A hierarchy of visual processing deficits in body dysmorphic disorder: a conceptual review and empirical investigation

ABSTRACT

Objective

Abnormal visual processing has been proposed as a mechanism underlying excessive focus on minor appearance flaws in body dysmorphic disorder (BDD). Existing BDD research has not differentiated the various stages of face processing (featural, first-order configural, holistic and second-order configural) that are required for higher-order processes such as emotion recognition. This study investigated a hierarchical visual processing model to examine the nature of abnormalities in face processing in BDD.

Method

Thirty BDD participants and 27 healthy controls completed the Navon task, a featural and configural face processing task and a facial emotion labelling task.

Results

BDD participants performed similarly to controls when processing global and local non-face stimuli on the Navon task, when detecting subtle changes in the features and spacing of a target face, and when labelling emotional faces. However, BDD participants displayed poorer performance when viewing inverted faces, indicating difficulties in configural processing.

Conclusions

The findings only partially support prior work. However, synthesis of results with previous findings indicates that heterogenous task methodologies may contribute to inconsistent findings. Recommendations are provided regarding the task parameters that appear most sensitive to abnormalities in BDD.

KEYWORDS:

• Body dysmorphic disorder
• visual processing
• cognitive psychology
• emotion processing
• visual perception

Body dysmorphic disorder (BDD) is characterised by preoccupation with a perceived flaw/s in physical appearance (American Psychiatric Association, 2013). Distortions in visual perception may underlie excessive focus on minor appearance flaws in BDD (e.g. Beilharz et al., 2017; Li et al., 2013), with a bias towards detailed visual processing, over holistic or global processing (Feusner, Moody, et al., 2010; Feusner et al., 2007; Kerwin et al., 2014; Li et al., 2013). Individuals with BDD describe heightened focus on disliked physical features when looking in the mirror or mentally picturing themselves (Veale, 2004; Veale & Riley, 2001); and eye-tracking studies find some BDD participants focus excessively on “problem areas’ in visual scanning (Greenberg et al., 2014; Toh et al., 2015, 2017). Further, neurobiological models of BDD propose an imbalance between first and second-order visual processing networks could underlie overreliance on detailed rather than holistic processing (Grace et al., 2017).

Whereas behavioural studies in BDD have investigated local and global processing in relation to faces specifically (e.g. Feusner et al., 2007; Jefferies et al., 2012; Monzani et al., 2013), this work has arguably not considered the multidimensional nature of human face processing (Maurer et al., 2002; Zhen et al., 2013). Research in healthy individuals has identified consequential steps in efficient processing of human faces (Maurer et al., 2002): featural processing (identifying local facial information, e.g. eyes or nose, in a piecemeal manner; Duchaine & Weidenfeld, 2003); first-order configural processing (identifying typical, shared facial configuration of two eyes situated earlier nose and mouth, hence distinguishing faces from other objects); holistic processing (combining individual features into a whole); and second-order configural processing (detecting subtle variation in spatial distances between features; (Maurer et al., 2002; Taubert et al., 2011). These processes facilitate higher-order facial perception, including accurate identification of faces and emotional expressions (Zhen et al., 2013); see Figure 1).

Figure 1. Proposed hierarchy of the stages of visual processing of human faces.

Notes: Holistic and first-order configural processes are often challenging to disentangle in existing cognitive tasks; as such, they are considered as separate, but parallel process in the current study.

Intact configural processing is crucial for higher-order visual processes, such as facial identification. This is demonstrated through the face inversion effect, whereby healthy individuals show reduced accuracy and longer reaction times (RT) when processing inverted rather than upright faces (Farah et al., 1995). Face inversion disrupts first-order configural information, leading to piecemeal processing of individual features, rather than more efficient holistic processing (Freire et al., 2000).¹ Whereas inversion can also disrupt the ability to discern second-order configural information from the face, this has not been found to contribute as strongly to the inversion effect as first-order configural information (Civile et al., 2016)

Face inversion studies in BDD have yielded inconsistent results, with some reporting reduced inversion effects relative to controls (Feusner, Moller, et al., 2010; Toh et al., 2017) and others demonstrating no differences (Monzani et al., 2013). Feusner, Moller and colleagues (2010) found reduced inversion effects in BDD participants on long stimulus duration trials (5000 ms), but similar performance to controls on short trials (500 ms). As such, they proposed that individuals with BDD prioritise detailed processing with longer durations, but rely on configural processing when there is insufficient time to process specific details (Feusner, Moller et al., 2010).

Second-order configural processing has been interrogated by manipulating spatial frequency of facial stimuli which alters the level of available visual detail. When processing faces at low (global/configural information), normal (configural and featural) and high (local/featural information) spatial frequencies, BDD participants and healthy controls (HC) show no differences in response accuracy or RTs (Feusner et al., 2007; Li, Lai, Bohon, et al., 2015; Li, Lai, Loo, et al., 2015; Moody et al., 2015). Similarly, when discriminating between pairs of faces digitally manipulated to display either featural or configural differences, BDD participants exhibit similar accuracy and RTs to HCs (Monzani et al., 2013). These findings suggest no second-order configural processing deficits in BDD.

Reduced accuracy and/or slower RTs in BDD participants on higher-order emotion labelling tasks has been more consistently demonstrated (Buhlmann et al., 2006; Buhlmann et al., 2004; Feusner, Bystritsky et al., 2010; Grace et al., 2019), with specific impairments for neutral or negative emotions (Buhlmann et al., 2011; Grace et al., 2019; Jefferies et al., 2012). Others have found BDD participants interpret negative emotions (e.g. disgust and anger) in neutral faces (Buhlmann et al., 2006, 2011), or mistake other emotions as anger (Toh et al., 2015). Conversely, one study found BDD and HCs did not differ in emotion recognition performances (Rossell et al., 2014). Discrepancies across studies may be explained by sampling (e.g. medication status, comorbidities, BDD severity) or methodological differences (e.g. task design). Additionally, it remains unclear whether emotion processing alterations in BDD are driven by top-down changes (e.g. attentional biases, emotional dysregulation), or by alterations in lower-level visual processes disrupting efficient facial emotion processing.

Research aims

We aimed to clarify facial processing aberrations in BDD, employing a hierarchical model (Figure 1). We used tasks which allowed distinct analysis of featural, first-order/holistic, second-order configural face processing, and higher-order facial emotion labelling, to investigate visual processing in BDD compared to healthy participants. Whilst the primary goal was to explore distorted face processing, we included the Navon task (which assesses global versus local processing of letters; Navon, 1977) to determine whether visual processing aberrations extend to simple, non-face stimuli, which would implicate lower-level processes on the hierarchy. Our specific research questions were:

1. Are there behavioural differences between BDD and HC groups when processing local and global features of nonface stimuli?

