Internet-Based Interactions and Psychological Wellness: Implications on Interpersonal Responses to Hypothetical Situations
Eloise O’Reilly and Pieter Rossouw
The experience of social interaction is important for well-being. Research has indicated that the nature of social interactions can initiate neurobiological changes and developments within the human brain. Interactions that foster an individual’s sense of safety can inform the development of healthy, adaptive neural pathways whereas social interactions that compromise this sense of safety can inform the development of unhelpful, maladaptive neural pathways. These neurological developments, defined as neural plasticity, further inform patterns of behavior, which, over time (and depending on the nature of social interaction), can result in psychological health or patterns of pathology. The present study investigated the relationship between negative social interaction on the Internet, psychological well-being, levels of aggression, and aggressive behavioral responses. Two hundred and four community members completed an online self-report questionnaire, assessing time spent online, interaction-based experiences online, perceived psychological well-being, perceived aggression, and aggressive responses to five hypothetical situations. Correlational analyses revealed a negative relationship between feeling abused or victimized when online and psychological well-being, and a positive relationship between experiencing negative interactions online and levels of aggression. However, the nature of online interactions did not significantly relate to aggressive behavioral responses. The significant correlational findings of the study illustrate the importance of considering individual exposure to negative Internet-based social interactions and the risk this poses to individual well-being. Implications and directions for future research are discussed
Until some decades ago, it was believed that human beings are born with hard-wired brains—brains that present at birth not so differently to the same brain some 50 years into life (Arden, 2010). In line with this opinion, neurological problems were associated with outward symptoms such as amnesia, or Alzheimer’s disease, not with psychological disorders (Arden, 2010). Individual “brain-based” ailments were automatically categorized into organic (neurological) ailments or otherwise deemed merely a psychological problem due to the lack of evidence suggesting observable or measurable changes in the neurobiology of the ailing individual (Walter, Berger, & Schnell, 2009). In recent decades, however, a body of research has challenged this perspective and presented a fresh one, namely, that psychological disturbances do have a neurological basis (Kandel, 1998; Walter et al., 2009). Furthermore, this neurological basis can be altered—that is, one’s neural architecture can be shifted and developed in a therapeutic manner through (amongst other factors) psychotherapy and positive environmental interactions (Grawe, 2007; Siegel, 2001). This change in neural architecture is referred to as neural plasticity, the brain’s ability to change in response to developmental processes and experience (Huttenlocher, 2002).
The interplay between psychology and neurobiology was first proposed by Sigmund Freud, the grandfather of modern psychology, who postulated that unconscious and conscious behavior is organized and stored within the brain’s neural construction (Freud, 1968). His “Project for a Scientific Psychology,” initiated in 1895, highlighted the neurological underpinnings of psychology. This project detailed a theory towards the integration of psychology and science, accompanied by sketches of interconnected neurons that represented psychoanalytical presentations in an individual. Freud had to abandon his project due to the lack of refined equipment existing at the time with which to study the brain. Instead, he turned to the use of metaphor to explain psychopathology: the birth of psychoanalysis (Cozolino, 2010). Thus, the idea of behavior, conscious and unconscious, being a function of neurobiology lay dormant for many decades. Then, in 1998, molecular neurobiologist Erik Kandel published a pivotal article that initiated a paradigm shift toward a new framework for thinking about psychology. He argued that behavior can be analysed through the integration of both social and biological determinants, suggesting there exists an interconnectedness between a person’s environment, genes, and neural architecture in determining behavior. In this paper, Kandel set out five principles (Kandel, 1998). First, he proposed that all mental processes, however complex, derive from functions of the brain. His second, third, and fourth principles focus on epigenetic processes—how genes and their protein products are important determining factors for the brain’s neurons and their patterns of functioning. He discussed gene expression and how this process contributes to behavior development and major mental illness, and he emphasized the importance of social factors (such as environment) in this gene expression process. Finally, in his fifth principle, Kandel proposed that psychotherapy produces long-term changes in an individual’s behavior through change in their neurological processes—or gene expression—which alters synaptic connections, forming new neural pathways and, ultimately, changing behavior.
Epigenetics and Neural Plasticity
It has been suggested that an individual’s psychological processes and behavior have a biological basis (Kandel, 1998; Shen et al., 2000). Furthermore, these properties can be explained by genes—specifically, the interplay between genes and the environment. This is the field of epigenetics (Kandel, 1998; Robinson, Fernald, & Clayton, 2008). In terms of the function of genes, Kandel (1998) explains that, contrary to popular opinion (the idea that genes are unchanging and only serve to transfer hereditary information from one generation to another), genes actually have dual functions. First, genes have a template function, which enables them to replicate reliably and thus transfer across generations. This template function is uninfluenced by social experience; it functions at an exclusively organic level and is beyond an individual’s experiential control (Kandel, 1998). The second function of genes is described as the transcriptional process—that is, the expression of genes within a cell (Kandel, 1998). Transcriptional genetics are responsible for more subtle organization within the brain: They manufacture the specific sculpting of later-developing neural networks (Black, 1998; Cozolino, 2010). This transcriptional process is responsive to both internal (biological development) and external (environmental) stimuli. Unique environmental interactions result in the expression of certain genes within a cell, which in turn synthesizes specific proteins that trigger the building of neural networks (Orphanides & Reinberg, 2002). Due to its responsiveness to environmental factors, genetic transcription can be altered, meaning new environmental stimuli can result in genetic expression where preexisting neurons grow differently and ultimately change neural pathways (Kandel, Schwartz, Jessell, Siegelbaum, & Hudspeth, 2013). Kandel (1998) introduced the notion that epigenetic processes, as influenced by unique social and environmental experiences, contribute importantly to one’s changing neural plasticity and, in turn, one’s behavior.
