Impact of antisocial and psychopathic traits on emotional facial expression recognition in alcohol abusers

Abstract

As there are independent reports that alcohol dependence and antisocial personality pathology (particularly psychopathy) are associated with impaired face affect recognition, we examined the relationship between antisocial personality traits and face affect recognition in 23 detoxified inpatient alcohol abusers and 26 healthy controls. All participants were rated on the Antisocial Personality Questionnaire (APQ), and the alcohol abuse sample was rated on the Psychopathy Checklist: Screening Version. A computerized face affect recognition task was used to assess the six basic emotions. Overall, alcohol abusers were significantly worse than healthy controls at recognizing emotional facial expression, particularly anger and sadness. Recognition of disgust was negatively correlated with psychopathy (particularly the social deviance factor) and with several subscales of the impulsivity/ hostility factor on the APQ. Fear recognition was also negatively correlated with a number of APQ subscales within the impulsive/hostility factor. Our findings point to the importance of assessing co-morbid antisocial personality pathology in studies looking at face affect recognition deficits in substance abusers. The work also suggests that the interpersonal difficulties seen in some alcohol abusers may be related to impairments in emotional information that are primarily due to co-morbid personality pathology.

Recognition of facial expressions by alcoholic patients: a systematic literature review

“…The majority of the clinical subjects were alcohol-abstinent individuals who were undergoing treatment. Of these individuals, 80.8% were inpatients (n=21), 3 , 4 , 7 , 9 , 11 – 15 , 19 – 26 , 28 , 31 – 33 and 7.7% were outpatients (n=2). 10 , 29 The samples ranged from eight to 52 subjects (mean 22.9 and median 24), with mean ages between 35.7 and 60.8 years (median 44.7 years) and varying education levels.…”

“… 32 The controls were primarily healthy subjects from the general population without a prior history of alcohol dependence (n=23). Exclusion criteria for both groups were as follows: presence of comorbidity with any Axis I psychiatric disorder (n=17); 3 , 4 , 9 – 12 , 14 , 20 , 22 , 25 – 28 , 30 – 33 presence of comorbidity with any Axis II psychiatric disorder (n=1); 33 visual or hearing impairment (n=8); 4 , 11 – 14 , 24 – 26 epilepsy (n=9); 11 – 14 , 24 – 27 , 32 health problems in general (n=8); 4 , 7 , 12 , 14 , 24 , 25 , 27 , 32 intellectual impairment (n=4); 3 , 9 , 20 , 27 Wernicke-Korsakoff syndrome (n=2); 26 , 27 and use of medications (n=2). 21 , 32 Five studies did not present their exclusion criteria.…”

“…Several studies found differences in accuracy2,14,20,21,23,24,48 between alcoholics and controls. For sadness3,9,10,28,30 and disgust,3,9,13,20,30,33 which are considered negative emotions, there was a slight tendency towards impairment in the group of alcoholics. The same result was observed for emotional intensity, as some studies found that alcoholics were less sensitive in FERT3,9 and required greater emotional intensity to judge anger7,9,23 and fear 7,9,26,31…”

Hostility and Cigarette Use: A Comparison Between Smokers and Nonsmokers in a Matched Sample of Adolescents

“…This facial emotion processing sensitivity may result in hostile individuals viewing their own environment as especially threatening , further exacerbating hostile attitudes and possibly creating a proximal risk for smoking. Past research with emotion processing tasks have shown that psychopathy is negatively related to the successful recognition of disgust (Acharya & Dolan, 2012) and sadness (Blair, Colledge, Murray, & Mitchell, 2001). Also, among adult smokers using 10 or more cigarettes per day, those with high scores on the Cook-Medley Hostility inventory required a more expressive face to successfully recognize happiness .…”

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

“…In the past ten years, many heuristic optimization algorithms, such as particle swarm optimization (PSO) [1][2][3], ant colony optimization (ACO) [4,5], bat algorithm (BA) with triangle-flipping strategy [6], fly algorithm (FA) [7][8][9], cuckoo search [10][11][12][13], pigeon-inspired optimization algorithm, and genetic algorithm (GA) [14], have been developed to solve complex computational problems. It became popular because of its superior ability, which deals with a variety of complex issues.…”