2. Do BDD participants demonstrate different face inversion effects to HCs (suggestive of disrupted holistic and/or first-order configural processing)?

3. How do BDD participants perform when detecting subtle configural and featural changes in upright faces (i.e. featural and second-order configural processing when inversion is not considered)?

4. How do BDD participants perform when identifying emotional faces?

5. Are emotion perception deficits related to lower-level (i.e. featural and configural) face processing abnormalities in BDD?

To contextualise the findings, we provide a conceptual review of the BDD visual face processing literature in our discussion. Here, we aimed to identify what task parameters are most sensitive to abnormalities in each of the aforementioned levels of processing in BDD (Research Question 6).

Method

The study was approved by the Alfred Hospital Ethics Committee, Australian Catholic University and Swinburne University of Technology Human Research Ethics Committees and abided by the Declaration of Helsinki. All participants provided written informed consent.

Participants

Thirty adults (18 years and older) with BDD and 27 HCs were recruited through community and outpatient psychiatric services and public advertisements. BDD diagnosis was confirmed using the BDD Diagnostic Module (Phillips, 2005) for the Diagnostic and Statistical Manual of Mental Disorders – Fifth Edition (DSM-V; American Psychiatric Association, 2013). Thirteen BDD (43.3%) participants were taking stabilised psychiatric medications. Psychiatric comorbidities in the BDD group were assessed using the Mini International Neuropsychiatric Interview 7.0.2 (MINI 7.0.2; Sheehan, 2016; see Supplementary Table 1). The MINI was used to confirm the absence of psychiatric disorders in the HC group. All participants were required to speak fluent English, have no history of brain injury or neurological disorders, or uncorrected visual or hearing impairments. They were also required to have an estimated IQ earlier 70 based on the Wechsler Test of Adult Reading, to facilitate meaningful participation and informed consent (Wechsler, 2001). BDD participants were administered the Yale-Brown Obsessive-Compulsive Scale – Modified for BDD (BDD-YBOCS; Phillips et al., 1997) to assess symptom severity, and the Brown Assessment of Beliefs Scale (Eisen et al., 1998) to measure insight.

Procedure and materials

All participants completed three visual processing tasks, presented on a laptop using Presentation® software (Version 18.0, Neurobehavioural Systems, Inc., Berkeley, CA).

Local and global non-face processing

Local and global processing of non-face stimuli was assessed using the Navon task (Navon, 1977) to address Research Question 1. Participants were presented with a global letter (“H” or “S”) formed by a configuration of smaller, local letters. Participants completed two counterbalanced blocks of 95 trials each, where they were instructed to identify either the local or global letter by pressing the H or S key on the keyboard. Trials began with a 500 ms fixation cross, followed by the stimulus, which remained on-screen until a response was made. Accuracy and RTs were recorded. Healthy participants generally demonstrate a global precedence effect (i.e. faster RTs and higher accuracy on global rather than local trials) on this task (Navon, 1977).

Featural and configural face processing

A facial discrimination task (Joshua et al., 2016) was used to investigate featural, first-order, and second-order configural processing (Research Questions 2 and 3). A neutral female face from the Pictures of Facial Affect Series (Ekman & Friesen, 1976) was manipulated to display either configural or featural changes (detailed description in Joshua et al., 2016). Featural manipulations involved replacing the eyes, nose, or mouth on the template face with features from other faces, whereas configural manipulations involved adjusting the spacing between the eyes, nose, and mouth (Figure 2). Four configural and four featural face manipulations were created. Pairs of face stimuli were presented on-screen for 5000 ms, followed by a 300 ms fixation cross. Participants indicated via two-button press whether the faces were the “same” or “different”. RTs and accuracy rates were recorded. The task was presented in two counterbalanced blocks (configural, featural sets; 160 trials each). To investigate inversion effects, each pair of faces (80 different pairs) was presented in both upright and inverted orientation, randomised within each block. Inversion effects were calculated by subtracting accuracy and RTs on inverted trials from upright trials.

Figure 2. Examples of stimuli from the featural and configural face processing task.

Notes: Pictures (a) and (b) demonstrate featural differences between the two faces, whereas pictures (c) and (d) depict configural adjustments in the spacing of the face.

Facial emotion labelling

The facial emotion labelling task (Van Rheenen & Rossell, 2014) was used to assess Research Questions 4 and 5. It was designed to assess participants’ ability to identify emotional expressions as portrayed by five male and five female greyscale models from the Ekman and Friesen (1976) series. Each model portrayed six basic expressions: happiness, sadness, anger, fear, disgust and neutrality. Individual face stimuli were presented on-screen for 2000ms (1500 ms interstimulus intervals). Participants identified the emotional expression displayed on the target face by pressing one of six corresponding keys. Stimuli were randomised across 160 trials. Accuracy rates (i.e. percentage of correctly identified emotions), RTs and mistakes (i.e. mistaking one emotional expression as another) were recorded.

Statistical analyses

Demographic and clinical group differences were assessed via t-tests or chi-squared analyses. A series of mixed-design analyses of variance (ANOVA) were computed to assess whether the BDD and HC groups differed on visual tasks. Significant interaction effects were followed up with planned post-hoc tests, with Bonferroni corrections for multiple comparisons. To explore non-face visual processing (Research Question 1), two mixed-design ANOVAs (group x trial type) were computed to assess group differences in RT and accuracy on global and local trials of the Navon task. To determine whether first-order/holistic configural processing differed between groups (Research Question 2), inversion effects were calculated from the facial discrimination task, providing accuracy and RT scores for four inversion effect conditions (configural same, configural different, featural same and featural different). Inversion effect scores were entered into a three-way mixed-design ANOVA (group x task type [configural or featural] x face pair [same or different]). To assess featural and second-order configural processing between groups (Research Question 3), RTs and accuracy rates from upright face trials only were entered into mixed-design ANOVAs (group x task type [configural or featural] x face pair [same or different]). To address the Research Question 4, group differences in RTs, accuracy rates and mistakes on the facial emotion labelling task were assessed via two-way mixed-design ANOVAs (group x emotion). Tasks that produced significant group differences were then correlated with one another to identify shared mechanisms (Research Question 5).

Results

There were no significant differences between the BDD and HC groups in age, IQ, educational attainment, or occupational status (Table 1). Performance on visual processing tasks did not differ by medication status in the BDD group (Supplementary Table 2).

Table 1. Demographic and clinical features of the BDD and HC groups.

