Poor Inhibition predicts Approach Bias
for Alcohol and Cannabis in Delinquent Youth
Eveline E. Schippers University of Amsterdam
Student ID number: 10434453
Address: Haarlemmerstraat 166 B, 2312 GG Leiden Contact: T: 0645277009, E: eveline.schippers@gmail.com
Supervision: dhr. prof. dr. R.W.H.J. Wiers (Developmental Psychology) External supervisor: dhr. H.S. van der Baan MSc
Abstract
The current paper investigated how inhibition and sensation seeking relate to attentional and approach bias for alcohol and cannabis in delinquent youth in a governmental juvenile detention center. The retraining and existence of these biases are not yet established in this population. Cognitive bias modification is used to reduce attentional bias and approach bias for alcohol or cannabis and to facilitate abstinence from use. A number of neurobiological markers are measured, as well as behavioral inhibition and sensation seeking. Poor inhibition significantly predicted approach bias for alcohol- and cannabis-related cues. Knowing how cognitive biases relate to sensation seeking and inhibition may improve future treatment to reduce substance use.
Keywords: cognitive bias modification training, approach bias, attentional bias, inhibition, sensation seeking
Introduction
Many problem behaviors originate from failures in self-control processes. Self-control can be modeled to consist of reflective processes, impulsive processes, and self-control abilities that govern both impulsive and reflective processes (Friese, Hofmann, & Wiers, 2011). Self-control and impulsivity are closely linked to substance use. Impulsivity is even mentioned as a predisposition to substance use disorders (SUDs), meeting all criteria for an endophenotype of SUDs (Verdejo-García, Lawrence, & Clark, 2008). Substance use in turn affects self-control processes, enlarging the impulsive processes and weakening the reflective processes (Wiers, Gladwin, Hofmann, Salemink, & Ridderinkhof, 2013). A way to improve self-control is to target the impulsive processes, by changing approach biases and attentional biases towards substances, a process called cognitive bias modification training (CBM; Friese et al., 2011).
An attentional bias exists when some stimuli attract more attention than others (Friese et al., 2011). The approach bias is described as the tendency to approach rather than avoid a stimulus (Cousijn, Goudriaan, & Wiers, 2011). Both biases are present in alcohol users and cannabis users and predict later use (Cousijn et al., 2011; Field, Eastwood, Bradley, & Mogg, 2006; Friese et al., 2011; Wiers et al., 2013).
The Visual Probe Task (VPT) measures the attentional bias. A substance-related picture and a matched neutral picture are presented simultaneously next to each other on the screen, after which a probe is presented on either the left or right side. A certain response to the probe is required. Response is faster after a substance-related stimulus for people with SUDs, which indicates an attentional bias for the particular substance (Field, Mogg, & Bradley, 2004; Robbins & Ehrman, 2004). CBM employs a modified version of the VPT where the probe consistently appears behind the neutral (instead of substance-related) cue. To be able to indicate the location of the probe, one has to direct attention away from the substance cue (Field & Eastwood, 2005; Friese et al., 2011). This way, the attentional bias is retrained and diminished.
The Approach-Avoidance Task (AAT) requires a response which visually pulls a stimulus (approach) or pushes it away (avoid), depending on neutral characteristics of the stimulus. For people with SUDs, approach seems to be faster for substance-related stimuli, indicating an approach bias for
the particular substance (Friese et al., 2011). A modified version of the AAT, where the participant consistently pushes the substance stimuli and pulls the non-substance stimuli, is effective in retraining the approach bias in alcoholics (Wiers, Eberl, Rinck, Becker, & Lindenmeyer, 2011). Its effectiveness for cannabis users remains to be investigated, although the bias is already found in heavy users (Cousijn et al., 2011). Both CBM trainings improve self-control abilities (Friese et al., 2011). Reducing these cognitive biases might help to reduce substance use.
