Playing It Safe: A Video Game Probing the Relationship Between Addiction, Gender, and Avoidance

This work may not be copied, distributed, displayed, published, reproduced, transmitted, modified, posted, sold, licensed, or used for commercial purposes. By downloading this file, you are agreeing to the publisher’s Terms & Conditions.

See article by Sheynin et al

Playing It Safe: A Video Game Probing the Relationship Between Addiction, Gender, and Avoidance

Zhenhao Shi, PhDa,b; An-Li Wang, PhDb; and Daniel D. Langleben, MDa,b,*

Heroin addiction is a growing public health problem in the United States, driven by an epidemic of opioid painkiller misuse.1 Individuals with opioid use disorders show a wide range of behavioral abnormalities that may have diagnostic and prognostic value.2,3 Avoidance is a common behavior intended to prevent aversive experiences.4 However, avoidance that exceeds the level of impending threat5 is maladaptive and could be symptomatic of psychopathology.2,6,7

In an innovative and thought-provoking study that appears in this issue of JCP, Sheynin et al8 used a video game to study avoidance behavior in heroin-dependent patients. In this paradigm, inspired by Molet et al,9 players control a spaceship and gain points by shooting enemy spaceships and avoiding being shot down.9,10 Warnings about an impending attack by enemy craft were displayed periodically on the screen. Players could avoid being shot down and losing points by "hiding" their virtual spaceship. However, a hiding spaceship could not shoot down enemy craft, preventing the player from accruing game points. During the acquisition phase of the task, warnings were followed by an attack from enemy craft. During the extinction phase, warnings were no longer followed by an attack. Authors found that while male patients and controls showed comparable avoidance during a virtual attack, male patients hid their spaceships more during the warning periods and were slower to stop hiding during the extinction trials. In contrast, there was no difference between female patients and controls.

Sheynin et al previously reported using this task to capture individual differences in vulnerability to anxiety,10 supporting its potential clinical relevance. Studies show that individuals with substance use disorders (SUD) tend to downplay the severity of their disease and are reluctant to actively cope with stressful real-life issues in general.3,11-13 Given the association between maladaptive avoidance and poor SUD treatment outcomes,14,15 reducing avoidance has become one of the key objectives in some addiction recovery programs.11,16 The computerized video game paradigm reported by Sheynin et al8 has the potential to provide a quantifiable index of maladaptive avoidance.

The male patients’ excessive hiding during the warning periods of both the acquisition and extinction phases suggests greater maladaptive avoidance in this cohort. While these results are consistent with an earlier study in SUD patients showing greater harm avoidance13 and more difficulty learning from prior mistakes,2 they seem to contrast the well-established tendency toward greater than normal risk taking in SUD patients.17 Such an apparent contradiction could be due to the multifaceted nature of avoidance behaviors that entail both cognitive and emotional components.4 Effective avoidance of threats requires accurate cognitive representations of the association between stimuli, behavior, and outcome.18,19 However, one’s lower tolerance of negative emotion associated with threat is a key factor that determines the degree of avoidance.6,20 Therefore, maladaptive avoidance in male heroin-dependent patients could result from either impaired cognitive processes, such as insufficient working memory and cognitive flexibility,21 or excessive emotional reactivity to aversive events.13,22

Sheynin et al8 found that while female patients and controls were equally good at avoiding being shot down during the video game task, male patients showed greater avoidance than the male controls. This important sex difference suggests that while the male patients’ avoidance was maladaptive, female patients’ avoidance was intact. All patients in the Sheynin et al8 study had a history of heroin dependence; however, during the study, most were maintained on methadone and a few on buprenorphine. The sex differences in neuroendocrinology and neurochemistry23-25 make agonist maintenance an important factor in the interpretation of the study findings. Some cognitive side effects of methadone are not sex-specific.26 However, methadone lowers plasma testosterone,27,28 a principal male sex hormone that not only controls male reproductive function, but also promotes aggressive behavior.29,30 Buprenorphine has a much smaller effect on plasma testosterone.31 Therefore, opioid agonist maintenance treatment could modulate the differences between the male and female patients, between the male patients receiving buprenorphine and methadone, and between all male patients and controls. It is possible that the exaggerated avoidance in the male patients was in part due to the effect of methadone on testosterone levels, which could have reduced aggressive behaviors in the video game and increased hiding behaviors. Future studies could test this hypothesis by comparing methadone-maintained patients with a control group maintained on buprenorphine or long-acting naltrexone or a group treated nonpharmacologically.