Variable	Controls (n = 27)		BDD (n = 30)
	M	SD	M	SD	Statistic	p
Age	28.07	6.86	29.67	11.37	t(55) = −.63	.53
Estimated IQ	105.41	8.07	107.50	9.26	t(55) = −.91	.37
BDD-YBOCS	–	–	29.23	6.17
Age of illness onset (years)	–	–	16.68	4.67
Onset to diagnosis (years)	–	–	6.23	7.16
Illness duration (years)	–	–	12.98	9.88
Insight (BABS)	–	–	14.93	5.99
	Controls (n = 27)		BDD (n = 30)
Variable	n	%	n	%	Statistic	p
Sex (Females)	12	44.4%	19	63.3%	χ^2(1) = 2.04	.15
Highest educational attainment					χ^2(6) = 12.03	.06
Secondary school	2	7.4%	11	36.7%
Trade qualification	0	–	2	6.7%
TAFE certificate or diploma	5	18.5%	5	16.7%
Undergraduate degree	13	48.1%	6	20.0%
Masters, PhD, other postgraduate	7	25.9%	5	16.7%
Other	0	–	1	3.3%
Occupational status					χ^2(3) = 2.42	.49
Full time employment/study	13	48.1%	11	36.7%
Part-time employment/study	7	25.9%	4	13.3%
Home/carer duties, or volunteering	0	–	2	6.7%
Unemployed or retired	4	14.8%	5	16.7%

Notes: BDD-YBOCS = Yale Brown Obsessive Compulsive Scale Modified for Body Dysmorphic Disorder. Scores presented are calculated from the first 10 BDD-YBOCS items (ranging from 0 to 40). BABS = Brown Assessment of Beliefs Scale (scores range from 0 to 24).

Global and local non-face processing: the Navon task

One outlier in the HC group was removed from Navon task analyses due to extended RTs. RTs were significantly faster on global (rather than local) trials for all participants, F(1, 54) = 47.39, p < .001, η²_p = .47. The BDD group responded more slowly overall (to both global and local trials) than HCs, F (1, 54) = 4.51, p = .04, η²_p = .08. There was no significant group-by-trial interaction effect for RT, F (1, 54) = 1.12, p = .30, η²_p = .02, and no significant main effects or interactions in accuracy rates.

First-order and holistic processing: inversion effects

RT

Mean inversion effects are presented in Table 2. There were no significant main effects of group for RT inversion effects (F(1, 53) = .001, p = .96, η²_p= .001) or trial type, F(1, 53) = .01, p = .92, η²_p= .001. A main effect was observed between “same” and “different” trials, whereby larger inversion effects occurred on “different” face pairs in both groups, F(1, 53) = 29.30, p < .001, η²_p= .36. A three-way interaction was observed between group, task type, and same/different trials, F(1, 53) = 4.03, p = .05, η²_p = .07. In the BDD group, significant inversion effects were observed on featural and configural “different” trials (faster and more accurate processing of upright faces), but RTs were unaffected by inversion on “same” trials (Figure 3(a) and (b)). The HC group displayed significant RT inversion effects only on “different” configural trials.

Figure 3. Inversion effects on the featural and configural face processing tasks.

Notes: RTs depicted for (a) featural trials and (b) configural trials. Accuracy rates are shown for (c) featural trials and (d) configural trials. HC = healthy control group. *denotes significant inversion effect at p < .001.

Table 2. Differences in face inversion effects between the BDD and HC groups.

Variable	Controls (n = 27)		BDD (n = 30)
	Mean (SD)		Mean (SD)		t	p	d	95% CI
RT (ms)
Featural Task
Same	−20.93 (342.03)		−2.70 (375.65)		−.19	.85	.05	[−208.95, 173.02]
Different	−195.18 (593.22)		−275.69 (337.51)		.66	.52	.18	[−171.80, 338.56]
Configural Task
Same	−53.00 (307.43)		197.94 (405.77)		−2.58	.01	.70	[−446.19, −55.70]
Different	−230.25 (279.95)		−429.29 (356.60)		2.30	.03	.62	[25.22, 372.86]
Accuracy (%)
Featural Task
Same	−1.39 (16.13)		1.25 (8.09)		−.75	.46	.20	[−9.26. 4.24]
Different	6.30 (9.13)		8.48 (8.72)		−.83	.41	.22	[−6.74, 2.78]
Configural Task
Same	−2.69 (11.93)		−8.13 (16.85)		1.38	.17	.37	[−2.48, 13.36]
Different	16.85 (12.10)		20.54 (13.25)		−1.08	.29	.29	[−10.55, 3.19]

Notes: CI = confidence interval around the mean difference between BDD and HC groups, presented as [lower limit, upper limit]. Accuracy and RTs listed are the inversion effects (i.e. inverted trials subtracted from upright trials) for each trial type. Post-hoc tests were only conducted on configural and featural “different” trials. However, results from additional group comparisons have been provided for completeness. *p-values significant at the Bonferroni-corrected p < .03 have been bolded.

Post-hoc tests are displayed in Table 2. Whereas inversion effects were similar between BDD and HCs on featural trials, significant differences emerged on configural trials. For “same” trials, BDD participants displayed a positive inversion effect (slower processing on upright rather than inverted trials), but a negative inversion effect (slower processing on inverted rather than upright trials) on “different” trials. For both “same” and “different” configural trials, BDD participants demonstrated larger inversion effects than controls.

Accuracy

Mixed-design ANOVA results revealed no significant main effect of group, F(1, 53) = .20, p = .65, η²_p= .001, or interaction terms. However, a main effect of task condition was observed, F(1, 53) = 6.15, p = .02, η²_p= .10, indicating that accuracy inversion effects were significantly larger for all participants on configural rather than featural trials. BDD and HC groups displayed reduced accuracy when faces were inverted on “different” trials (Figure 3(c) and (d)). Accuracy was unaffected by inversion on “same” trials in both groups.

Second-order configural processing

Accuracy and RTs on upright face trials are presented in Table 3. The mixed-design ANOVA revealed a significant main effect of trial type, F(1, 53) = 7.91, p = .007, η²_p = .13, with faster RTs overall on featural rather than configural trials. BDD and HC participants performed faster on “different” rather than “same” trials, F(1, 53) = 55.87, p < .001, η²_p = .51. There were no significant main effects of group, F(1, 53) = .38, p = .54, η²_p = .01, or interactions between group and trial type, F(1, 53) = 2.99, p = .09, η²_p = .05. The overall sample also achieved higher accuracy on featural rather than configural trials, F(1, 53) = 8.40, p = .005, η²_p = .14, and on “different” rather than “same” trials, F(1, 53) = 38.77, p < .001, η²_p = .42. There was no significant main effect of group, F(1, 53) = .88, p = .35, η²_p = .02, or interaction between group and trial type, F(1, 53) = 1.60, p = .21, η²_p = .03.

Table 3. RTs and accuracy rates for upright featural and configural trials.

