Introduction
Psychopathy is a complex neuropsychiatric disorder characterised by emotional, interpersonal, and behavioural disturbances (Rijnders et al. 2021). The increased levels of narcissism, impulsivity, and antisocial behaviour evident in psychopathy (Dawel et al. 2012) contribute to heightened levels of criminality, violence, suicidality, and homelessness, placing considerable financial and social burdens on prison systems, society, and the individual themselves (Reidy et al. 2015; Wilson et al. 2011). Between 1.2% and 4.5% of the global population have clinically high levels of psychopathic traits (Sanz-García et al. 2021), with that number expected to grow, particularly within the prison context, where 15-25% of inmates display high levels of psychopathic traits (Kiehl and Hoffman 2011).
The Hare Psychopathy Checklist-Revised (PCL-R; Hart et al. 1992) is the gold standard (Cunha et al. 2020, p. 254) and most frequently used measure of psychopathic traits (Skeem et al. 2003). The PCL-R is a 20-item semi-structured interview that measures characteristics associated with psychopathy, including interpersonal (e.g., manipulation), affective (e.g., lack of remorse), impulsive (e.g., irresponsibility), and antisocial features (e.g., poor behavioural control). Scores range from 0 to 40, with higher scores indicating higher levels of psychopathy. People in the general population typically score 4 or 5 on the PCL-R, with 30 out of 40 the benchmark clinical level of psychopathy (Miller 2008).
Emotion recognition in psychopathy
One of psychopathy’s most notable traits is a lack of remorse and empathy (Rijnders et al. 2021). Empathy is a multidimensional construct related to the ability to recognise, understand, and feel the emotional states of others (Riess 2017). Previous research has suggested that psychopaths experience empathetic deficits (e.g., Prado et al. 2015) which may interfere with moral socialisation (i.e., understanding what society deems to be ‘good’ and ‘bad’), thus increasing the susceptibility to engage in antisocial behaviours (Prado et al. 2015).
Facial emotion recognition is an important aspect of empathy as it helps us understand the intentions of others by inferring their emotional states (Ko 2018). Non-verbal forms of emotion expression, such as clenched teeth or frowning to indicate anger (Prado et al. 2015), make up approximately two-thirds of human communication (Ko 2018). Thus, the ability to perceive and interpret facial emotion is a vital component of social communication. Though one may expect impaired facial emotion recognition in psychopathy, research is inconsistent: some studies indicate that people with higher levels of psychopathic traits have poorer facial emotion recognition than people with lower levels of psychopathic traits (e.g., Eisenbarth et al. 2008), whilst others report that psychopathy does not affect facial emotion recognition abilities (e.g., Mowle et al. 2019).
Impaired facial emotion recognition in psychopathy
Many studies have reported that people with higher levels of psychopathic traits show poorer facial emotion recognition abilities than individuals with lower levels of psychopathic traits. This research includes both clinical (e.g., Kosson et al. 2002; Pera-Guardiola et al. 2016) and community samples (e.g., Kranefeld and Blickle 2022; Prado et al. 2015), and previous meta-analyses (Dawel et al. 2012; Wilson et al. 2011). For example, Eisenbarth et al. (2008) assessed the recognition of emotional facial expressions in psychopathic and non-psychopathic female prisoners. They found that prisoners with high levels of psychopathy were significantly worse at identifying sad, disgusted, neutral, and surprised facial expressions compared to non-psychopathic prisoners and controls. Other studies have similarly reported poorer facial emotion recognition abilities in both females (e.g., Amiri and Behnezhad 2017) and males (e.g., Kosson et al. 2002) with high levels of psychopathic traits, suggesting that facial emotion perception is impaired in psychopathy.
Additionally, Barwiñski (2014) demonstrated that the effects of psychopathy on facial emotion recognition are graded. The study used PCL-R scores to categorise 78 male criminal offenders into high- (> 26, n = 19), moderate- (13-25, n = 31), and low-intensity (< 12, n = 28) psychopathy groups. The participants completed a facial emotion recognition test, with the results confirming a significant effect of psychopathy on emotion recognition accuracy: high-intensity psychopaths (50% correct) performed more poorly than medium-intensity psychopaths (60% correct) who, in turn, performed worse than low-intensity psychopaths (71% correct). These findings thus suggest a negative relationship between emotion recognition and psychopathy: the higher the level of psychopathic traits, the poorer the ability to accurately perceive facial emotion.
