On the Development of Implicit and Control Processes in Relation to Substance Use in Adolescence

Current Addiction Reports

Volumn 2, Issue 2, 2015, Pages 141-155

  Wiers, Reinout W ^a   Boelema, Sarai R ^b   Nikolaou, Kiki ^a   Gladwin, Thomas E ^a,c  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UTRECHT UNIVERSITY   (Netherlands)

^c Ministerie van Defensie   (Netherlands)

Author keywords

Adolescence; Alcohol; Approach bias; Attentional bias; Brain development; Cigarette use; Cognitive control; Dual process models; Executive functions; fMRI; Marijuana; Memory bias; Review; Substance use

Indexed keywords

EID: 84969430229     PISSN: None     EISSN: 21962952     Source Type: Journal    
DOI: 10.1007/s40429-015-0053-z     Document Type: Review

References (109)

1. Bechara A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci. 2005;8(11):1458–63. Important neurocognitive dual-process model paper in addiction.
2. Stacy AW, Wiers RW. Implicit cognition and addiction: a tool for explaining paradoxical behavior. Annu Rev Clin Psychol. 2010;6:551–75.
3. Wiers RW, Bartholow BD, van den Wildenberg E, Thush C, Engels RC, Sher KJ, et al. Automatic and controlled processes and the development of addictive behaviors in adolescents: a review and a model. Pharmacol Biochem Behav. 2007;86(2):263–83. Initial review and model of automatic and controlled processes with a focus on adolescence.
4. Munakata Y, Herd SA, Chatham CH, Depue BE, Banich MT, O’Reilly RC. A unified framework for inhibitory control. Trends Cogn Sci (Regul Ed). 2011;15(10):453–9. Important conceptual paper on neurocognitive mechanisms underlying executive functions.
5. Steinberg L. Cognitive and affective development in adolescence. Trends Cogn Sci (Regul Ed). 2005;9(2):69–74.
6. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2(10):861–3.
7. Lenroot RK, Giedd JN. Sex differences in the adolescent brain. Brain Cogn. 2010;72(1):46–55.
8. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A. 2004;101(21):8174–9.
9. Quevedo KM, Benning SD, Gunnar MR, Dahl RE. The onset of puberty: effects on the psychophysiology of defensive and appetitive motivation. Dev Psychopathol. 2009;21(01):27–45. This study showed that after controlling for age, onset of puberty results in stronger reactivity to emotional/motivational cues (positive and negative).
10. Hardee JE, Weiland BJ, Nichols TE, Welsh RC, Soules ME, Steinberg DB, et al. Development of impulse control circuitry in children of alcoholics. Biol Psychiatry 2014.
11. Crone EA, Dahl RE. Understanding adolescence as a period of social–affective engagement and goal flexibility. Nat Rev Neurosci. 2012;13(9):636–50. Very important review highlighting the importance of affective and motivational processes in adolescent brain development.
12. Bava S, Tapert SF. Adolescent brain development and the risk for alcohol and other drug problems. Neuropsychol Rev. 2010;20(4):398–413.
13. Casey B, Jones RM. Neurobiology of the adolescent brain and behavior: implications for substance use disorders. J Am Acad Child Adolescent Psychiatry. 2010;49(12):1189–201.
14. Gladwin TE, Figner B, Crone EA, Wiers RW. Addiction, adolescence, and the integration of control and motivation. Dev Cognitive Neurosci. 2011;1(4):364–76. Reconceptualization of dual process models from neurocognitive perspective.
15. Field M, Wiers RW, Christiansen P, Fillmore MT, Verster JC. Acute alcohol effects on inhibitory control and implicit cognition: implications for loss of control over drinking. Alcohol Clin Exp Res. 2010;34(8):1346–52.
16. Keren G, Schul Y. Two is not always better than one a critical evaluation of two-system theories. Perspect Psychol Sci. 2009;4(6):533–50.