Variable	Controls (n = 27)	BDD (n = 30)
	Mean (SD)	Mean (SD)
RT (ms)
Featural task
Same	2156.39 (603.59)	1977.20 (757.90)
Different	1648.96 (696.35)	1497.72 (396.96)
Configural task
Same	2216.40 (488.94)	2373.76 (719.83)
Different	1711.81 (377.66)	1608.82 (365.62)
Accuracy (%)
Featural task
Same	77.50 (20.53)	85.09 (16.10)
Different	90.00 (19.89)	93.97 (16.43)
Configural task
Same	73.15 (18.04)	69.55 (18.23)
Different	87.04 (11.81)	90.54 (13.25)

Facial emotion labelling

Average RTs and accuracy rates on the facial emotion labelling task are depicted in Table 4.

RT

RTs differed across emotions, F(5, 52) = 67.90, p < .001, η²_p= .88, with both groups responding fastest to happy faces, and slowest to angry faces (see Table 4). Overall RTs were similar between BDD and HC groups, F(1, 52) = .001, p = .95, η²_p= .001, with no significant group-by-emotion interaction, F(5, 52) = .52, p = .76, η²_p= .01.

Table 4. RTs and accuracy rates on the facial emotion labelling task.

	RT (ms)		Accuracy (%)
Emotion	Controls	BDD	Controls	BDD
Anger	1533.68 (205.15)	1519.32 (190.41)	48.46 (15.64)	44.64 (15.02)
Disgust	1409.25 (200.17)	1394.21 (169.37)	53.72 (16.37)	58.33 (18.07)
Fear	1484.16 (183.37)	1469.72 (174.00)	73.72 (16.66)	76.67 (14.57)
Sadness	1374.73 (154.70)	1388.69 (148.51)	58.08 (17.31)	63.45 (14.59)
Happiness	1164.99 (185.03)	1164.95 (140.48)	89.62 (9.77)	92.74 (4.97)
Neutral	1272.17 (220.05)	1316.67 (172.29)	86.92 (13.20)	84.29 (17.94)
Total	1384.32 (158.56)	1381.52 (126.76)	66.11 (9.54)	68.24 (6.50)

Notes: Due to a technical error on the facial emotion labelling task leading to missing data, there were n = 26 participants included in the control group, and n = 28 in the BDD group.

Accuracy

Results from the mixed-design ANOVA (group x emotion) for accuracy revealed a significant main effect of emotion, F(5, 52) = 83.25, p < .001, η²_p= .62, with accuracy being highest for happy faces and lowest for angry faces in both groups. There were no significant group differences in accuracy rates, F(1, 52) = .61, p = .44, η²_p= .01, and no group-by-emotion interaction, F(5, 52) = 1.00, p = .41, η²_p= .02.

Emotional biases

A significant main effect of emotion, F(5, 52) = 65.26, p < .001, η²_p= .56, indicated that all participants were more likely to mistake other emotions as neutral, and least likely to mistake them as happy. There were no significant differences between groups in their mistake biases towards a particular emotion (F(1, 52) = 1.60, p = .21, η²_p= .03) or group-by-emotion interaction effects (F(5, 52) = .68, p = .60, η²_p= .01).

Discussion

We examined a multidimensional model of facial processing in BDD, to determine whether individuals with BDD display specific or widespread face processing abnormalities. Results revealed that BDD participants performed similarly to HCs on the Navon task, suggesting no differences in global and local processing of simple non-face stimuli (Research Question 1). Further, BDD and HC groups displayed similar accuracy inversion effects, with both groups being significantly less accurate in discriminating between inverted rather than upright faces. The BDD group further displayed slower RTs on both configural and featural inverted trials, whereas HCs were affected by inversion on configural trials only. Moreover, BDD participants displayed larger (rather than smaller) inversion effects than HCs on the configural task, indicating some deficits in first-order configural/holistic processing in BDD (Research Question 2). There were no differences between BDD and HCs in accuracy or RTs when identifying manipulations to details or spacing of facial features, suggesting no difficulties in featural and second-order configural processing (Research Question 3). Contrary to expectation, the BDD and HC groups achieved similar RTs and accuracy rates on the facial emotional labelling task, and BDD participants did not demonstrate any mistake biases (Research Question 4). Due to the lack of group differences on the emotion labelling task, correlations between the visual processing tasks were not computed (Research Question 5).

Our results are consistent with previous studies showing BDD patients perform normally on the Navon task (Monzani et al., 2013) and do not differ from HCs in their ability to detect subtle changes in facial features or spacing in upright faces (Feusner et al., 2007; Li, Lai, Loo, et al., 2015; Monzani et al., 2013; Moody et al., 2015). However, our findings regarding larger facial inversion effects in BDD contradict prior research (Feusner, Moller, et al., 2010; Jefferies et al., 2010; Toh et al., 2017). Notably, significant inversion effects only emerged on “different” pair trials. Other researchers have not separated these from “same” pair trials, which may require more “visual checks’ and thus produce longer RTs (Sergent, 1984). Furthermore, our finding of similar facial emotion recognition results among BDD and HCs contrasts with previous studies which showed poorer performance in BDD participants (Buhlmann et al., 2006; Buhlmann et al., 2004; Feusner, Bystritsky et al., 2010; Grace et al., 2019; Toh et al., 2015).

The discrepancy with prior results is unlikely due to sampling variability, as our participants exhibited moderate-to-severe symptom severity and poor insight, similar to previous studies. Thus, we suggest that discrepancies between findings of visual processing abnormalities in BDD may be due to methodological heterogeneity within this research domain. Our following synthesis of the existing literature regarding featural, configural, and emotional face processing in BDD within the context of the visual processing hierarchy (Figure 1) indeed suggests that task characteristics may play a role in discrepancies across findings (15 studies; Table 5; two were excluded due to additional complexity or task demands beyond visual processing, see Supplementary Table 3).

Table 5. Summary of results and task methodologies used to assess face processing in BDD.

