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. Author manuscript; available in PMC: 2020 Aug 4.
Published in final edited form as: Addict Behav. 2020 Feb 19;106:106359. doi: 10.1016/j.addbeh.2020.106359

Impact of polysubstance use on social and non-affective cognitive performance among treatment-seeking individuals with alcohol use disorders

Ben Lewis a,b,c,*, Christian C Garcia b,c, Riley Bohan c, Sara Jo Nixon a,b,c
PMCID: PMC7400962  NIHMSID: NIHMS1612161  PMID: 32109774

Abstract

Background:

Individuals with alcohol use disorder (AUD) display deficits across a range of cognitive processes. Decrements in social cognition may be particularly important for interpersonal functioning and post-treatment adaptation. Although social cognitive deficits are associated with chronic use of numerous substances, the role of polysubstance use in AUD-associated deficits remains largely unaddressed.

Methods:

Community volunteers (n = 49; 22 men) and inpatient treatment-seekers with AUD were administered neurocognitive tasks indexing emotion processing and non-affective cognitive functioning. Tasks included an emotion discrimination task, a working memory task with affective stimuli, a general face processing (control) task, two measures of executive function, and two measures of visual spatial function. AUD subgroups included individuals with no recent (6-month) polysubstance use (AUD-Only; n = 22; 15 men), and those with at least weekly use (Poly-SU; n = 22; 18 men).

Results:

Poly-SU individuals evinced disadvantaged performance relative to other groups on the emotion discrimination task [ps ≤ 0.001], affective working memory task [ps ≤ 0.050], and two executive function measures [ps ≤ 0.051]. No differences were observed for visual spatial functioning [ps ≥ 0.498] or general face processing [ps ≥ 0.190]. No performance differences between AUD-Only and community volunteers were noted.

Conclusions:

Results extend the emerging literature exploring emotion processing in AUD and add to the established literature regarding cognitive deficits in this population. The data suggest that among individuals with AUD, those with polysubstance use may be particularly vulnerable to deficits in decoding emotional face content. The current work highlights the need to incorporate more nuanced and careful considerations of poly-substance use in the design and analysis for future investigations of alcohol-associated deficits in emotion processing.

Keywords: Alcohol use disorder, Polysubstance use, Emotion processing, Social cognition, Face processing

1. Background

Individuals with alcohol use disorder (AUD) display impairments in neurocognitive function, frequently including deficits in executive function (for reviews see Bates, Bowden, & Barry, 2002; Sullivan, Harris, & Pfefferbaum, 2010; Oscar-Berman et al., 2014; Le Berre, Fama, & Sullivan, 2017). While the nature and severity of cognitive perturbations are heterogeneous, current estimates indicate 50–80% of AUD individuals are affected (Bates et al., 2002), with 30–40% exhibiting clinically relevant levels of compromise throughout the first 2 months of abstinence (Diagnostic & Statistical Manual of Mental Disorders Fifth Edition, Association, 2013). Although the literature interrogating the nature of these deficits is substantive, at least two facets remain understudied, including the degree to which social cognition is impacted and the extent to which polysubstance use affects outcomes; both are addressed in the current study.

Social interaction and interpersonal functioning depend on perception, identification, and implementation of cues transmitting affective information. A growing literature has described disruptions across these processes among individuals with AUDs, specifically including deficits in the capacities to interpret affective states, feelings, and emotions of others (e.g., Philippot et al., 1999; Maurage et al., 2011a; for reviews see Uekermann & Daum, 2008; Onuoha, Quintana, Lyvers, & Guastella, 2016; Le Berre, 2019). While the broad range of tasks indexing emotion identification has included body postures (e.g., Maurage et al., 2009), vocal prosody (e.g., Monnot, Nixon, Lovallo, & Ross, 2001), and music (Kornreich et al., 2013), emotional facial expressions (EFEs) are employed most commonly and appear particularly sensitive to alcohol-related compromise (e.g., Frigerio, Burt, Montagne, Murray, & Perrett, 2002; Oscar-Berman, Hancock, Mildworf, Hutner, & Weber, 1990; Philippot et al., 1999; Townshend & Duka, 2003; Foisy et al., 2005; for reviews see Castellano et al., 2015; Donadon & Osorio Fde, 2014). This work reveals AUD-associated deficits in identification accuracy and processing speed (e.g., Maurage, Campanella, Philippot, Martin, & De Timary, 2008, Kornreich et al., 2003; Foisy et al., 2007), altered neural responses to emotionally valent stimuli (e.g., Marinkovic, Oscar-Berman, & Urban, 2009; Maurage, Campanella, Philippot, Martin et al., 2008; Salloum et al., 2007), and impairments in judging intensity which vary by emotion (e.g., overestimation of anger, underestimation of sadness; Maurage et al., 2009). In addition to these “first-person” measures, AUD-associated decrements persist in more complex indices of emotional reasoning and affective theory of mind, including humor processing (e.g., Uekermann, Channon, Winkel, Schlebusch, & Daum, 2007), social error (e.g., Cox et al., 2018), and capacity to perceive the emotional perspective of others (e.g., Maurage et al., 2011).

Several groups have interrogated EFE processing in samples with other substance use disorders (SUDs; e.g., Bayrakci et al., 2015; Henry, Mazur, & Rendell, 2009; Verdejo-Garcia et al., 2013; Yip & Lee, 2006). In a recent meta-analysis, Castellano and colleagues (2015) reported that despite substantial heterogeneity within the AUD and SUD literatures, the estimated effect sizes between literatures were remarkably comparable, i.e., Cohen’s ds of 0.67 and 0.65, respectively. Unfortunately, the few examinations explicitly addressing polysubstance use (e.g., Fernandez-Serrano, Lozano, Perez-Garcia, & Verdejo-Garcia, 2010) lack specific comparison of use within AUD contexts.

Given the prevalence of polysubstance use in AUD populations, among whom approximately one third are estimated to have lifetime histories of comorbid SUDs (Staines, Magura, Foote, Deluca, & Kosanke, 2001), the common practice of excluding polysubstance using individuals from AUD-focused examination substantially limits sample representation and ecological validity. The paucity of attention to these issues represents a substantive gap in the extant literature, and is the focus of the current study.

