Abstract
Background and aims
Despite the inclusion of the Compulsive Sexual Behavior Disorder (CSBD) in the 11th edition of the International Classification of Diseases, emotional and cognitive impairments related to CSBD remains unclear. This study aimed to investigate the behavioral and neuronal effects of emotional interference on cognition among CSBD patients.
Methods
Thirty heterosexual males with CSBD and matched healthy controls (HC) were studied with the Emotional Stroop Task using 5 categories of emotionally arousing words (sex-related, positive, fear-related, negative, neutral) during functional magnetic imaging.
Results
At the behavioral level, we found the main effect of the condition: sex-related words evoked a stronger Stroop effect than other conditions. At the neural level, we found a significant group effect. Among CSBD patients processing of sex-related words was related to increased activity in the right putamen, right thalamus, hippocampi, and left pulvinar, when compared to HC. We also found a negative correlation between neuronal activation and time spent on sexual activity during the week preceding study and numerous group differences in brain regions connected to the emotional and motivational processing of sexually explicit material, correlating with CSBD symptoms.
Conclusions
Behavioral results indicate a specific attentional bias toward sex-related stimuli in both groups, while neural data uncovered stronger reactivity to sex-related words in CSBD compared to HC. This reactivity is related to CSBD symptoms and provides evidence for the interference of sex-related stimuli with cognition. Such results are firmly in line with the Incentive Salience Theory and conceptualizing CSBD as a behavioral addiction.
Introduction
Compulsive Sexual Behavior Disorder (CSBD), recently included in the 11th edition of the International Classification of Diseases (ICD-11), has been increasingly studied during the last 10 years (Gola & Potenza, 2018; Kraus, Voon, & Potenza, 2016; Kühn & Gallinat, 2016). CSBD is characterized by a pattern of repeated failure to resist an impulse or urge to engage in different kinds of sexual behaviors (e.g. pornography watching, compulsive masturbation, engaging in sexual behaviors with others etc.), which turn into harmful symptoms impairing essential areas of life. To fulfill CSBD criteria patient for at least 6 months needs to meet following criteria: (a) failure to control repetitive, intrusive, strong sexual impulses or urges; (b) these impulses lead to different forms of compulsive sexual behavior; (c) engaging into those behaviors has become a central focus of the patient's life causing the neglect of health/personal care or other interests (d) these sexual behaviors are associated with personal distress or difficulties in patient's important areas of life; (e) subject continues repetitive sexual behavior despite adverse consequences or (f) deriving little or no satisfaction from it. The pattern of failure to control repetitive sexual urges could not be better accounted for by another mental disorder (e.g., manic episode) or other medical condition and is not due to the effects of a substance abuse or medication. Also, if distress connected to sexual behaviors is entirely related to moral judgments/religious beliefs and disapproval, CSBD should not be diagnosed (WHO, 2023). Unfortunately, current research does not provide a transparent model explaining the mechanism underlying CSBD symptoms, taking into consideration both cognitive impairments (Chatzittofis et al., 2016; Kor, Fogel, Reid, & Potenza, 2013; Kowalewska et al., 2018; Liberg et al., 2022; Mechelmans et al., 2014) and processing of emotional events/stimuli (Bőthe et al., 2019; Draps et al., 2021; Gola & Draps, 2018; Miner, Dickenson, & Coleman, 2019; Sinke et al., 2020).
Regarding cognitive impairments, there is evidence for attentional bias toward sexual stimuli (Mechelmans et al., 2014), impairment in general attentional abilities (Draps et al., 2021), higher sensitivity to novelty and cue-reactivity (Banca, Morris, et al., 2016; Brand, Snagowski, Laier, & Maderwald, 2016; Draps et al., 2021; Gola et al., 2017). All these impairments correlate with CSBD symptoms, similar to other findings related to the cognitive domain, such as inhibition capacities (Antons & Brand, 2018; Draps, et al., 2021), impulsivity (Antons et al., 2019; Banca, Harrison, & Voon, 2016; Bothe et al., 2019; Draps, et al., 2021; Miner et al., 2016; Reid, Garos, Carpenter, & Coleman, 2011; Wetterneck, Burgess, Short, Smith, & Cervantes, 2012), and impaired executive control (Seok & Sohn, 2018).
The second important aspect is processing of emotional events/stimuli, especially strategies for coping with negative emotions. It is known that individuals with CSBD experience increased level of shame, low self-esteem, and loneliness and that CSBD symptoms are often accompanied by stress (Odlaug et al., 2013; Reid, Temko, Moghaddam, & Fong, 2014; Schreiber, Grant, & Odlaug, 2012; Spenhoff, Kruger, Hartmann & Kobs, 2013) and severe anxiety (Gola, Miyakoshi, & Sescousse, 2015; Wordecha et al., 2018). There is a hypothesis that CSBD symptoms serve as a coping mechanism to self-regulate negative emotional states (Lew-Starowicz, Lewczuk, Nowakowska, Kraus, & Gola, 2020; Miner et al., 2019; Wordecha et al., 2018). Therefore impaired emotion regulation was not included in the CSBD criteria as potentially not specific symptom, yet it is important to understand its functional role (Gola & Kraus, 2021). Unfortunately, the amount of scientific studies in this domain is limited. Farthermost previous studies investigated either cognitive processes or emotional processing in CSBD, and there are very few attempts (e.g. our previous work: Draps et al., 2021) to examine both and look into the potential interference between them. Understanding such interference could shed new light on the nature of CSBD and its underlying mechanisms.
One of the measures of interaction between cognitive and emotional processing is cognitive conflict, occurring when the processing of task-relevant information is coalbed by a distractor (Nee, Wager, & Jonides, 2007). A proper example of a daily distractor is emotionally salient stimuli of danger or any other important negative emotion (LeDoux, 2000). In this situation, cognitive conflict can arise from this emotional interference caused by danger and compromise the ability to complete tasks requiring cognitive control (Etkin, Egner, Peraza, Kandel, & Hirsch, 2006). So, the ability to efficiently resolve emotional interference seems to be a relevant human skill. Lastly, researchers started to view emotional processing and cognitive control skills as brain functions that do not operate independently. Conversely, numerous studies suggest that both functions share the same neural circuitry (Mueller, 2011; Pessoa, 2008; Shackman et al., 2011). For example, there is compelling evidence that brain regions commonly associated with cognitive control, such as the dorsolateral prefrontal cortex (DLPFC), also play an important role in emotion processing (Okon-Singer, Hendler, Pessoa, & Shackman, 2015). The meta-analysis of 43 studies with different emotional tasks intermixed with a variety of cognitive control tasks (e.g., Stroop, n-back, stop signal, or the go/no-go task), showed consistent brain activation in both cognitive control (e.g., dorsolateral prefrontal cortex (DLPFC), and inferior frontal gyrus (IFG)) and emotion processing structures (e.g., subgenual anterior cingulate cortex (ACC) and amygdala regions) (Cromheeke & Mueller, 2014).