Study	Participants (n)	F/M	Age	Stimuli	Duration	Number of Trials and Randomisation	Response type	Results	Visual Abnormality
Featural Processing
Current Study	30 BDD 27 HC	19/11 12/15	29.7 ± 11.4 28.1 ± 6.9	Grey-scale neutral female face (Ekman & Friesen, 1976) manipulated to depict featural changes (i.e. eyes, nose, and mouth)	Face pairs presented for 5000ms 300 ms interstimulus interval	160 featural trials = 80 upright face pairs, 80 inverted face pairs. Inversion conditions and same/different face pairs were counterbalanced and randomised.	Same/different decision: two button press	BDD vs HC: No group differences in RT or accuracy on upright featural trials.	BDD = HC
Feusner et al. (2007)	12 BDD 13 HC	10/2 11/2	28.7 ± 10.0 31.3 ± 11.3	Neutral expression male and female faces (Macbrain database; Tottenham, 1998). Faces altered to normal, high, or low spatial frequency.	Trio of faces presented for 4000ms 1000 ms interstimulus interval	32 trials  =  Two runs, each consisting of 4 sets of the same face (NSF, HSF, or LSF or the control task). Each set of 4 blocks (A-B-C-control) was repeated 4 times in each run.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
Monzani et al. (2013)	25 BDD 25 HC	14/11 16/9	29.4 ± 7.5 30.4 ± 9.4	Grey-scale male face manipulated to depict featural changes (i.e. mouth or eyes).	Face pairs presented for 250ms 1000 ms interstimulus interval	80 trials in two blocks = 40 upright face pairs, 40 inverted face pairs. Inversion conditions and same/different face pairs were counterbalanced and randomised.	Same/different decision: two button press Same and different trials combined for analyses	BDD vs. HC: no group differences in RT or accuracy.	BDD = HC
Li et al. (2015)	15 BDD 15 HC	13/2 13/2	24.9 ± 5.2 22.1 ± 3.9	Neutral expression male and female faces (Macbrain database; Tottenham, 1998). Faces altered to normal, high, or low spatial frequency.	Trio of faces presented for 4000ms 500 ms interstimulus interval	216 trials = 72 trials of each spatial frequency (HSF, NSF, LSF, control) with blocks counterbalanced.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
Li et al. (2015)	20 BDD 20 HC	18/2 18/2	24.6 ± 5.1 22.6 ± 4.0	Neutral expression male and female faces (Macbrain database; Tottenham, 1998). Faces altered to normal, high, or low spatial frequency.	Trio of faces presented for 2000ms 500 ms interstimulus interval	216 trials = 72 trials of each spatial frequency (HSF, NSF, LSF, control) with blocks counterbalanced.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
Moody et al. (2015)	20 BDD 20 HC	15/5 17/3	21.5 ± 3.5 23.3 ± 4.6	Neutral expression faces (novel stimuli) altered to normal, high, or low spatial frequency.	Trio of faces presented for 4000ms 500 ms interstimulus interval	48 trials = presented in alternating blocks of HSF, LSF, NSF, or control, with 4 sets of images per block. Order of blocks counterbalanced.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
First-Order/Holistic Configural Processing
Current Study	30 BDD 27 HC	19/11 12/15	29.7 ± 11.4 28.1 ± 6.9	Face inversion effects: Grey-scaled neutral female face from Ekman and Friesen (1976) face set. Faces manipulated to display featural or configural (spacing) changes.	Face pairs presented on screen for 5000ms 300 ms interstimulus interval	320 trials in two blocks = 160 featural and 160 configural. Faces were inverted/upright and same/different. Conditions were counterbalanced and randomised.	Same/different decision: two-button press	BDD vs HC: -BDD group showed larger RT inversion effects on configural trials (i.e. BDD group faster on inverted same trials, but slower on inverted different trials). - No differences in inversion effects on featural trials or accuracy inversion effects.	BDD < HC
Feusner, Moller et al., (2010)	18 BDD 17 HC	10/8 10/7	28.6 ± 6.7 28.1 ± 5.4	Face inversion effects: Grey-scale neutral faces rated as neutral attractiveness. Incorrect faces morphed 50% with another face to create both configural and featural changes in each face.	Target face presented for 500 ms or 5000 ms, followed by a screen with two faces for 3000ms 1000 ms interstimulus interval	112 trials in 4 blocks (28 trials each) of conditions = 500 ms inverted, 500 ms upright, 5000 ms inverted, 5000 ms upright. 500 ms blocks presented first, followed by 5000 ms blocks. Order of blocks counterbalanced.	Matching task: Participants select whether left or right face matches target image	BDD vs. HC: - No difference in inversion effects on short-duration trials, but reduced inversion effects in BDD group (i.e. superior RT on inverted faces) for long duration stimuli only. - No difference in accuracy inversion effects.	BDD > HC
Jefferies et al. (2010)	12 BDD 16 HC	7/5 10/6	30.1 ± 8.9 35.8 ± 12.1	Face inversion effects: task information unavailable.	Task information unavailable.	Task information unavailable.	Task information unavailable.	BDD vs HC: BDD group displayed superior accuracy when recognising inverted faces (accuracy only).	BDD > HC
Monzani et al. (2013)	25 BDD 25 HC	14/11 16/9	29.4 ± 7.5 30.4 ± 9.4	Face inversion effects: Grey-scale male face manipulated to depict featural or configural changes.	Pairs of stimuli presented for 250ms 1000 ms interstimulus interval.	80 trials in two blocks = 40 upright faces, 40 inverted faces. Same/different and configural/featural differences randomised in each block.	Same/different decision: two-button press Same/different combined for analysis	BDD vs. HC: No group differences in accuracy or RT inversion effects.	BDD = HC
Monzani et al. (2013)	25 BDD 25 HC	14/11 16/9	29.4 ± 7.5 30.4 ± 9.4	Composite face task: Grey-scale male and female faces. Composite faces created by pasting top and bottom halves of target stimuli together.	Face pairs presented for 200 ms. 300 ms interstimulus interval.	96 trials in two blocks: 48 “aligned” trials, 48 “misaligned” trials. Same/different face pairs randomised within each block.	Same/different decision: two-button press Same/different combined for analysis	BDD vs. HC: No group differences. Both groups displayed superior performance on misaligned trials.	BDD = HC
Toh et al. (2017)	21 BDD 21 HC	16/5 13/8	34.3 ± 11.9 35.7 ± 10.6	Mooney Face Inversion: Set of 40 Mooney faces.	Mooney face presented for 2000ms 500 ms interstimulus interval	120 trials in 2 blocks = 20 upright faces, 20 inverted faces, 20 scrambled faces.	Participants decided if target image was a human face, object or neither.	BDD vs HC: BDD group showed smaller accuracy and RT inversion effects.	BDD > HC
Second-order Configural Processing
Current study	30 BDD 27 HC	19/11 12/15	29.7 ± 11.4 2 8.1 ± 6.9	Grey-scale neutral female face (Ekman & Friesen, 1976) manipulated to depict configural changes.	Face pairs presented for 5000ms 300 ms interstimulus interval	160 configural trials = 80 upright face pairs, 80 inverted face pairs. Inversion conditions and same/different face pairs were counterbalanced and randomised.	Same/different decision: two-button press	BDD vs HC: No group differences in RT or accuracy on upright configural trials.	BDD = HC
Feusner et al. (2007)	12 BDD 13 HC	10/2 11/2	28.7 ± 10.0 31.3 ± 11.3	Neutral male and female faces (Macbrain database; Tottenham, 1998) altered to normal, high or low spatial frequency.	Trio of faces presented for 4000ms 1000 ms interstimulus interval	32 trials  =  Two runs, each consisting of 4 sets of the same face (NSF, HSF, or LSF or the control task). Each set of 4 blocks (A-B-C-control) was repeated 4 times in each run.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