17. Cunningham WA, Zelazo PD. Attitudes and evaluations: a social cognitive neuroscience perspective. Trends Cogn Sci (Regul Ed). 2007;11(3):97–104. Important neurocognitive reconceptualization of implicit attitudes.
18. Hazy TE, Frank MJ, O’Reilly RC. Banishing the homunculus: making working memory work. Neuroscience. 2006;139(1):105–18. Proof of principle paper on how to banish the homunculus out of accounts of executive functions.
19. Noël X, Brevers D, Bechara A. A neurocognitive approach to understanding the neurobiology of addiction. Curr Opin Neurobiol. 2013;23(4):632–8.
20. Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2009;35(1):217–38. Important review paper on different neuroadaptations in addiction.
21. Chassin L, Sher KJ, Hussong A, Curran P. The developmental psychopathology of alcohol use and alcohol disorders: research achievements and future directions. Dev Psychopathol. 2013;25(4pt2):1567–84.
22. van Holst RJ, Lemmens JS, Valkenburg PM, Peter J, Veltman DJ, Goudriaan AE. Attentional bias and disinhibition toward gaming cues are related to problem gaming in male adolescents. J Adolesc Health. 2012;50(6):541–6.
23. Hommer RE, Seo D, Lacadie CM, Chaplin TM, Mayes LC, Sinha R, et al. Neural correlates of stress and favorite‐food cue exposure in adolescents: a functional magnetic resonance imaging study. Hum Brain Mapp. 2013;34(10):2561–73.
24. Fernie G, Peeters M, Gullo MJ, Christiansen P, Cole JC, Sumnall H, et al. Multiple behavioural impulsivity tasks predict prospective alcohol involvement in adolescents. Addiction. 2013;108(11):1916–23.
25. Pieters S, Burk WJ, Van der Vorst H, Engels RC, Wiers RW. Impulsive and reflective processes related to alcohol use in young adolescents. Frontiers Psychiatry 2014;5.
26. Pieters S, Van Der Vorst H, Burk WJ, Schoenmakers TM, Van Den Wildenberg E, Smeets HJ, et al. The effect of the OPRM1 and DRD4 polymorphisms on the relation between attentional bias and alcohol use in adolescence and young adulthood. Dev Cognitive Neurosci. 2011;1(4):591–9.
27. Cousijn J, Watson P, Koenders L, Vingerhoets W, Goudriaan A, Wiers R. Cannabis dependence, cognitive control and attentional bias for cannabis words. Addict Behav. 2013;38(12):2825–32.
28. van Hemel-Ruiter ME, de Jong PJ, Ostafin BD, Wiers RW. Reward sensitivity, attentional bias, and executive control in early adolescent alcohol use. Addict Behav. 2015;40:84–90. Interesting study describing possible antecedents of attentional bias in adolescence.
29. van Hemel-Ruiter ME, de Jong PJ, Oldehinkel AJ, Ostafin BD. Reward-related attentional biases and adolescent substance use: the TRAILS study. Psychol Addict Behav. 2013;27(1):142–50.
30. Larsen H, Kong G, Becker D, Cousijn J, Boendermaker W, Cavallo D, et al. Implicit motivational processes underlying smoking in American and Dutch adolescents. Frontiers Psychiatry 2014;5.
31. Janssen T, Larsen H, Vollebergh WA, Wiers RW. Longitudinal Relations between Cognitive Bias and Adolescent Alcohol Use. Addict Behav 2014;In press.
32. Willem L, Vasey MW, Beckers T, Claes L, Bijttebier P. Cognitive biases and alcohol use in adolescence and young adulthood: the moderating role of gender, attentional control and inhibitory control. Personal Individ Differ. 2013;54(8):925–30.
33. Field M, Christiansen P, Cole J, Goudie A. Delay discounting and the alcohol Stroop in heavy drinking adolescents. Addiction. 2007;102(4):579–86.