Kandel’s (1998) proposition that genes can indirectly (through social interactions) contribute to one’s mental functioning is supported by a breadth of research on the rates of schizophrenia in monozygotic (identical) twins (Jang, Woodward, Lang, Honer, & Livesly, 2005; Kandel, 2006; McGuffin, Owen, & Farmer, 1995; Tsuang, 2000; Tsuang, Stone, & Faraone, 2001). Such studies endeavored to determine a genetic basis for schizophrenia, with the assumption that if one twin had the illness, the other would have it too due to their shared identical genes. A genetic susceptibility to developing schizophrenia in monozygotic twins was found; however, this susceptibility was not as strong as expected, suggesting that genetic factors are not the only determinants of the disease (Tsuang, 2000). Further, a meta-analysis of 12 published twin studies found that, in addition to a genetic liability to schizophrenia, environmental factors significantly contributed to the disease’s etiology (Sullivan, Kendler, & Neale, 2003). These findings support the notion that genes (through organic and environmental means) are important determinants of the brain’s neurological construction that underpins behavior (Kandel, 1998).
Following Kandel’s (1998) research, further attention has been paid to the key neurological mechanisms within the brain that may explain further the influence of unique social interactions on neural plasticity and, beyond this, individual psychological well-being. The brain’s neural architecture suggests that humans need meaningful connections and interactions with one another in order to develop individual wellness (Cozolino, 2006). One neural mechanism key to this interconnectedness perspective is the mirror neuron system. Discovered in the mid-1990s through various experiments involving macaque monkeys, the mirror neuron system is a class of premotor neurons that fire during the execution of a goal-oriented motor act and the observation of another performing the same or very similar act (Casile, Caggiano, & Ferrari, 2011; Rizzolatti & Craighero, 2004). This finding indicated that an action does not need to be individually performed (it can simply be observed) for the action or the intention of the action to be understood (Casile et al., 2011). Various studies have identified that the mirror neuron system functions similarly in humans (Fabbri-Destro & Rizzolatti, 2008; Iacoboni et al., 2005). In short, this system is the mechanism by which humans are able to understand the intentions behind another individual’s actions (Rizzolatti, Fabbri-Destro, & Cattaneo, 2009).
The relevance of the mirror neuron system to human interconnectedness at a conscious and neural level, and to human wellness, is significant (Cozolino, 2006; Kandel, 2006). It is thought that mirror neurons are an integral basis not only for understanding intention but also for expressing empathy. When empathy—that is, the ability to genuinely understand, feel, and reflect the emotions of another (Keeran, 2012)—is expressed during a social interaction, the brain is able to create an internal state that resonates with the internal state of another individual, thus achieving a neural and cognitive connection between the two minds (Siegel, 2006). This interpersonal connection, achieved through the firing of mirror neurons, can result in a change in the neural hardware of the involved individuals such that one individual’s well-being can positively affect the well-being of another (Rossouw, 2011). The more positive connections and interactions experienced between individuals, the more effective are the neural pathways being developed and, consequently, the more effective the patterns of well-being created for the individual (Rossouw, 2011). Antithetically, if one’s environment and social interactions are detrimental (e.g., traumatic interactions such as bullying or abuse; Rossouw, 2013), mirror neurons can respond by developing dysfunctional neural networks and unhelpful patterns of behavior. So, just as one’s well-being can positively affect another’s well-being via mirror neuron functioning as a result of social interaction, one’s un-wellness can negatively affect another’s through the same mechanism (Rossouw, 2011; Siegel, 2006).
The present study has discussed the brain as plastic and malleable, capable of reshaping and restructuring its neural connections through environmental experiences. In addition to genetic transcription and mirror neuron processes, it is important here to address the specific regions of the brain that are particularly susceptible to this change in neural plasticity. In the first few months of life, an individual’s brain has trillions of synapses (Siegel, 1999). However, almost immediately after birth, a process called pruning begins, discarding millions of these random synaptic connections. The synaptic connections that remain are, essentially, the individual’s neural networks, ready to be sculpted and formed through social and environmental interactions (Joseph, 1998). Whereas some brain regions (such as the brainstem and cerebellum) are mostly developed at birth, other parts of the brain develop at a slower pace and in response to external environmental factors (Joseph, 1998). An example of the latter is the limbic system—comprised of the thalamus, amygdala, hypothalamus, and hippocampus. This system is responsible for the experience of emotions and the formation of new memories (Siegel, 1999). These processes are triggered initially by external sensory stimuli (one’s environment) being received by the thalamus, which then sends a signal to the amygdala, where these stimuli are assigned emotional significance such as danger or threat (Siegel, 1999). In response to the amygdala, the hypothalamus prepares the body to respond to the incoming stimuli, the fight or flight response (Arden, 2010). In further response, the hippocampus registers the details of the sensory input into context, that is, it maps the emotion generated by the amygdala onto space and time, forming new memories (Siegel, 1999). This collective limbic system activation (which occurs in milliseconds) sends neurotransmitters to the left prefrontal cortex, which rationalizes the sensory input through problem-solving and arranging the information and details of the input linearly (Arden, 2010).
The question, then, is how the limbic system relates to neural plasticity and external environmental effects on well-being. A breadth of research has suggested that abnormalities in limbic system structures contribute to negative psychological well-being and pathology (Frodl et al., 2008; Grawe, 2007). For example, individuals with a major depressive disorder have possessed significantly larger amygdalae volumes, suggesting consistent hyper-activation of this region (Frodl et al., 2008; Lange & Irle, 2004), and a study by Bremner and colleagues (2000) found an association between loss of hippocampal volume and depressive disorders. It can be deduced, therefore, that the absence of neural structures adequately regulating or “making sense” of one’s environment (or of the emotions that environment elicits) can result in the development of defensive coping mechanisms—unhelpful neural loops of behavior—or even pathology (Cozolino, 2010). Grawe (2007) explains this behavior as approach- or avoidance-oriented responses, and Cozolino (2010) suggests these behaviors can result in well-being or illness:
The neural connections that result in defenses shape our lives by selecting what we approach and avoid, what our attention is drawn to, and the assumptions we use to organize our experiences. Our cortex then provides us with rationalizations and beliefs about our behaviors that help keep our coping strategies and defenses in place, possibly for a lifetime. These neural and psychic structures can lead to either psychological and physical health or illness and disability/pathology. (Cozolino, 2010, p. 23).