We conducted a cross-sectional, between-groups analysis of performance across several measures, including an EFE processing task, an attend/ignore working memory task with emotionally salient stimuli, and four non-affective tasks (two executive function; two visual processing). Groups included community controls (CCs) and treatment-seeking individuals with AUD, subgrouped into those with significant current polysubstance use (Poly-SU) and those without (AUD-Only). We have previously reported deficits in executive functioning and emotion processing in a sample of AUD individuals overlapping the current group (Hoffman, Lewis, & Nixon, 2019), thus we expected both AUD groups would perform more poorly relative to CCs. We hypothesized that if alcohol use patterns/severity were equivalent between groups, deficits among Poly-SU individuals would be compounded by consequences of additional substance use, relative to other groups. However, given the equivalent effect sizes between AUD and SUD populations regarding emotion processing deficits (Castellano et al., 2015), potential differences between Poly-SU and AUD-Only individuals under conditions of non-equivalent alcohol consumption/severity remained an empirical question. Of further interest was the consistency of group differences across the variety of affective and non-affective cognitive measures.

2. Materials and methods

All participants provided written informed consent and were compensated for their time. The University of Florida Medical IRB approved all procedures.

2.1. Participants

Participants (N = 93) included 49 CCs (22 men) and 44 inpatient treatment seekers with AUD, including subgroups of 22 AUD-Only (15 men) and 22 Poly-SU individuals (18 men). Participants provided basic demographic (e.g., age, education) information and self-reported medical histories. Substance use histories were collected, including chronicity, quantity, and frequency (Quantity Frequency Index; QFI; Cahalan, Cissin, & Crossley, 1969) of recent consumption. Probabilistic psychiatric disorders were assessed with the computerized Diagnostic Interview Schedule, Version IV (cDIS-IV; Robins, Cottler, Bucholz, Compton, North, & Rourke, 2000). Utilization of the alcohol craving questionnaire (Singleton, Tiffany, & Henningfield, 2000) facilitated characterization of DSM-5 symptom severity. AUD groups met DSM-5 criteria for moderate-to-severe AUD and were at least 21 days abstinent. Individuals were designated AUD-Only if they reported no recent (6 month) use of other substances (excluding nicotine and less than weekly cannabis use). Poly-SU individuals reported greater than weekly use of at least one substance not including alcohol, nicotine, or cannabis. Substance use data were gathered explicitly in the context of non-prescription use. In the current sample, this included use of opioids, psychostimulants, and/or benzodiazepines. Among CCs, histories of substance use disorders were exclusionary, as were recent drinking histories (i.e., > 2/3 drink per day average, for women/men, respectively) suggestive of patterns that significantly exceeded “low-risk” guidelines (Department of Health and Human Services, 2015). Negative consequences of recent alcohol and drug use were quantified using the Short Inventory of Problems (SIP; Miller, Dolgoy, Friese, & Sita, 1996). The alcohol-specific SIP-2R (Miller et al., 1996) and a parallel form assessing drug use (SIP-DU) were both employed. A derived consequence score was computed using the highest value across substances for each SIP item. SIP subscales address physical, intrapersonal, work/financial, impulse control, and interpersonal consequences. Given the relevance of emotion processing deficits to interpersonal functioning (e.g., Hoffman et al., 2019), analyses included both total and interpersonal scores.

Study inclusion was limited to individuals between 25 and 59 years of age with between 10 and 16 years of education. Exclusionary conditions included: 1) significant neurologic disorder/insult (e.g., stroke); 2) medical conditions (e.g. epilepsy) challenging interpretation of neurobehavioral function; 3) current medications compromising neurobehavioral function (e.g., benzodiazepines); 4) lifetime psychotic or bipolar disorder; and 5) current panic disorders or social phobias.

On testing day, negative results for breath alcohol content (Intoxylizer® 400PA; CMI, Inc., Owensboro, KY), urine toxicology screen for substance use (10-panel screen including cannabis, benzodiazepines, psychostimulants, opioids; Alere San Diego Inc., San Diego CA), and pregnancy tests were required for continued participation in all groups.

2.2. Face processing tasks

EFE processing was assessed with an emotion judgement task (EJT; adapted from Maurage, Campanella, Philippot, Martin, & De Timary, 2008). To facilitate interpretation of EJT performance, a sex judgement task (SJT) provided control for non-affective facial processing. A two-choice discrimination procedure required responding on a two-button response pad with the index finger of the dominant hand. Task instructions emphasized the importance of response speed and accuracy. Task stimuli were adapted from the Pictures of Facial Affect stimulus set (Ekman & Friesen, 1976). Response windows were initiated and terminated with stimulus onset and offset (1500 ms presentation time). Interstimulus intervals were 300 ms. Exemplar stimuli from both tasks are presented in Fig. 1.

Fig. 1.

Fig. 1.

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Fig. 1A depicts an exemplar trial from the emotion judgement task (EJT), including face stimuli and instructional cue. The depicted trial includes an angry face at the 65% morph level presented during a “Sad or Angry” block. Fig. 1B depicts an exemplar trial from the sex judgement task (SJT). The depicted trial includes a face morphed at 65%/35% male/female. Instructional cues were maintained on-screen for all tasks/trials. Right/left position of on-screen cues was kept consistent with right/left response button position.

Emotion Judgement Task

For each face model, four facial expressions (happy, angry, sad and neutral) were used to create the experimental stimuli. Task stimuli were created using digital morphing software (FantaMorph) to combine each of eight (4 men; 4 women) models’ neutral images with each of their emotional images, at two levels of intensity. Thus, 6 stimuli were generated for each model, including happy/neutral, angry/neutral, and sad/neutral combinations at 65% and 95% levels of emotion intensity. Preceding each task block, participants were informed of the two emotions to be discriminated (e.g., Sad vs. Happy) and their corresponding response buttons, which were alternated across blocks. Each block contained only a single pair of emotions to be discriminated, with all combinations of unique face model (8), emotion (2), and morph level (2) included (32 trials/block). Sequences of stimuli presentation were pseudorandomized such that sequential repetition of images containing either the same poser or same sex/morph/emotion combination was rare (< 5% chance per block). Each of the three emotion pairs were repeated across 5 blocks (15 blocks total), with no sequential pair repetition.

Sex Judgement Task

Stimuli were created from pairs of male and female face models. Different models were used in the SJT and EJT tasks. Each of 8 models was used in only one of the 4 male/female pairs. Neutral faces from each unique pair were used to generate morphed face stimuli at 65% and 95% for each sex. Thus, 16 unique stimuli were generated, with 4 stimuli from each of the following categories: 1) 95% female; 2) 65% female; 3) 65% male; and 4) 95% male. Each stimulus was presented once per block, with pseudorandom distribution to avoid sequential pair/morph repetitions. In all 16 blocks, participants indicated whether the face was more masculine or feminine.