Substance use is among the best predictors of self-reported delinquency in the community (Young, Misch, Collins, & Gudjonsson, 2011). The use of cannabis is a clear problem even in clinical forensic setting, with 64% of youngsters using cannabis during detention, and levels of cannabis use being much higher than that of their peers outside of juvenile justice (Kepper et al., 2009). Our own experiences and interviews with juvenile justice facility staff underscore the problematic influence of substance use on behavior and treatment outcome of the youngsters. Substance use and delinquent behavior are thus clearly related. The current research investigates if improved self-control reduces alcohol and cannabis use. Focus will be on alcohol and cannabis, since the use thereof is relatively high in this population.
A second part of the research concerns the relationship of sensation seeking to substance use, measured by means of neurobiological data. Disruptive and antisocial behavior are predicted by several neurobiological measures: low basal cortisol levels (Alink et al., 2008; van de Wiel, van Goozen, Matthys, Snoek, & van Engeland, 2004), low resting state heart rate and higher heart rate variability (i.e. more strongly decreased heart rate) in response to stress (de Wied, van Boxtel, Matthys, & Meeus, 2012; Ortiz & Raine, 2004; Scarpa, Haden, & Tanaka, 2010), and increased skin conduction (Scarpa et al., 2010). These measures together indicate low stress-sensitivity, which relates to more problematic behavior (conform van Goozen & Fairchild, 2008).
If fitted to the framework of the sensation seeking theory (Zuckerman, 1984), it makes sense that youngsters with low stress-sensitivity (under-arousal) search for stimulating activities such as drug use or criminal activities to create an optimal level of arousal. The trait of sensation seeking is indeed linked to substance use (disorders) and criminal or rule-violation activities (Crawford, Pentz, Chou, Li,
& Dwyer, 2003; Wills, Vaccaro, & Mcnamara, 1994; Zuckerman & Neeb, 1979; Zuckerman, 1994). Low inhibition underlies sensation seeking and is measured as part of the sensation seeking scale as disinhibition (Zuckerman, 1994). Sensation seeking and deficits in inhibition together predict substance use (Noël et al., 2005; Verdejo-García et al., 2008; Woicik, Stewart, Pihl, & Conrod, 2009).
The current paper will investigate how inhibition and sensation seeking relate to attentional and approach bias for alcohol and cannabis in delinquent youth. The retraining and existence of these biases are not yet established in this population. Knowing how cognitive biases relate to sensation seeking and inhibition may improve future treatment. The current training is ready to be implemented in treatment, and can directly be used in juvenile justice settings.
Note: Since the neurobiological data will not be available by time for data analysis, sensation seeking will be measured by a questionnaire (see Procedure). Bias retraining outcome data will not be available either, since these need measures of later relapse rate.
Methods Procedure
Attentional bias, approach bias, sensation seeking, inhibition and substance use will be measured in a population of delinquent youth (12-23 years old) within a juvenile detention center. The current research proposal is of an observational and cross-sectional nature. This internship is part of a larger project, consisting of self-control training to reduce cognitive biases for substances and neurobiological measures of antisocial behavior.
Our team has free access to juvenile detention center the Hartelborgt in Spijkenisse, the Netherlands. The procedure consists of recruiting participants; a Screening session, in which several questionnaires and tasks are administered on a laptop; and a Training only when there is an indication of substance use in the past year. The training is administered on a laptop and consists of six sessions in which a modified version of the VPT and a modified version of the AAT are administered. The kind of training (alcohol or cannabis) depends on whether subjects score higher on Alcohol Use Disorder
Identification Test (AUDIT) or Cannabis Use Disorder Identification Test (CUDIT). The collected data are supplemented with information from dossiers, to minimize the demands made on our participants. Participants
To calculate the number of participants, a power analysis is done that includes four dependent variables: CUDIT scores, AUDIT scores, VPT reaction time (RT), and AAT RT. There are two independent variables: Stroop RT and SURP-SS scores. With an effect size of .50, an alpha level set at .05, and power set at .95, a power analysis calculates a minimal sample size of 44 subjects.