34. Cox WM, Fadardi JS, Pothos EM. The addiction-Stroop test: theoretical considerations and procedural recommendations. Psychol Bull. 2006;132(3):443–76.
35. MacLeod C, Mathews A, Tata P. Attentional bias in emotional disorders. J Abnorm Psychol. 1986;95(1):15. introduction of the famous visual probe test.
36. Ataya AF, Adams S, Mullings E, Cooper RM, Attwood AS, Munafò MR. Internal reliability of measures of substance-related cognitive bias. Drug Alcohol Depend. 2012;121(1):148–51.
37. Zvielli A, Bernstein A, Koster EH. Temporal dynamics of attentional bias. Clin Psychol Sci 2014:2167702614551572. Potentially important and promising new scoring method for notoriously unreliable probe tests (see reference #36).
38. Price RB, Kuckertz JM, Siegle GJ, Ladouceur CD, Silk JS, Ryan ND, et al. Empirical recommendations for improving the stability of the dot-probe task in clinical research. Psychol Assessment 2014;In press.
39. Zetteler JI, Stollery BT, Weinstein AM, Lingford-Hughes AR. Attentional bias for alcohol-related information in adolescents with alcohol-dependent parents. Alcohol Alcohol. 2006;41(4):426–30.
40. Wiers RW, Gunning WB, Sergeant JA. Do young children of alcoholics hold more positive or negative alcohol‐related expectancies than controls? Alcohol Clin Exp Res. 1998;22(8):1855–63.
41. Peeters M, Wiers RW, Monshouwer K, Schoot R, Janssen T, Vollebergh WA. Automatic processes in at‐risk adolescents: the role of alcohol‐approach tendencies and response inhibition in drinking behavior. Addiction. 2012;107(11):1939–46.
42. Peeters M, Monshouwer K, Schoot RA, Janssen T, Vollebergh WA, Wiers RW. Automatic processes and the drinking behavior in early adolescence: a prospective study. Alcohol Clin Exp Res. 2013;37(10):1737–44. Prospective replication of reference #41, showing that the same interplay between strong automatically triggered approach tendencies for alcohol and relatively weak control functions predicts escalation of drinking in high-risk adolescents.
43. Pieters S, Burk WJ, Van der Vorst H, Wiers RW, Engels RC. The moderating role of working memory capacity and alcohol‐specific rule‐setting on the relation between approach tendencies and alcohol use in young adolescents. Alcohol Clin Exp Res. 2012;36(5):915–22.
44. van Hemel-Ruiter ME, de Jong PJ, Wiers RW. Appetitive and regulatory processes in young adolescent drinkers. Addict Behav. 2011;36(1):18–26.
45. Cousijn J, Goudriaan AE, Wiers RW. Reaching out towards cannabis: approach‐bias in heavy cannabis users predicts changes in cannabis use. Addiction. 2011;106(9):1667–74. This study showed that automatically triggered approach tendencies are predictive of escalation of cannabis use in youth and young adults.
46. Wiers R, Rinck M, Dictus M, Van Den Wildenberg E. Relatively strong automatic appetitive action‐tendencies in male carriers of the OPRM1 G‐allele. Genes Brain Behav. 2009;8(1):101–6. Paper introducing the “feature irrelevant” substance approach avoidance task (aat).
47. De Houwer J, Crombez G, Baeyens F, Hermans D. On the generality of the affective Simon effect. Cogn Emo. 2001;15(2):189–206. Paper introducing the stimulus response compatibility (SRC) task as measure of approach/avoid tendencies.
48. Rinck M, Becker ES. Approach and avoidance in fear of spiders. J Behav Ther Exp Psychiatry. 2007;38(2):105–20. Paper introducing the original (feature relevant) AAT.
49. De Houwer J, Musch J, Klauer K. A structural analysis of indirect measures of attitudes. In: Musch J, Klauer K, editors. The psychology of evaluation. Affective processes in cognition and emotion. Mahwah: Erlbaum; 2003. p. 219–44. Conceptual chapter on different task-mechanisms in indirect measures.