2.3. Emotional attend/ignore working memory task

The capacity to appropriately attend to or ignore emotionally salient stimuli constitutes a significant, if often-overlooked, facet of emotion processing. Thus, the current work incorporated an attend/ignore working memory task, modified from Gazzaley, Cooney, Mcevoy, Knight, and D’esposito (2005) to incorporate images from the FACES stimuli set (Ebner & Johnson, 2010). The task includes presentation of two “face” stimuli and two “scene” stimuli per trial (i.e., 4 target stimuli per trial). On any given trial, participants were instructed to attend to only one of the two stimuli sets; scene stimuli were utilized due to their discernibility from facial stimuli. All stimuli were grayscale. Facial stimuli were evenly distributed between male/female face models across the task, but same-sex stimuli were maintained within each trial.

Target stimuli were presented for 800 ms each, with 200 ms ISIs. All possible target sequences (e.g., Face-Scene-Face-Scene) were utilized, with equal pseudorandom distribution across trials. After presentation of the four target stimuli, a 9000 ms delay was introduced, with only a fixation cross presented onscreen. Following the delay, a probe image was presented for 1500 ms. Participants used a two-button response pad to indicate whether the probe image matched (50% trials) or did not match (50% trials) stimuli in the target set (4000 ms response period). A simplified diagram of this sequence is presented in Fig. 2.

Fig. 2.

Fig. 2.

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Fig. 2 depicts stimuli presentation timing/sequencing in the attend/ignore working memory task. Fixation crosses occurred between each target stimulus (200 ms each) but are not depicted in the figure. An exemplar target stimuli sequence is utilized (sequence of face/scene stimuli was varied across trials). The figure depicts stimuli presentation during a “remember scenes/ignore faces” instructional set in which irrelevant stimuli included surprised female faces and the probe image matched one of the relevant scene targets.

Two instructional sets were utilized. Participants were instructed to either “Remember scenes but ignore faces” or “Remember faces but ignore scenes”. Order of instruction sets was counterbalanced, with equal distribution across participants. Probe images remained consistent with instructions (i.e., all probes images were scenes for ‘remember scenes’ condition). Both sets included instruction to respond “as quickly and accurately as possible”. Following each instruction set, participants completed a block of 72 trials. Emotionality of facial stimuli was varied within each block, with each trial including either neutral, happy, or fearful faces (24 trials/block for each).

2.4. Non-affective cognitive measures

Selection of non-affective measures was directed to indexing executive and visual processing. The former were selected based on extant literature suggesting relationships with social cognitive processes. The latter, to elucidate any differences in visual processing which might potentially impact performance on the visually-dependent emotion processing tasks. Executive functions were assessed with standard administrations of the Digit Symbol Substitution Task (DSST; Wechsler, 1981; Reitan & Wolfson, 1985) and Trail-Making Test Form B (TMT-B; Reitan, 1955; Reitan & Wolfson, 1985). Visual processing abilities were assessed with two measures. Mental rotation abilities were assessed using the “Little Man” Task (LMT; Acker & Acker, 1982), in which participants must discriminate in which hand (i.e., Left vs. Right) a cartoon figure is holding an object. Presentation of the figure varied on two axes, including presentation facing toward/away from the viewer, and presentation upside-down/rightside-up, creating four possible presentation axis combinations with equal distribution across trials. A visual-perceptual analysis task (VPAT; Acker & Acker, 1982) assessed visual discrimination abilities. In each trial, three complex figures were presented onscreen, one of which deviated slightly from the other two identical figures. Participants were instructed to select the nonmatching figure. All tasks included instruction to respond “as quickly and accurately as possible”.

2.5. Data analysis

Planned analyses were conducted using PROC MIXED in SAS Version 9.4. All models were fit using restricted maximum likelihood estimation. Alpha was set at p = .05. Group (CC vs. AUD-Only vs. Poly-SU) was a fixed factor in all analyses. For EJT, SJT, and Attend/Ignore analyses, task condition (Attend/Ignore: Remember Faces vs. Ignore Faces), Face Content ([EJT: Happy vs. Angry vs. Sad]; [Attend/Ignore: Happy vs. Neutral vs. Fearful]; [SJT: Male vs. Female]) and Face Morph (EJT/SJT: 65% vs. 95%) were fixed and repeated factors. All models included Group by repeated factor interaction terms as fixed effects. Accuracy and reaction time (correct responses only) were dependent measures.

Following planned analyses, two sets of exploratory analyses were conducted: 1) Informed by results of demographic and drinking analyses, which indicated differences between AUD groups on several relevant measures, post hoc covariate analyses were conducted; 2) To examine relationships between DVs, pairwise correlations were conducted among a constrained set of outcomes (accuracy measures from emotion processing tasks; composite scores for executive function and visual processing). To characterize potential group differences in these relationships, Fisher’s r-to-z transformations were conducted to compare correlation coefficients between groups. These analyses served to both aid interpretation and provide preliminary guidance for future investigations.

3. Results

3.1. Participants

Participants (N = 93 [49 CCs; 22 AUD-Only; 22 Poly-SU]) included Caucasian (n = 67), Black/African-American (n = 17), Asian/Asian American (n = 2), and American Indian (n = 1) individuals. 6 participants endorsed “Other”, or failed to endorse a racial group. Five participants endorsed Hispanic ethnicity. Among individuals in the Poly-SU group, recent substance use included at least weekly use of cannabis (27%), psychostimulants (55%), opioids (55%), and benzodiazepines (32%). Consistent with the extant literature, differences in education, affective symptomatology, quantities of alcohol consumption, and negative consequences of drinking were observed between CCs and AUD groups. AUD-Only individuals were substantially older than the Poly-SU group, had later onset of alcohol problems, later initial entry into treatment, higher average alcohol consumption, and lower derived SIP scores (both total and interpersonal subscale). No difference between AUD subgroups was observed for days of alcohol abstinence. Descriptive statistics and summary of results are presented in Table 1. To further characterize substance use among AUD subgroups, proportional endorsements of lifetime problem use (excluding alcohol) were compared; problem use histories were endorsed by approximately 27% of the AUD-Only group and all (100%) participants in the Poly-SU group.

Table 1.

Demographic, affective, and substance-related measures.