Participants that do not possess a basic understanding of Dutch will be unable to participate, as they cannot read the questionnaires. Any participant that has no indication of alcohol or cannabis use in the past year on the screening measurement will not be asked for the training sessions.
Unfortunately, the desired number of subjects was not reached yet at time of data-analysis. Statistical analysis were done with N = 29. For demographic characteristics, see Table 1. A Dutch nationality was reported by 23 subjects, whilst six subjects reported other nationality.
Materials
Alcohol use is measured with the Alcohol Use Disorders Identification Test (AUDIT). This questionnaire includes the domains of alcohol consumption, drinking behavior, and alcohol-related problems in ten items. Responses to each item are scored from 0 to 4 (maximum total score 40). A score above eight is considered hazardous or harmful alcohol use (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). The AUDIT has good reliability and is validated for use for adolescents (Reinert & Allen, 2007).
Cannabis use is measured with the Cannabis Use Disorders Identification Test (CUDIT), a ten item questionnaire adapted to the AUDIT. Scores higher than eight indicate a current cannabis use disorder (Adamson & Sellman, 2003). The CUDIT has good reliability and is validated for use for adolescents (Piontek, Kraus, & Klempova, 2008).
Sensation seeking is measured using the Substance Use Rick Profile Scale (SURPS), which measures sensation seeking (among four personality dimensions), referred to here as SURPS-SS. The SURPS questionnaire contains 23 items, six of which are used to measure sensation seeking. Participants need to indicate to what extent they agree with statements about themselves (response options range
from 1: strongly disagree to 4: strongly agree). The SURPS has good reliability and is validated for an early adolescent population (Krank et al., 2011; Woicik et al., 2009).
Inhibition is measured using a short version of the Stroop Color Word Test (original by Stroop, 1935; short version by Klein, Ponds, Houx, & Jolles, 1997). The task consists of three cards, with either color words printed in black (card I), colored blocks (card II), or color words printed in distractor colors (card III). Subjects must name the words for card I, and the printed colors for card II and III as fast as possible. The time (RT) difference between reading card III and card I and II is due to inhibitory mechanisms: distractor information must be inhibited (Klein et al., 1997). This time difference is called Interference. The larger this score, the weaker inhibition. The Stroop Color Word Test has good reliability and validity (Siegrist, 2010).
Approach bias is measured using the Approach-Avoidance Task (AAT) adapted to alcohol and cannabis stimuli (Cousijn et al., 2011; Wiers et al., 2011). Participants must visually pull (approach) or push away (avoid) a substance-related or neutral picture, depending on neutral characteristics of the stimulus. For statistical analysis, the median approach-RT is subtracted from the median avoid-RT. Positive scores (shorter RT to substance stimuli) indicate approach bias (Cousijn et al., 2011). A modified version of the AAT is used for the training, where the participant must push away the substance stimuli and pull the neutral stimuli. All training sessions include a cognitive bias measure.
Attentional bias is measured using the Visual Probe Task (VPT) adapted to substance use (Field et al., 2004; Robbins & Ehrman, 2004). A pair of a substance-related picture and a matched neutral picture are presented simultaneously next to each other on the screen, after which a probe is presented on either the left or right side. Conform the statistical analysis of the AAT, the median RT to probes after substance-related stimuli is subtracted from the median RT to neutral stimuli. Positive scores (shorter RT to substance stimuli) indicate an attentional bias (Field et al., 2004). A modified version of the VPT is used for the training, where the probe appears behind the neutral (instead of substance-related) cue (Field & Eastwood, 2005). All training sessions include a cognitive bias measure.
Data analysis
According to the proposed hypotheses, the statistical predictions are as follows:
Stroop RT positively predict CUDIT scores, AUDIT scores, VPT RT and AAT RT. Stroop RT positively interacts with SURP-SS scores.