50. Field M, Caren R, Fernie G, De Houwer J. Alcohol approach tendencies in heavy drinkers: comparison of effects in a relevant stimulus–response compatibility task and an approach/avoidance Simon task. Psychol Addict Behav. 2011;25(4):697–701.
51. Wiers RW, Gladwin TE, Rinck M. Should we train alcohol-dependent patients to avoid alcohol? Commentary on Spruyt et al. “On the predictive validity of automatically activated approach/avoidance tendencies in abstaining alcohol-dependent patients”. Frontiers Psychiatry 2013;4(33).
52. Ames SL, Sussman S, Dent CW, Stacy AW. Implicit cognition and dissociative experiences as predictors of adolescent substance use. Am J Drug Alcohol Abuse. 2005;31(1):129–62. Interesting paper on antecedents of substance-memory associations in adolescents.
53. Ames SL, Grenard JL, Thush C, Sussman S, Wiers RW, Stacy AW. Comparison of indirect assessments of association as predictors of marijuana use among at-risk adolescents. Exp Clin Psychopharmacol. 2007;15(2):204–18.
54. Belles S, Budde A, Moesgen D, Klein M. Parental problem drinking predicts implicit alcohol expectancy in adolescents and young adults. Addict Behav. 2011;36(11):1091–4.
55. O'Connor RM, Lopez-Vergara HI, Colder CR. Implicit cognition and substance use: the role of controlled and automatic processes in children. J Stud Alcohol Drugs. 2012;73(1):134–43.
56. Grenard JL, Ames SL, Wiers RW, Thush C, Sussman S, Stacy AW. Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychol Addict Behav. 2008;22(3):426–32. First paper to show the moderating effect of working memory on the impact of automatically activated memory associations in adolescent smoking and drinking.
57. Thush C, Wiers RW, Ames SL, Grenard JL, Sussman S, Stacy AW. Apples and oranges? Comparing indirect measures of alcohol-related cognition predicting alcohol use in at-risk adolescents. Psychol Addict Behav. 2007;21(4):587–91.
58. Thush C, Wiers RW. Explicit and implicit alcohol-related cognitions and the prediction of future drinking in adolescents. Addict Behav. 2007;32(7):1367–83.
59. Thush C, Wiers RW, Ames SL, Grenard JL, Sussman S, Stacy AW. Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents. Drug Alcohol Depend. 2008;94(1):116–24.
60. Van Der Vorst H, Krank M, Engels RC, Pieters S, Burk WJ, Mares SH. The mediating role of alcohol‐related memory associations on the relation between perceived parental drinking and the onset of adolescents’ alcohol use. Addiction. 2013;108(3):526–33.
61. Sherman SJ, Chassin L, Presson C, Seo D, Macy JT, Sherman SJ, et al. The intergenerational transmission of implicit and explicit attitudes toward smoking: predicting adolescent smoking initiation. J Exp Soc Psychol. 2009;45(2):313–9. Interesting study relating implicit association test (IAT)-scores of children and their parents to smoking onset in adolescents.
62. O'Connor RM, Fite PJ, Nowlin PR, Colder CR. Children's beliefs about substance use: an examination of age differences in implicit and explicit cognitive precursors of substance use initiation. Psychol Addict Behav. 2007;21(4):525–33.
63. Pieters S, van der Vorst H, Engels RC, Wiers RW. Implicit and explicit cognitions related to alcohol use in children. Addict Behav. 2010;35(5):471–8.
64. Coronges K, Stacy AW, Valente TW. Social network influences of alcohol and marijuana cognitive associations. Addict Behav. 2011;36(12):1305–8.
65. Thush C, Wiers RW, Moerbeek M, Ames SL, Grenard JL, Sussman S, et al. Influence of motivational interviewing on explicit and implicit alcohol-related cognition and alcohol use in at-risk adolescents. Psychol Addict Behav. 2009;23(1):146–51.