The Emotional Stroop Task (EST) is particularly efficient for studies of emotion and cognition interactions (Etkin et al., 2006; Melcher, Born, & Gruber, 2011). In this task, emotional words are displayed in various colors, and the subject is asked to indicate the color. The assumption is that the reaction times will be significantly different if the words are emotionally charged as opposed to neutral words (Draps et al., 2021). In 2017, Albery and colleagues showed on healthy controls that there is a negative relationship between sexual experience and bias towards sex-related stimuli in modified EST, suggesting that attentional preference for sexual stimuli varies as a function of the interaction between how long a person has been active sexually and how compulsive their sexual behavior is (Albery et al., 2017). But there is almost no data on EST collected with a CSBD group. Our recent study (Draps et al., 2021) showed a difference between CSBD patients and healthy controls in the processing of positive (also sexually related) and negative stimuli, concluding that the sexual stimuli could play a more distractive role during cognitive processing (causing more attentional biases) in CSBD patients. Unfortunately, this did not involve collecting any neuronal measures. Wang & Zhang (2021) using electroencephalographic data collected from a non-clinical group with CSBD symptoms, demonstrated that sexual stimuli induce greater amplitudes of P200 and LPP (late positive potential) evoked response potentials related to the salience of stimuli, than neutral stimuli, especially at the frontal, frontocentral, and central regions. This result suggests that individuals with CSBD symptoms tend to automatically allocate more attention to sexually explicit images than neutral ones (Wang & Zhang, 2021).
Taking into consideration all matters described above we decided to conduct the study aiming to examine interference between emotional stimuli (including sexual stimuli) and cognitive processing using EST during functional magnetic resonance imagining (fMRI) among CSBD patients and healthy controls. We hypothesized that the CSBD group would show higher levels of interference on task performance for sex-related and negative stimuli apparent in both measures of reaction time and altered pattern of neural reactivity. More precisely, we hypothesized that positive emotional stimuli and sex-related stimuli would be connected to bigger motivation to correct and quit response (Draps et al., 2021; Gola et al., 2017) seemed as the shortening of behavioral results in both groups, but the difference between neutral and sex-related trials would be more prominent among CSBD patients what would be in line with specific sensitization hypothesis (Berridge, 2012). If the hypothesis of generally impaired processing of emotional stimuli (Lew-Starowicz et al., 2020; Miner et al., 2019; Wordecha et al., 2018) is correct, negative and fearful emotional stimuli would be connected with bigger cognitive interference, causing an increase in reaction times (RTs) in CSBD. Regarding previous findings (Wang & Zhang, 2021) on the neuronal level, we expected increased neuronal activation in the CSBD group, compared to healthy controls, during the processing of sex-related stimuli, especially in areas connected to the processing of sexually arousing material. We also expected an increased activation in the CSBD group, compared to healthy controls, during processing of negative stimuli (general category and specific fearful words) in areas related to emotional processing.
Methods
Recruitment
Individuals with CSBD (N = 30) were recruited by a psychologist, based on clinical interviews (CSBD criterion-ICD-11 World Health Organization, 2023), and scores in Sexual Addiction Screening Test >8 (SASTR; Gola et al., 2016, the scale is created by Patrick Carnes to assist in the assessment of sexually compulsive behavior, Polish version consisted with 20 items measuring different kinds of sexual behaviors with restriction that more than 5 points can indicate the need for additional clinical interviews for CSBD), scores >4 in Brief Pornography Screening Test (BPS; Kraus et al., 2020, 5-items scale created by Shane Kraus to assist in the assessment of problematic pornography use- PPU, scores equal or more than 4 is considered a positive screen for possible PPU), Hypersexual Behavior Inventory with no cut-off score (HBI; Reid et al., 2011, 19-item scale created by Rory Reid to assesses different aspects of hypersexuality through three factors: control, coping, and consequences of sexual behaviors). All participants were screened for comorbid addictions ((included if scores <14 on Alcohol Use Disorder Identification Test (AUDIT; Babor, de la Fuente, Saunders, & Grant, 1992, 10-item screening tool developed by the World Health Organization (WHO) to assess alcohol consumption, drinking behaviors, and alcohol-related problems) and included if scores <5 on the South Oaks Gambling Screen (SOGS; Stinchfield, 2002, 20-item questionnaire tool based on DSM criteria to screening for pathological gambling)), as well as identifying as exclusively or predominantly heterosexual on the Kinsey Scale (included if scores <14; Polish adaptation: Wierzba et al., 2015, the scale is a tool to describe a person's sexual orientation based on one's experience). Thirty age, and income-matched Healthy Controls (HC) without psychopathological symptoms (including CSBD symptoms) were also recruited from a community sample. Additional exclusion criteria for both groups were: history of other psychiatric disorders, neurological or medical serious issues, contraindications for magnetic resonance imaging procedures, and color blindness (Ishihara's Test (1987)). One CSBD patient was excluded from the analysis due to the lack of data.
Procedure
CSBD patients were recruited from treatment clinics in Warsaw, Poland while HC were recruited through online announcements. After the positive enrollment, participants were invited to the Laboratory of Brain Imaging of Nencki Institute, PAS (Warsaw, Poland) for the experimental examination. After receiving detailed information about the study's aims and confirmation of anonymity and confidentiality participants signed informed consent. For anonymity reasons, we applied a double-blind procedure, so the research team, during data acquisition, had no access to the data from recruitment. The procedure took approximately 1.5 h to complete training in mock scanner and to complete structural and functional magnetic resonance imaging. Addition of training in the mock scanner was related to need of ensure that all participant understood the procedure and the task and mainly involved learning which button (in the left or right thumb or index finger) corresponds to which color. It took between 10 and 15 trials for each subject. Participants received a financial compensation of 150 PLN (∼34 EUR).
Task
We used a modified version of Dresler's Emotional Stroop Task (Dresler, Mériau, Heekeren, & Van Der Meer, 2009). In our version, instead of 3, we had 5 categories of emotionally arousing words (sex-related, positive, fear-related, negative, and neutral), details information is provided in Draps et al., 2021 and in Fig. 1.