Data are analyzed in a Multivariate General Linear Model with Stroop Interference score and mean SURPS-SS score as independent variables and mean VPT score, mean AAT score, mean CUDIT score and mean AUDIT score as dependent variables. Data are analyzed using IBM SPSS Statistics Version 20 (www.ibm.com/software/analytics/spss). The assumptions for multivariate regression were not all met. The VPT and AAT data was not entirely normally distributed, which is probably due to the small N. Results should thus be interpreted with caution.
Table 1
Sample Characteristics
N (gender) 29 (2 F)
Age, mean (SD) 18.16 (1.95)
AAT score, mean (SD) 0.06 (0.50)
VPT score, mean (SD) 18.72 (40.37)
AUDIT score, mean (SD) 2 (0.14)
CUDIT score, mean (SD) 1 (0.45)
SURP-SS score, mean (SD) 2.67 (0.65) Stroop Interference score, mean (SD) 12.53 (6.68)
Results
The sample characteristics are shown in Table 1. Multivariate regression showed that Stroop Interference score significantly predicted AAT score, F(1,13) = 6.65, p < .05. Interference did not significantly predict VPT score, F(1,13) = .35, p = .57. There was a nonsignificant relation between Interference and mean CUDIT, F(1,13) = 3.26, p = .09, and Interference and mean AUDIT score, F(1,13) = 4.00, p = .07, however, a trend was visible for these variables. Mean SURPS-SS score did not significantly predict AAT score F(1,13) = .60, p = .45, nor VPT score, F(1,13) = .21, p = .65, nor mean CUDIT score, F(1,13) = .19, p = .67, nor mean AUDIT score F(1,13) = .03, p = .88. The model was run again, but now included an interaction effect of Interference and mean SURPS-SS score. The interaction
was not a significantly predictor for VPT score, F(1,14) = .46, p = .51, nor the other variables, but showed a trend for AAT score F(1,14) = 4.35, p = .06, mean CUDIT score, F(1,14) = .3.09, p = .10, and mean AUDIT score F(1,14) = 3.86, p = .07.
Conclusion
The current paper investigates a relationship between sensation seeking, inhibition, alcohol and cannabis use and related problems, and cognitive biases for the substances. High Stroop Interference scores positively predict AAT scores for both alcohol- and cannabis-related cues. This means that subjects with poor inhibition more often have an approach bias for both substances compared to subjects with good inhibition. There is a trend for Interference to predict CUDIT and AUDIT scores. If this relationship holds in a larger sample, this would mean that subjects with poor inhibition experience more alcohol- and cannabis use and related problems, which is consistent with previous research. The interaction between Interference and mean SURPS-SS score shows trends for a relationship with all variables but VPT score, but this is probably due to the effect of Interference alone.
Some of the expected hypotheses are not accepted in the current research. High sensation seeking does not predict approach bias and cannabis use and related problems in this sample, nor does poor inhibition predict attentional bias. A few possible explanations exist. First, the number of subjects was not big enough, leaving the current sample underpowered to find significant results. Second, an inadequate measure of sensation seeking may be responsible for not finding a relationship with substance use and approach bias. A general sensation seeking score will be more reliable with the availability of the neurobiological data, since it will consist of more than a few self-report questions. A more theoretical explanation for the lack of significant results might be that sensation seeking initiates first drug use, but is not responsible for the persistence of use. This would mean that sensation seeking itself is not related to the biases. Finally, the expected predictors of substance use do not function as usual for populations within detention facilities, as these juveniles are more restricted in their access to the substances than they would be in regular life. The restriction of freedom and choice inherent in these
institutions may skew the results, possibly indicating that substance use becomes a matter of opportunity rather than inclination.
In sum, we found that poor inhibition predicts an approach bias for alcohol- and cannabis-related cues. This is relevant for a population that often has inhibition problems, since the effect of (poor) inhibition on substance use might be extra strong here. Cognitive bias modification training might decrease substance use among delinquent juveniles in a different way than conventional methods. Both practical and societal relevance are significant. If we know what underlies cognitive biases for substances, we will know the best way to retrain these biases. This way substance use may be decreased, treatments may be improved, criminal behavior may be reduced, and costs for society may shrink.
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