Seven of 27 patients in the Sheynin et al8 study reported having a diagnosis of schizophrenia, while 7 more reported depression, panic disorder, bipolar disorder, or cluster B personality disorder. Schizophrenia coincides with a range of cognitive deficits from impaired language processing to poor working memory,32,33 functions that may be required to perform the study video game task. Considering the gender differences in the prevalence of both schizophrenia34 and depression,35 it would be important to ensure that these disorders were balanced across genders in Sheynin et al.8

Sheynin and colleagues’ 8 study highlights the rapidly growing use of video games, mobile phone apps, and virtual reality techniques as outcome measures and therapeutic interventions in clinical research and practice.36-39 Such "gamification" increases ecological validity of the test and is especially relevant in the context of impaired motivation that hampers experimental assessments in SUD.40 While Sheynin et al8 are making an important contribution to our understanding of the mechanisms of avoidance in opioid addiction, its potential clinical applications would require additional research directly linking the video game performance to the real-life maladaptive avoidance in SUD patients. It would also be interesting to determine whether video game performance can predict treatment outcomes, similar to recent studies using neurophysiological biomarkers.41,42 To disentangle the cognitive and emotional mechanisms of exaggerated avoidance, one could add standard measures to the video game paradigm. For instance, the Wechsler Adult Intelligence Scale43 or the Wisconsin Card Sorting Test44 would address cognitive performance, and the Affective Go/No-Go Task45 would address emotional responses. Since real life almost invariably involves social interactions, it would be interesting to examine SUD patients’ maladaptive avoidance in a social context. For example, a 2-player46 rather than a solo video game could probe aspects of social cognition such as theory of mind, cooperation, and empathy, which are known to exhibit gender differences47,48 that may be affected by SUD. Finally, it is yet to be determined whether the findings in Sheynin et al8 can be generalized to other substances of abuse. Answering these questions will help to unravel the complex interactions between addiction, gender, and avoidance behavior and translate the approaches and results presented here to clinical practice.

Submitted: February 16, 2016; accepted February 16, 2016.

Potential conflicts of interest: None reported.

Funding/support: None reported.

REFERENCES

1. Volkow ND, Frieden TR, Hyde PS, et al. Medication-assisted therapies—tackling the opioid-overdose epidemic. N Engl J Med. 2014;370(22):2063-2066. PubMed doi:10.1056/NEJMp1402780

2. Thompson LL, Claus ED, Mikulich-Gilbertson SK, et al. Negative reinforcement learning is affected in substance dependence. Drug Alcohol Depend. 2012;123(1-3):84-90. PubMed doi:10.1016/j.drugalcdep.2011.10.017

3. Verdejo-Garcí­a A, Pérez-Garcí­a M. Substance abusers’ self-awareness of the neurobehavioral consequences of addiction. Psychiatry Res. 2008;158(2):172-180. PubMed doi:10.1016/j.psychres.2006.08.001

4. Krypotos AM, Effting M, Kindt M, et al. Avoidance learning: a review of theoretical models and recent developments. Front Behav Neurosci. 2015;9:189. PubMed doi:10.3389/fnbeh.2015.00189

5. Barlow DH. Anxiety and Its Disorders: The Nature and Treatment of Anxiety and Panic. New York, NY: Guilford Press; 2002.

6. Pittig A, Brand M, Pawlikowski M, et al. The cost of fear: avoidant decision making in a spider gambling task. J Anxiety Disord. 2014;28(3):326-334. PubMed doi:10.1016/j.janxdis.2014.03.001

7. Schneier FR, Rodebaugh TL, Blanco C, et al. Fear and avoidance of eye contact in social anxiety disorder. Compr Psychiatry. 2011;52(1):81-87. PubMed doi:10.1016/j.comppsych.2010.04.006

8. Sheynin J, Moustafa AA, Beck KD, et al. Exaggerated acquisition and resistance to extinction of avoidance behavior in treated heroin-dependent men. J Clin Psychiatry. 2016;77(3):386-394.

