Peer-reviewed critiques of Prause et al., 2015

Introduction

EEG study: “Modulation of late positive potentials by sexual images in problem users and controls inconsistent with porn addiction” (Prause et al., 2015)

Claim: To this day, former UCLA researcher Nicole Prause boldly claims that her solitary EEG study “falsified the porn addiction model.”

Reality: The results indicate habituation/desensitization in the more frequent porn users. Because this paper reported greater porn use related to less brain activation to vanilla porn it is listed on this website as supporting the hypothesis that chronic porn use down regulates sexual arousal. Put simply, the frequent porn users were bored by static images of ho-hum porn. (These findings parallel Kuhn & Gallinat., 2014.) These findings are consistent with tolerance, a sign of addiction. Tolerance is defined as a person’s diminished response to a drug or stimulus that is the result of repeated use. The nine peer-reviewed papers listed below agree with this YBOP assessment of Prause et al., 2015.

Twenty seven studies have reported findings consistent with sensitization/cue-reactivity. Because frequent porn users had lower EEG readings than controls, lead author Nicole Prause claimed her paper, with its anomalous conclusions, “falsified” the porn addiction model. She claims that her EEG readings assessed “cue-reactivity,” rather than habituation. Even if Prause were correct, she conveniently ignores the gaping hole in her “falsification” assertion. Regardless of her claims about Prause et al. 2015 finding less cue-reactivity in frequent porn users, 26 other neurological studies have reported cue-reactivity or cravings (sensitization) in compulsive porn users: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27. Science doesn’t go with the lone anomalous study hampered by serious methodological flaws and biased spokespersons; science goes with the preponderance of evidence.

Note: In this 2018 presentation Gary Wilson exposes the truth behind 5 questionable and misleading studies, including the two Nicole Prause EEG studies (Steele et al., 2013 and Prause et al., 2015): Porn Research: Fact or Fiction?

Ten peer-reviewed analyses of Prause et al., 2015. Over the intervening years many more neuroscience-based studies have been published  (MRI, fMRI, EEG, neuropsychological, hormonal). All provide strong support for the addiction model as their findings mirror the neurological findings reported in substance addiction studies. The real experts’ opinions on porn/sex addiction can be seen in this list of 25 recent literature reviews & commentaries (all consistent with the addiction model). The papers below all agree that the Prause et al. findings of habituation lend support to the porn addiction model. Paper #2 (by Gola) is solely devoted to parsing Prause et al., 2015. The other 9 papers contain brief sections analyzing Prause et al., 2015 (all say the EEG study actually found habituation or desensitization). The papers are listed by date of publication.

1) Neuroscience of Internet Pornography Addiction: A Review and Update (2015)

Excerpt critiquing Prause et al., 2015 (citation 309)

Another EEG study involving three of the same authors was recently published [309]. Unfortunately, this new study suffered from many of the same methodological issues as the prior one [303]. For example, it used a heterogeneous subject pool, the researchers employed screening questionnaires that have not been validated for pathological internet pornography users, and the subjects were not screened for other manifestations of addiction or mood disorders.

In the new study, Prause et al. compared EEG activity of frequent viewers of Internet pornography with that of controls as they viewed both sexual and neutral images [309]. As expected, the LPP amplitude relative to neutral pictures increased for both groups, although the amplitude increase was smaller for the IPA subjects. Expecting a greater amplitude for frequent viewers of Internet pornography, the authors stated, “This pattern appears different from substance addiction models”.

While greater ERP amplitudes in response to addiction cues relative to neutral pictures is seen in substance addiction studies, the current finding is not unexpected, and aligns with the findings of Kühn and Gallinat [263], who found more use correlated with less brain activation in response to sexual images. In the discussion section, the authors cited Kühn and Gallinat and offered habituation as a valid explanation for the lower LPP pattern. A further explanation offered by Kühn and Gallinat, however, is that intense stimulation may have resulted in neuroplastic changes. Specifically, higher pornography use correlated with lower grey matter volume in the dorsal striatum, a region associated sexual arousal and motivation [265].

It’s important to note that the findings of Prause et al. were in the opposite direction of what they expected [309]. One might expect frequent viewers of Internet pornography and controls to have similar LPP amplitudes in response to brief exposure to sexual images if pathological consumption of Internet pornography had no effect. Instead, the unexpected finding of Prause et al. [309] suggests that frequent viewers of Internet pornography experience habituation to still images. One might logically parallel this to tolerance. In today’s world of high-speed Internet access, it is very likely that frequent consumers of Internet pornography users view sexual films and videos as opposed to still clips. Sexual films produce more physiological and subjective arousal than sexual images [310] and viewing sexual films results in less interest and sexual responsiveness to sexual images [311]. Taken together, the Prause et al., and Kühn and Gallinat studies lead to the reasonable conclusion that frequent viewers of internet pornography require greater visual stimulation to evoke brain responses comparable to healthy controls or moderate porn users.

In addition, the statement of Prause et al. [309] that, “These are the first functional physiological data of persons reporting VSS regulation problems” is problematic because it overlooks research published earlier [262,263]. Moreover, it is critical to note that one of the major challenges in assessing brain responses to cues in Internet pornography addicts is that viewing sexual stimuli is the addictive behavior. In contrast, cue-reactivity studies on cocaine addicts utilize pictures related to cocaine use (white lines on a mirror), rather than having subjects actually ingest cocaine. Since the viewing of sexual images and videos is the addictive behavior, future brain activation studies on Internet pornography users must take caution in both experimental design and interpretation of results. For example, in contrast to the one-second exposure to still images used by Prause et al. [309], Voon et al. chose explicit 9-second video clips in their cue reactivity paradigm to more closely match Internet porn stimuli [262]. Unlike the one-second exposure to still images (Prause et al. [309]), exposure to 9-second video clips evoked greater brain activation in heavy viewers of internet pornography than did one-second exposure to still images. It is further concerning that the authors referenced the Kühn and Gallinat study, released at the same time as the Voon study [262], yet they did not acknowledge the Voon et al. study anywhere in their paper despite its critical relevance.