Approach and Avoidance Orientation
An interplay exists between one’s environment and one’s neural development in establishing patterns of behavior (Cozolino, 2010): An individual evaluates their environmental interaction as positive (safe) or compromising (unsafe). This evaluation is registered through the individual’s experience of emotion, through limbic system activation (Grawe, 2007). In response to this, the individual’s neural processes fire toward approach or avoidance motivation, thus informing their behavior (Grawe, 2007). Approach and avoidance motivation refers to the individual’s goals and the neural systems that underpin these. For example, approach goals are associated with the experience of an enriched environment and the proliferation of neural synapses developing into helpful loops of behavior. The individual experiences positive emotion from environmental interactions that trigger and reinforce positive neural patterns, promoting healthy approach behaviors (such as aspiration for attainment of positive life goals) and generating a positive cycle of wellness (Grawe, 2007). Contrastingly, avoidance goals act as a protective (or survival) system for the individual (Grawe, 2007). When one evaluates one’s environment as unsafe—such as an environment in which one’s basic needs are compromised—one’s neural processes trigger in such a way to protect oneself from the experience of distress. This process informs one’s orientation toward avoidance behavior, the aforementioned defensive coping mechanisms (Cozolino, 2010; Grawe, 2007). These protective avoidance behaviors are executed in order to minimize the individual’s distress in what they perceive to be a dangerous or unsafe environment (Grawe, 2007). If an individual is consistently evaluating their environment as compromised, the neural patterns that underpin avoidance behavior will activate more readily than neural patterns of approach behavior, causing the individual to be more susceptible to avoidance goals (Grawe, 2007).
To take an example: A young boy grows up in an abusive household. This environment is evaluated by the boy’s amygdala as unsafe by way of amygdalae activation. Once the amygdala sends a signal to the prefrontal cortex, the boy perceives his basic needs as compromised and experiences heightened fear and anxiety. To decrease these feelings of alarm and fear, the boy retreats to his room and locks himself inside. Consequently, his amygdala ceases firing and he begins to feel safer and less distressed. The boy’s neural pathways are reinforcing his behavior whereby avoiding the negative environment results in enhanced survival experiences. As an isolated event, this behavior may in fact be necessary to ensure the boy’s safety. However, over time and repetition of this behavior, the boy’s neural patterns of avoidance are strengthened and reinforced, encouraging orientation toward avoidance goals in other life situations. He develops a default neural pattern of avoidance: The child’s brain is wired (or trained) to avoid distress through avoidance behavior that can manifest in different ways as he progresses through life. Situations that threaten exposure to distress (such as an episode of significant bullying or heightened job stress) will trigger his default neural pattern of unhelpful, maladaptive behaviors executed in the interest of protecting himself (e.g., he lashes out in aggression to the bullying or neglects his job). Such behaviors, albeit in the interest of personal survival, are costly to general wellness and can result in the emergence of pathology (Grawe, 2007). In fostering wellness, environmental interactions play a crucial role—a role that expands as modern developments increase opportunity or risk for social interaction.
The Internet as a Social Environment
The present study has so far discussed the effect of the environment on a person’s neural pathways through defining epigenetic processes, key neural mechanisms, brain structure responses, and approach and avoidance behaviors. It is important to clarify what is meant by environment, which is used in this context to describe the nature of social experiences or interactions one has with other individuals and the world—a person’s interconnectedness. To give a few examples, these experiences may include: social and family relationships, occupation and workload, exercise, sleep, diet, leisure, and events such as the death of a loved one or going on a holiday. An additional component to the social environment considered in this study is the Internet. Over the past decade, the Internet has served as a platform for individuals to network and connect with one another at a global level. The opportunity for online social interaction offered by the Internet is significant: online game forums, dating websites, private and public blogs, file-share spaces, and social networking sites, to name a few. In 2009, 62% of Australians (aged 5 to 65 years and over) were using the Internet at home (Australian Bureau of Statistics [ABS], 2010; 2011). By 2012, this percentage had risen to 94%, with approximately 21.5 million Australians using online services (ABS, 2013; Public Relations Institute of Australia, 2012). An international survey found that Internet users in the United States of America spend an average of 30 hours per week online, with Facebook, Twitter and YouTube being among the most visited websites (European Travel Commission, 2013).
Previous writings illustrate the effect of social interactions on individual well-being, identifying an extensive amount of time spent socializing on the Internet (Cozolino, 2010; Grawe, 2007; Huttenlocher, 2002). This significant amount of time spent online gives rise to an important postulation: That is, if environmental experiences influence neural and approach/avoidance development, and the Internet is a significant part of a person’s environment, then Internet-based interactions should also influence the development of behavioral responses via neural processes. Moreover, if a person’s interactions on the Internet are consistently anxiety provoking or compromising, then that person’s neural processes should develop a default pathway that orients towards avoidance behavioral patterns (such as the tendency to act aggressively) and generally reinforces psychological un-wellness.
Previous research has investigated the impact of specific Internet use on psychological well-being, with mixed results. Whilst some studies suggest that social use of the Internet can enhance well-being by increasing perceptions of social support (Kang, 2007), improving communication skills, and decreasing feelings of loneliness and depression (Shaw & Gant, 2002), other research suggests that excessive Internet use is related to lowered psychological well-being (Chen, 2012; Kraut et al., 1998). Such studies have found that excessive and problematic Internet users (i.e., individuals who do not possess self-control over the time they spend online) report higher feelings of depression and loneliness and lower perceived self-esteem than those who use the Internet non-excessively or not problematically (Chen, 2012; van den Eijnden, Meerkerk, Vermulst, Spijkerman, & Engels, 2008). Further research examining behavioral outcomes of excessive Internet use (largely focused on online game playing) has found this to be directly associated with levels of aggression and hostility (Guan & Subrahmanyam, 2009; Iacoboni, 2005; Yang, 2012) and both perpetuating and being a victim of cyber-bullying (Kim, Namkoong, Ku, & Kim, 2008; Mehroof & Griffiths, 2010). Such studies highlight that the Internet provides a platform for social interaction that can lead to the lowered psychological well-being of an individual. The online environment contains potentially harmful interactions such as bullying, exposure to explicit or violent materials, and viral embarrassment (e.g., uploaded pictures or videos on Facebook or YouTube). Such interactions may cause an individual significant distress, influencing the neural development of unhelpful pathways, which, once activated, reinforce avoidance behaviors and decrease individual wellness. The present study examines this relationship between the Internet and individual well-being further.