9. Molet M, Leconte C, Rosas JM. Acquisition, extinction and temporal discrimination in human conditioned avoidance. Behav Processes. 2006;73(2):199-208. PubMed doi:10.1016/j.beproc.2006.05.009

10. Sheynin J, Beck KD, Pang KC, et al. Behaviourally inhibited temperament and female sex, two vulnerability factors for anxiety disorders, facilitate conditioned avoidance (also) in humans. Behav Processes. 2014;103:228-235. PubMed doi:10.1016/j.beproc.2014.01.003

11. Ferrari JR, Groh DR, Rulka G, et al. Coming to terms with reality: predictors of self-deception within substance abuse recovery. Addict Disord Their Treat. 2008;7(4):210-218. PubMed doi:10.1097/ADT.0b013e31815c2ded

12. Goldstein RZ, Craig AD, Bechara A, et al. The neurocircuitry of impaired insight in drug addiction. Trends Cogn Sci. 2009;13(9):372-380. PubMed doi:10.1016/j.tics.2009.06.004

13. Le Bon O, Basiaux P, Streel E, et al. Personality profile and drug of choice; a multivariate analysis using Cloninger’s TCI on heroin addicts, alcoholics, and a random population group. Drug Alcohol Depend. 2004;73(2):175-182. PubMed doi:10.1016/j.drugalcdep.2003.10.006

14. Beutler LE, Moos RH, Lane G. Coping, treatment planning, and treatment outcome: discussion. J Clin Psychol. 2003;59(10):1151-1167. PubMed doi:10.1002/jclp.10216

15. Eftekhari A, Turner AP, Larimer ME. Anger expression, coping, and substance use in adolescent offenders. Addict Behav. 2004;29(5):1001-1008. PubMed doi:10.1016/j.addbeh.2004.02.050

16. Shaffer HJ, Simoneau G. Reducing resistance and denial by exercising ambivalence during the treatment of addiction. J Subst Abuse Treat. 2001;20(1):99-105. PubMed doi:10.1016/S0740-5472(00)00152-5

17. Lane SD, Cherek DR. Analysis of risk taking in adults with a history of high risk behavior. Drug Alcohol Depend. 2000;60(2):179-187. PubMed doi:10.1016/S0376-8716(99)00155-6

18. Mowrer OH. Two-factor learning theory: summary and comment. Psychol Rev. 1951;58(5):350-354. PubMed doi:10.1037/h0058956

19. Rescorla RA, Solomon RL. Two-process learning theory: relationships between Pavlovian conditioning and instrumental learning. Psychol Rev. 1967;74(3):151-182. PubMed doi:10.1037/h0024475

20. Timpano KR, Keough ME, Traeger L, et al. General life stress and hoarding: examining the role of emotional tolerance. Int J Cogn Ther. 2011;4(3):263-279. doi:10.1521/ijct.2011.4.3.263

21. Baldacchino A, Balfour DJ, Passetti F, et al. Neuropsychological consequences of chronic opioid use: a quantitative review and meta-analysis. Neurosci Biobehav Rev. 2012;36(9):2056-2068. PubMed doi:10.1016/j.neubiorev.2012.06.006

22. Robinson TE, Berridge KC. Addiction. Annu Rev Psychol. 2003;54(1):25-53. PubMed doi:10.1146/annurev.psych.54.101601.145237

23. Back SE, Payne RL, Wahlquist AH, et al. Comparative profiles of men and women with opioid dependence: results from a national multisite effectiveness trial. Am J Drug Alcohol Abuse. 2011;37(5):313-323. PubMed doi:10.3109/00952990.2011.596982

24. Fattore L, Melis M, Fadda P, et al. Sex differences in addictive disorders. Front Neuroendocrinol. 2014;35(3):272-284. PubMed doi:10.1016/j.yfrne.2014.04.003

25. Lynch WJ, Roth ME, Carroll ME. Biological basis of sex differences in drug abuse: preclinical and clinical studies. Psychopharmacology (Berl). 2002;164(2):121-137. PubMed doi:10.1007/s00213-002-1183-2

26. Langleben DD, Ruparel K, Elman I, et al. Extended-release naltrexone modulates brain response to drug cues in abstinent heroin-dependent patients. Addict Biol. 2014;19(2):262-271. PubMed doi:10.1111/j.1369-1600.2012.00462.x

27. Cicero TJ, Bell RD, Wiest WG, et al. Function of the male sex organs in heroin and methadone users. N Engl J Med. 1975;292(17):882-887. PubMed doi:10.1056/NEJM197504242921703

28. Mendelson JH, Mendelson JE, Patch VD. Plasma testosterone levels in heroin addiction and during methadone maintenance. J Pharmacol Exp Ther. 1975;192(1):211-217. PubMed

29. Archer J. The influence of testosterone on human aggression. Br J Psychol. 1991;82(pt 1):1-28. PubMed doi:10.1111/j.2044-8295.1991.tb02379.x