Intact facial emotion recognition in psychopathy
However, whilst some studies have reported impaired facial emotion recognition in people with high versus low levels of psychopathic traits, others have found no effect of psychopathy on emotion recognition (e.g., Glass and Newman 2006; Stanković et al. 2015). For example, Mowle et al. (2019) reported that though people with high levels of psychopathy spend less time looking at emotional face stimuli, their ability to accurately recognise emotions does not differ from people with low levels of psychopathy. Such findings raise the possibility that psychopaths may experience a deficit in attentional allocation, rather than emotion recognition.
Furthermore, Stanković et al. (2015) compared facial emotion recognition abilities in criminal and non-criminal psychopaths. After moderating for criminality, the results indicated no significant difference in facial emotion recognition ability between psychopaths and non-psychopaths. Glass and Newman’s (2006) investigation of male prisoners appears consistent, finding that inmates with high (30+, n = 50) and low (20 and below, n = 61) PCL-R scores showed no difference in their ability to recognise facial affect. Overall, these studies suggest that psychopathy does not affect facial emotion recognition abilities, in marked contrast to the research detailed previously. As such, meta-analysis would be beneficial to reconcile the contradictory findings and help determine whether psychopathic adults experience deficits in recognising facial emotion.
The present study
Investigating psychopaths’ facial emotion recognition abilities is necessary to improve our understanding of whether and how psychopathic traits affect moral socialisation and help explain psychopaths’ increased susceptibility to engage in antisocial behaviours (Prado et al. 2015; Reidy et al. 2015). Research on facial emotion recognition abilities associated with psychopathy is inconsistent, and previous meta-analyses are now dated (Dawel et al. 2012; Wilson et al. 2011). The current systematic review and meta-analysis was thus designed to provide an updated synthesis of the literature investigating facial emotion recognition in psychopathy, to better understand the underlying cognitive mechanisms in psychopathy, and determine whether psychopaths experience deficits in perceiving facial emotion.
Method
This meta-analysis was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines (Page et al. 2021).
Search strategy
Four databases were comprehensively searched: Embase, MEDLINE, PsycINFO, and Web of Science. These databases were chosen as they have a recall rate of > 95% (Bramer et al. 2017). The following search terms were used, with the terms intentionally broad to ensure a comprehensive search: Psychopath*, AND criminal* OR offend*, AND fac* affect* recogni*, OR fac* emotion* recogni*, OR emotion* recognition deficits, OR fac* emotion* process*, OR “facial express*”, OR fac* process*, OR perception, OR “cognit*. The truncation symbol (*) allowed the databases to search for all forms of a word (e.g., psychopath* searched for papers containing the terms ‘psychopathy’, ‘psychopaths’, and/or ‘psychopathic’). Database searches began on the 23rd of May 2023, and were finalised on the 20th of January, 2025.
Study selection
Studies had to meet eight criteria to be eligible for inclusion in the meta-analysis (Table 1). Study screening was conducted independently by two reviewers (ZG and AL) in Covidence (Veritas Health Innovation 2022). Following duplicate removal, titles and abstracts were screened independently against the eligibility criteria to establish general relevance to the research question. Studies were either excluded or proceeded to full text screening, with studies that did not meet the full set of criteria excluded. At each screening stage, any discrepancies between reviewers were resolved by rechecking the papers and discussing to reach a consensus.
Data extraction
Data from the eligible studies were extracted independently by ZG and NM. Any discrepancies were discussed and resolved by reviewing the relevant paper and arriving at a consensus.