Experimental procedure
We used a modified version of Dresler's Emotional Stroop Task (Dresler et al., 2009). In our version, instead of 3 categories of emotionally arousing words (positive, negative and neutral) we used 5 (sex-related, positive, fear-related, negative, and neutral) with 4–8 letters long words (the mean number in each category was about 6 letters). We used sex-related words despite not being a typical emotional category, based on our clinical observations, that patients often report processing of sexual stimuli as inseparably linked with emotional states, such as e.g. pleasure, sadness, or anger (Wordecha et al., 2018). All words were taken from Nencki Affective Word List – NAWL (Riegel et al., 2015) and displayed in four different colors (yellow, red, blue, and purple on a screen that could be seen through a tilted mirror attached to the fMRI's head coil. Participants had to indicate the color of the word by pressing one of the 4 corresponding buttons with the left or right thumb or index finger (the same configuration for all participants). Trials were presented in event-related design in a semi-randomized order counterbalances across conditions in 4 separate scanning sessions. Functional MRI scanning consisted of 4 runs with 20 words in each sex-related, positive, fear-related, and negative category and 40 words in the neutral category. All the words we presented twice in different runs. All 4 runs of the task were counterbalanced across participants in both groups. Each word was presented on a gray screen until a response was given with a maximum duration of 2,000 ms, with intertrial intervals (ITI) ranging between 300 and 700 ms
Citation: Journal of Behavioral Addictions 13, 3; 10.1556/2006.2024.00033
Data acquisition
Data acquisition took place at the Laboratory of Brain Imaging of Nencki Institute, PAS (Warsaw, Poland) on a 3-Tesla MRI scanner (Siemens Magnetom Trio TIM, Erlangen, Germany) equipped with a 32-channel phased array head coil. All information about data acquisition is provided in Table 1.
Information about data acquisition. * Field mapping was done based on a method outlined in Jezzard and Balaban (1995)
| Type of data | Sequence | Parameters |
| Anatomical data of the brains | T1-weighted sequence | time repetition = 2,530 ms, time echo = 3.32 ms, flip angle = 7°, field of view = 256 mm, and 176 axial slices with 1 mm slice thickness |
| Functional data from the Emotional Stroop Task | T2*-weighted gradient echo planar imaging sequence | time repetition = 2500 ms, time echo = 28 ms, flip angle = 90°, in plane resolution = 64 × 64 mm, the field of view = 192 mm, and 38 axial slices with 3 mm slice thickness with no gap between slices |
| Field mapping | double echo FLASH | echo time 1 = 4.92, echo time 2 = 7.38, time repetition = 600 ms with the same spatial properties as functional scans |
Statistical analyses of behavioral data
To investigate the magnitude of the emotional Stroop effect measured with reaction times (RTs), we calculated an emotional interference value, defined as the difference between mean RTs in the neutral category and mean RTs in emotional categories (e.g. neutral minus sex-related). The reason for such calculation was an assumption that (a) the positive emotional category and sex-related category would be connected with the shortening of RTs and differences would be over 0 and (b) both negative emotional categories would be connected with cognitive load interference causing an increase in RTs and differences will be under the 0. To test differences between 4 different emotional categories and between 2 groups (CSBD vs. HC), repeated measures ANOVA (4 × 2) with RTs (in ms) as a dependent variable was conducted. Post hoc comparisons between emotional categories were calculated with Bonferroni correction (p < 0.05). In addition to examining evidence for the null hypothesis stating a lack of between-group differences in certain conditions we have conducted Bayesian Repeated Measures ANOVA and t-tests using JASP software (JASP Team, 2019).
Data preprocessing and statistical analyses of neuronal data
We used Statistical Parametric Mapping (SPM12) for data preprocessing and statistical analyses. Functional data were corrected for the head motion with field map realignment option (FieldMap Toolbox; Jezzard, Balaban, 1995), then coregister to T1w image, normalized and smooth in 6 mm full-width at half-maximum isotropic Gaussian kernel. Structural scans were automatically classified into types of brain matter with the „New Segmentation” tool (Ashburner, Friston, 2005). Then a two-step random-effects approach was used for all analyses. Firstly, all activation during the conditions of interest (5 categories of events: sex-related, positive, fear-related, negative and neutral trials) were fitted using the general linear model (GLM) as a 2 s box-car events for each participant individually with 6 regressors of head motions. We modeled 4 runs separately as blocks in the same order for all participants. All models were masked on threshold 0.2. and a high-pass filter with a cut-off of 128 s was applied to the time series. Secondly, to investigate group differences, the contrasts of interest (each emotion minus neutral contrasts) were entered as the dependent variables with the group variable as the independent variable in simple two-sample t-tests. The reason for that was the assumption that all emotional categories will be connected with more neuronal activation than processing words in neutral trials. The last step of analysis included region-of-interests (ROI) analysis. We selected a list of ROIs (from Automated Anatomical Labelling atlas (https://www.gin.cnrs.fr/en/tools/aal/) to identify amygdala, anterior cingulate cortex (ACC), caudate, pallidum, putamen, nucleus accumbens, (NAcc), insula, thalamus and ventral tegmental area (VTA), posterior orbitofrontal cortex (pOFC), anterior orbitofrontal cortex (aOFC) and we created the two spheres left and right ventral striatum (ROI defined a priori based on a previous meta-analysis of reward anticipation (Liu, Hairston, Schrier, & Fan, 2011); as a 8 mm sphere centered around: Left: x = −12, y = 10 z = −6; Right: x = 12, y = 10, z = −4) based on meta-analysis on emotional and motivational processing of sexually explicit material described by Stark et al. (2019) and extracted the percentage of BOLD signal change in these ROIs during processing sex-related words by the usage of the MarsBaR toolbox (http://marsbar.sourceforge.net/). We also did exploratory correlation analysis between severity of CSBD symptoms' and percentage of BOLD signal change (extracted by MarsBaR) in clusters that statistically significantly differentiated the studied groups (in CSBD minus HC comparison) in previous contrasts of interest i.e. sex-related trials minus neutral trials and with selected ROIs. The last exploratory correlation analysis was done only in the CSBD group, because we were interested in showing how the clinical severity of the symptoms translates into increased neural activity during the task processing and in the HC group there had no clinical symptoms to explore.
Ethics
All the procedures were carried out in accordance with the Declaration of Helsinki. The study was approved by the local ethics committee of the Institute of Psychology Polish Academy of Sciences.