2) Decreased LPP for sexual images in problematic pornography users may be consistent with addiction models. Everything depends on the model: Commentary on Prause, Steele, Staley, Sabatinelli, & Hajcak, 2015 (2016)

Biol Psychol. 2016 May 24. pii: S0301-0511(16)30182-X. doi: 10.1016/j.biopsycho.2016.05.003.

Gola Matuesz¹. ¹Swartz Center for Computational Neuroscience, Institute for Neural Computations, University of California San Diego, San Diego, USA; Institute of Psychology, Polish Academy of Science, Warsaw, Poland. Electronic address: [email protected].

Full Paper

Internet technology provides affordable and anonymous access to a wide range of pornography content (Cooper, 1998). Avail-able data show that 67.6% of male and 18.3% of female Danish young adults (18–30 years old) use pornography on the regular weekly basis (Hald, 2006). Among USA college students 93.2% of boys and 62.1% of girls were watching online pornography before age of 18 (Sabina, Wolak, & Finkelhor, 2008). For the majority of users, pornography viewing plays a role in entertainment, excitement, and inspiration (Rothman, Kaczmarsky, Burke, Jansen, & Baughman, 2014) (Häggström-Nordin, Tydén, Hanson,& Larsson, 2009), but for some, frequent pornography consumption is a source of suffering (about 8% out of users according to Cooper et al., 1999) and becomes a reason for seeking treatment (Delmonico and Carnes, 1999; Kraus, Potenza, Martino, & Grant,2015; Gola, Lewczuk, & Skorko, 2016; Gola and Potenza, 2016). Due to its widespread popularity and conflicting clinical observations, pornography consumption is an important social issue, garnering much attention in the media, (e.g., high-profile movies: “Shame” by McQueen and “Don Jon” by Gordon-Levitt) and from politicians(e.g., UK prime minister David Cameron’s 2013 speech on pornography use by kids), as well as neuroscience research (Steele, Staley,Fong, & Prause, 2013; Kühn and Gallinat, 2014; Voon et al., 2014).One of the most frequently asked questions is: whether pornography consumption can be addicting?

The finding of Prause, Steele, Staley, Sabatinelli, & Hajcak, (2015) published in the June issue of Biological Psychology delivers interesting data on this topic. The researchers showed that men and women reporting problematic pornography viewing (N = 55),1 exhibited lower late positive potential (LPP – an event related potential in EEG signaling associated with significance and subjective silence of the stimuli) to sexual images as compared with non-sexual images, when compared with the responses of controls. They also show that problematic pornography users with higher sexual desire have smaller LPP differences for sexual and non-sexual images. The authors concluded that: “This pattern of results appears inconsistent with some predictions made by addiction models” (p. 196) and announced this conclusion in the article’s title: “Modulation of late positive potentials by sexual images in problem users and controls inconsistent with “porn addiction””.

Unfortunately, in their article, Prause et al. (2015) did not explicitly define which model of addiction they were testing. Presented results when considered in relation to the most established models either do not provide clear verification of the hypothesis that problematic pornography use is an addiction (like in case of Incentive Salience Theory; Robinson and Berridge, 1993; Robinson, Fischer, Ahuja, Lesser, & Maniates, 2015) or support this hypothesis (like in case of Reward Deficiency Syndrome; Blum et al., 1996; 1996; Blum, Badgaiyan, & Gold, 2015). Below I explain it in details.

Correspondence address: Swartz Center for Computational Neuroscience, Institute for Neural Computations, University of California San Diego, 9500 Gilman Drive, San Diego, CA 92093-0559, USA. E-mail address: [email protected]

1 It is worthy to notice that the authors present results for male and female participants together, while recent studies shows that sexual images ratings of arousal and valence differs dramatically between genders (see: Wierzba et al., 2015)

2 This guess is supported by fact that references used in Prause et al. (2015) also refer to IST (i.e. Wölfling et al., 2011

Why theoretical framework and clear hypothesis matter

Based on the multiple uses of the term “cue-reactivity” by the authors we may guess that the authors have in mind Incentive Salience Theory (IST) proposed by Robinson and Berridge (Berridge, 2012; Robinson et al., 2015).2 This theoretical frame-work distinguishes two basic components of motivated behavior − “wanting” and “liking”. The latter is directly linked to the experienced value of the reward, while the former is related to the expected value of the reward, typically measured in relation to a predictive cue. In terms of Pavlovian learning, reward is an unconditioned stimulus (UCS) and cues associated with this reward through learning are conditioned stimuli (CS). Learned CSs acquire incentive salience and evoke “wanting”, reflected in motivated behavior (Mahler and Berridge, 2009; Robinson & Berridge, 2013). Thus they acquire similar properties as the reward itself. For example domesticated quail willingly copulate with a terrycloth object (CS) previously paired with the opportunity to copulate with a female quail (UCS), even if a real female is available (Cetinkaya and Domjan, 2006)

According to IST, addiction is characterized by increased “wanting” (elevated cue-related reactivity; i.e. higher LPP) and decreased “liking” (diminished reward-related reactivity; i.e. lower LPP). In order to interpret data within the IST framework researchers must clearly disentangle cue-related “wanting” and reward-related “liking.” Experimental paradigms testing both processes introduce separate cues and rewards (i.e. Flagel et al., 2011; Sescousse, Barbalat, Domenech, & Dreher, 2013; Gola, Miyakoshi, & Sescousse, 2015). Prause et al. (2015) instead use a much simpler experimental paradigm, wherein subjects passively view different pictures with sexual and non-sexual content. In such simple experimental design the crucial question from the IST perspective is: Do the sexual images play the role of cues (CS) or rewards (UCS)? And therefore: does the measured LPP reflect “wanting” or “liking”?