Quality assessment and risk of bias
Risk of bias and study quality were assessed using the Standard Quality Assessment Criteria (QualSyst; Kmet et al. 2004). QualSyst is a 14-item checklist that evaluates the quality of qualitative and quantitative primary research papers using a three-item scale: ‘yes’ = 2, ‘partial’ = 1, or ‘no’ = 0. Papers that score 80% or above are considered ‘strong’, 70-79% is ‘good’, 50-69% is ‘adequate’, and scores below 50% indicate ‘limited’ quality (Black et al. 2017). Items 5-7 relate to interventional designs and so were excluded as they were not relevant to the present study. Finally, the reviewers assessed the objective alignment between the study and the current meta-analysis, based on a recommendation by Mikolajewicz and Komarova (2019). The adapted scale thus consisted of 12 items, with a maximum score of 24 (Table 2). Two reviewers (ZG and NM) independently assessed study quality. The percentage of discrepancies between the reviewers was approximately 5%; these were resolved by taking a conservative approach and opting for the lower rating.
Data analysis
The data were analysed using JASP version 0.16.3 (JASP Team 2022). A random effects model was used as it acknowledges the heterogeneity and variance in effect sizes between studies that are not functionally equivalent (Higgins et al. 2003; Mikolajewicz and Komarova 2019). Prior to running the analysis, Hedges’ g effect sizes and standard errors were calculated and combined using the restricted maximum likelihood method with 95% confidence intervals. Hedges’ g was chosen over other statistics such as Cohen’s d as it is less susceptible to the effects of small sample sizes (Hedges 1983). The outcomes were interpreted using Cohen’s benchmarks of small (0.20), medium (0.50), and large (0.80) effect sizes (Cohen 1988). Studies that included multiple experimental or control groups were treated separately, with separate effect sizes and standard errors calculated. Effect sizes were visualised using forest plots.
Heterogeneity was measured using the I2 statistic with 95% confidence intervals, and was interpreted as minimal (0-40%), moderate (30-50%), substantial (50-90%), and considerable (75-100%; Higgins et al. 2022). I2 was chosen over the frequently employed Cochran’s Q test because the magnitude of heterogeneity is independent of the quantity of studies included in the meta-analysis (Higgins and Thompson 2002).
Publication bias was assessed using Egger’s regression test (Egger et al. 1997), and presented visually using a funnel plot. Where the meta-analysis found significant results, Rosenthal’s Failsafe-N (Rosenthal 1979) was calculated to determine the number of unpublished or non-significant studies required to refute the meta-analytic findings.
Results
Study selection
A total of 4584 studies were identified in the database searches. After removing 1625 duplicates, 2959 studies underwent title and abstract screening against the eligibility criteria. Of these, 2899 studies were deemed irrelevant; the remaining 60 studies underwent full-text screening against the eligibility criteria. Following full text screening, a further forty-seven studies were excluded, with the final thirteen studies included in the meta-analysis. Figure 1 presents a PRISMA flow diagram illustrating the study selection process (Page et al. 2021). The characteristics of each study are displayed in Table 3.
Quality assessment and risk of bias
Quality assessment was conducted on the 13 studies meeting the inclusion criteria. All received a score of 17 out of 24 or above (M = 21.00, SD = 1.63), with percentages ranging from 71 to 100% (M = 87.96%, SD = 6.90%). Twelve of the 13 studies indicated ‘strong’, and one study ‘good’, study quality (Table 4), and thus all were included in the meta-analysis.
Meta-analysis
A random effects meta-analysis using the restricted maximum likelihood method indicated that adults with high levels of psychopathy displayed poorer facial affect recognition than adults with low/no psychopathy, with a small effect size (g = –0.24, 95% CI: –0.41 to –0.06, p = 0.01; Fig. 2). Heterogeneity was considered moderate (I2 = 37.38%). Egger’s test (p = 0.89) and inspection of the funnel plot revealed no publication bias (Fig. 3). Rosenthal’s fail-safe N indicated that 37 unpublished or non-significant studies would be required to render the results of the meta-analysis non-significant.