Measure CCs M (SD) AUD-OnlyM (SD) Poly-SU M (SD) Group Contrasts
Age (yrs) 41.9 (12.4) 46.4 (8.8) 37.7 (7.7) Only > Poly, p < .01
Education (yrs) 14.8 (1.5) 13.2 (1.5) 12.9 (1.6) AUDs < CC; ps < 0.01
Depressive Symptomsa 5.2 (6.0) 15.5 (10.1) 15.0 (9.4) AUDs > CC; ps < 0.01
Anxiety Symptomsb 43.6 (7.7) 52.6 (22.7) 56.3 (13.3) AUDs > CC; ps ≤ 0.02
Avg. alcohol (oz./day)c 0.2 (0.3) 17.0 (10.4) 12.5 (8.7) Only > Poly > CC; ps ≤ 0.03
Max. alcohol (single day) 1.8 (1.5) 20.5 (12.2) 20.7 (9.7) AUDs > CC; ps ≤ 0.03
Substance Use Consequencesd 0.8 (1.7) 31.1 (10.0) 39.5 (7.4) Poly > Only > CC; ps < 0.01
Interpersonal Consequencesd 0.2 (0.6) 6.9 (2.7) 8.5 (1.5) Poly > Only > CC; ps < 0.01
Alcohol Consequencese 0.8 (1.7) 31.1 (10.0) 32.9 (10.7) AUDs > CC; ps < 0.01
Drug Consequencesf – – 35.4 (13.1) –
Alc. Problem Onset Age – 26.5 (10.3) 20.0 (5.1) Only > Poly, p = .01
Initial Treatment Age – 42.0 (10.8) 32.6 (7.1) Only > Poly, p < .01
Days Abstinent – 44.1 (18.3) 41.7 (13.7) Only = Poly, p = .62
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a

Beck Depression Inventory-II;

b

Spielberger State Anxiety Index (age-adjusted scores);

c

Alcohol Quantity/Frequency Index - averaged across 6 mo. prior to treatment initiation;

d

Derived SIP score across substances;

e

Short Inventory of Problems - Revised;

f

Short Inventory of Problems – Drug Use.

3.2. Emotion judgement task

Poly-SU individuals were less accurate in decoding face emotions [F (2,87) = 8.17, p < .001], with contrasts confirming reduced accuracy relative to CC and AUD-Only groups [ts ≥ 3.58, ps ≤ 0.001], which did not differ [p = .508]. Reaction time results indicated a significant group effect [F(2,87) = 7.61, p < .001], albeit with unexpected directionality; group comparisons indicated AUD-Only individuals responded more rapidly relative to CC and Poly-SU groups [ts ≥ 3.04, ps ≤ 0.003], which did not differ [p = .173]. No group interactions with either emotion or morph level were observed in accuracy or reaction time models. Results are depicted in Fig. 3.

Fig. 3.

Fig. 3.

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Fig. 3 depicts EJT accuracy by Group (LS Means ± SE). ** Denotes significant differences (ps ≤ 0.001) relative to other groups.

3.3. Sex judgement task

To afford comparison with effects observed in the EJT task, full SJT univariate and group contrast results are presented, regardless of significance. No effect of group [F(2,88) = 0.89, p = .415] or significant contrasts [ts ≤ 1.32, ps ≥ 0.190] were observed for SJT accuracy. Reaction time analyses yielded no group effect [F(2,87) = 1.85, p = .163], although group contrasts suggested a trend toward slower responding in ALC-Poly individuals relative to ALC-Only individuals [t = 1.81, p = .074]. No other differences we noted [ts ≤ 1.60, ps ≥ 0.112]. Results are depicted in Fig. 4.

Fig. 4.

Fig. 4.

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Fig. 4 depicts SJT accuracy by Group (LS Means ± SE).

3.4. Emotional attend/ignore working memory task

Poly-SU individuals displayed accuracy deficits in the attend/ignore task [F(2,87) = 3.45, p = .036], with contrasts confirming lower accuracies relative to CC and AUD-Only groups [ts ≥ 1.98, ps ≤ 0.050], which did not differ [p = .322]. Analysis of reaction time yielded similar results, with Poly-SU individuals responding more slowly than other groups [F(2,87) = 3.85, p = .025]. Contrasts confirmed delayed responding relative to CC and AUD-Only groups [ts ≥ 2.03, ps ≤ 0.045], which did not differ [p = .255]. No interactions with instruction set or face emotion were observed for accuracy or reaction time measures. Results are depicted in Fig. 5.

Fig. 5.

Fig. 5.

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Fig. 5 depicts Attend/Ignore Working Memory Task accuracy by Group (LS Means ± SE). * Denotes significant differences (ps ≤ 0.050) relative to other groups.

3.5. Non-affective measures

TMT-B results revealed longer time-to-completion among Poly-SU individuals [F(2,88) = 5.71, p = .005], with contrasts confirming disadvantaged performance relative to both CC and AUD-Only groups [ts ≥ 1.98, ps ≤ 0.051], which were indistinguishable [p = .309]. Similarly, Poly-SU individuals translated fewer symbols in the DSST [F (2,88) = 6.59, p = .002], relative to both other groups [ts ≥ 2.60, ps ≤ 0.011], which did not differ [p = .634]. In contrast to these executive measures, no group differences were observed in either visual processing task [Fs ≤ 0.49, ps ≥ 0.498]. Results are depicted in Fig. 6.

Fig. 6.

Fig. 6.

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Fig. 6 depicts performance in executive measures by Group (LS Means ± SE). Fig. 6A depicts task completion time in the Trail Making Test-Form B. Fig. 6B depicts scaled scores on the Digit Symbol Substitution Task. * Denotes significant differences (ps ≤ 0.051) relative to other groups.

3.6. Covariate analyses

Given the differences between AUD-Only and Poly-SU groups on age, average alcohol consumption, substance use consequences, onset of alcohol problems, and initial treatment entry, these variables were considered as potential covariates. Correlation analyses revealed no significant associations between DVs and either consequences, problem onset, or initial treatment entry. Relationships with age and alcohol consumption were modest and not consistently observed, (r = 0.28 for age and EJT accuracy; r = 0.27 for QFI and attend/ignore accuracy), but appeared sufficient to warrant inclusion in covariate analyses. However, excepting reaction time measures, for which age had an expected positive association, neither covariate accounted for significant variance in DVs. In all cases, the significance and directionality of group effects persisted.

3.7. Exploratory/descriptive analyses

Given the paucity of reports comparing performance either between emotion processing tasks, or between emotion processing and non-affective measures, we conducted exploratory analyses to characterize relationships between DVs. Pearson correlations were conducted between accuracy measures on the affective tasks and composite scores of executive and visual spatial function (averaged z-scores of TMT-B/DSST and LMT/VPAT, respectively). Pairwise correlations between all four measures were significant, including a particularly strong association between affective measures (r = 0.82). Correlation coefficients for the full sample are reported in Table 2a. Fisher’s r-to-z transformations were employed to facilitate comparison of coefficients between groups. Coefficients were equivalent between AUD subgroups across all four comparisons (zs ≤ 1.36, ps ≥ 0.174), thus subgroups were combined to facilitate comparison between CC and AUD groups. Correlations between performance on visual spatial and affective tasks were stronger among CCs (ps ≤ 0.001). In contrast, relationships between executive and affective task performance appeared stronger among AUD individuals (ps ≤ 0.061). Correlation matrices for each group are presented in Tables 2b/c, respectively, with p-values indicating between-group significance.