The authors assume that sexual images are cues, and there-fore interpret decreased LPP as a measure of diminished “wanting.”Diminished “wanting” with respect to cues would indeed be inconsistent with the IST addiction model. But many studies show that sexual pictures are not mere cues. They are rewarding in them-selves (Oei, Rombouts, Soeter, van Gerven, & Both, 2012; Stoléru,Fonteille, Cornélis, Joyal, & Moulier, 2012; reviewed in: Sescousse,Caldú, Segura, & Dreher, 2013; Stoléru et al., 2012). Viewing sexual images evokes ventral striatum (reward system) activity (Arnowet al., 2002; Demos, Heatherton, & Kelley, 2012; Sabatinelli, Bradley,Lang, Costa, & Versace, 2007; Stark et al., 2005; Wehrum-Osinskyet al., 2014), dopamine release (Meston and McCall, 2005) and both self-reported and objectively measured sexual arousal (review: Chivers, Seto, Lalumière, Laan, & Grimbos, 2010).

The rewarding properties of sexual images may be innate due to the fact that sex (like food) is a primary reward. But even if some-one rejects such innate rewarding nature, rewarding properties of erotic stimuli may be acquired due to Pavlovian learning. Under natural conditions, visual erotic stimuli (such as a naked spouse or pornographic video) may be a cue (CS) for sexual activity leading to the climax experience (UCS) as a result of either dyadic sex or solitary masturbation accompanying pornography consumption. Furthermore in the case of frequent pornography consumption, visual sexual stimuli (CS) are strongly associated with orgasm (UCS) and may acquire properties of reward (UCS; Mahler and Berridge, 2009; Robinson & Berridge, 2013) and then lead to approach (i.e.seeking pornography) and consummatory behaviors (i.e., hours of viewing before reaching climax).

Regardless of innate or learned reward value, studies show that sexual images are motivating in themselves, even without the possibility of climax. Thus they have intrinsic hedonic value for humans (Prévost, Pessiglione, Météreau, Cléry-Melin, & Dreher,2010) as well as rhesus macaques (Deaner, Khera, & Platt, 2005).Their rewarding value may even be amplified in an experimental setting, where a climax experience (natural UCS) is unavailable, as in the Prause et al.’s (2015) study (“participants in this study were instructed not to masturbate during the task”, p. 197). According to Berridge, task context influences reward prediction (Berridge,2012). Thus, as no other pleasure than sexual images was available here, the viewing of pictures was the ultimate reward (rather than simply a cue).

Decreased LPP for sexual rewards in problematic pornography users is consistent with addiction models

Taking all of the above into account we may assume that sexual images in the Prause et al. (2015) study, instead of being cues, might have played the role of rewards. If so, according to the IST framework, lower LPP for sexual vs. non-sexual pictures in problematic pornography users and subjects with high sexual desire indeed reflects diminished “liking”. Such a result is in line with the addiction model proposed by Berridge and Robinson (Berridge, 2012; Robinson et al., 2015). However, to fully verify an addiction hypothesis within IST framework, more advanced experimental studies, disentangling cue and reward are required. A good example of a well designed experimental paradigm was used in studies on gamblers by Sescousse, Redouté, & Dreher (2010). It employed monetary and sexual cues (symbolic stimuli) and clear rewards (monetary wins or sexual pictures). Due to lack of well defined cues and rewards in Prause et al. (2015) study, role of sexual pictures remains unclear and therefore obtained LPP effects are ambiguous within IST framework. For sure conclusion presented in the study’s title “Modulation of late positive potentials by sexual images in problem users and controls inconsistent with “porn addiction” is ungrounded with respect to IST

If we take another popular addiction model – Reward Deficency Syndrome (RDS; Blum et al., 1996, 2015) the data obtained by the authors actually speaks in favor of addiction hypothesis. RDS frame-work assumes that genetic predisposition to lower dopaminergic response for rewarding stimuli (expressed in diminished BOLD and electrophysiological reactivity) is related to sensation-seeking, impulsivity and higher risk of addiction. The authors’ findings of lower LPPs in problematic pornography users is entirely consistent with the RDS addiction model. If Prause et al. (2015) were testing some other model, less well known than IST or RDS, it would be highly desirable to present it briefly in their work.

Final remarks

The study by Prause et al. (2015) delivers interesting data on problematic pornography consumption.3 Yet, due to the lack of clear hypothesis statement which addiction model is tested and ambiguous experimental paradigm (hard to define role of erotic pictures), it is not possible to say if the presented results are against, or in favor of, a hypothesis about “pornography addiction.” More advanced studies with well defined hypotheses are called for. Unfortunately the bold title of Prause et al. (2015) article has already had an impact on mass media,4 thus popularizing scientifically unjustified conclusion. Due to the social and political importance of the topic of the effects of pornography consumption, researchers should draw future conclusions with greater caution. (emphasis supplied)

3 It is worthy to notice that in Prause et al. (2015) problematic users consume pornography in average for 3.8 h/week (SD = 1.3) it is almost the same as non-problematic pornography users in Kühn and Gallinat (2014) who consume in average 4.09 h/week (SD = 3.9). In Voon et al. (2014) problematic users reported 1.75 h/week (SD = 3.36) and problematic 13.21 h/week (SD = 9.85) – data presented by Voon during American Psychological Science conference in May 2015.