Results
Participant's characteristics
There was no between-group difference in the mean age (t (57) = 0.159 p = 0.874). CSBD patients obtained significantly higher scores of CSBD severity across all used scales (SASTR: t (57) = 19.687 p < 0.001; BPS: t (57) = 19.107 p < 0.001; HBI: t (57) = −11.890, p < 0.001). For all participants, the scores measuring comorbid addiction symptoms were below the threshold (AUDIT: t (57) = −0.496 p = 0.622; SOGS: t (57) = 0.526 p = 0.601). Groups did differ at the: Obsessive-Compulsive Inventory-Revised (t (57) = −4.357, p < 0.001; OCI-R, Foa et al., 2002), Hospital Anxiety and Depression Scale (anxiety sub-scale: t (57) = −4.897, p < 0.001; depression sub-scale: t (57) = −3.609, p < 0.001; Zigmond & Snaith, 1983), State-Trait Anxiety Inventory (state sub-scale: t (57) = −3.831, p < 0.001; Spielberger, 1989), Barratt Impulsiveness Scale (attentional impulsiveness sub-scale: t (57) = −5.265, p < 0.001; motor impulsiveness sub-scale: t (57) = −3.140, p = 0.003; non-planning impulsiveness sub-scale; t (57) = −2.197, p = 0.032; Stanford et al., 2009), UPPS-P Impulsive Behavior Scale (positive urgency sub-scale: t (57) = −3.050, p = 0.003; negative urgency sub-scale: t (57) = −6.642, p < 0.001; lack of perseverance sub-scale t (57) = −2.377, p = 0.021; Lynam, Smith, Whiteside, & Cyders, 2006), Sensitivity to Punishment and Reward Questionnaire (punishment sub-scale: t (57) = −3.402, p < 0.001; Cooper & Gomez, 2008). There were no significant group differences in certain sub-scales of State-Trait Anxiety Inventory (trait sub-scale: t (57) = 0.833, p = 0.408), UPPS-P Impulsive Behavior Scale (lack of premeditation sub-scale: t (57) = −0.361, p = 0.720 and sensation seeking sub-scale: t (57) = −0.089, p = 0.929; Lynam et al., 2006), and in Monetary Choice Questionnaire (overall K value: t (57) = 1.784, p = 0.080; MCQ, Kirby & Maraković, 1996) and Sensitivity to Punishment and Reward Questionnaire (reward scale: t (57) = −0.590, p = 0.558; Cooper & Gomez, 2008) (Table 2).
Participants' characteristics – comparison of the questionnaire statistics obtained for CSBD and HC groups (significance value of < 0.05)
| CSBD (N = 29) mean (SD) | HC (N = 30) mean (SD) | p value | Cohen's d | SE Cohen's d | |
| Age | 33.93 (6.573) | 33.43 (6.399) | 0.769 | −0.077 | 0.261 |
| Brief Pornography Screening Test | 7.79 (2.059) | 1 (1.050) | <0.001 | −4.177 | 0.599 |
| Sexual Addiction Screening Test-Revised | 12.93 (2.927) | 1.37 (1.273) | <0.001 | −5.155 | 0.715 |
| Hypersexual Behavior Inventory | 59.069 (14.709) | 24.63 (6.494) | <0.001 | −3.047 | 0.472 |
| Alcohol Use Disorder Identification Test | 6.24 (2.773) | 6.53 (2.968) | 0.698 | 0.102 | 0.261 |
| South Oaks Gambling Screen | 0.76 (1.480) | 0.57 (0.935) | 0.552 | −0.156 | 0.261 |
| Obsessive Compulsive Inventory-Revised | 20.14 (9.775) | 10.31 (6.448) | <0.001 | −1.187 | 0.305 |
| Hospital Anxiety and Depression Scale anxiety scale | 8.72 (3.575) | 4.59 (2.732) | <0.001 | −1.301 | 0.313 |
| Hospital Anxiety and Depression Scale depression scale | 5.93 (3.605) | 2.93 (2.789) | <0.001 | −0.931 | 0.290 |
| State-Trait Anxiety Inventory state scale | 44.07 (12.218) | 33.23 (8.443) | <0.001 | −1.035 | 0.293 |
| State-Trait Anxiety Inventory trait scale | 48.14 (2.489) | 48.57 (2.161) | 0.482 | 0.184 | 0.261 |
| Barratt Impulsiveness Scale attentional impulsiveness scale | 12.72 (2.389) | 9.03 (2.580) | <0.001 | −1.484 | 0.323 |
| Barratt Impulsiveness Scale motor impulsiveness scale | 18.62 (3.736) | 15.83 (3.130) | 0.003 | −0.810 | 0.281 |
| Barratt Impulsiveness Scale non-planning impulsiveness scale | 23.207 (4.799) | 20.30 (4.669) | 0.022 | −0.614 | 0.272 |
| UPPS-P Impulsive Behavior Scale positive urgency scale | 32.69 (6.872) | 28.03 (4.313) | 0.003 | −0.811 | 0.283 |
| UPPS-P Impulsive Behavior Scale negative urgency scale | 32.24 (5.396) | 23.45 (4.339) | <0.001 | −1.796 | 0.353 |
| UPPS-P Impulsive Behavior Scale lack of premeditation scale | 24.55 (2.515) | 24.17 (2.989) | 0.603 | −0.137 | 0.263 |
| UPPS-P Impulsive Behavior Scale lack of perseverance scale | 23.17 (4.343) | 20.34 (4.490) | 0.018 | −0.640 | 0.276 |
| UPPS-P Impulsive Behavior Scale sensation seeking scale | 32.80 (6.15) | 32.65 (6.32) | 0.983 | 0.006 | 0.263 |
| Monetary Choice Questionnaire overall K value | 0.01 (0.019) | 0.03 (0.055) | 0.088 | 0.456 | 0.269 |
| Sensitivity to Punishment and Reward Questionnaire punishment scale | 5.41 (3.459) | 2.47 (3.277) | <0.001 | −0.875 | 0.284 |
| Sensitivity to Punishment and Reward Questionnaire reward scale | 5.17 (2.189) | 4.90 (2.203) | 0.636 | −0.124 | 0.261 |
Behavioral results
Reaction times were influenced by the experimental condition (type of emotion) F (3,232) = 10.608; p = 0.001 with strong effect size (eta2 = 0.109; interpretation based on Cohen, 1988). To examine between-group differences within each emotional category, we have conducted Bonferroni-corrected post hoc comparisons. Results of this analysis revealed a significantly higher difference in RTs (neutral minus sex-related) trials (more precisely there was a bigger emotional Stroop effect in sex-related trials) than in positive trials (neutral minus positive trials) (p = 0.002), negative trials (neutral minus negative trials) (p = 0.002), and fear-related trials (neutral minus fear-related trials) (p = 0.001). RTs did not differ significantly between positive and negative words, positive and fear-related, and between negative and fear-related. Interaction between the emotional category and the group was insignificant (F (3,232) = 1.385; p = 0.249; eta2 = 0.014) and we did not find any significant group effect (F (1,116) = 3.547; p = 0.065; eta2 = 0.017; for details see Fig. 2) Mean RTs are listed in Table 3 as a difference (neutral minus each category) and as an absolute value, worth to mention is the fact that our assumption about shortening RTs in sex-related category seems to be opposite.