Aim of the Study
As described above, previous research has shown that environmental experiences play an important role in establishing and reinforcing neural networks that are the basis of behavior and well-being (Cozolino, 2006 Kandel, 1998; Rossouw, 2011). To date, research has not focused on the implications of Internet-based environmental interactions on individual behavior and well-being, nor have prior samples included Australian participants. The present study addressed this gap. It aimed to measure the influence of environment (defined as interactions on the Internet and the nature of such interactions) on psychological well-being. In addition, it addressed the influence Internet-based interactions may have on the development of behavioral patterns. That is, if interactions are consistently confrontational or uncomfortable, this may influence neural processes in such a way that reinforces unhelpful and avoidant behavioral patterns (which may be present even in non-Internet-based situations). When examining unhelpful behaviors, the current study decided to focus on the presentation of aggression. This decision was based on the existence of prior research highlighting that high aggression is correlated with poorer outcomes across domains of life success and lower psychosocial functioning (Anooshian, 2005; Huesmann, Dubow, & Boxer; 2009). The current study employed an aggression scale and a hypothetical scenario questionnaire to measure aggressive responses. In measuring well-being, a general psychological well-being measure was included. The following hypotheses were investigated:
Hypothesis 1a. Spending time engaging in negative internet-based interactions (feeling abused or victimized, using coarse language, engaging in arguments) will compromise participants’ psychological well-being.
Hypothesis 1b. Participants’ general aggression will be influenced by engaging in negative online-based interactions.
Hypothesis 1c. Participants’ tendency to respond aggressively to actual situations (non-online) will also be influenced by engaging in negative online-based interactions.
There were 246 participants involved in the study. Of this sample, only 204 participants were retained in the final analyses. One hundred and sixty-four of these were female (80.4%) and 40 were male (19.6%). The age range was 13 to 65 years (M = 28.9, SD = 12.2). Participants were recruited by a second-year postgraduate student as part of compulsory course credit for a core subject, PSYC7800 (Applied Psychology Dissertation). Recruiting was conducted via the provision of a URL link to participants through a social networking site, email, and face-to-face interaction. There were no specific eligibility criteria for inclusion in the study except that participants needed to have access to the Internet. Participants’ involvement in the study was voluntary and they completed the study in their own time.
The study used a correlational research approach to observe the relationships between five variables: time spent online, type of experience online, psychological well-being, levels of aggression, and aggressive responses to actual (non-online) situations.
Demographic information. Participants were asked to provide their gender, age, ethnicity, current relationship status, and sexual orientation. They provided responses by either selecting from prewritten options or manually entering responses.
Non-standardized measures. In order to ascertain the time and nature of the participants’ interactions online, non-standardized measures were employed. Such measures included questions about which websites they visited, and how much time they spent on them. Ten different websites or domains were provided, and participants indicated which sites they visited by selecting one or more of the sites given (for example, Facebook and YouTube). Participants were asked to manually enter the amount of time on average (in hours) they spent on each listed website. To assess online engagement and experience, three items were implemented. For each of the ten websites, participants were asked to indicate on a 5-point Likert scale ranging from 1 (always) to 5 (never) the extent to which they experienced feelings of being abused or victimized, used coarse language, and engaged in an online argument.
Standardized measures: Buss–Perry Aggression Questionnaire—short form (BPAQ-SF). The short-form Buss–Perry Aggression Questionnaire (Bryant & Smith, 2001) is a shortened version of its parent questionnaire, the 29-item Buss–Perry Aggression Questionnaire (Buss & Perry, 1992). The BPAQ-SF was developed and assessed by Bryant and Smith (2001) who found that the shortened version had better construct validity than the original scale and as good internal consistency (Cronbach’s alpha = .92). A 12-item self-report scale, the BPAQ-SF assesses aggression using a four-factor model: physical aggression, verbal aggression, anger, and hostility. Of the 12 items, four items measure physical aggression, three items measure verbal aggression, three items measure hostility, and two items measure anger. Participants were instructed to rate the extent to which they identified with each item on a 5-point Likert scale ranging from 1 (extremely uncharacteristic of me) to 5 (extremely characteristic of me). Examples of items within the four categories are: I have threatened people I know (physical aggression); I often find myself disagreeing with people (verbal aggression); I have trouble controlling my temper (anger); I wonder why sometimes I feel so bitter about things (hostility). In scoring responses on the BPAQ-SF, researchers can either adopt a unidimensional approach, assuming that all items reflect a single underlying construct of aggression, thus one total score is summed, or use the multidimensional, four-factor approach, where aggression is measured separately by four constructs, thus summing four separate subscales (Bryant & Smith, 2001; Buss & Perry, 1992). The present study adopted the former, unidimensional approach for scoring purposes. For each participant, scores on each item were summed to give a final score of Aggression.
Adapted BBC Well-Being scale. The BBC Well-Being scale is a 24-item self-report measure developed by Kinderman and colleagues (2011) that aims to assess general well-being. The 24 items cover participants’ life satisfaction and sense of well-being in three areas: psychological well-being (12 items), physical health and well-being (7 items), and relationships (5 items). The present study included only the psychological well-being subscale of the BBC Well-Being scale as a general measure of psychological well-being. Participants were asked to rate the degree to which they identified with each item on a 4-point Likert scale ranging from 1 (not at all) to 4 (extremely). The following is an example of an item measuring psychological well-being: Do you feel depressed or anxious? This item was also reverse scored, therefore any response of 1 became 4, 2 became 3, and so on (Kinderman, Schwannauer, Pontin, & Tai, 2011). Accounting for this reverse-scored item, all items were scored positively from 1 to 4, with 4 indicating greater well-being. For each participant, a total scale score of psychological well-being was averaged (Kinderman et al, 2011). The BBC Well-Being scale possesses strong internal consistency for the psychological well-being subscale (α = .93; Kinderman et al., 2011).