30. Olweus D, Mattsson A, Schalling D, et al. Testosterone, aggression, physical, and personality dimensions in normal adolescent males. Psychosom Med. 1980;42(2):253-269. PubMed doi:10.1097/00006842-198003000-00003

31. Bliesener N, Albrecht S, Schwager A, et al. Plasma testosterone and sexual function in men receiving buprenorphine maintenance for opioid dependence. J Clin Endocrinol Metab. 2005;90(1):203-206. PubMed doi:10.1210/jc.2004-0929

32. Kurtz MM, Moberg PJ, Gur RC, et al. Approaches to cognitive remediation of neuropsychological deficits in schizophrenia: a review and meta-analysis. Neuropsychol Rev. 2001;11(4):197-210. PubMed doi:10.1023/A:1012953108158

33. Pomarol-Clotet E, Oh TM, Laws KR, et al. Semantic priming in schizophrenia: systematic review and meta-analysis. Br J Psychiatry. 2008;192(2):92-97. PubMed doi:10.1192/bjp.bp.106.032102

34. Picchioni MM, Murray RM. Schizophrenia. BMJ. 2007;335(7610):91-95. PubMed doi:10.1136/bmj.39227.616447.BE

35. Cyranowski JM, Frank E, Young E, et al. Adolescent onset of the gender difference in lifetime rates of major depression: a theoretical model. Arch Gen Psychiatry. 2000;57(1):21-27. PubMed doi:10.1001/archpsyc.57.1.21

36. Abroms LC, Leavitt LE, Van Alstyne JM, et al. A motion videogame for opioid relapse prevention. Games Health J. 2015;4(6):494-501. PubMed doi:10.1089/g4h.2014.0100

37. Jansari AS, Froggatt D, Edginton T, et al. Investigating the impact of nicotine on executive functions using a novel virtual reality assessment. Addiction. 2013;108(5):977-984. PubMed doi:10.1111/add.12082

38. Paris MM, Carter BL, Traylor AC, et al. Cue reactivity in virtual reality: the role of context. Addict Behav. 2011;36(7):696-699. PubMed doi:10.1016/j.addbeh.2011.01.029

39. Son JH, Lee SH, Seok JW, et al. Virtual reality therapy for the treatment of alcohol dependence: a preliminary investigation with positron emission tomography/computerized tomography. J Stud Alcohol Drugs. 2015;76(4):620-627. PubMed doi:10.15288/jsad.2015.76.620

40. Fox LG, Barnes D. BoosterBuddy: using gamification as a compensatory strategy for motivational deficits. Psychiatr Serv. 2016;67(1):141-142. PubMed doi:10.1176/appi.ps.661101

41. Brewer JA, Worhunsky PD, Carroll KM, et al. Pretreatment brain activation during stroop task is associated with outcomes in cocaine-dependent patients. Biol Psychiatry. 2008;64(11):998-1004. PubMed doi:10.1016/j.biopsych.2008.05.024

42. Wang AL, Elman I, Lowen SB, et al. Neural correlates of adherence to extended-release naltrexone pharmacotherapy in heroin dependence. Transl Psychiatr. 2015;5(3):e531. PubMed doi:10.1038/tp.2015.20

43. Wechsler D, Coalson DL, Raiford SE. WAIS-IV: Wechsler Adult Intelligence Scale. San Antonio, TX: Pearson; 2008.

44. Heaton RK. Wisconsin Card Sorting Test: Computer Version 2. Odessa, FL: Psychological Assessment Resources; 1993.

45. Elliott R, Ogilvie A, Rubinsztein JS, et al. Abnormal ventral frontal response during performance of an affective go/no go task in patients with mania. Biol Psychiatry. 2004;55(12):1163-1170. PubMed doi:10.1016/j.biopsych.2004.03.007

46. Kätsyri J, Hari R, Ravaja N, et al. The opponent matters: elevated FMRI reward responses to winning against a human versus a computer opponent during interactive video game playing. Cereb Cortex. 2013;23(12):2829-2839. PubMed doi:10.1093/cercor/bhs259

47. Cross SE, Madson L. Models of the self: self-construals and gender. Psychol Bull. 1997;122(1):5-37. PubMed doi:10.1037/0033-2909.122.1.5

48. Holt CL, Ellis JB. Assessing the current validity of the Bem Sex-Role Inventory. Sex Roles. 1998;39(11-12):929-941. doi:10.1023/A:1018836923919