4 Examples of titles of popular science articles about Prause et al. (2015):“Porn is not as harmful as other addictions, study claims” (http://metro.co.uk/2015/07/04/porn-is-not-as-harmful-as-other-addictions-study-claims-5279530/), “Your Porn Addiction Isn’t Real” (http://www.thedailybeast.com/articles/2015/06/26/your-porn-addiction-isn-t-real.html), “Porn ’Addiction’ Isn’t Really Addiction, Neuroscientists Say” (http://www.huffingtonpost.com/2015/06/30/porn-addiction- n7696448.html)

References

1. Arnow, B. A., Desmond, J. E., Banner, L. L., Glover, G. H., Solomon, A., Polan, M. L., . . .& Atlas, S. W. (2002). Brain activation and sexual arousal in healthy, heterosexual males. Brain, 125(Pt. 5), 1014–1023.

2. Berridge, K. C. (2012). From prediction error to incentive salience: mesolimbic computation of reward motivation. European Journal of Neuroscience, 35(7),1124–1143. http://dx.doi.org/10.1111/j.1460-9568.2012.07990.x

3. Blum, K., Sheridan, P. J., Wood, R. C., Braverman, E. R., Chen, T. J., Cull, J. G., &Comings, D. E. (1996). The D2 dopamine receptor gene as a determinant of reward deficiency syndrome. Journal of the Royal Society of Medicine, 89(7),396–400.

4. Blum, K., Badgaiyan, R. D., & Gold, M. S. (2015). Hypersexuality addiction and withdrawal: phenomenology, neurogenetics and epigenetics. Cureus, 7(7), e290. http://dx.doi.org/10.7759/cureus.290

5. Cetinkaya, H., & Domjan, M. (2006). Sexual fetishism in a quail (Coturnix japonica) model system: test of reproductive success. Journal of Comparative Psychology, 120(4), 427–432. http://dx.doi.org/10.1037/0735-7036.120.4.427

6. Chivers, M. L., Seto, M. C., Lalumière, M. L., Laan, E., & Grimbos, T. (2010).Agreement of self-reported and genital measures of sexual arousal in men and women: a meta-analysis. Archives of Sexual Behavior, 39(1), 5–56. http://dx.doi.org/10.1007/s10508-009-9556-9

7. Cooper, A., Scherer, C. R., Boies, S. C., & Gordon, B. L. (1999). Sexuality on the Internet: from sexual exploration to pathological expression. Professional Psychology: Research and Practice, 30(2), 154. Retrieved from. http://psycnet.apa.org/journals/pro/30/2/154/

8. Cooper, A. (1998). Sexuality and the Internet: surfing into the new millennium. CyberPsychology & Behavior,. Retrieved from. http://online.liebertpub.com/doi/abs/10.1089/cpb.1998.1.187

9. Deaner, R. O., Khera, A. V., & Platt, M. L. (2005). Monkeys pay per view: adaptive valuation of social images by rhesus macaques. Current Biology, 15(6),543–548. http://dx.doi.org/10.1016/j.cub.2005.01.044

10. Delmonico, D. L., & Carnes, P. J. (1999). Virtual sex addiction: when cybersex becomes the drug of choice. Cyberpsychology and Behavior, 2(5), 457–463.http://dx.doi.org/10.1089/cpb.1999.2.457

11. Demos, K. E., Heatherton, T. F., & Kelley, W. M. (2012). Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. The Journal of Neuroscience, 32(16), 5549–5552. http://dx.doi.org/10.1523/JNEUROSCI.5958-11.2012

12. Flagel, S. B., Clark, J. J., Robinson, T. E., Mayo, L., Czuj, A., Willuhn, I., . . . & Akil, H.(2011). A selective role for dopamine in stimulus-reward learning. Nature,469(7328), 53–57. http://dx.doi.org/10.1038/nature09588

13. Gola, M., & Potenza, M. (2016). Paroxetine treatment of problematic pornography use—a case series. The Journal of Behavioral Addictions, in press.

14. Gola, M., Miyakoshi, M., & Sescousse, G. (2015). Sex impulsivity, and anxiety :interplay between ventral striatum and amygdala reactivity in sexual behaviors. The Journal of Neuroscience, 35(46), 15227–15229.

15. Gola, M., Lewczuk, K., & Skorko, M. (2016). What matters: quantity or quality of pornography use? Psychological and behavioral factors of seeking treatment for problematic pornography use. The Journal of Sexual Medicine, 13(5),815–824.

16. Häggström-Nordin, E., Tydén, T., Hanson, U., & Larsson, M. (2009). Experiences ofand attitudes towards pornography among a group of Swedish high school students. European Journal of Contraception and Reproductive Health Care, 14(4),277–284. http://dx.doi.org/10.1080/13625180903028171

17. Hald, G. M. (2006). Gender differences in pornography consumption among young heterosexual Danish adults. Archives of Sexual Behavior, 35(5), 577–585. http://dx.doi.org/10.1007/s10508-006-9064-0

18. Kühn, S., & Gallinat, J. (2014). Brain structure and functional connectivity associated with pornography consumption: the brain on porn. JAMA Psychiatry, 71 (7), 827–834. http://dx.doi.org/10.1001/jamapsychiatry.2014.93

19. Kraus, S. W., Potenza, M. N., Martino, S., & Grant, J. E. (2015). Examining the psychometric properties of the Yale-Brown Obsessive-Compulsive Scale in a sample of compulsive pornography users. Comprehensive Psychiatry, http://dx.doi.org/10.1016/j.comppsych.2015.02.007

20. Mahler, S. V., & Berridge, K. C. (2009). Which cue to want? Central amygdala opioid activation enhances and focuses incentive salience on a prepotent reward cue. The Journal of Neuroscience, 29(20), 6500–6513. http://dx.doi.org/10.1523/JNEUROSCI.3875-08.2009

21. Meston, C. M., & McCall, K. M. (2005). Dopamine and norepinephrine responses to film-induced sexual arousal in sexually functional and sexually dysfunctional women. Journal of Sex and Marital Therapy, 31(4), 303–317. http://dx.doi.org/10.1080/00926230590950217