66. Greenwald AG, McGhee DE, Schwartz JL. Measuring individual differences in implicit cognition: the implicit association test. J Pers Soc Psychol. 1998;74(6):1464–80. Introduction of the famous IAT.
67. Conrey FR, Sherman JW, Gawronski B, Hugenberg K, Groom CJ. Separating multiple processes in implicit social cognition: the quad model of implicit task performance. J Pers Soc Psychol. 2005;89(4):469–87. Interesting decomposition of IAT scores using mathematical modelling (QUAD).
68. Tapert SF, Cheung EH, Brown GG, Frank LR, Paulus MP, Schweinsburg AD, et al. Neural response to alcohol stimuli in adolescents with alcohol use disorder. Arch Gen Psychiatry. 2003;60(7):727–35. In this early cue reactivity study with adolescents, alcohol use disorder, alcohol consumption, and desire to drink were associated with increased brain activation by alcoholic vs non-alcoholic stimuli. This increased activation was found in a broad network of regions including amygdala, anterior cingulate, and precuneus.
69. Dager AD, Anderson BM, Rosen R, Khadka S, Sawyer B, Jiantonio‐Kelly RE, et al. Functional magnetic resonance imaging (fMRI) response to alcohol pictures predicts subsequent transition to heavy drinking in college students. Addiction. 2014;109(4):585–95.
70. Rubinstein ML, Luks TL, Dryden WY, Rait MA, Simpson GV. Adolescent smokers show decreased brain responses to pleasurable food images compared with nonsmokers. Nicotine Tob Res. 2011;13(8):751–5.
71. Bunge SA. How we use rules to select actions: a review of evidence from cognitive neuroscience. Cognitive Affective Behav Neurosci. 2004;4(4):564–79.
72. Korucuoglu O, Gladwin TE, Wiers RW. Preparing to approach or avoid alcohol: EEG correlates, and acute alcohol effects. Neurosci Lett. 2014;559:199–204.
73. Ames SL, Wong SW, Bechara A, Cappelli C, Dust M, Grenard JL, et al. Neural correlates of a Go/NoGo task with alcohol stimuli in light and heavy young drinkers. Behav Brain Res. 2014;274:382–9. In an alcohol Go-NoGo task similar to study (reference #72), activation of the insula was found to increase in heavy drinkers when they had to inhibit responses after an alcoholic cue, providing a further generalization of cue reactivity studies to conflict reactivity predicted by dual process models.
74. Ames SL, Grenard JL, Stacy AW, Xiao L, He Q, Wong SW, et al. Functional imaging of implicit marijuana associations during performance on an implicit association test (IAT). Behav Brain Res. 2013;256:494–502.
75. Cousijn J, Goudriaan AE, Ridderinkhof KR, van den Brink W, Veltman DJ, Wiers RW. Approach-bias predicts development of cannabis problem severity in heavy cannabis users: results from a prospective FMRI study. PLoS One. 2012;7(9):e42394.
76. Ames SL, Grenard JL, He Q, Stacy AW, Wong SW, Xiao L, et al. Functional imaging of an alcohol‐implicit association test (IAT). Addict Biol. 2014;19(3):467–81.
77. Gladwin TE, ter Mors-Schulte MH, Ridderinkhof KR, Wiers RW. Medial parietal cortex activation related to attention control involving alcohol cues. Frontiers Psychiatry 2013;4. In an alcohol cued attention task, brain activation in the precuneus/posterior cingulate was found to increase when attention had to be directed towards a distracting alcoholic cue, and risky drinking was associated with weaker activation near this region of activation. This may indicate that the role of the precuneus is to attempt to control the influence of alcoholic cues on cognition.
78. Korucuoglu O, Gladwin TE, Wiers RW. Alcohol-induced changes in conflict monitoring and error detection as predictors of alcohol use in late adolescence. Neuropsychopharmacology. 2015;40(3):614–21. To the best of our knowledge the first psychophysiological study to use an implicit measures-style task targeting alcohol-related automatic processes in adolescents. Reduced sensitivity to acute alcohol, as measured by the error-related negativity in a Go-NoGo task, was found to predict alcohol escalation.