Table 2.

Correlation matrices: accuracy on affective measures & executive/visual composite scores.

2a. All Groups Executive Function Vis-Spatial Function Emotion Judge Ace
Vis-Spatial Function 0.348**
Emotion Judge Acc 0.487** 0.562**
Attend/Ignore Acc 0.454** 0.544** 0.822**
**All ps < 0.001
2b. AUD Executive Function Vis-Spatial Function Emotion Judge Ace
Vis-Spatial Function 0.367
Emotion Judge Acc 0.617¥ 0.322**
Attend/Ignore Acc 0.653* 0.262** 0.811
Differences relative to CCs: ¥p = .061; *p = .022; **ps≤0.001
2c. CCs Executive Function Vis-Spatial Function Emotion Judge Ace
Vis-Spatial Function 0.336
Emotion Judge Acc 0.367¥ 0.773**
Attend/Ignore Acc 0.270* 0.769** 0.834
Differences relative to AUD groups: ¥p = .O61; *p = .022; **ps≤ 0.001
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4. Discussion

Individuals with AUD who endorsed polysubstance use displayed disadvantaged performance in tasks probing emotion processing, attention to emotionality, and non-affective measures of executive function. These findings are consistent with diverse literatures examining neurocognitive concomitants of chronic alcohol/substance use. However, the observed performance disadvantages among this group, relative to non-polysubstance using AUD individuals, are novel. These findings contribute to several extant bodies of literature, including studies of emotion processing in chronic substance users and investigations attempting to disentangle complex consequences of poly-substance use.

Results were consistent with the hypothesis that polysubstance use would exacerbate neurocognitive deficits in treatment-seeking individuals with AUD. However, we proposed this hypothesis under conditions of similar alcohol use patterns and severity. Severity measures for negative consequences of drinking (SIP-2R) were comparable between AUD-Only and Poly-SU individuals, however appropriate comparison may have been challenged by ceiling effects, given the exceptionally high scores in both groups. Notably, comparison of the derived SIP score identified greater use consequences in the Poly-SU group. Consistent with observed emotion processing deficits, Poly-SU individuals also reported more severe interpersonal consequences of use (SIP subscale). Consideration of between-group drinking patterns revealed the AUD-Only group first experienced alcohol-related problems approximately six years later than the Poly-SU group, and were approximately ten years older when first entering treatment. Taken together, these findings suggest that Poly-SU individuals experienced more severe substance use consequences and drinking trajectories, consistent with their disadvantaged cognitive performance. However, the substantially greater quantities of daily alcohol consumed by AUD-Only individuals highlights the multivariate nature of this comparison and challenges between-group severity assessment.

Emotional face processing deficits observed among the Poly-SU group are broadly consistent with those described in other (non-alcohol) substance use disorders, specifically including studies employing EFE paradigms (e.g., Verdejo-Garcia, Rivas-Perez, Vilar-Lopez, & Perez-Garcia, 2007; Craparo et al., 2016; Foisy et al., 2005). However, while emotion processing impairments are reported in AUD and SUD populations, few studies have attempted to disentangle complex relationships regarding polysubstance use or explicitly compared AUD subgroups on the basis of their polysubstance use. Given estimates that approximately two thirds of individuals with AUDs endorse recent polysubstance use and approximately one third have lifetime histories of comorbid substance use disorders (Staines et al., 2001), this gap is somewhat surprising. In much of the relatively small literature focusing on emotion processing in AUD, polysubstance use is commonly addressed via exclusion of individuals with comorbid SUDs (e.g., Dethier, El Hawa, Duchateau, & Blairy, 2014; Kornreich et al., 2013; Maurage, Campanella, Philippot, Martin, & De Timary, 2008; Uekermann, Daum, Schlebusch, & Trenckmann, 2005). While appropriate, the exclusion of comorbid use disorders fails to eliminate significant subclinical use, which remains largely unaddressed in these studies. Although less common, some either fail to address use (e.g., Foisy et al., 2007; Kornreich et al., 2001) or describe use among the sample without further investigation or statistical control (e.g., Salloum et al., 2007; Lewis, Price, Garcia, & Nixon, 2019). Among the notable exceptions, Kornreich et al. (2003) examined EFE processing among several SUD groups, including AUD-Only individuals and those with comorbid opioid use disorders. These subgroups were indistinguishable from one another, but displayed deficits relative to controls. While Poly-SU performance in the current work was consistent with Kornreich’s observations, the equivalence between AUD-Only and CC groups was inconsistent with much of the extant literature, but not unprecedented (e.g., Uekermann et al., 2005). This discrepancy may be related to specifics of the EFE tasks utilized, which often vary substantially across studies. The current EFE task was modified from Maurage, Campanella, Philippot, Martin, & De Timary, 2008, and included conditions under which emotionality was salient and relatively easily discriminated, with mean accuracies across groups ranging from ~ 84–91%. In contrast, the paradigm utilized by Kornreich and colleagues employed less discriminable stimuli (e.g., morph levels of 30%) and required selection from a larger array of eight potential emotions, resulting in accuracies of < 50%, even among controls. As noted by Fein, Key, and Szymanski (2010), increasingly difficult tasks are more likely to reveal group differences in EFE decoding. Thus, the current task may have been insufficient to detect relatively subtle differences in the AUD-Only group, but adequate for observation of more exaggerated deficits among Poly-SU individuals. While plausible, this interpretation fails to explain either the similar pattern of results observed in the attend/ignore task or the between group differences noted in other studies utilizing the task (e.g., Maurage, Campanella, Philippot, Martin, & De Timary, 2008). However, an advantage of this task lies in its capacity to demonstrate the potential functional impact of affective processing deficits. For instance, despite easily discriminable stimuli (e.g., Fig. 1a), individuals in the Poly-SU group misidentified emotions at a rate of over 50% higher than that of community controls.