Differences of mean reaction times in 4 categories of trials. In the CSBD group in the neutral minus sex-related category M = −28.23 ms (SD = 52.22), in neutral minus positive emotional category M = 2.19 ms (SD = 25.29), in neutral minus negative emotional category M = −0.67 ms (SD = 23.52) and in neutral minus fear-related category M = 6.70 ms (SD = 23.70). Among healthy controls (HC) in neutral minus sex-related category M = −8.81 ms (SD = 25.99), in neutral minus positive emotional category M = 11.84 ms (SD = 24.55), in neutral minus negative emotional category M = 4.37 ms (SD = 29.25) and in neutral minus fear-related category M = 5.59 ms (SD = 21.63). There was a significant main effect of emotion type on differences of mean reaction times F (3,232) = 10.608; p = 0.001 with strong effect size (eta2 = 0.109). Interaction between emotional category and the group (F (3,232) = 1.385; p = 0.249; eta2 = 0.014) and main group effect (F (1,116) = 3.547; p = 0.065; eta2 = 0.017) were insignificant. The blue lines represents if the differences are in over or under 0, to test if right is an assumption that (a) the positive emotional category and sex-related category would be connected with the shortening of RTs and differences would be over 0 and (b) both negative emotional categories will be connected with cognitive load interference causing an increase in RTs and differences will be under the 0
Citation: Journal of Behavioral Addictions 13, 3; 10.1556/2006.2024.00033
Mean reaction times
| CSBD (N = 29) mean (SD) | HC (N = 30) mean (SD) | |
| RTs in neutral minus sex-related category | −28.23 ms (52.22) | −8.81 ms (25.99) |
| RTs in neutral minus positive emotional category | 2.19 ms (25.29) | 11.84 ms (24.55) |
| RTs in neutral minus negative emotional category | −0.67 ms (23.52) | 4.37 ms (29.25) |
| RTs in neutral minus fear-related category | 6.70 ms (23.70) | 5.59 ms (21.63) |
| RTs in neutral category | 823.63 ms (158.03) | 794.90 ms (118.35) |
| RTs in sex-related category | 851.86 ms (182.76) | 803.72 ms (120.25) |
| RTs in positive emotional category | 821.43 ms (156.04) | 783.06 ms (109.96) |
| RTs in negative emotional category | 824.29 ms (159.50) | 790.53 ms (121.10) |
| RTs in fear-related category | 816.92 ms (159.41) | 789.31 ms (116.99) |
Therefore we have conducted additional Bayesian analysis focused on the Bayesian Factor 01 (BF01) indicating the level of evidence in favor of the null hypothesis on the lack of differences. Obtained results were at anecdotal levels for between-group differences in sex-related category (BF01 = 0.964) and positive category (BF01 = 1.507), so we cannot say that groups do not differ in this matter. In negative category and fear-related category BF01 were on the moderate level (negative: BF01 = 3.033, fear-related: BF01 = 3.728), therefore is it hard to tell that both groups definitively do not differ in these two conditions.
Neuronal results
Neuronal analyses were limited to comparisons between CSBD and HC. On the whole-brain level of analyses we found a significant difference between CSBD and HC in contrast sex-related words minus neutral words in two clusters located in the right and left hemispheres (Fig. 3) located respectively within the right hippocampus, right putamen, and right thalamus (peak in MNI: x = 28, y = −14, z = −8; FWEc = 181; t = 4.53; p < 0.001) and left pulvinar (posterior part of the thalamus) and left hippocampus (peak in MNI: x = −14, y = −32, z = 14; FWEc = 181; t = 5.07; p < 0.001). By examining the contrast between positive and neutral trials, we found a significant difference between HC and CSBD in the left middle frontal gyrus (peak in MNI: x = −52, y = 16, z = 46; FWEc = 91; t = 4.70; p = 0.006; see Fig. 3). Two remaining contrasts (fear vs. neutral and negative vs. neutral) were insignificant.
Neuronal activations during sex-related words processing (contrast sex-related words minus neutral words): We found a significant difference between CSBD vs. HC in two clusters located in the right and left hemispheres located respectively within right hippocampus, right putamen, and right thalamus (peak in MNI: x = 28, y = −14, z = −8; FWEc = 181; t = 4.53; p < 0.001) and left pulvinar (posterior part of the thalamus) and left hippocampus (peak in MNI: x = −14, y = −32, z = 14; FWEc = 181; t = 5.07; p < 0.001)
Citation: Journal of Behavioral Addictions 13, 3; 10.1556/2006.2024.00033
To examine the relation between the above-described neural activations (see Figs 3 and 4) and CSBD symptoms, we have conducted an exploratory correlation analysis of the percent of BOLD signal change within each of the described above clusters (as a regions of interest – ROIs) and CSBD symptoms measured with questionnaires and self-reported frequency of sexual behavior (only in CSBD patients). Neural activation in the left cluster (peak in MNI: x = −14, y = −32, z = 14; FWEc = 181; t = 5.07; p < 0.001) was significantly negatively correlated with time spent on sexual activity during the week preceding fMRI recording (r = −0.556; p < 0.05; see Table 4).