79. Schuckit MA. Low level of response to alcohol as a predictor of future alcoholism. Am J Psychiatry. 1994;151(2):184–9.
80. Frijda NH. The emotions: Cambridge University Press; 1986.
81. O'Doherty JP, Hampton A, Kim H. Model‐based fMRI and its application to reward learning and decision making. Ann N Y Acad Sci. 2007;1104(1):35–53.
82. Vollstädt‐Klein S, Loeber S, Richter A, Kirsch M, Bach P, von der Goltz C, et al. Validating incentive salience with functional magnetic resonance imaging: association between mesolimbic cue reactivity and attentional bias in alcohol‐dependent patients. Addict Biol. 2012;17(4):807–16.
83. Gladwin TE, Wiers RW. Alcohol‐related effects on automaticity due to experimentally manipulated conditioning. Alcohol Clin Exp Res. 2012;36(5):895–9.
84. Jacobus J, Tapert SF. Neurotoxic effects of alcohol in adolescence. Annu Rev Clin Psychol. 2013;9:703–21. Thorough review of the available literature on the relation between neurocognition and alcohol use in adolescence.
85. Lisdahl KM, Gilbart ER, Wright NE, Shollenbarger S. Dare to delay? The impacts of adolescent alcohol and marijuana use onset on cognition, brain structure, and function. Frontiers Psychiatry 2013;4. Review on how substance use in adolescence is related to cognition and brain anatomy and function.
86. Boelema SR. Adolescent alcohol use. A longitudinal study of its effect on cognitive functioning. Utrecht University: Unpublished doctoral thesis; 2014.
87. Squeglia LM, Spadoni AD, Infante MA, Myers MG, Tapert SF. Initiating moderate to heavy alcohol use predicts changes in neuropsychological functioning for adolescent girls and boys. Psychol Addict Behav. 2009;23(4):715–22.
88. Verdejo-García A, Pérez-García M. Ecological assessment of executive functions in substance dependent individuals. Drug Alcohol Depend. 2007;90(1):48–55.
89. De Wit H. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol. 2009;14(1):22–31.
90. Winward JL, Hanson KL, Bekman NM, Tapert SF, Brown SA. Adolescent heavy episodic drinking: neurocognitive functioning during early abstinence. J Int Neuropsychol Soc. 2014;20(02):218–29.
91. Schulte MH, Cousijn J, den Uyl TE, Goudriaan AE, van den Brink W, Veltman DJ, et al. Recovery of neurocognitive functions following sustained abstinence after substance dependence and implications for treatment. Clin Psychol Rev. 2014;34(7):531–50.
92. White HR, Marmorstein NR, Crews FT, Bates ME, Mun E, Loeber R. Associations between heavy drinking and changes in impulsive behavior among adolescent boys. Alcohol Clin Exp Res. 2011;35(2):295–303.
93. Hicks BM, Durbin CE, Blonigen DM, Iacono WG, McGue M. Relationship between personality change and the onset and course of alcohol dependence in young adulthood. Addiction. 2012;107(3):540–8.
94. Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014;512(7513):185–9. Very informative study which tested models of current and future adolescent alcohol misuse that incorporated several measures including: life experiences, personality and cognitive differences, as well as biological measures including candidate genes and indices of brain function during performance of a stop-signal task.
95. Norman AL, Pulido C, Squeglia LM, Spadoni AD, Paulus MP, Tapert SF. Neural activation during inhibition predicts initiation of substance use in adolescence. Drug Alcohol Depend. 2011;119(3):216–23.