Adapted Aggressive Provocation Questionnaire. The current study implemented an adaptation of the original Aggressive Provocation Questionnaire (APQ), a 12-item self-report scale developed by O’Connor and colleagues (2001). The APQ presents 12 different vignette-style scenarios designed as valid representations of real-life provocative situations. These scenarios are then followed by a question pertaining to how this scenario would make the participant feel, providing three options (angry, frustrated, or irritated). Participants indicate their perceived level of emotional response on a 5-point Likert scale ranging from 0 (not at all) to 4 (extremely). The APQ possesses strong internal consistency for the three subscales of anger (α = .94), frustrated (α = .93), and irritated (α = .89). Due to the categorical nature of the action responses, no reliability analyses or properties are reported for this scale (O’Connor, Archer, & Wu, 2001).
The adapted version differs from the original in that it employs only five vignettes with corresponding responses—three from the original scale and two novel scenarios. The retained vignettes are numbers 1, 3, and 7 from the original scale. Due to the current study’s examination of responses to online-based interactions, the two novel scenarios featured online-based provocation. The following is an example of a novel vignette in the adapted version: You are uploading some photographs of yourself and your friends onto your Facebook. Someone comments that you should take them down immediately because nobody wants to see your or your friends’ disgusting faces. Furthermore, the adapted version uses a 5-point Likert scale (as opposed to the 4-point scale used in the original version). A 5-point scale was employed to allow participants a neutral response of moderately in the event that the other options did not seem an accurate representation of their perceived reaction. Similar to the original scale, participants’ scores on each emotion subscale (Anger, Upset, and Irritated) were summed (O’Connor et al., 2001).
Prior to releasing the survey online, the study was submitted for ethical approval, which was cleared by the University of Queensland School of Psychology Ethics Committee (code: 13-PSYCH-MAP-42-TS; see Appendix A). The study was administered online during the participants’ own time. Participants were provided with a URL link to the survey via the social networking website, Facebook, emails, and face-to-face interaction. Once connected to the survey, all participants were presented with an information sheet (see Appendix B). This briefed them on the purpose of the study, what was involved in completing the study, survey length (approximately 20 minutes), any potential risks in participating in the study, and who to contact if they wished to obtain further information regarding the outcome of the study. It was also clarified that participants’ responses would be stored confidentially and anonymously, that their participation was completely voluntary, and that they were free to withdraw from the study at any time without prejudice or penalty. Upon beginning the survey, each participant was given the option to create a unique de-identifying code by manually entering a number between 1 and 99 followed by a letter of the alphabet. The purpose of this code, as it was explained to the participants, was that in the event of a participant wishing to enquire about their specific responses, they could quote this code to the researcher who could then extract that particular participant’s responses from the data. Unless the participant offered their identity to the researcher when requesting their response information, this code system ensured their responses remained anonymous.
Participants were then asked to provide demographic information, followed by responses to a number of items comprising the aforementioned non-standardized measures, standardized scales, and the scale adaptation. Upon completion of the study, participants were presented with a debriefing note (see Appendix D), which thanked them for their contribution, reiterated the purpose of the study, and provided the researcher’s email address should they wish to make contact regarding the results of the study. In the event of a participant experiencing any negative feeling as a result of completing the survey, this debrief included a number for Lifeline.
Initially, a series of stepwise regressions were conducted on the data with each variable being entered as both a predictor and an outcome variable. It was found, however, that for each regression analysis conducted, less than 5% of the variance was accounted for by different predictor variables (see Appendix E). It was decided, therefore, to run bivariate correlations on the data in order to observe any existing relationships between variables. The dependent measures observed were: experiences on Facebook and/or YouTube (feelings of abuse/victimization, used coarse language, engaged in an online argument); levels of aggression; psychological well-being; and aggressive responses to five different hypothetical scenarios.
Treatment of Data
The data were analysed using SPSS v17.0. Relevant items were reverse scored. Prior to conducting regression and correlation analyses, the data were screened for any violations of univariate or multivariate assumptions. Of the original 246 participants, only 204 participants were retained in the final analyses. The exclusion criterion for 39 of the omitted participants was that they indicated to never having used Facebook or YouTube. The remaining three cases were excluded from further analyses because their standardized z-scores were greater than three standard deviations from the mean, thus they were regarded as univariate outliers (Tabachnick & Fidell, 2007). Separate correlation analyses were run, including and excluding the three cases. Inclusion of the outliers generated significantly different results compared to exclusion, supporting the decision to remove these three cases from the data.
The normality, skew, and kurtosis for each scale distribution were inspected. It was found that both time spent on Facebook and/or YouTube and experiences on Facebook and/or YouTube did not prescribe a normal distribution. To overcome this violation of normality, both variables were categorized into: time spent on Facebook and/or YouTube (low use, moderate use, and high use); and experiences on Facebook and/or YouTube (yes or no). The variable of aggression was slightly skewed (greater than twice the standard error of skewness). According to Field (2009), transformation of skewed variables may result in interpretation difficulties because the original construct being measured changes when transformations are conducted. Therefore, in the interest of interpretational simplicity, the variable of aggression was not transformed. Skewness, kurtosis, and descriptive statistics of each variable are displayed in Table 1.