Neuronal activations during positive words processing (contrast positive words minus neutral words): We found a significant difference between HC vs. CSBD in the left middle frontal gyrus (peak in MNI: x = −52, y = 16, z = 46; FWEc = 91; t = 4.70; p = 0.006)
Citation: Journal of Behavioral Addictions 13, 3; 10.1556/2006.2024.00033
Correlation analysis of neuronal activation and CSBD symptoms (Pearson's r or Spearman's rho statistics based on normality of distribution measured with Shapiro-Wilk test) among CSBD group (significance value *p < 0.05; **p < 0.01)
| BOLD signal changed during processing of sex-related words | The sum of all subscales in Hypersexual Behavior Inventory | Coping Subscale in Hypersexual Behavior Inventor | Consequences Subscales in Hypersexual Behavior Inventory | Control Subscale in Hypersexual Behavior Inventory | Sexual Addiction Screening Test-Revised version | Brief Pornography Screening Test | Onset of pornography watched the first time | Time of pornography watching during the week proceeding study | Numbers of masturbation during the week proceeding study | Numbers of intercourse during the week proceeding study | mean reaction times in sex-related trials | mean reaction times in neutral vs sex-related trials |
| left cluster (peak in MNI: x = -14, y = -32, z = 14) | 0.201 | 0.111 | 0.288 | 0.148 | 0.114 | −0.015 | −0.227 | 0.095 | 0.142 | −0.556* | −0.219 | 0.01 |
| right cluster (peak in MNI: x = 28, y = -14, z = -8) | −0.011 | −0.043 | −0.124 | 0.085 | −0.131 | −0.184 | 0.199 | 0.139 | 0.022 | 0.008 | −0.091 | 0.08 |
| left Anterior Cingulate Cortex | 0.112 | 0.161 | 0.091 | 0.028 | 0.019 | 0.105 | −0.063 | 0.273 | 0.289 | −0.226 | 0.049 | −0.23 |
| right Anterior Cingulate Cortex | 0.003 | 0.031 | 0.001 | −0.022 | −0.083 | 0.045 | −0.064 | 0.191 | 0.125 | −0.145 | 0.040 | −0.19 |
| left amygdala | −0.059 | 0.022 | −0.080 | −0.097 | −0.307 | −0.218 | −0.110 | −0.247 | 0.017 | −0.321 | −0.068 | 0.04 |
| right amygdala | 0.073 | 0.033 | 0.060 | 0.085 | −0.122 | 0.066 | 0.054 | −0.060 | −0.130 | −0.092 | −0.065 | −0.02 |
| left caudate | 0.010 | −0.113 | 0.022 | 0.113 | −0.156 | −0.011 | −0.031 | 0.086 | 0.098 | −0.316 | −0.108 | −0.11 |
| right caudate | 0.019 | −0.043 | 0.012 | 0.071 | −0.157 | −0.090 | −0.033 | 0.121 | 0.145 | −0.355 | −0.006 | −0.14 |
| left insula | 0.289 | 0.368* | 0.173 | 0.153 | 0.031 | 0.036 | 0.000 | 0.085 | 0.173 | −0.039 | −0.048 | −0.19 |
| right insula | 0.309 | 0.378* | 0.192 | 0.174 | 0.013 | 0.081 | 0.010 | 0.075 | 0.079 | 0.120 | 0.016 | −0.16 |
| left Nucleus accumbens | 0.023 | −0.058 | −0.038 | 0.120 | −0.179 | −0.029 | −0.060 | −0.007 | 0.050 | 0.235 | 0.081 | −0.18 |
| right Nucleus accumbens | 0.040 | −0.003 | 0.029 | 0.068 | −0.108 | −0.168 | −0.254 | 0.162 | 0.255 | 0.059 | 0.211 | −0.32 |
| left Anterior Orbitofrontal Cortex | 0.144 | 0.037 | 0.109 | 0.198 | 0.122 | 0.236 | −0.107 | 0.114 | 0.092 | 0.226 | 0.300 | −0.519** |
| right Anterior Orbitofrontal Cortex | 0.208 | 0.156 | 0.181 | 0.179 | 0.144 | 0.248 | −0.261 | 0.062 | 0.042 | 0.201 | 0.257 | −0.437* |
| left Posterior Orbitofrontal Cortex | 0.230 | 0.142 | 0.213 | 0.218 | 0.114 | 0.174 | −0.089 | 0.235 | 0.207 | −0.117 | 0.167 | −0.382* |
| right Posterior Orbitofrontal Cortex | 0.167 | 0.113 | 0.165 | 0.145 | −0.003 | 0.254 | −0.053 | −0.003 | −0.069 | −0.039 | 0.199 | −0.394* |
| left pallidum | −0.019 | 0.121 | −0.124 | −0.081 | −0.263 | −0.268 | −0.037 | 0.038 | 0.169 | −0.039 | −0.046 | −0.04 |
| right pallidum | −0.178 | 0.017 | −0.228 | −0.252 | −0.211 | −0.383* | −0.047 | −0.216 | 0.077 | −0.405 | −0.137 | 0.03 |
| left putamen | −0.121 | 0.054 | −0.244 | −0.162 | −0.378* | −0.219 | −0.078 | −0.034 | 0.032 | 0.014 | −0.158 | −0.03 |
| right putamen | −0.104 | 0.071 | −0.234 | −0.147 | −0.363 | −0.156 | −0.048 | 0.022 | 0.069 | −0.042 | −0.101 | −0.06 |
| left Ventral Striatum | 0.216 | 0.047 | 0.298 | 0.232 | 0.175 | 0.120 | −0.148 | −0.024 | 0.132 | −0.123 | 0.132 | 0.01 |
| right Ventral Striatum | 0.267 | 0.261 | 0.249 | 0.165 | −0.028 | 0.028 | −0.139 | 0.211 | 0.140 | −0.263 | 0.082 | −0.23 |
| left Ventral Tegmental Area | 0.009 | 0.015 | −0.036 | 0.025 | −0.141 | −0.163 | −0.079 | −0.033 | 0.093 | 0.070 | 0.001 | −0.14 |
| right Ventral Tegmental Area | 0.110 | 0.140 | 0.046 | 0.070 | −0.103 | −0.086 | −0.027 | −0.006 | 0.132 | −0.045 | 0.129 | −0.22 |
*p < 0.05; **p < 0.01.
ROI analysis across brain regions related with emotional and motivational processing of sexually explicit material justified by review of literature and described previously by Stark et al. (2019) and listed in Table 5, showed group differences (differences between CSBD vs. HC without correction for multiple comparisons) in: left caudate (t (57) = −2.055 p = 0.044), right caudate (t (57) = −2.167 p = 0.034), right insula (t (57) = −1.976 p = 0.053), left pallidum (t (57) = −2.368 p = 0.021), left putamen (t (57) = −2.012 p = 0.049), left thalamus (t (57) = −2.688 p = 0.009), right thalamus (t (57) = −2.451 p = 0.017) and right ventral striatum (t (57) = −2.1 p = 0.040). In some of these ROIs BOLD signal correlated with CSBD symptoms (calculated only in CSBD group). We found positive trends (correlation without correction for multiple comparisons) between the signal from the left and right insula and scores in the HBI coping subscale, a negative trend between activation in right pallidum and BPS score, a negative correlation between activation of left putamen and SASTR score and negative trend between activation in bilateral aOFC and pOFC and mean RT in neutral vs sex-related trials and also significant negative trend between numbers of intercourse during the week proceeding study and activation in left cluster (peak in MNI: x = −14, y = −32, z = 14) (see Table 4). Unfortunately, none of the results survived Bonferroni-Holm's (1979) correction for multiple comparisons.