22. Oei, N. Y., Rombouts, S. A., Soeter, R. P., vanGerven vanGerven, J. M., & Both, S. (2012). Dopamine modulates reward system activity during subconscious processing of sexual stimuli. Neuropsychopharmacology, 37 (7), 1729–1737. http://dx.doi.org/10.1038/npp.2012.19

23. Prévost, C., Pessiglione, M., Météreau, E., Cléry-Melin, M. L., & Dreher, J. C. (2010).Separate valuation subsystems for delay and effort decision costs. The Journal of Neuroscience, 30(42), 14080–14090. http://dx.doi.org/10.1523/JNEUROSCI.2752-10.2010

24. Prause, N., Steele, V. R., Staley, C., Sabatinelli, D., & Hajcak, G. (2015). Modulation of late positive potentials by sexual images in problem users and controls inconsistent with porn addiction. Biological Psychology, 109, 192–199. http://dx.doi.org/10.1016/j.biopsycho.2015.06.005

25. Robinson, T. E., & Berridge, K. C. (1993). The neural basis of drug craving: an incentive-sensitization theory of addiction? Brain Research. Brain Research Reviews, 18(3), 247–291.

26. Robinson, M. J., & Berridge, K. C. (2013). Instant transformation of learned repulsion into motivational wanting. Current Biology, 23(4), 282–289. http://dx.doi.org/10.1016/j.cub.2013.01.016

27. Robinson, M. J., Fischer, A. M., Ahuja, A., Lesser, E. N., & Maniates, H. (2015). Roles o fwanting and liking in motivating behavior: gambling food, and drug addictions. Current Topics in Behavioral Neurosciences, https://link.springer.com/chapter/10.1007/7854_2014_300 2015 387

28. Rothman, E. F., Kaczmarsky, C., Burke, N., Jansen, E., & Baughman, A. (2014).Without porn . . . I wouldn’t know half the things I know now: a qualitative study of pornography use among a sample of urban, low-income, black and Hispanic youth. Journal of Sex Research, 1–11. http://dx.doi.org/10.1080/00224499.2014.960908

29. Sabatinelli, D., Bradley, M. M., Lang, P. J., Costa, V. D., & Versace, F. (2007). Pleasure rather than salience activates human nucleus accumbens and medial prefrontal cortex. Journal of Neurophysiology, 98(3), 1374–1379. http://dx.doi.org/10.1152/jn.00230.2007

30. Sabina, C., Wolak, J., & Finkelhor, D. (2008). The nature and dynamics of internet pornography exposure for youth. Cyberpsychology and Behavior, 11(6),691–693. http://dx.doi.org/10.1089/cpb.2007.0179

31. Sescousse, G., Redouté, J., & Dreher, J. C. (2010). The architecture of reward value coding in the human orbitofrontal cortex. The Journal of Neuroscience, 30(39),13095–13104. http://dx.doi.org/10.1523/JNEUROSCI.3501-10.2010

32. Sescousse, G., Barbalat, G., Domenech, P., & Dreher, J. C. (2013). Imbalance in the sensitivity to different types of rewards in pathological gambling. Brain, 136(Pt.8), 2527–2538. http://dx.doi.org/10.1093/brain/awt126

33. Sescousse, G., Caldú, X., Segura, B., & Dreher, J. C. (2013). Processing of primary and secondary rewards: a quantitative meta-analysis and review of human functional neuroimaging studies. Neuroscience and Biobehavioral Reviews, 37(4), 681–696. http://dx.doi.org/10.1016/j.neubiorev.2013.02.002

34. Stark, R., Schienle, A., Girod, C., Walter, B., Kirsch, P., Blecker, C., . . . & Vaitl, D.(2005). Erotic and disgust-inducing pictures—differences in the hemodynamic responses of the brain. Biological Psychology, 70(1), 19–29. http://dx.doi.org/10.1016/j.biopsycho.2004.11.014

35. Steele, V. R., Staley, C., Fong, T., & Prause, N. (2013). Sexual desire, nothypersexuality, is related to neurophysiological responses elicited by sexual images. Socioaffective Neuroscience & Psychology, 3, 20770. http://dx.doi.org/10.3402/snp.v3i0.20770

36. Stoléru, S., Fonteille, V., Cornélis, C., Joyal, C., & Moulier, V. (2012). Functional neuroimaging studies of sexual arousal and orgasm in healthy men and women: a review and meta-analysis. Neuroscience and Biobehavioral Reviews,36(6), 1481–1509. http://dx.doi.org/10.1016/j.neubiorev.2012.03.006

37. Voon, V., Mole, T. B., Banca, P., Porter, L., Morris, L., Mitchell, S., . . . & Irvine, M.(2014). Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours. Public Library of Science, 9(7), e102419.http://dx.doi.org/10.1371/journal.pone.0102419

38. Wehrum-Osinsky, S., Klucken, T., Kagerer, S., Walter, B., Hermann, A., & Stark, R.(2014). At the second glance: stability of neural responses toward visual sexual stimuli. The Journal of Sexual Medicine, 11(11), 2720–2737. http://dx.doi.org/10.1111/jsm.12653

39. Wierzba, M., Riegel, M., Pucz, A., Lesniewska, Z., Dragan, W., Gola, M., . . . &Marchewka, A. (2015). Erotic subset for the Nencki Affective Picture System (NAPS ERO): cross-sexual comparison study. Frontiers in Psychology, 6, 1336.