While EFE performance is likely impacted by individual differences in the degree to which emotionally-laden stimuli may tax attentional resources, EFE judgement paradigms provide an insufficient means by which to evaluate susceptibility to affective distractors. To address this, we utilized a modified attend/ignore working memory task (Gazzaley et al., 2005), in which the capacity to ignore task-irrelevant information (including emotional faces) should advantage performance. However, performance disadvantages in the Poly-SU group persisted across task conditions, i.e., Poly-SU individuals were less accurate in remembering target images regardless of the task relevance or emotional salience of the faces. While no group by condition interactions were observed, the conditions functioned as anticipated (e.g., greater difficulties ignoring faces vs scenes and negative vs. positive emotions). While disadvantaged Poly-SU performance is notable, the lack of sensitivity to task conditions challenges interpretation. Given the deficits in TMT-B and DSST performance, one potential interpretation is that these results reflect working memory deficits, with no specific contributions of emotion processing or attention to emotionality, despite the affective components of the task. This interpretation is supported by the strong correlation (r = 0.65) between attend/ignore performance and executive functioning among the AUD groups. Notably, although relevance of face content varied by task condition, the emotional content of the faces remained irrelevant across instructional sets. Thus, an alternative interpretation is that inappropriate attention to emotionality may have impacted both instructional sets, impairing both maintenance of scene-specific memories during the ‘remember scenes’ condition and encoding of face-specific features during the ‘remember faces’ condition. This assertion is supported by the exceptionally strong correlation across groups (r = 0.82) between performance on both emotion processing tasks. Importantly, these interpretations are not mutually exclusive; both provide guiding framework for future extensions of this work.

Results from non-affective cognitive measures were generally consistent with an extensive literature identifying particular susceptibility of executive functioning in AUD (e.g., Le Berre et al., 2017; Pitel et al., 2007;), including AUD groups with polysubstance use (Nixon, Paul, & Phillips, 1998), as well as some sparing of visual spatial function in these populations (Nixon et al., 1998). The pattern of group differences across affective and non-affective measures was interesting, but not unexpected given an extensive literature associating the capacity to infer mental states of others with multiple executive processes (e.g., Wade et al., 2018). However, empirical study of relationships between executive functions and emotion-specific inferences are limited, even in community samples. Work by Maurage et al. (2011b, 2016) suggests that relative to non-affective mental inferences, which appear largely conserved, perceptions of emotionality may be particularly vulnerable to AUD-associated insult. Thus, the most parsimonious interpretation of associated affective/executive performance deficits among individuals with AUD is shared vulnerability to alcohol effects, perhaps due to shared reliance on prefrontal cortical functioning, as suggested in reviews conducted by Uekermann and Daum (2008) and Le Berre (2019). While results from the current Poly-SU sample are consistent with this interpretation, the difference between AUD subgroups and equivalence between AUD-Only and CC groups both suggest that use of additional substances in AUD samples may have an underappreciated contribution.

5. Limitations

Several limitations to this work should be noted: 1) Substance use in the Poly-SU group was heterogeneous, including use of multiple drug classes at frequencies varying from weekly to daily use. While the observed differences relative to AUD-Only individuals are notable, extensions of this work statistically powered to differentiate contributions of specific drug classes and use patterns remain critical. 2) The cross-sectional nature of the study allows for characterization of recently detoxified individuals, but precludes generalization to functioning prior to treatment entry or during mid- to long-term sobriety. Future investigations of emotion processing employing longitudinal designs will substantially improve generalizability and better clarify the potential role of emotion processing in sustained recovery. 3) The current work includes only behavioral and self-report data. Utilization of neuroimaging and/or electrophysiological methods will be critical to elucidating neural mechanisms underlying these observed behavioral differences. 4) While the employment of a well-validated and commonly-used stimuli set is a strength of this study, the set is limited to images of Caucasian individuals. The use of multi-racial stimuli sets or racially ambiguous stimuli would improve the generalizability of findings. 5) The sample size (n = 22) in both AUD subgroups was modest, potentially resulting in lower-than-optimal power, especially given the heterogeneity in substances/patterns among the POLY-SU group.

6. Summary

Taken together, our results extend the emerging literature exploring emotion processing in AUD and add to the established literature regarding cognitive deficits in this population. They suggest that among individuals with AUD, those with chronic polysubstance use may be particularly vulnerable to deficits in decoding emotional face content. While results from the attend/ignore task imply greater sensitivity to emotionally salient distractors among this polysubstance using group, further investigation is required to disentangle sensitivity to emotional stimuli from the observed deficits in executive functions. Given recent and growing attention to emotion processing in the AUD/SUD literatures and the range of unanswered questions regarding import to recovery, relapse, and function, the current work highlights the need to incorporate more nuanced and careful considerations of polysubstance use in the design and analysis of future emotion processing investigations.

Supplementary Material

Supplementary Table

HIGHLIGHTS.

  • Emotion processing was examined among individuals with alcohol use disorders.

  • Polysubstance users evinced disadvantaged performance relative to other groups.

  • Polysubstance users may be particularly vulnerable to deficits in decoding emotionality.

  • Polysubstance use must be considered in analyses of AUD-associated cognitive deficits.

Acknowledgements

The authors thank Robert Prather, Julianne Price, Lauren Hoffman, Ian Frazier, and Natalie Ebner for their contributions to data collection and/or task design. The authors thank the participating treatment facilities and all study volunteers.

7. Role of funding sources

Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under Grant Number R01 AA022456 (Sara Jo Nixon, PI). Further support was provided by K01AA026893 (Ben Lewis, PI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.addbeh.2020.106359.