Descriptive Statistics for All Variables
|Aggressive Response 1||174||2.90||1.10||.12||-.94|
|Aggressive Response 2||176||2.91||1.29||-.15||-1.18|
|Aggressive Response 3||178||3.25||1.18||-.39||-.72|
|Aggressive Response 4||176||2.66||1.22||-.19||-.98|
|Aggressive Response 5||178||3.25||1.18||-.32||-.89|
Frequencies and Reliability Analyses
Frequencies for the categorical variable—experiences on Facebook and/or YouTube—are presented in Figure 1. Only participants who responded “Yes” to experiences online are reported.
|Figure 1. Participants’ reports of negative experiences on Facebook and YouTube.|
Reliability analyses were conducted to determine the internal consistency of the aggression, psychological well-being, and aggressive response variables, as depicted in Table 2. The reliability indices for each of the measures were found to be good to very good.
Reliability Values (Cronbach’s Alpha)
|Measure||N||Cronbach’s Alpha Coefficient|
Correlational research is conducted with the purpose of observing the natural relationships occurring between variables without direct interference or manipulation of these variables (Field, 2009). The present study’s aim was to do just this: that is, to observe any influence that online interactions have on psychological well-being and the development of avoidant-oriented behavior (measured by aggression levels and aggressive responses to hypothetical situations). Correlational analyses, therefore, were conducted to observe the relationships between these variables.
Bivariate Pearson’s product–moment correlations were performed to observe the relationships between time online, experiences online, aggression, psychological well-being, and aggressive responses to each of the five scenarios. Correlations are displayed in Table 3 (Facebook) and Table 4 (YouTube).
Correlations Between Time and Experiences on Facebook, Aggression, Psychological Well-Being, and Aggressive Responses to Each of the Five Scenarios
|Variable (measurement scale)||1||2||3||4||5||6||7||8||9||10||11|
|1. Time on Facebook||–||.29**||.21**||.40**||.10||.06||.01||.06||.00||-.06||-.03|
|2. Felt Abused/Victimized||–||.33**||.48**||.32**||-.15*||.13||.19*||.16*||.09||.05|
|3. Used Coarse Language||–||.40**||.28**||.07||.12||.20*||.10||-.07||.10|
|4. Engaged in Online Argument||–||.38**||-.14||.11||.11||.05||-.09||.01|
|6. Psychological Well-Being||–||-.14||-.04||-.14||-.04||-.05|
|7. Aggressive Response to Scenario 1||–||.36**||.43**||.16*||.58**|
|8. Aggressive Response to Scenario 2||–||.55*||.35*||.36**|
|9. Aggressive Response to Scenario 3||–||.36*||.47**|
|10. Aggressive Response to Scenario 4||–||.27**|
|11. Aggressive Response to Scenario 5||–|
**Correlation is significant at p < .01; *p < .05
Time on Facebook. As displayed in Table 3, time spent on Facebook was significantly positively correlated with participants’ negative experiences on Facebook, suggesting the more time spent on Facebook, the greater the experience of abuse or victimization, use of coarse language, and engagement in online arguments. However, the relationship between time spent on Facebook and levels of aggression or psychological well-being did not achieve statistical significance. Similarly, time on Facebook did not significantly correlate with aggressive responses to any of the five scenarios.
Aggression. Aggression was significantly positively correlated with negative experiences on Facebook but not time spent on Facebook. A significant negative correlation was found between aggression and psychological well-being, r = −.31, p < .01, suggesting that higher levels of aggression are associated with a decreased sense of psychological well-being. Aggression was also significantly positively correlated with aggressive responses on all of the five scenarios.
Psychological well-being. A significant negative correlation was found between feelings of abuse or victimization on Facebook and psychological well-being, r = −.15, p < .05, suggesting that participants who experienced higher rates of abuse or victimization on Facebook also reported decreased levels of psychological well-being.
Aggressive responses to scenarios. A significantly positive relationship was found between negative experiences on Facebook (feelings of abuse or victimization and using coarse language) and responding aggressively to two (of the five) scenarios. Experiencing feelings of abuse or victimization on Facebook significantly correlated to responding aggressively to Scenarios 2 and 3, r = .19, p < .05 and r = −.16, p < .05 respectively. A higher rate of experience of abuse or victimization was related to a higher tendency to respond aggressively to particular scenarios. Furthermore, responding aggressively to Scenario 2 was also significantly positively correlated with using coarse language on Facebook, r= −.20, p < .05. Significant positive correlations were found between all aggressive response variables, suggesting that participants who tended to respond aggressively to one scenario were more likely to respond aggressively to the other scenarios.
Correlations Between Time and Experiences on YouTube, Aggression, Psychological Well-Being, and Aggressive Responses to Each of the Five Scenarios
|Variable||Time on YouTube||Abuse||Coarse Lang.||Online Argument||Aggress.||Psych. Well-Being||AggR 1||AggR 2||AggR 3||AggR 4||AggR 5|
**Correlation is significant at p < .01; *p < .05.
Note. Correlations for variables 5–11 are not shown on the Y-axis because the values are identical to that in Table 3. Variables 7–11 represent Aggressive Response (AggR) to Scenarios 1 to 5.
Time on YouTube. As displayed in Table 4, time spent on YouTube was significantly positively correlated with experiencing feelings of abuse or victimization, using coarse language, and engaging in online arguments on YouTube. Time spent on YouTube was also significantly positively correlated with aggression, suggesting that the more time spent on YouTube, the higher reported levels of aggression, r = −.15, p < .05.
Aggression. Aggression was found to be significantly positively correlated with negative experiences on YouTube, suggesting that those who reported experiencing feelings of abuse or victimization, use of coarse language, and engagement in arguments on YouTube reported higher levels of aggression, r = −.22, p < .05, r = −.21, p < .05, and r = −.29, p < .05 respectively.
Psychological well-being. A significant negative correlation was found between experiencing feelings of abuse or victimization on YouTube and psychological well-being, r = −.21, p < .01, suggesting that participants who reported experiencing higher rates of abuse or victimization on YouTube also reported decreased levels of psychological well-being.
Aggressive responses. Relationships between responding aggressively to different scenarios and time and experiences on YouTube did not achieve statistical significance.