96. Wetherill RR, Squeglia LM, Yang TT, Tapert SF. A longitudinal examination of adolescent response inhibition: neural differences before and after the initiation of heavy drinking. Psychopharmacol (Berl). 2013;230(4):663–71. A very interesting study which found that adolescents who transition into heavy drinking, relative to those that do not go on to drink heavily, show reduced activation at baseline on no-go trials in 12 brain regions, including right inferior frontal gyrus, left dorsal and medial frontal areas, bilateral motor cortex, cingulate gyrus, left putamen, bilateral middle temporal gyri, and bilateral inferior parietal lobules.
97. Schuckit MA, Tapert S, Matthews SC, Paulus MP, Tolentino NJ, Smith TL, et al. fMRI differences between subjects with low and high responses to alcohol during a stop signal task. Alcohol Clin Exp Res. 2012;36(1):130–40.
98. Anderson BM, Stevens MC, Meda SA, Jordan K, Calhoun VD, Pearlson GD. Functional imaging of cognitive control during acute alcohol intoxication. Alcohol Clin Exp Res. 2011;35(1):156–65.
99. Nikolaou K, Critchley H, Duka T. Alcohol affects neuronal substrates of response inhibition but not of perceptual processing of stimuli signalling a stop response. PLoS One. 2013;8(9):e76649.
100. Petit G, Kornreich C, Noël X, Verbanck P, Campanella S. Alcohol-related context modulates performance of social drinkers in a visual Go/No-Go task: a preliminary assessment of event-related potentials. PLoS One. 2012;7(5):e37466.
101. López‐Caneda E, Cadaveira F, Crego A, Gómez‐Suárez A, Corral M, Parada M, et al. Hyperactivation of right inferior frontal cortex in young binge drinkers during response inhibition: a follow‐up study. Addiction. 2012;107(10):1796–808.
102. Squeglia LM, Sorg SF, Schweinsburg AD, Wetherill RR, Pulido C, Tapert SF. Binge drinking differentially affects adolescent male and female brain morphometry. Psychopharmacol (Berl). 2012;220(3):529–39. A study showing that adolescent female binge drinkers presented ~8% thicker cortices in left frontal regions of their brain, relative to matched non-binge-drinking females. Additionally, these differences related to worse performance on attention and inhibition tasks. In contrast, adolescent male binge drinkers had ~7 % thinner cortices in these areas than non-drinking males. These effects were also related to worse performance on the tasks.
103. Maurage P, Pesenti M, Philippot P, Joassin F, Campanella S. Latent deleterious effects of binge drinking over a short period of time revealed only by electrophysiological measures. J Psychiatry Neurosci. 2009;34(2):111–8.
104. Reich RR, Below MC, Goldman MS. Explicit and implicit measures of expectancy and related alcohol cognitions: a meta-analytic comparison. Psychol Addict Behav. 2010;24(1):13.
105. Rooke SE, Hine DW, Thorsteinsson EB. Implicit cognition and substance use: a meta-analysis. Addict Behav. 2008;33(10):1314–28.
106. Larsen H, Engels RC, Wiers RW, Granic I, Spijkerman R. Implicit and explicit alcohol cognitions and observed alcohol consumption: three studies in (semi) naturalistic drinking settings. Addiction. 2012;107(8):1420–8.
107. Wiers RW, Gladwin TE, Hofmann W, Salemink E, Ridderinkhof KR. Cognitive bias modification and cognitive control training in addiction and related psychopathology mechanisms, clinical perspectives, and ways forward. Clin Psychol Sci. 2013;1(2):192–212. Recent review of Cognitive Bias Modification studies in the field of addiction.
108. Jensen CD, Cushing CC, Aylward BS, Craig JT, Sorell DM, Steele RG. Effectiveness of motivational interviewing interventions for adolescent substance use behavior change: a meta-analytic review. J Consult Clin Psychol. 2011;79(4):433–40.
109. Conrod PJ, Castellanos-Ryan N, Strang J. Brief, personality-targeted coping skills interventions and survival as a non–drug user over a 2-year period during adolescence. Arch Gen Psychiatry. 2010;67(1):85–93. Important study showing long-term effects of personality targeted interventions in adolescents.