40. Wölfling, K., Mörsen, C. P., Duven, E., Albrecht, U., Grüsser, S. M., & Flor, H. (2011).To gamble or not to gamble: at risk for craving and relapse—learned motivated attention in pathological gambling. Biological Psychology, 87(2), 275–281. http://dx.doi.org/10.1016/j.biopsycho.2011.03.010

3) Neurobiology of Compulsive Sexual Behavior: Emerging Science (2016)

COMMENTS: While this paper is only a brief summation, it contains a few key observations. For example, it states that both Prause et al., 2015 and Kuhn & Gallinat, 2014 report a similar finding: greater porn use correlating with greater habituation to porn. Both studies reported lower brain activation in response to brief exposure to photos of vanilla porn. In the following excerpt “Lower late positive-potential” refers to the EEG findings of Prause et al.:

“In contrast, studies in healthy individuals suggest a role for enhanced habituation with excessive use of pornography. In healthy men, increased time spent watching pornography correlated with lower left putaminal activity to pornographic pictures (Kühn and Gallinat, 2014). Lower late positive-potential activity to pornographic pictures was observed in subjects with problematic pornography use.” (emphasis supplied)

Paper is saying that both Prause et al., 2015 and Kuhn & Gallinat, 2014 found habituation in more frequent porn users.

The full commentary:

Compulsive sexual behavior (CSB) is characterized by craving, impulsivity, social/occupational impairment, and psychiatric comorbidity. Prevalence of CSB is estimated around 3–6%, with a male predominance. Although not included in DSM-5, CSB can be diagnosed in ICD-10 as an impulse control disorder. However, debate exists about CSB’s classification (eg, as an impulsive-compulsive disorder, a feature of hypersexual disorder, an addiction, or along a continuum of normative sexual behavior).

Preliminary evidence suggests that dopamine may contribute to CSB. In Parkinson’s disease (PD), dopamine replacement therapies (Levo-dopa, dopamine agonists) have been associated with CSB and other impulse control disorders (Weintraub et al, 2010). A small number of case studies using naltrexone support its effectiveness at reducing urges and behaviors associated with CSB (Raymond et al, 2010), consistent with the possible opioidergic modification of mesolimbic dopamine function in reducing CSB. Currently, larger, adequately powered, neurochemical investigations and medication trials are needed to further understand CSB.

Incentive motivational processes relate to sexual cue reactivity. CSB vs non-CSB men had greater sex-cuerelated activation of the anterior cingulate, ventral striatum, and amygdala (Voon et al, 2014). In CSB subjects, functional connectivity of this network associated with cue-related sexual desire, thus resonating with findings in drug addictions (Voon et al, 2014). CSB men further show enhanced attentional bias to pornographic cues, implicating early attentional orienting responses as in addictions (Mechelmans et al, 2014). In CSB vs non-CSB PD patients, exposure to pornographic cues increased activation in the ventral striatum, cingulate and orbitofrontal cortex, linking also to sexual desire (Politis et al, 2013). A small diffusion-tensor imaging study implicates prefrontal abnormalities in CSB vs non-CSB men (Miner et al, 2009).

In contrast, studies in healthy individuals suggest a role for enhanced habituation with excessive use of pornography. In healthy men, increased time spent watching pornography correlated with lower left putaminal activity to pornographic pictures (Kühn and Gallinat, 2014). Lower late positive- potential activity to pornographic pictures was observed in subjects with problematic pornography use. These findings, while contrasting, are not incompatible. Habituation to picture cues relative to video cues may be enhanced in healthy individuals with excessive use; whereas, CSB subjects with more severe/pathological use may have enhanced cue reactivity.

Although recent neuroimaging studies have suggested some possible neurobiological mechanisms of CSB, these results should be treated as tentative given methodological limitations (eg, small sample sizes, cross-sectional designs, solely male subjects, and so on). Current gaps in research exist complicating definitive determination whether CSB is best considered as an addiction or not. Additional research is needed to understand how neurobiological features relate to clinically relevant measures like treatment outcomes for CSB. Classifying CSB as a ‘behavioral addiction’ would have significant implications for policy, prevention and treatment efforts; however, at this time, research is in its infancy. Given some similarities between CSB and drug addictions, interventions effective for addictions may hold promise for CSB, thus providing insight into future research directions to investigate this possibility directly. (emphasis supplied)

1. Kühn S, Gallinat J (2014). Brain structure and functional connectivity associated with pornography consumption: the brain on porn. JAMA Psychiatry 71: 827–834.

2. Mechelmans DJ, Irvine M, Banca P, Porter L, Mitchell S, Mole TB et al (2014). Enhanced attentional bias towards sexually explicit cues in individuals with and without compulsive sexual behaviours. PloS One 9: e105476.

3. Miner MH, Raymond N, Mueller BA, Lloyd M, Lim KO (2009). Preliminary investigation of the impulsive and neuroanatomical characteristics of compulsive sexual behavior. Psychiatry Res 174: 146–151.

4. Politis M, Loane C, Wu K, O’Sullivan SS, Woodhead Z, Kiferle L et al (2013). Neural response to visual sexual cues in dopamine treatment-linked hypersexuality in Parkinson’s disease. Brain 136: 400–411.

5. Raymond NC, Grant JE, Coleman E (2010). Augmentation with naltrexone to treat compulsive sexual behavior: a case series. Ann Clin Psychiatry 22: 55–62.

6. Voon V, Mole TB, Banca P, Porter L, Morris L, Mitchell S et al (2014). Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours. PloS One 9: e102419.

7. Weintraub D, Koester J, Potenza MN, Siderowf AD, Stacy M, Voon V et al (2010). Impulse control disorders in Parkinson disease: a cross-sectional study of 3090 patients. Arch Neurol 67: 589–595. Neuropsychopharmacology Reviews (2016) 41, 385–386; doi:10.1038/npp.2015.300

4) Should compulsive sexual behavior be considered an addiction? (2016)

COMMENTS: This review, like the other papers, says that Prause et al., 2015 aligns with Kühn & Gallinat, 2014 (Citation 72) which found that more porn use correlated with less brain activation in response to pictures of vanilla porn.