The present study aimed to examine social online-based interactions and the effect that these interactions may have on psychological well-being and behavior. Negative experiences on social networking sites were examined to determine any existing relationships between these experiences and psychological well-being, aggression, and aggressive behavioral responses. Previous research has proposed that unique social interactions can influence the development of neural systems through epigenetic processes and the effect of the mirror neuron system on neural responses (Cozolino, 2010; Kandel, 1998; Kandel, 2006; Siegel, 2006). These interactive neural processes inform well-being and the development of behavioral patterns. The nature of an interaction can elicit different motivations that underpin behavior (Grawe, 2007). If interactions compromise an individual’s safety, avoidance-oriented behavior may develop—that is, unhelpful, inhibited behavior executed in the interest of self-protection yet resulting in poor well-being (Grawe, 2007). In contrast, safe and positive interactions can facilitate the development of approach-oriented behavior—that is, healthy behavior in coherence with self-improvement and positive well-being (Grawe, 2007). The present study endeavored to observe this interplay between social interaction, psychological well-being, and behavior. The study’s hypotheses were partially supported, as discussed below.
Hypothesis 1a. The prediction that spending time online and engaging in negative interactions (defined as feeling abused or victimized, using coarse language, and engaging in an argument) would compromise participants’ psychological well-being was partially supported. The study found that the more time participants spent on Facebook or YouTube, the more likely they were to experience negative interactions. Interpretation of this finding should be approached with caution, however, as the present study did not compare this finding with participants’ experiences of positive reactions. It may be that excessive time spent on Facebook and YouTube increases exposure to experiencing negative interactions such as abuse or arguments, but exposure to positive interactions would likely be increased also. To make a stronger argument for the risk of negative social interactions posed by online sites, this finding should be compared to the rate at which people experience positive social interactions online—an idea for further research that will be discussed later.
In terms of psychological well-being, the study found that the amount of time spent on Facebook or YouTube had no effect on participants’ perceived psychological well-being. However, the types of experiences participants were having online did influence psychological well-being. The study hypothesized that engaging in negative online interactions (feeling abused or victimized, using coarse language, and having an online argument) would have an influence on participants’ psychological well-being. Using coarse language and engaging in online arguments did not affect psychological well-being; on the other hand, feeling abused or victimized (on both Facebook and YouTube) negatively affected participants’ perceived psychological well-being. It is possible to explain these results by focusing on the nature of the three negative experiences. Two of these instances—using coarse language and engaging in an online argument—are both experiences in which the participant actively and intentionally engages; for example, he/she types the curse word or initiates the argument. In both instances, the participant retains some sense of control. However, this is not the case for the third negative experience—that of feeling abused or victimized. This interaction sees the participant as a recipient of negative feedback who lacks control over the situation. Research suggests that when a person lacks control, anxiety and distress are experienced, which can result in the lessening of well-being (Grawe, 2007).
Hypothesis 1b. Hypothesis 1b predicted that participants’ general aggression would be influenced by time spent online and engagement in negative experiences. This hypothesis was partially supported. While the amount of time spent on Facebook and YouTube had no effect on participants’ levels of aggression, it was found (for both Facebook and YouTube) that participants who reported experiencing negative interactions also reported having higher levels of aggression. The correlational nature of this finding renders it unable to be interpreted as a causal relationship, in that it cannot be concluded whether experiencing negative interactions leads to higher levels of aggression, or higher levels of aggression leads to negative interactions. However, this finding does highlight the existence of a relationship, albeit undefined, between negative experiences and aggression. The literature has proposed that environment influences behavior (Grawe, 2007; Kandel, 1998) and may be able to provide some further explanation for the findings provided here. It has been suggested that unique environmental experiences contribute importantly to changing neural plasticity, which in turn leads to the orientation of behavioral goals (approach or avoidant) and general well-being (Grawe, 2007). In experiencing a negative online interaction, a person’s limbic system (the amygdala) would be activated (Grawe, 2007; Siegel, 1999). This activation would cause the person distress, an unwanted feeling that threatens their basic need for safety or survival. As a response, the person seeks to eliminate this distress by executing whatever behavior is necessary. As mentioned earlier in the study, this behavior could be approach-oriented (healthy behavior such as asserting oneself toward the perpetrator of the online abuse) or avoidance-oriented (protective behavior—yet costly in the long term—such as reacting with aggression toward the perpetrator of the online abuse). The present study found a relationship between these negative social interactions and participants’ heightened aggression. In line with the literature (Cozolino, 2010; Grawe, 2007), the notion that these participants may have developed neural patterns that reinforce their tendency to engage in avoidant behaviors (aggression) as a mechanism to protect themselves from the distress of negative online interactions can therefore be considered.
Alternative explanations for the identified relationship between aggression and the engagement in negative online interactions should be considered. It may be that participants who reported experiencing negative online interactions had aggressive tendencies prior to these negative experiences, and the Internet simply provides a platform for them to express this aggression (such as using coarse language, engaging in arguments). However, this alternative explanation does not quite fit with the study’s finding that feeling abused or victimized whilst online is related to higher levels of aggression. Rather, an explanation provided by previous research is more convincing. This explanation suggests that negative experiences (which elicit feelings of distress) can influence neural development that informs protective, avoidant behavior—aggression (Cozolino, 2010; Grawe, 2007).
Hypothesis 1c. Lastly, it was hypothesized that participants’ tendency to respond aggressively to five different hypothetical situations would be influenced by time spent online engaging in negative social interactions. This prediction was generally unsupported. Results did show, however, that participants who experienced feelings of abuse or victimization on Facebook (only) were more likely to respond aggressively to the second hypothetical situation. This particular hypothetical situation prompted responses to a situation where someone insults them on Facebook. The crossover between the hypothetical content of the situation (being abused on Facebook) and the study’s questions about being abused on Facebook may explain why this particular situation yielded a tendency to elicit aggressive responses (as opposed to the other four situations). It may be that participants, having halfway completed a survey asking them about their negative experiences on Facebook and their levels of aggression, might by this stage of the questionnaire have been somewhat primed to respond aggressively when asked about a negative experience on Facebook (the hypothetical situation). The same relationship was not found for those who had experienced feelings of abuse or victimization on YouTube, reinforcing the possibility that perhaps the Facebook-oriented hypothetical situation primed the participants’ responses.