Between-group comparison of neural activations across all regions of interest (ROI) (significance value of <0.05)
| CSBD (N = 29) mean (SD) | HC (N = 30) mean (SD) | p value | Cohen's d | SE Cohen's d | |
| Left Anterior Cingulate Cortex (ACC) | 0.829 (1.251) | 0.273 (1.134) | 0.079 | −0.466 | 0.267 |
| Right Anterior Cingulate Cortex (ACC) | 0.445 (0.921) | 0.143 (0.779) | 0.179 | −0.354 | 0.264 |
| Left Amygdala | 0.360 (0.683) | 0.286 (0.645) | 0.670 | −0.111 | 0.261 |
| Right Amygdala | 0.428 (0.581) | 0.199 (0.563) | 0.129 | −0.401 | 0.266 |
| Left Caudate | 0.391 (0.771) | 0.038 (0.530) | 0.044 | −0.535 | 0.269 |
| Right Caudate | 0.383 (0.690) | 0.032 (0.551) | 0.034 | −0.564 | 0.270 |
| Left Insula | 0.402 (0.655) | 0.141 (0.561) | 0.106 | −0.428 | 0.266 |
| Right Insula | 0.458 (0.733) | 0.078 (0.745) | 0.053 | −0.515 | 0.269 |
| Left Nucleus accumbens (NAcc) | 0.316 (0.843) | 0.107 (0.750) | 0.318 | −0.262 | 0.263 |
| Right Nucleus accumbens (NAcc) | 0.176 (0.759) | 0.008 (0.699) | 0.380 | −0.231 | 0.262 |
| Left Anterior Orbitofrontal Cortex (aOFC) | 0.384 (0.928) | 0.295 (0.865) | 0.706 | −0.099 | 0.261 |
| Right Anterior Orbitofrontal Cortex (aOFC) | 0.263 (0.907) | 0.145 (0.964) | 0.629 | −0.127 | 0.261 |
| Left Posterior Orbitofrontal Cortex (pOFC) | 1.099 (1.232) | 0.787 (0.823) | 0.256 | −0.299 | 0.263 |
| Right Posterior Orbitofrontal Cortex (pOFC) | 1.028 (1.088) | 0.586 (0.959) | 0.103 | −0.432 | 0.266 |
| Left Pallidum | 0.134 (0.332) | −0.061 (0.299) | 0.021 | −0.617 | 0.272 |
| Right Pallidum | 0.092 (0.357) | −0.068 (0.351) | 0.088 | −0.453 | 0.267 |
| Left Putamen | 0.151 (0.556) | −0.108 (0.427) | 0.049 | −0.524 | 0.269 |
| Right Putamen | 0.109 (0.483) | −0.117 (0.434) | 0.063 | −0.493 | 0.268 |
| Left Thalamus | 0.291 (0.466) | −0.023 (0.431) | 0.009 | −0.700 | 0.276 |
| Right Thalamus | 0.228 (0.439) | −0.047 (0.422) | 0.017 | −0.638 | 0.273 |
| Left Ventral Striatum (VStr) | 0.111 (0.633) | 0.004 (0.589) | 0.505 | −0.175 | 0.261 |
| Right Ventral Striatum (VStr) | 0.338 (0.539) | 0.032 (0.578) | 0.040 | −0.547 | 0.270 |
| Left Ventral Tegmental Area (VTG) | 0.318 (0.655) | 0.001 (0.600) | 0.057 | −0.506 | 0.268 |
| Right Ventral Tegmental Area (VTG) | 0.359 (0.637) | 0.049 (0.682) | 0.077 | −0.470 | 0.267 |
Discussion
In our study, we did not find any evidence on behavioral and/or neuronal levels supporting the hypothesis of generally impaired emotional processing in CSBD, which was to be seen through increased cognitive conflict (EST interference) both in generally negative trials and in fearful trials (based on results from frequent and Bayesian statistics). There was also no evidence for bigger cognitive interference in positive trials in results from frequent statistics, but the BF stats provided only anecdotal evidence supporting the null hypothesis in the positive condition, so the groups probably differ in this matter. Instead, our study revealed that there is an increased EST effect (conclusion based on results from frequent and Bayesian statistics) increasing cognitive load visible in increased RTs) in sex-related trials, especially in the CSBD group. Taking into consideration all of this results contrary to hypothesis, CSBD could be connected to the cognitive conflict during processing of positive and symptom's related stimuli. What is more, our results can be understood as evidence of impaired cognitive coping strategies during exposure to a stimulus associated with problematic sexual behavior. It is well established that CSBD symptoms are correlated with increased biases during attentional processing of sex-related stimuli. For example Pekal et al. study (2018) showed that problematic pornography use was associated with attentional bias toward sexual stimuli. A similar effect was found in Mechelmans et al. study (2014). Other studies using the Stroop task and emotional Stroop task with word-related emotional and sexual words also showed behavioral markers of attentional biases toward sex-related words among CSBD patients and correlation of the magnitude of these biases with severity of CSBD symptoms (Albery et al., 2017; Draps et al., 2021; Wang, Chen, & Zhang, 2021).
This pattern of behavioral reaction could be discussed in reference to Incentive Salience Theory (IST). The Incentive Salience Theory framework was developed in the field of addiction studies and proposed by Robinson and Berridge (1993) to distinguish between two primary components of motivated behavior: “liking” and “wanting.” “Liking” is linked to the experienced value of the reward, usually carried by an unconditional stimulus such as alcohol consumption or pornography watching. “Wanting” is related to the expected value of the reward, often carried by conditional stimulus (e.g. presence of people with whom the patient used to drink alcohol, or conditions indicating an opportunity to watch pornography). The IST assumes that during the repetitive engagement in certain stimuli, they gain salience due to the neural sensitization, and become powerful triggers of problematic behavior evoking an urge in response to substance-related cues which manifests itself in shortening of reaction times among addicted individuals (Berridge, 2012). While many previous studies shows shortening of reaction times among CSBD individuals for sex-related cues (review in Gola & Draps, 2018), they don't examine cognitive conflict during salient stimuli processing. Franken (2003) argued that after repeated experience with a substance learned problematic cues become salient and are more likely to interfere with attention due to dopamine release in the corticostriatal circuit they evoke. This line of reasoning was confirmed in Albery et al. (2017) results. So it could be that cognitive processing during emotionally arousing conditions, salient stimuli (in CSBD it would be for example sex-related words) affect RTs more than any other emotional stimuli (e.g. positive or negative words) and our results observed among CSBD patients are in line with these IST assumptions.