Excerpt describing Prause et al., 2015 (citation 73):

In contrast, other studies focusing on individuals without CSB have emphasized a role for habituation. In non-CSB men, a longer history of pornography viewing was correlated with lower left putaminal responses to pornographic photos, suggesting potential desensitization [72]. Similarly, in an event-related potential study with men and women without CSB, those reporting problematic use of pornography had a lower late positive potential to pornographic photos relative to those not reporting problematic use. The late positive potential is elevated commonly in response to drug cues in addiction studies [73]. These findings contrast to, but are not incompatible with, the report of enhanced activity in the fMRI studies in CSB subjects; the studies differ in stimuli type, modality of measure and the population under study. The CSB study used infrequently shown videos compared to repeated photos; the degree of activation has been shown to differ to videos versus photos and habituation may differ depending on the stimuli. Furthermore, in those reporting problematic use in the event-related potential study, the number of hours of use was relatively low [problem: 3.8, standard deviation (SD) = 1.3 versus control: 0.6, SD = 1.5 hours/week] compared to the CSB fMRI study (CSB: 13.21, SD = 9.85 versus control: 1.75, SD = 3.36 hours/week). Thus, habituation may relate to general use, with severe use potentially associated with enhanced cue-reactivity. Further larger studies are required to examine these differences. (emphasis supplied)

5) Is Internet Pornography Causing Sexual Dysfunctions? A Review with Clinical Reports (2016)

COMMENTS: This review, like the other papers, says that Prause et al., 2015 aligns with Kühn & Gallinat, 2014 (Citation 72) which found that more porn use correlated with less brain activation in response to pictures of vanilla porn.

Excerpt analyzing Prause et al., 2015 (citation 130):

A 2015 EEG study by Prause et al. compared frequent viewers of Internet pornography (mean 3.8 h/week) who were distressed about their viewing to controls (mean 0.6 h/week) as they viewed sexual images (1.0 s exposure) [130]. In a finding that parallels Kühn and Gallinat, frequent Internet pornography viewers exhibited less neural activation (LPP) to sexual images than controls [130]. The results of both studies suggest that frequent viewers of Internet pornography require greater visual stimulation to evoke brain responses when compared with healthy controls or moderate Internet pornography users [167,168]. In addition, Kühn and Gallinat reported that higher Internet pornography use correlated with lower functional connectivity between the striatum and the prefrontal cortex. Dysfunction in this circuitry has been related to inappropriate behavioral choices regardless of potential negative outcome [169]. In line with Kühn and Gallinat, neuropsychological studies report that subjects with higher tendency towards cybersex addiction have reduced executive control function when confronted with pornographic material [53,114]. (emphasis supplied)

6) “Conscious and Non-Conscious Measures of Emotion: Do They Vary with Frequency of Pornography Use?” (2017)

COMMENTS: This EEG study on porn users cited 3 Nicole Prause EEG studies. The authors believe that all 3 Prause EEG studies actually found desensitization or habituation in frequent porn users (which often occurs with addiction). The excerpts below these 3 citations indicate the following Nicole Prause EEG studies (#8 is Prause et al., 2015):

• 7 – Prause, N.; Steele, V.R.; Staley, C.; Sabatinelli, D. Late positive potential to explicit sexual images associated with the number of sexual intercourse partners. Soc. Cogn. Affect. Neurosc. 2015, 10, 93–100.
• 8 – Prause, N.; Steele, V.R.; Staley, C.; Sabatinelli, D.; Hajcak, G. Modulation of late positive potentials by sexual images in problem users and controls inconsistent with “porn addiction”. Biol. Psychol. 2015, 109, 192–199.
• 14 – Steele, V.R.; Staley, C.; Fong, T.; Prause, N. Sexual desire, not hypersexuality, is related to neurophysiological responses elicited by sexual images. Socioaffect. Neurosci. Psychol. 2013, 3, 20770

Excerpts describing Prause et al., 2015 (citation 8):

Event-related potentials (ERPs) have often been used as a physiological measure of reactions to emotional cues, e.g., [24]. Studies utilizing ERP data tend to focus on later ERP effects such as the P300 [14] and Late-Positive Potential (LPP) [7, 8] when investigating individuals who view pornography. These later aspects of the ERP waveform have been attributed to cognitive processes such as attention and working memory (P300) [25] as well as sustained processing of emotionally-relevant stimuli (LPP) [26]. Steele et al. [14] showed that the large P300 differences seen between viewing of sexually explicit images relative to neutral images was negatively related to measures of sexual desire, and had no effect on participants’ hypersexuality. The authors suggested that this negative finding was most probably due to the images shown not having any novel significance to the participant pool, as participants all reported viewing high volumes of pornographic material, consequently leading to the suppression of the P300 component. The authors went on to suggest that perhaps looking at the later occurring LPP may provide a more useful tool, as it has been shown to index motivation processes. Studies investigating the effect pornography use has on the LPP have shown the LPP amplitude to be generally smaller in participants who report having higher sexual desire and problems regulating their viewing of pornographic material [7, 8]. This result is unexpected, as numerous other addiction-related studies have shown that when presented with a cue-related emotion task, individuals who report having problems negotiating their addictions commonly exhibit larger LPP waveforms when presented images of their specific addiction-inducing substance [27]. Prause et al. [7, 8] offer suggestions as to why the use of pornography may result in smaller LPP effects by suggesting that it may be due to a habituation effect, as those participants in the study reporting overuse of pornographic material scored significantly higher in the amount of hours spent viewing pornographic material.

———–

Studies have consistently shown a physiological downregulation in processing of appetitive content due to habituation effects in individuals who frequently seek out pornographic material [3, 7, 8]. It is the authors’ contention that this effect may account for the results observed.