Monzani et al. (2013)	25 BDD 25 HC	14/11 16/9	29.4 ± 7.5 30.4 ± 9.4	Grey-scale male face manipulated to depict configural changes (i.e. spacing between nose and eyes).	Pairs of stimuli presented for 250ms 1000 ms interstimulus interval	80 trials in two blocks = 40 upright face pairs, 40 inverted face pairs. Inversion conditions and same/different face pairs were counterbalanced and randomised.	Same/different decision: two-button press Same and different trials combined for analyses	BDD vs. HC: no group differences in RT or accuracy on configural trials.	BDD = HC
Moody et al. (2015)	20 BDD 20 HC	15/5 17/3	21.5 ± 3.5 23.3 ± 4.6	Neutral expression faces (novel stimuli) altered to normal, high or low spatial frequency.	Trio of faces presented for 4000ms 500 ms interstimulus interval	48 trials = presented in alternating blocks of HSF, LSF, NSF, or control, with 4 sets of images per block. Order of blocks counterbalanced.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
Li et al. (2015)	15 BDD 15 HC	13/2 13/2	24.9 ± 5.2 22.1 ± 3.9	Neutral male and female faces (Macbrain database; Tottenham, 1998) altered to normal, high or low spatial frequency.	Trio of faces presented for 4000ms 500 ms interstimulus interval	216 trials = 72 trials of each spatial frequency (HSF, NSF, LSF, control) with blocks counterbalanced.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
Li et al. (2015)	20 BDD 20 HC	18/2 18/2	24.6 ± 5.1 22.6 ± 4.0	Neutral expression male and female faces (Macbrain database; Tottenham, 1998). Faces altered to normal, high or low spatial frequency.	Trio of faces presented for 2000ms 500 ms interstimulus interval	216 trials = 72 trials of each spatial frequency (HSF, NSF, LSF, control) with blocks counterbalanced.	Matching: Two-button press to indicate which face matches the target image.	BDD vs. HC: No group differences in RT or accuracy.	BDD = HC
Facial Emotion Recognition
Current Study	30 BDD 27 HC	19/11 12/15	29.7 ± 11.4 2 8.1 ± 6.9	10 faces (5 male, 5 female) from Ekman and Friesen (1976) set displaying 6 emotions (happiness, sadness, anger, fear, disgust, and neutrality).	Face displayed for 2000ms 1500 ms interstimulus interval	160 trials with emotions randomised	Identification task: Participants identify emotion by pressing one of six corresponding keys (first letter of the emotion)	BDD vs. HC: No group differences in accuracy, RT or biases for any of the 6 emotions.	BDD = HC
Buhlmann et al. (2004)	20 BDD 20 HC	12/8 13/7	32.7 ± 11.3 32.9 ± 11.7	6 faces (3 male, 3 female) from Ekman and Friesen (1976) displaying 7 emotions (anger, disgust, fear, happiness, sadness, surprise, neutral).	Face displayed for 15s.	42 trials with order of emotions randomised.	Identification task: Participant circled the corresponding emotion on an answer sheet.	BDD vs HC: - BDD displayed lower accuracy overall (neutral and disgust specifically) - BDD mistook disgust as anger more often. - BDD mistook more faces as angry overall than controls. - No differences in mistakes made on neutral faces.	BDD < HC
Buhlmann et al. (2006)	18 BDD 18 HC	14/4 15/3	28.4 ± 9.2 29.0 ± 8.3	6 faces (3 male, 3 female) from Ekman and Friesen (1976) displaying 4 emotions (anger, disgust, neutral, surprise).	No time limits.	48 trials: 24 trials in each block (self-referent or other-referent), each model/emotion combination presented once.	Identification task: Participants presented with self-referent and other-referent scenarios, followed by instruction to identify the person’s emotion. Participant circled best answer from list of 8 emotions.	BDD vs HC: - BDD group less accurate than controls in self-referent situations only. - BDD group mistook neutral faces as anger or contempt more often. - No group differences for other-referent emotions.	BDD < HC
Feusner et al. (2010)	12 BDD 11 HC	9/3 9/2	32.0 ± 9.0 32.0 ± 9.3	Faces from Macbrain database (Tottenham, 1998) displaying 6 emotions (angry, sad, fearful, happy, disgusted, surprised)	Trio of faces presented for 4000ms (emotional face presented with two neutral faces [one of same person, one of different person]) 1000 ms interstimulus interval	32 trials = 6 trials each of angry, sad, fearful, happy, and disgusted faces and 2 trials for surprise.	Matching task: Participants instructed to choose which one of two faces was the same person as the target, regardless of emotional expression.	BDD vs HC: - BDD group made significantly more errors when matching emotional faces than controls, but not for neutral faces, ovals/circles. - Slower RT in the BDD group for emotional faces overall.	BDD < HC
Buhlmann et al. (2011)	34 BDD 34 HC	23/11 19/15	32.3 ± 8.3 30.8 ± 10.6	4 faces (2 women, 2 men) displaying 7 emotions (anger, disgust, happy, sad, neutral, scared, surprised)	No time limit	28 trials = each model/emotion combination presented once	Identification task: Participants instructed to identify the corresponding emotion by mouse click. RT was not measured.	BDD vs HC: - No group differences in overall accuracy. - BDD less accurate for neutral expressions.	BDD < HC
Jefferies et al. (2012)	12 BDD 16 HC	7/5 10/6	30.1 ± 8.9 35.8 ± 12.1	Facial Expression of Emotion: Stimuli and Test (FEEST). Faces from Ekman and Friesen (1976) displaying 6 emotions (happy, surprise, fear, sadness, disgust, anger)	Face presented for 5000 ms.	60 trials = 10 of each of the six emotions.	Identification task: Participants identify emotion by choosing the correct label, using a mouse click.	BDD vs. HC: - BDD less accurate in detecting fear, but no group differences for other emotions. - BDD group recognised non-threatening emotions similarly to controls, but were worse at detecting negative emotions (fear, anger, disgust).	BDD < HC
Rossell et al. (2014)	14 BDD 14 HC	9/5 9/5	33.1 ± 13.4 32.9 ± 13.4	Faces from Ekman and Friesen (1976) displaying 5 emotions (happy, sad, neutral, angry, or afraid).	Face presented for 2000ms 1500 ms interstimulus interval	Each emotion displayed once on each model.	Identification task: Participants identify emotion by pressing corresponding key (first letter of the emotion)	BDD vs. HC: - No significant differences between groups in accuracy, RT or mistakes.	BDD = HC
Toh et al. (2015)	21 BDD 21 HC	16/5 13/8	34.4 ± 11.9 35.7 ± 10.6	6 faces (3 male, 3 female) from Ekman and Friesen (1976) set displaying 7 emotions (anger, disgust, fear, happiness, sadness, surprise, neutral).	Face displayed for 8000 ms free-viewing 2000ms interstimulus interval for response	84 trials = 4 blocks of 21 faces with order of emotions pseudo-randomised.	Identification task: Participants viewed fixation cross, followed by target. Once face disappeared, participants identified emotion using corresponding key. Only accuracy obtained.	BDD vs. HC: - BDD group showed reduced accuracy in identifying emotional faces. - BDD participants misinterpreted more faces as angry than HC participants.	BDD < HC
Grace et al. (2019)	19 BDD 21 HC	13/6 14/7	34.7 ± 11.8 31.5 ± 11.2	10 faces (5 male, 5 female) from Ekman & Friesen set (1976) displaying 5 emotions (happy, sad, angry, fear, neutral).	Presentation time varied between 200 ms or 2000ms. 1700ms interstimulus interval.	200 trials = each identity displayed four times with order counterbalanced.	Identification task: Participants identify emotion by pressing corresponding key (first letter of the emotion)	BDD vs HC: - BDD group less accurate when identifying angry, neutral and sad faces only. - BDD group slower than controls for angry, neutral and happy faces. - Presentation time did not affect results.	BDD < HC