Moreover, the processing of salient stimuli is associated with the increased dopaminergic activity of the reward system and structures essential for the motivational system (Robinson & Berridge, 1993). We assessed neuronal activity in regions that have been previously linked to the emotional and motivational processing of sexually explicit material. We found significant group differences in the left and right caudate, the right insula, the left pallidum, the left putamen, the left and right thalamus, and the right ventral striatum. In our previous datasets, we revealed that men with and without CSBD symptoms differed in their right ventral striatal responses to cues predicting erotic outcomes (Gola et al., 2017; Golec, Draps, Stark, Pluta, & Gola, 2021). A literature review on this topic found that neuronal hyperactivity in the ventral striatum during the processing of sex-related stimuli among CSBD patients seems to be a crucial mechanism underlying the CSBD symptoms (Gola & Draps, 2018). Ventral striatal activations in CSBD were reported not only in the response to cues predicting erotic outcomes but also for watching sexually explicit pictures and videos (Brand et al., 2016; Voon et al., 2014). Banca et al. (2016) showed also decreased ventral striatal activations among CSBD patients as a response to a lack of erotic or monetary reward in a conditioning task, while Klucken, Wehrum-Osinsky, Schweckendiek, Kruse, and Stark (2016) showed decreased coupling between the ventral striatum and prefrontal cortex in the CSBD during appetitive conditioning.
Unlike ventral, the dorsal part of the striatum was not as often reported in CSBD studies. The dorsal striatum consists of the caudate nucleus and the putamen. In 2014 Kühn & Gallinat found that there is a negative correlation between the amount of pornography consumption per week and gray matter values extracted from the cluster in the right caudate, and this lowered density of the right caudate is associated with negative correlation between the amount of pornography consumption per week and functional reactivity during a sexual cue–reactivity paradigm of the left putamen during sexual stimuli. However, those data were collected from non-clinical sample. In 2015, Seok & Sohn showed higher activation of the left caudate nucleus in response to erotic pictures in the CSBD group when compared to controls and lower activation for neutral pictures in the left caudate nucleus, the inferior parietal lobe, the dorsal anterior cingulate gyrus, the thalamus, and the dorsolateral prefrontal cortex which is more in line with our results.
Other interesting results are related to the group differences in the right insular cortex and the left and right thalamus, as the reactivity of those regions was shown in the context of motivation towards obtaining a reward (insula; Kühn & Gallinat, 2011; Stark et al., 2019) and suggested as encoding expected reward salience (thalamus; Sescousse, Caldú, Segura, & Dreher, 2013). All those results align with the IST assumption of increased dopaminergic activity in reward circuits during the processing of salient stimuli related to the problematic behavior.
Moreover, our whole-brain analysis, showed that sex-related trials were related to neuronal differences between CSBD and HC groups. These differences were visible in two clusters located respectively in the right and left hemispheres. The first contains the right hippocampus, the right putamen, and the right thalamus, while the second contains the left pulvinar and the left hippocampus. Interestingly there was a significant negative correlation between neuronal activation in the left cluster and time spent on sexual activity during the week preceding data collection. Considering the functional role of mentioned brain regions, such a pattern of neuronal activations sheds new light on the mechanism of CSBD. The hippocampus is a crucial part of the limbic system (Martin, 2012), plays an important role in the consolidation of information from short-term memory to long-term memory and in spatial memory (Bachevalier, 2019; Kovács, 2020; Squire, 2009) and has a functional role in approach-avoidance conflict and learning reaction to negative (related to anxiety) stimuli (Chan, Morell, Jarrard, & Davidson, 2001; Etkin & Wager, 2007; Ito & Lee, 2016; Satpute, Mumford, Naliboff, & Poldrack, 2012). The putamen with caudate nucleus (parts of the basal ganglia system) form the dorsal striatum (Martin, 2012). A primary function of the putamen is to regulate different stages of movement (e.g. preparation and execution) (Marchand et al., 2008; Shirinbayan, Dreyer, & Rieger, 2019). It also affects reinforcement learning and implicit learning (Packard & Knowlton, 2002). Both learning processes are enforced to maximize the outcome and to perceive knowledge from repetitive exposure to a particular type of stimulus, e.g., pornographic or any other sex-related stimulus. The pulvinar is a part of the thalamus, and its function is related to attentional deficits (Arend, Rafal, & Ward, 2008). The thalamus is a larger structure of the brain and obviously has multiple functions, mainly as a hub, relaying information between different subcortical areas and the cerebral cortex (Gazzaniga, Ivry, & Mangun, 2006). These reveal the involvement of a whole network of brain structures, which has not been previously investigated in the context of CSBD symptoms and is worth attention in future studies.
Limitation
Despite the importance of revealed results, our study does have some limitations. Only heterosexual males aged between 21 and 47 years resident in the capital city of WEIRD (Western, Educated, Industrialized, Rich, Democratic) type country were recruited. In future research, greater diversity (e.g. female or gay and/or lesbian participants) would be needed. There is also a methodological limitation to the study. Namely, different types of the Emotional Stroop Task have been used in neuroimaging studies, which results with various levels of induced cognitive interference (compare to Song et al., 2017) and difficulty with direct comparison of the results. The one used here seems to result in only mild emotional interference as the emotional words stimuli are not semantically-relevant to the task instructions (Etkin et al., 2006), as previously shown in healthy subjects (reviews in: Song et al., 2017; Williams, Mathews, & MacLeod, 1996), so the usage of other types of task is needed e.g. task with sexually arousing pictures or videos.
Conclusion
Our findings suggest that in accordance with IST (Robinson & Berridge, 1993) and similar to what is observed in substance and gambling addictions (Berridge, 2012), the neuronal and behavioral mechanisms associated with the processing of sex-related words could be understood as evidence of attentional biases toward those stimuli. This behavioral and neuronal bias is more robust among CSBD patients than healthy controls and is related to clinically relevant features of CSBD. Moreover, the bias is a source of cognitive conflict, which results in the decreased level of cognitive processing among CSBD patients when exposed to sex-related conditions, but not during the processing of other negative stimuli. There is also a possible influence of positive stimuli when CSBD patients are exposed to positive stimuli. These results need further investigation and replications.
Funding sources
This study was supported by the Polish National Science Centre- NCN, PRELUDIUM grant (2016/23/N/HS6/02906) (for MD). MD is also supported by the Foundation for Polish Science (FNP), scholarship number START 014.2023.
Authors' contribution
MD contributed to study and methods design, subject recruitment, conducting data collection, data analysis and interpretation, manuscript writing, and obtaining funding. MG contributed to study and methods design, data interpretation, and manuscript writing. MK, AG, JSz, KL, WŻ contributed to data collection. All authors contributed to and have approved the final manuscript.
Conflict of interest
The authors report no conflicts of interest with respect to the content of this manuscript. Polish National Science Centre (NCN) had no role in the study design, collection, analysis, or interpretation of the data, writing the manuscript, or submitting the paper for publication. Foundation for Polish Science (FNP) had no role in the study design, collection, analysis, or interpretation of the data, writing the manuscript, or submitting the paper for publication. The authors report no conflicts of interest with respect to the content of this manuscript.
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