Discussion
This meta-analysis aimed to determine whether facial emotion recognition is impaired in people with high levels of psychopathic traits. Of the 4584 studies identified in initial database searches, 13 studies of ‘good’ and ‘strong’ quality (Kmet et al. 2004) met the stringent inclusion criteria. The random effects meta-analysis confirmed that adults with higher levels of psychopathic traits performed significantly worse on facial emotion recognition tasks than adults with lower levels of psychopathy. These findings suggest that high levels of psychopathy are associated with deficits in recognising facial emotion, likely contributing to the behavioural disturbances exhibited by psychopaths, and potentially compromising the ability to act in accordance with societal norms.
Facial emotion recognition in adult psychopathy
The results of the present meta-analysis indicate that adults with high levels of psychopathy exhibit deficits in facial emotion recognition. The meta-analysis calculated effect sizes for facial emotion recognition abilities in psychopathy across all six core emotions (fear, happiness, sadness, disgust, anger and surprise). Because the impairment extended across both positive and negative emotions, the results appear consistent with previous meta-analyses examining children, adolescents and adults across multiple modalities (facial, verbal/vocal, postural; Dawel et al. 2012; Wilson et al. 2011). As empathy relates to the ability to feel the emotional states of others, these results imply that adults with high levels of psychopathic traits have poorer ability to understand and/or feel the emotions of others (Riess 2017; Rijnders et al. 2021). Given the importance of empathy in building positive relationships and meaningfully contributing to society, impaired facial emotion recognition can help explain why psychopaths display antisocial behaviours.
Limitations
Because high levels of psychopathic traits are more common in forensic populations (15-25% vs 1.2-4.5% in the general population; Kiehl and Hoffman 2011; Sanz-García et al. 2021), researchers investigating the effects of psychopathic traits on performance often assess incarcerated groups. Such samples offer the benefit of having an increased likelihood of heightened levels of psychopathic traits (though this, in turn, renders such individuals more susceptible to criminal engagement; Reidy et al. 2015). Not surprisingly then, of the 13 primary studies included in the present meta-analysis, 11 tested high psychopathy samples that were currently incarcerated (in either a prison or a forensic hospital), or that had a history of criminal offending; only two studies assessed healthy young adults. Though that may raise concerns about the potential generalisability of the findings, given that forensic samples may have psychopathy levels at the more extreme end of the scale, inspection of the included studies’ mean scores (Table 3) indicates that they were often below the threshold for a clinical diagnosis. For example, a PCL-R score of 30 or higher is used to indicate a diagnosis of psychopathy; over half the studies that used the PCL-R and reported their scores had a high psychopathy group mean < 30. Of the studies with a mean score exceeding the clinical threshold, none reported means higher than 33.65 (Kosson et al. 2002); thus, even the forensic samples did not have psychopathy scores at ceiling level (PCL-R max score = 40). Whilst this engenders greater confidence in the generalisability of the findings, the relative lack of research assessing non-offending samples with high psychopathy highlights an opportunity for future researchers. In particular, research is needed to determine the threshold at which the emotion recognition deficits emerge and assess whether the effect is linear, increasing consistently with increasing levels of psychopathic traits.
Comorbidities frequently cooccur in people with diagnosed psychopathy. Of the studies included in the present meta-analysis, comorbidities including schizophrenia, anxiety, depression, and impulsivity were reported (Table 3). However, it is worthy of note that eight of the 13 studies did not provide comorbidity data in their papers, and none of the included studies reported antisocial personality disorder as a comorbidity. Given the high incidence of comorbid psychopathy and antisocial personality disorder (e.g., Werner et al. 2015), perhaps this is not surprising; securing a high psychopathy sample while excluding antisocial personality disorder is likely to be challenging, if not impossible. Hence, it is important to be circumspect when drawing conclusions: given the paucity of comorbidity reporting, it is not possible to rule out the potential contributions of comorbid psychiatric diagnoses to the observed deficits in facial emotion recognition observed in people with high levels of psychopathy. Researchers are encouraged to explicitly report comorbidity data to enable more definitive inferences to be drawn in future.