Notes: n = 15 individual studies. Some studies have been repeated within the table as they have included multiple tasks probing different levels of visual processing. The table has been structured to reflect the proposed hierarchy of visual processing presented in Figure 1. Task methodology and results from the current study have been bolded in each section.

Featural face processing

Five studies have assessed featural face processing in BDD by digitally manipulating facial features on a target face, akin to our study (Monzani et al., 2013), or by presenting high spatial-frequency images (Feusner et al., 2007; Li, Lai, Bohon, et al., 2015; Li, Lai, Loo, et al., 2015; Moody et al., 2015). These studies varied in use of male and/or female faces, duration of stimulus presentation (ranging from 250 ms to 5000 ms), and response paradigms (i.e. utilising either a trio of faces requiring matching one of the bottom stimuli with the top face, or discrimination between two faces presented side-by-side). As no significant differences between BDD and HC have emerged in these studies, it is unlikely that stimuli presentation times or response format affect performance. Instead, as processing of facial features is proposed to be the most basic level of the hierarchy, it is possible that these tasks have been too easy, with ceiling effects concealing subtle abnormalities in the BDD group in both our study and previous works.

First-order and holistic configural processing

Inversion effects have been explored in six prior studies, using neutral expression faces (Feusner, Moller, et al., 2010; Monzani et al., 2013) or Mooney faces (fragmented, black and white faces; Toh et al., 2017). Our study and that of Monzani et al. (2013) manipulated neutral human face photographs to display either featural or configural changes, whereas others manipulated both featural and configural elements in the same image (Feusner, Moller, et al., 2010). The former approach allows separate exploration of the effects of priming participants to configural or featural differences. Specially, inversion may have a greater effect on configural face processing conditions, as individuals are primed towards spatial information that is disrupted by inversion (Schwaninger & Mast, 2005; Sergent, 1984). Contrastingly, featural conditions prime participants toward facial features, which is considered a superior strategy for processing inverted faces (Schwaninger & Mast, 2005). Such effects were demonstrated in the current study, where larger accuracy inversion effects (i.e. poorer performances) were observed on configural rather than featural trials in both groups.

Unlike the current study, Toh et al. (2017) used Mooney faces to examine inversion effects, revealing smaller inversion effects in BDD participants compared to healthy controls. Mooney faces include limited visual detail but are recognisable to healthy individuals as human faces when upright (McKone, 2004; Schwiedrzik et al., 2018). As Mooney stimuli lack detailed first- or second-order facial information, holistic processing becomes the primary mode of perception (Latinus & Taylor, 2005). Further, Toh et al. (2017) used the Mooney task as a facial identification task where participants determine whether the target is a face or an object, rather than comparing elements between two face stimuli. The latter is considered a more challenging task (Tsao & Livingstone, 2008), and thus may be more sensitive to visual processing abnormalities. However, Mooney faces provide a more robust measure of holistic processing, which could assist in separating abnormalities of first-order and holistic stages of visual processing.

Cross-study differences in task design may also explain discrepancies across findings. For example, our study and Monzani et al. (2013) required participants to determine whether simultaneously presented faces were the same or different. Conversely, Feusner, Moller et al. (2010) required participants to view a target stimulus (for either 500 ms or 5000 ms), then determine which face in a subsequently presented pair of face images matched the target. This task design may have created additional memory and attentional demands (as compared to our design) that could explain study differences. Further, at short stimulus presentations (250-500 ms), neither Monzani et al. (2013) or Feusner, Moller et al. (2010) identified significant inversion effects among BDD participants. At longer stimulus presentations (5000 ms), Feusner, Moller et al. (2010) reported significantly smaller inversion effects in the BDD group, though the opposite was found in our present study. This suggests that investigations of first-order configural processing in BDD should use presentation lengths of 5000 ms or longer. Yet, given these inconsistencies in inversion effect results, further research is needed to investigate first-order configural and/or holistic processing in BDD.

Finally, Monzani et al. (2013) utilised the composite faces task, which asks participants to determine whether the top or bottom halves of two composite faces are the same or different (Monzani et al., 2013; Young et al., 1987). When the composite face halves are aligned, it is harder to attend to one half whereas ignoring the other, suggesting a reliance on holistic processing (for a review and illustration of the task, see Murphy et al., 2017). Misaligned composites facilitate better discrimination by disrupting the holistic template. Thus, individuals with BDD, who are purported to engage in detailed (over holistic) processing should perform better than controls when faces are aligned (Monzani et al., 2013). However, this was not supported by Monzani et al.’s (2013) results, albeit the 200 ms presentation time might have been too brief. Future research should include both composite and inversion face tasks to disentangle effects of holistic and first-order configural processing.

Second-order configural processing

Five studies have assessed second-order configural processing using discrimination tasks involving digitally manipulated faces (Monzani et al., 2013) or by presenting low spatial-frequency images (Feusner et al., 2007; Li, Lai, Bohon, et al., 2015; Li, Lai, Loo, et al., 2015; Moody et al., 2015). Some have used matching paradigms, or same/different discrimination between two faces (Monzani et al., 2013). Varying stimuli, presentation times, and discrimination or matching designs have all failed to reveal significant differences between BDD and HCs. Taken together with our findings, second-order configural processing does not seem to be impaired in BDD.

