. J., Huppert, J. D., Bernstein, A., … Lenze, E. J. (2016). Unreliability as a threat to understanding psychopathology: The cautionary tale of attentional bias. Journal of Abnormal Psychology, 125(6), 840851. doi: 10.1037/abn0000184.CrossRefGoogle ScholarPubMed
Schmidt, L. J., Belopolsky, A. V., & Theeuwes, J. (2015). Attentional capture by signals of threat. Cognition and Emotion, 29(4), 687694. doi: 10.1080/02699931.2014.924484.CrossRefGoogle ScholarPubMed
Sears, C., Quigley, L., Fernandez, A., Newman, K., & Dobson, K. (2019). The reliability of attentional biases for emotional images measured using a free-viewing eye-tracking paradigm. Behavior Research Methods, 51(6), 27482760. doi: 10.3758/s13428-018-1147-z.CrossRefGoogle ScholarPubMed
Shamai-Leshem, D., Lazarov, A., Pine, D. S., & Bar-Haim, Y. (2020). A randomized controlled trial of gaze-contingent music reward therapy for major depressive disorder. Depression and Anxiety, 38, 134145. doi:10.1002/da.23089.CrossRefGoogle ScholarPubMed
Skinner, I. W., Hubscher, M., Moseley, G. L., Lee, H., Wand, B. M., Traeger, A. C., … McAuley, J. H. (2017). The reliability of eyetracking to assess attentional bias to threatening words in healthy individuals. Behavior Research Methods, 50(5), 17781792. doi: 10.3758/s13428-017-0946-y.CrossRefGoogle Scholar
Soleymani, A., Ivanov, Y., Mathot, S., & de Jong, P. J. (2020). Free-viewing multi-stimulus eye tracking task to index attention bias for alcohol versus soda cues: Satisfactory reliability and criterion validity. Addictive Behaviors, 100, 106117. doi: 10.1016/j.addbeh.2019.106117.CrossRefGoogle ScholarPubMed
Suslow, T., Hußlack, A., Kersting, A., & Bodenschatz, C. M. (2020). Attentional biases to emotional information in clinical depression: A systematic and meta-analytic review of eye tracking findings. Journal of Affective Disorders, 274, 632642. doi: 10.1016/j.jad.2020.05.140.CrossRefGoogle ScholarPubMed
Thomas, C. L., Goegan, L. D., Newman, K. R., Arndt, J. E., & Sears, C. R. (2013). Attention to threat images in individuals with clinical and subthreshold symptoms of post-traumatic stress disorder. Journal of Anxiety Disorders, 27(5), 447455. doi: 10.1016/j.janxdis.2013.05.005.CrossRefGoogle ScholarPubMed
Waechter, S., Nelson, A. L., Wright, C., Hyatt, A., & Oakman, J. (2014). Measuring attentional bias to threat: Reliability of dot probe and eye movement indices. Cognitive Therapy and Research, 38(3), 313333. doi: 10.1007/s10608-013-9588-2.CrossRefGoogle Scholar
Wald, I., Degnan, K. A., Gorodetsky, E., Charney, D. S., Fox, N. A., Fruchter, E., … Bar-Haim, Y. (2013). Attention to threats and combat-related posttraumatic stress symptoms: Prospective associations and moderation by the serotonin transporter gene. JAMA Psychiatry, 70(4), 401409. doi: 10.1001/2013.jamapsychiatry.188.CrossRefGoogle ScholarPubMed
Wald, I., Shechner, T., Bitton, S., Holoshitz, Y., Charney, D. S., Muller, D., … Bar-Haim, Y. (2011). Attention bias away from threat during life threatening danger predicts PTSD symptoms at one-year follow-up. Depression and Anxiety, 28(5), 406411. doi: 10.1002/da.20808.CrossRefGoogle ScholarPubMed
Weathers, F. W., Blake, D. D., Schnurr, P. P., Kaloupek, D. G., Marx, B. P., & Keane, T. M. (2013a). The clinician administered PTSD scale for DSM-5 (CAPS-5) [Assessment]. Retrieved from http://www.ptsd.va.gov.Google Scholar
Weathers, F. W., Blake, D. D., Schnurr, P. P., Kaloupek, D. G., Marx, B. P., & Keane, T. M. (2013b). The Life Events Checklist for DSM-5 (LEC-5). Retrieved from http://www.ptsd.va.gov.Google Scholar
Wermes, R., Lincoln, T. M., & Helbig-Lang, S. (2017). How well can we measure visual attention? Psychometric properties of manual response times and first fixation latencies in a visual search paradigm. Cognitive Therapy and Research, 41(4), 588599. doi: 10.1007/s10608-016-9830-9.CrossRefGoogle Scholar
Figure 0

Table 1. Demographic and psychopathological characteristics by group

Figure 1

Fig. 1. An example of a single matrix for (a) the angry-neutral block; (b) the fear-neutral block; and (c) the sad-neutral block. In each block, the eight emotional faces comprise the angry/fearful/sad area of interest (AOI) and the eight neutral faces comprise the neutral AOI.

Figure 2

Fig. 2. Mean averaged total dwell time (in seconds) by area of interest (AOI) and group. Higher values indicate higher mean average dwell time. Error bars denote standard error of the mean. HC, healthy controls; PTSD, posttraumatic stress disorder; TEHC, trauma-exposed healthy control.

Figure 3

Fig. 3. Averaged first-fixation dwell time (in milliseconds) by area of interest (AOI) and group. Higher values indicate higher average dwell time. Error bars denote standard error of the mean. HC, healthy controls; PTSD, posttraumatic stress disorder; TEHC, trauma-exposed healthy control.

Figure 4

Table 2. Internal consistency and test-retest reliability

Supplementary material: File

Lazarov et al. supplementary material

Lazarov et al. supplementary material

Download Lazarov et al. supplementary material(File)
File 34.8 KB
Supplementary material: File

Lazarov et al. supplementary material

Figures S1-S2

Download Lazarov et al. supplementary material(File)
File 308.3 KB