References

  1. Acker W & Acker W 1982. Bexley maudsley automated processing screening and bexley maudsley category sorting test manual, Great Britain, NFER-Nelson Publishing. [Google Scholar]
  2. American Psychiatric Association DSM-5 Task Force. 2013. Diagnostic and statistical manual of mental disorders: Dsm-5, Washington, D.C., American Psychiatric Association. [Google Scholar]
  3. Bates ME, Bowden SC, & Barry D (2002). Neurocognitive impairment associated with alcohol use disorders: Implications for treatment. Experimental and Clinical Psychopharmacology, 10, 193–212. [DOI] [PubMed] [Google Scholar]
  4. Bayrakci A, Sert E, Zorlu N, Erol A, Saricicek A, & Mete L (2015). Facial emotion recognition deficits in abstinent cannabis dependent patients. Comprehensive Psychiatry, 58, 160–164. [DOI] [PubMed] [Google Scholar]
  5. Cahalan D, Cissin L & Crossley H 1969. American drinking practices: A national study of drinking behaviors and attitudes (monograph no. 6), New Brunswick, NJ, Rutgers Center of Alcohol Studies. [Google Scholar]
  6. Castellano F, Bartoli F, Crocamo C, Gamba G, Tremolada M, Santambrogio J, et al. (2015). Facial emotion recognition in alcohol and substance use disorders: A meta-analysis. Neuroscience & Biobehavioral Reviews, 59, 147–154. [DOI] [PubMed] [Google Scholar]
  7. Cox S, Bertoux M, Turner JJD, Moss A, Locker K, & Riggs K (2018). Aspects of alcohol use disorder affecting social cognition as assessed using the mini social and emotional assessment (mini-sea). Drug and Alcohol Dependence, 187, 165–170. [DOI] [PubMed] [Google Scholar]
  8. Craparo G, Gori A, Dell’aera S, Costanzo G, Fasciano S, Tomasello A, & Vicario CM (2016). Impaired emotion recognition is linked to alexithymia in heroin addicts. PeerJ, 4, e1864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dethier M, El Hawa M, Duchateau R, & Blairy S (2014). Emotional facial expression recognition and expressivity in type I and type II alcohol dependent patients. Journal of Nonverbal Behavior, 38, 89–105. [Google Scholar]
  10. Donadon MF, & Osorio Fde L (2014). Recognition of facial expressions by alcoholic patients: A systematic literature review. Neuropsychiatric Disease and Treatment, 10, 1655–1663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ebner NC, & Johnson MK (2010). Age-group differences in interference from young and older emotional faces. Cognition and Emotion, 24, 1095–1116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Ekman P & Friesen WV (1976). Pictures of facial affect, Palo Alto (CA), Consulting Psychologists Press. [Google Scholar]
  13. Fein G, Key K, & Szymanski MD (2010). Erp and rt delays in long-term abstinent alcoholics in processing of emotional facial expressions during gender and emotion categorization tasks. Alcoholism, Clinical and Experimental Research, 34, 1127–1139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Fernandez-Serrano MJ, Lozano O, Perez-Garcia M, & Verdejo-Garcia A (2010). Impact of severity of drug use on discrete emotions recognition in polysubstance abusers. Drug and Alcohol Dependence, 109, 57–64. [DOI] [PubMed] [Google Scholar]
  15. Foisy ML, Kornreich C, Petiau C, Parez A, Hanak C, Verbanck P, et al. (2007). Impaired emotional facial expression recognition in alcoholics: Are these deficits specific to emotional cues? Psychiatry Research, 150, 33–41. [DOI] [PubMed] [Google Scholar]
  16. Foisy ML, Philippot P, Verbanck P, Pelc I, Van Der Straten G, & Kornreich C (2005). Emotional facial expression decoding impairment in persons dependent on multiple substances: Impact of a history of alcohol dependence. Journal of Studies on Alcohol, 66, 673–681. [DOI] [PubMed] [Google Scholar]
  17. Frigerio E, Burt DM, Montagne B, Murray LK, & Perrett DI (2002). Facial affect perception in alcoholics. Psychiatry Research, 113, 161–171. [DOI] [PubMed] [Google Scholar]
  18. Gazzaley A, Cooney JW, Mcevoy K, Knight RT, & D’esposito M (2005). Top-down enhancement and suppression of the magnitude and speed of neural activity. Journal of Cognitive Neuroscience, 17, 507–517. [DOI] [PubMed] [Google Scholar]
  19. Henry JD, Mazur M, & Rendell PG (2009). Social-cognitive difficulties in former users of methamphetamine. British Journal of Clinical Psychology, 48, 323–327. [DOI] [PubMed] [Google Scholar]
  20. Hoffman LA, Lewis B, & Nixon SJ (2019). Neurophysiological and interpersonal correlates of emotional face processing in alcohol use disorder. Alcoholism, Clinical and Experimental Research, 43, 1928–1936. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kornreich C, Blairy S, Philippot P, Hess U, Noel X, Streel E, et al. (2001). Deficits in recognition of emotional facial expression are still present in alcoholics after mid- to long-term abstinence. Journal of Studies on Alcohol, 62, 533–542. [DOI] [PubMed] [Google Scholar]
  22. Kornreich C, Brevers D, Canivet D, Ermer E, Naranjo C, Constant E, et al. (2013). Impaired processing of emotion in music, faces and voices supports a generalized emotional decoding deficit in alcoholism. Addiction, 108, 80–88. [DOI] [PubMed] [Google Scholar]
  23. Kornreich C, Foisy ML, Philippot P, Dan B, Tecco J, Noel X, et al. (2003). Impaired emotional facial expression recognition in alcoholics, opiate dependence subjects, methadone maintained subjects and mixed alcohol-opiate antecedents subjects compared with normal controls. Psychiatry Research, 119, 251–260. [DOI] [PubMed] [Google Scholar]
  24. Le Berre AP (2019). Emotional processing and social cognition in alcohol use disorder. Neuropsychology, 33, 808–821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Le Berre AP, Fama R, & Sullivan EV (2017). Executive functions, memory, and social cognitive deficits and recovery in chronic alcoholism: A critical review to inform future research. Alcoholism, Clinical and Experimental Research, 41, 1432–1443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lewis B, Price JL, Garcia CC, & Nixon SJ (2019). Emotional face processing among treatment-seeking individuals with alcohol use disorders: Investigating sex differences and relationships with interpersonal functioning. Alcohol and Alcoholism, 54(4), 361–369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Marinkovic K, Oscar-Berman M, Urban T, O’reilly CE, Howard JA, Sawyer K et al. (2009) Alcoholism and dampened temporal limbic activation to emotional faces. Alcoholism: Clinical and Experimental Research 33: 1880–1892. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Maurage P, Campanella S, Philippot P, Charest I, Martin S, & De Timary P (2009). Impaired emotional facial expression decoding in alcoholism is also present for emotional prosody and body postures. Alcohol and Alcoholism, 44, 476–485. [DOI] [PubMed] [Google Scholar]