Facial emotion recognition

Prior facial emotion recognition studies in BDD (n = 8) have typically used 6–10 faces from the Ekman and Friesen (1976) series, half male and half female, assessing 4–7 basic emotions (Buhlmann et al., 2006; Buhlmann et al., 2004; Feusner, Bystritsky et al., 2010; Grace et al., 2019; Jefferies et al., 2012; Rossell et al., 2014; Toh et al., 2015). However, stimulus presentation times have varied widely (200 ms up to 15s, or no time limit). The present study and Rossell et al. (2014) presented stimuli for 2000ms and did not observe significant differences between BDD and HC groups. Studies which presented stimuli >2000ms found BDD participants performed less accurately than controls (Buhlmann et al., 2006; Buhlmann et al., 2004; Toh et al., 2015). However, Grace et al. (2019) directly compared 200 ms versus 2000ms stimulus presentations and found poorer performances in BDD (versus HC) across both durations. As our task design was similar to Grace et al. (2019), our lack of group differences is unexpected but could reflect sampling of different neurocognitive subtypes in BDD. One of the largest investigations of cognition in BDD identified subgroups of individuals with “broadly impaired” or “selectively impaired” cognition, indicating significant cognitive heterogeneity within BDD samples which may be masked in aggregate data like the current study (Malcolm et al., 2020).

Furthermore, findings of group difference have been inconsistent in showing reduced accuracy overall, or reduced accuracy or RTs for specific emotions only (i.e. angry, neutral, sad). Moreover, Buhlmann et al. (2006) found no differences between BDD and HC groups in “other-referent” situations, but BDD participants were less accurate in identifying emotions during self-referent scenarios (Buhlmann et al., 2006). The lack of group differences in our study could be influenced by participants not finding the stimuli personally relevant. Available evidence suggests that people with BDD have alterations in emotion processing, yet further research is needed to determine if stimulus presentation times (<2000ms to >2000ms) and self- or other-referent contexts impact processing performances.

General findings and limitations

Across all domains of face processing in prior BDD research, task instructions and randomisation strategies have varied. Methodological factors related to priming and stimulus presentation duration may explain inconsistent results among these studies and our current study. Firstly, the current study and studies which manipulated spatial frequency of faces (Li, Lai, Loo, et al., 2015; Moody et al., 2015) presented featural and configural stimuli in separate blocks, rather than interspersing these conditions randomly across trials. Block presentations may prompt participants to focus on either featural or configural elements, creating priming and attentional effects which could override natural visual processing tendencies (Monzani et al., 2013). Accordingly, Monzani et al.’s (2013) study using separate global and local Navon task blocks did not report significant group differences, but Kerwin et al. (2014), who randomised global and local Navon trials, found a greater fixation on visual detail and difficulty shifting away from local processing in BDD. Secondly, a similar argument could explain the finding that shorter trial durations do not produce significant visual processing abnormalities in BDD, but longer trials do. Allowing more time to respond may create greater opportunity to selectively attend to most salient visual aspects. As such, top-down influences may underlie difficulties on visual processing tasks in BDD, rather than a visual processing abnormality per se (Johnson et al., 2018). Thus, visual processing tasks should be designed to minimise demands on working memory or attention (e.g. randomising task conditions and minimising priming effects), to facilitate more direct assessment of visual system distortions. If group differences are not demonstrated on “pure” visual processing tasks, selective attention may be more central to the phenomenology of BDD, than perceptual abnormalities (Johnson et al., 2018; Kollei et al., 2017; Lambrou et al., 2011).

Several limitations are worth noting. The scope of this study was to investigate hierarchical visual processing difficulties on behavioural tasks. However, some studies described here may not have revealed behavioural group differences but identified unusual patterns in eye-tracking and neural activation (Beilharz et al., 2017). Incorporating behavioural, neuroimaging and eye-tracking studies into a future review may elucidate whether individuals with BDD experience altered visual processing patterns, which do not necessarily translate into differences in task performance. Further, the current research could be enhanced by multi-level modelling of the interrelationships between visual processing domains in BDD, though this requires larger samples. Additionally, we used a face inversion paradigm to explore holistic and first-order configural processing simultaneously. Future research should include other tasks that directly probe holistic visual processes (e.g. a composite or Mooney face task; Monzani et al., 2013; Toh et al., 2017), which would further increase the utility of a multidimensional model of face processing.

Conclusions

In summary, we found no widespread deficits in featural, configural and emotion processing in BDD. Given the discrepancy between these findings and previous literature, a synthesis and review of the task designs of previous studies was conducted, revealing wide variation in methodology. This variation emphasises the need for a unified consensus on the cognitive difficulties present in BDD, and the behavioural tasks which are most sensitive to these differences. A cognitive consensus battery could reinforce existing evidence regarding cognitive and visual difficulties in BDD, and may facilitate further exploration of the “cognitive subtype” hypothesis where some individuals with BDD present with cognitive difficulties whereas others do not (Malcolm et al., 2020). Further, a cognitive consensus battery could aid the development, monitoring and evaluation of visual and attention retraining programmes (e.g. Beilharz et al., 2018) to enhance clinical outcomes in BDD.

Acknowledgements

The views expressed herein are the authors’ opinions and do not reflect any official positions of the institutional affiliations or funding bodies. The authors do not have any conflicts to declare in relation to this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that supports the findings of this study are available from the corresponding author, TP, upon reasonable request.

Additional information

Funding

This work was sponsored by the Australian Government Research Training Program Stipend and Professor Susan Rossell’s Senior National Health and Medical Research Council (NHMRC) Fellowship (GNT1154651).

Notes on contributors

Toni D. Pikoos

Toni D. Pikoos is a postdoctoral researcher at Swinburne University, and a clinical psychologist working primarily with body image conditions such as body dysmorphic disorder in Melbourne, Australia.

Amy Malcolm

Amy Malcolm is a postdoctoral researcher at Swinburne University, and is currently completing her Masters in Clinical Psychology in Melbourne, Australia.

David J. Castle

David J. Castle is a Professor of Psychiatry at the University of Tasmania and Co-Director of the Centre for Mental Health Service Innovation in Tasmania, Australia.

Susan L. Rossell

Susan L. Rossell is a Professor and Director of Clinical Trials at Swinburne University in Melbourne, Australia. She is also an Adjunct Research Professor in Psychiatry at St Vincent's Hospital, Melbourne.

Notes

1 First-order and holistic visual processes are often difficult to decouple, as they are simultaneously affected in inversion paradigms and are both required for efficient discrimination of second-order facial configuration (Piepers & Robbins, 2012; Taubert et al., 2011; Tsao & Livingstone, 2008). As such, first-order configural and holistic processing are considered as separate, but parallel processes within the current study.