Implications and future directions
The impaired facial emotion recognition highlighted in the present meta-analysis is likely a significant factor contributing to the behaviour of psychopaths because it hinders their ability to understand and respond to social cues appropriately. Poorer ability to recognize emotions compromises the capacity to perceive others’ distress or discomfort (e.g., Bird and Viding 2014); when people with higher levels of psychopathic traits engage in antisocial behaviours, their inability to interpret emotional expressions effectively may lead to a lack of remorse or guilt, as they fail to fully grasp the emotional impact of their actions on others (Blair et al. 2004; Prado et al. 2015).
Moreover, impaired facial emotion recognition in psychopaths may exacerbate the tendency toward callousness and interpersonal exploitation. For example, difficulty in recognizing emotions such as anger or fear could diminish the ability to predict and respond to potential threats or negative reactions (e.g., Mienaltowski et al. 2019), resulting in more risky and/or impulsive behaviours. This deficiency may further isolate psychopaths from healthy social interactions, reinforcing detachment from societal norms and enhancing traits such as egocentricity and reduced concern for others’ welfare (e.g., Bresin et al. 2013). Such patterns are often associated with the development and persistence of psychopathic traits, making impaired emotion recognition a crucial element in understanding psychopathic behaviour (e.g., Moul et al. 2012).
Whilst the current meta-analysis indicates that people with high levels of psychopathy show deficits in facial emotion recognition, training can yield significant improvements (e.g., Penton-Voak et al. 2013). For example, Penton-Voak et al. (2013) found that training individuals to perceive ambiguous facial expressions as more happy than angry resulted in a decrease in self-reported anger and aggression in healthy adults and high-risk delinquent youth. Additionally, Hubble et al. (2015) found that training juvenile offenders with antisocial behaviour to recognise negatively valanced emotions, such as anger, sadness, and fear, reduced the severity of their juvenile delinquency. Therefore, research developing targeted intervention strategies for adolescent delinquent samples would be valuable, aiming to mitigate emotion recognition impairments before the adolescent reaches adulthood.
As the findings of the present meta-analysis indicate that emotion recognition is impaired in people with high levels of psychopathic traits, research is needed to help identify the cause(s) of the deficit. Attention is one promising avenue worthy of investigation, as Díaz Vázquez et al.’s (2024) recent systematic review suggests that impaired facial recognition is moderated by attentional deficits. In particular, reduced attention toward key regions (e.g., eyes and mouth) may make an important contribution, compromising the encoding of facial features that are vital to identifying emotional expressions. Examination of eye gaze and fixation patterns during emotion evaluation will be beneficial in helping to determine the contribution of attentional atypicalities to emotion recognition deficits in people with high levels of psychopathic traits.
It is also important to note that the majority of the studies identified for the present meta-analysis assessed Western samples using Western facial emotion recognition tasks (e.g., AFFECT, Dolan and Fullam 2006; NimStim, Mowle et al. 2019). Whilst these are reliable, empirically validated emotion recognition tasks, restriction to Western measures limits the generalisability of findings to non-Western cultures. For example, previous studies have found differences between individualist and collectivist cultures: because individualist cultures encourage emotional moderation and discourage expression, particularly for negatively valanced emotions (Elfenbein and Ambady 2002), people from individualist cultures are typically better at recognising emotions than people from collectivist cultures (Yang et al. 2019). Further research is needed to determine whether the differences in facial affect recognition identified in the present study extend to people from other cultural groups.
Conclusions
The current meta-analysis is the first to synthesise studies examining facial emotion recognition abilities in psychopathy, focussing on the adult population. The results indicate that adults with higher levels of psychopathic traits have impaired facial emotion recognition. As the ability to recognise and appropriately respond to others’ emotions forms a vital component of empathy, impaired ability to accurately recognise and interpret emotional facial cues is a likely contributor to the interpersonal disturbances that characterise psychopathy (American Psychiatric Association 2013), and subsequently contribute to the societal burdens imposed by antisocial and delinquent behaviours (Reidy et al. 2015). Identification and intervention are thus important in helping to reduce the impact of psychopathy on both the individual and broader society.
Disclosures
This research received no external funding.
Institutional review board statement: Not applicable.
The authors declare no conflict of interest.
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