  29. Maurage P, Campanella S, Philippot P, De Timary P, Constant E, Gauthier S, et al. (2008). Alcoholism leads to early perceptive alterations, independently of comorbid depressed state: An erp study. Neurophysiologie Clinique, 38, 83–97. [DOI] [PubMed] [Google Scholar]
  30. Maurage P, Campanella S, Philippot P, Martin S, & De Timary P (2008). Face processing in chronic alcoholism: A specific deficit for emotional features. Alcoholism, Clinical and Experimental Research, 32, 600–606. [DOI] [PubMed] [Google Scholar]
  31. Maurage P, D’hondt F, De Timary P, Mary C, Franck N, & Peyroux E (2016). Dissociating affective and cognitive theory of mind in recently detoxified alcohol-dependent individuals. Alcoholism, Clinical and Experimental Research, 40, 1926–1934. [DOI] [PubMed] [Google Scholar]
  32. Maurage P, Grynberg D, Noel X, Joassin F, Hanak C, Verbanck P, et al. (2011). The “reading the mind in the eyes” test as a new way to explore complex emotions decoding in alcohol dependence. Psychiatry Research, 190, 375–378. [DOI] [PubMed] [Google Scholar]
  33. Maurage P, Grynberg D, Noel X, Joassin F, Philippot P, Hanak C, et al. (2011). Dissociation between affective and cognitive empathy in alcoholism: A specific deficit for the emotional dimension. Alcoholism, Clinical and Experimental Research, 35, 1662–1668. [DOI] [PubMed] [Google Scholar]
  34. Miller SB, Dolgoy L, Friese M, & Sita A (1996). Dimensions of hostility and cardiovascular response to interpersonal stress. Journal of Psychosomatic Research, 41, 81–95. [DOI] [PubMed] [Google Scholar]
  35. Monnot M, Nixon S, Lovallo W, & Ross E (2001). Altered emotional perception in alcoholics: Deficits in affective prosody comprehension. Alcoholism, Clinical and Experimental Research, 25, 362–369. [PubMed] [Google Scholar]
  36. Nixon SJ, Paul R, & Phillips M (1998). Cognitive efficiency in alcoholics and poly-substance abusers. Alcoholism, Clinical and Experimental Research, 22, 1414–1420. [DOI] [PubMed] [Google Scholar]
  37. Onuoha RC, Quintana DS, Lyvers M, & Guastella AJ (2016). A meta-analysis of theory of mind in alcohol use disorders. Alcohol and Alcoholism, 51, 410–415. [DOI] [PubMed] [Google Scholar]
  38. Oscar-Berman M, Valmas MM, Sawyer KS, Ruiz SM, Luhar RB, & Gravitz ZR (2014). Profiles of impaired, spared, and recovered neuropsychologic processes in alcoholism. Handbook of Clinical Neurology, 125, 183–210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Oscar-Berman M, Hancock M, Mildworf B, Hutner N, & Weber DA (1990). Emotional perception and memory in alcoholism and aging. Alcoholism, Clinical and Experimental Research, 14, 383–393. [DOI] [PubMed] [Google Scholar]
  40. Philippot P, Kornreich C, Blairy S, Baert I, Den Dulk A, Le Bon O, et al. (1999). Alcoholics’ deficits in the decoding of emotional facial expression. Alcoholism, Clinical and Experimental Research, 23, 1031–1038. [PubMed] [Google Scholar]
  41. Pitel AL, Beaunieux H, Witkowski T, Vabret F, Guillery-Girard B, Quinette P, et al. (2007). Genuine episodic memory deficits and executive dysfunctions in alcoholic subjects early in abstinence. Alcoholism, Clinical and Experimental Research, 31, 1169–1178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Reitan RM (1955). The relation of the trail making test to organic brain damage. Journal of Consulting Psychology, 19, 393–394. [DOI] [PubMed] [Google Scholar]
  43. Reitan RM & Wolfson D 1985. The halstead-reitan neuropsychological test battery: Theory and clinical interpretation, Tucson, Ariz., Neuropsychology Press. [Google Scholar]
  44. Robins LN, Cottler L, Bucholz KK, Compton W, North CS & Rourke KM 2000. The diagnostic interview schedule, version iv, St. Louis, Washington University. [Google Scholar]
  45. Salloum JB, Ramchandani VA, Bodurka J, Rawlings R, Momenan R, George D, et al. (2007). Blunted rostral anterior cingulate response during a simplified decoding task of negative emotional facial expressions in alcoholic patients. Alcoholism, Clinical and Experimental Research, 31, 1490–1504. [DOI] [PubMed] [Google Scholar]
  46. Singleton E, Tiffany ST, & Henningfield JE (2000). Alcohol craving questionnaire (ACQ-Now): Background, scoring, and administration (manual), Baltimore, MD, Intramural Research Program, National Institute on Drug Abuse. [Google Scholar]
  47. Staines GL, Magura S, Foote J, Deluca A, & Kosanke N (2001). Polysubstance use among alcoholics. Journal of Addictive Diseases, 20, 53–69. [DOI] [PubMed] [Google Scholar]
  48. Sullivan E, Harris RA, & Pfefferbaum A (2010). Alcohol’s effects on brain and behavior. Alcohol Research & Health, 33, 127–143. [PMC free article] [PubMed] [Google Scholar]
  49. Townshend JM, & Duka T (2003). Mixed emotions: Alcoholics’ impairments in the recognition of specific emotional facial expressions. Neuropsychologia, 41, 773–782. [DOI] [PubMed] [Google Scholar]
  50. Uekermann J, Channon S, Winkel K, Schlebusch P, & Daum I (2007). Theory of mind, humour processing and executive functioning in alcoholism. Addiction, 102, 232–240. [DOI] [PubMed] [Google Scholar]
  51. Uekermann J, & Daum I (2008). Social cognition in alcoholism: A link to prefrontal cortex dysfunction? Addiction, 103, 726–735. [DOI] [PubMed] [Google Scholar]
  52. Uekermann J, Daum I, Schlebusch P, & Trenckmann U (2005). Processing of affective stimuli in alcoholism. Cortex, 41, 189–194. [DOI] [PubMed] [Google Scholar]
  53. United States Department of Health and Human Services. Dietary Guidelines Advisory Committee. 2015. Dietary guidelines for Americans, 2015–2020, Washington, D.C., U.S. [Google Scholar]
  54. Verdejo-Garcia A, Albein-Urios N, Molina E, Ching-Lopez A, Martinez-Gonzalez JM, & Gutierrez B (2013). A maoa gene*cocaine severity interaction on impulsivity and neuropsychological measures of orbitofrontal dysfunction: Preliminary results. Drug and Alcohol Dependence, 133, 287–290. [DOI] [PubMed] [Google Scholar]
  55. Verdejo-Garcia A, Rivas-Perez C, Vilar-Lopez R, & Perez-Garcia M (2007). Strategic self-regulation, decision-making and emotion processing in poly-substance abusers in their first year of abstinence. Drug and Alcohol Dependence, 86, 139–146. [DOI] [PubMed] [Google Scholar]
  56. Wade M, Prime H, Jenkins JM, Yeates KO, Williams T, & Lee K (2018). On the relation between theory of mind and executive functioning: A developmental cognitive neuroscience perspective. Psychonomic Bulletin & Review, 25, 2119–2140. [DOI] [PubMed] [Google Scholar]
  57. Wechsler D (1981). Wechsler adult intelligence scale–revised: Manual, New York, The Psychological Corporation. [Google Scholar]
  58. Yip JT, & Lee TM (2006). Selective impairment of sadness and disgust recognition in abstinent ecstasy users. Neuropsychologia, 44, 959–965. [DOI] [PubMed] [Google Scholar]

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