————

Future studies may need to utilise a more up-to-date standardised image database to account for changing cultures. Also, maybe high porn users downregulated their sexual responses during the study. This explanation was at least used by [7, 8] to describe their results which showed a weaker approach motivation indexed by smaller LPP (late positive potential) amplitude to erotic images by individuals reporting uncontrollable pornography use. LPP amplitudes have been shown to decrease upon intentional downregulation [62, 63]. Therefore, an inhibited LPP to erotic images may account for lack of significant effects found in the present study across groups for the “erotic” condition. (emphasis supplied)

7) Neurocognitive mechanisms in compulsive sexual behavior disorder (2018)

Excerpt analyzing Prause et al., 2015 (which is citation 87):

A study using EEG, conducted by Prause and colleagues, suggested that individuals who feel distressed about their pornography use, as compared to a control group who do not feel distress about their use of pornography, may require more/greater visual stimulation to evoke brain responses [87]. Hypersexual participants—individuals‘ experiencing problems regulating their viewing of sexual images’ (M=3.8 hours per week)—exhibited less neural activation (measured by late positive potential in the EEG signal) when exposed to sexual images than did the comparison group when exposed to the same images. Depending on the interpretation of sexual stimuli in this study (as a cue or reward; for more see Gola et al. [4]), the findings may support other observations indicating habituation effects in addictions [4]. In 2015, Banca and colleagues observed that men with CSB preferred novel sexual stimuli and demonstrated findings suggestive of habituation in the dACC when exposed repeatedly to the same images [88]. Results of the aforementioned studies suggest that frequent pornography use may decrease reward sensitivity, possibly leading to increased habituation and tolerance, thereby enhancing the need for greater stimulation to be sexually aroused. However, longitudinal studies are indicated to examine this possibility further. Taken together, neuroimaging research to date has provided initial support for the notion that CSB shares similarities with drug, gambling, and gaming addictions with respect to altered brain networks and processes, including sensitization and habituation. (emphasis supplied).

8) Online Porn Addiction: What We Know and What We Don’t—A Systematic Review (2019)

Excerpt critiquing Prause’s 2 EEG studies: Steele et al., 2013 & Prause et al., 2015 (citation 105 is Steele, citation 107 is Prause):

Evidence of this neural activity signalizing desire is particularly prominent in the prefrontal cortex [101] and the amygdala [102,103], being evidence of sensitization. Activation in these brain regions is reminiscent of financial reward [104] and it may carry a similar impact. Moreover, there are higher EEG readings in these users, as well as the diminished desire for sex with a partner, but not for masturbation to pornography [105], something that reflects also on the difference in erection quality [8]. This can be considered a sign of desensitization. However, Steele’s study contains several methodological flaws to consider (subject heterogeneity, a lack of screening for mental disorders or addictions, the absence of a control group, and the use of questionnaires not validated for porn use) [106]. A study by Prause [107], this time with a control group, replicated these very findings. The role of cue reactivity and craving in the development of cybersex addiction have been corroborated in heterosexual female [108] and homosexual male samples [109].

Comments: The above critique states that Prause’s 2015 EEG replicated the findings from her 2013 EEG study (Steele et al.): Both studies reported evidence of habituation or desensitization, which is consistent with the addiction model (tolerance). Let me explain.

It’s important to know that Prause et al., 2015 AND Steele et al., 2013 had the same “porn addicted” subjects. The problem is that Steele et al. had no control group for comparison! So Prause et al., 2015 compared the 2013 subjects from Steele et al., 2013 to an actual control group (yet it suffered from the same methodological flaws named above). The results: Compared to controls “individuals experiencing problems regulating their porn viewing” had lower brain responses to one-second exposure to photos of vanilla porn. The ACTUAL results of Prause’s two EEG studies:

1. Steele et al., 2013: Individuals with greater cue-reactivity to porn had less desire for sex with a partner, but not less desire to masturbate.
2. Prause et al., 2015: “Porn addicted users” had less brain activation to static images of vanilla porn. Lower EEG readings mean that the “porn addicted” subjects were paying less attention to the pictures.

A clear pattern emerges from the 2 studies: The “porn addicted users” were desensitized or habituated to vanilla porn, and those with greater cue-reactivity to porn preferred to masturbate to porn than have sex with a real person. Put simply they were desensitized (a common indication of addiction) and preferred artificial stimuli to a very powerful natural reward (partnered sex). There is no way to interpret these results as falsifying porn addiction. The findings support the addiction model.

10) Do Varying Levels of Exposure to Pornography and Violence Have an Effect on Non-Conscious Emotion in Men (2020)

Comments: Ignoring Prause et al.’s unsupported title, the authors accepted the most likely explanation mentioned in Prause et al., 2015: “Prause et al. suggested that this unexpected finding may be due to habituation effects, as the participants who presented with the reduced LPP waveform also scored significantly higher in the amount of hours they spent viewing pornographic material.”

Excerpt mentioning Prause et al., 2015:

Studies investigating neural attributes to problematic or frequent pornographic material use are relatively scarce. Unproblematic or infrequent use of pornographic material generally induces an enhanced LPP waveform when individuals are presented with erotic visual information (Prause et al., 2015). A larger amplitude LPP is an index of sustained processing of emotionally relevant stimuli and is a marker of motivational significance (Voon et al., 2014). In contrast, with regard to ERP effects of problem viewing of visual sexual stimuli, existing literature has generally shown a reduced amplitude LPP component. Prause et al. presented individuals who either reported or denied problematic pornography use with emotion-inducing images (including explicit sexual images). Individuals who reported problems policing their pornography use and who had a stronger desire for sex demonstrated lower LPP amplitudes in response to the explicit sexual images. Prause et al. suggested that this result was unexpected. Numerous studies of individuals with addictive behaviors have employed cuerelated emotional tasks. Typically, these studies have found an increased LPP amplitude when presented with images of the individual’s addiction-inducing substance (Minnix et al., 2013). Prause et al. suggested that this unexpected finding may be due to habituation effects, as the participants who presented with the reduced LPP waveform also scored significantly higher in the amount of hours they spent viewing pornographic material.