Strengths and Limitations
When considering the interpretability of this research and the extension to future research, it is necessary to examine the strengths and limitations of the current study. The use of an Australian sample in the research was novel in that, to date, no Australian sample has been used to investigate the relationship between Internet-based social interactions, psychological well-being, and behavior. The use of this sample is a strength of the study. It addresses a gap in the literature, and its findings relate specifically to Australian trends, furthering what is known about social Internet use and the implications of this for the well-being of the Australian population.
A further strength of the research was its implementation of reliable and valid measures. Both the psychological well-being subscale of the BBC Well-Being scale (Kinderman et al., 2011) and the short-form Buss–Perry Aggression Questionnaire (Bryant & Smith, 2001) provide reliable indications of psychological well-being and aggression. The online administration of the study proved advantageous, being inexpensive and easily advertised, and a large number of participants were recruited. The online nature of the study also allowed participants to complete the questionnaire in their own time and space, thus accentuating a feeling of anonymity. This sense of anonymity (together with the confidentiality and anonymity clauses given prior to the survey) is invaluable when administering self-report measures because it increases the likelihood of obtaining genuine responses. A shortcoming of the online administration in this particular study was that the data may have been limited due to the constrained sample of adults, as the survey was primarily advertised on Facebook using the researcher’s account. Consequently, the majority of participants who received the URL link to the survey were “Facebook friends” with the researcher and approximately the same age (in their mid-twenties). National census research has shown that, of all age groups, 15- to 17-year-olds use social networking more than any other (Australian Communications and Media Authority, 2013), therefore the present research (and the topical relevance of its findings) could have benefited by including more adolescent participants.
The study has supported the general premise that environmental interactions influence psychological well-being. However, the results were not supportive of such interactions informing behavior (that is, the tendency to respond aggressively to actual situations). It is possible this result can be attributed to methodological shortcomings of the present study.
The study endeavored to measure a relationship between social interactions and behavioral outcomes based on theoretical elements that were not directly measured. The hypothesis posed here stemmed from prior research suggesting that unique changes take place at a neural level in response to social interaction, and that this then informs behavior (Grawe, 2007; Kandel, 1998). These changes include epigenetic processes, mirror neuron firing and neural structure responses, all of which were not measured by the present study. Instead, the study measured participant responses, with the expectation that certain patterns would emerge, and that these patterns could then be explained in terms of this neurological-based theory. Such patterns were, for the most part, not found and the study did not fully confirm its primary objective. Further research could address this methodological limitation by implementing measures to observe neurological changes in the individual during Internet-based interactions.
A further methodological limitation of the research was the failure to question participants about their experience of positive online interactions—such as feeling connected with friends. This information could have been used as a comparative measure when considering the relationship between negative online interactions, psychological well-being, and behavior. In its current form, the research can only present an argument for the influence of negative social interactions on well-being, with no knowledge of how, or even if, this differs from the influence of positive social interactions. In the interest of thorough and quality research, the inclusion of positive social interactions could provide valuable information that could strengthen the implications of the study’s findings. At the very least, the inclusion of this comparative measure would render the research less biased and more thorough.
In further regard to the nature of experiences online, the research only enquired about three specific types of negative experiences, and two of these (using coarse language and engaging in an online argument) may not necessarily be interpreted by a participant as negative. For example, a participant may regularly use coarse language and not associate this with negative connotations. Furthermore, a participant may engage in online arguments, but these may be light-hearted in nature or even provide the participant with some satisfaction (perhaps they engaged in the argument and won). Consequently, the inclusion of only three predefined negative experiences limited the study’s opportunity to thoroughly explore the influence of negative social online-based interactions, a shortcoming that can be remedied by future research.
Directions for Future Research
The present research is a preliminary snapshot of the relationships between Internet-based social experiences, psychological well-being, and behavior. The findings here provide a starting point that can be advanced by future research through methodological improvements. As identified previously, future research could directly measure any occurring changes in participants at the neural level. This would require the inclusion of brain-scanning equipment (such as fMRI or PET), which, despite the financial implications, would enable direct observation of the firing patterns of neurons and activation of certain brain areas in response to Internet-based interactions. Further, participants could engage in different types of interactive online experiences whilst having their brain activity monitored by such equipment, and this could provide information about which parts of the brain are responding to certain types of online experiences—for example, the different responses involved when playing a first-person shooter online game compared to chatting with friends. Such information would be an invaluable tool in the quest to promote well-being. If more was known about the potential risk of Internet-based social interactions to neural development, behavior, and well-being, then more could be done, first, to facilitate healthy and positive Internet-based interactions and, second, to introduce protective measures (such as increased education, stricter monitoring of cyber-bullying, and more stringent security measures to prevent underage sign-up to social media websites).
In addition to the inclusion of technical equipment to future research, the inclusion of both negative and positive social online interactions would provide a useful basis for comparison. For example, a larger variety of types of online experience could be included, with the opportunity for participants to provide examples of experiences they have personally identified as negative or positive. When observing participants’ behavior, future research could include additional measures of avoidance-oriented behavior (such as negative self-evaluation scales or anxiety measures), and compare these findings to measures of approach-oriented behavior (further general well-being measures). Improved future research would be valuable both in the Australian context and globally. Knowledge could be gained as to how human wellness is not only being influenced but also potentially being compromised by interactions on the Internet. Such knowledge could then inform the development of protective measures to promote well-being, highlighting the importance of social interactions and their effect on human development and behavior.
Anooshian, L. J. (2005). Violence and aggression in the lives of homeless children. Journal of Family Violence, 20, 373–387. doi:10.1007/s10896-005-7799-3
Arden, J. B. (2010). Rewire your brain: Think your brain to a better life. New York, NY: Wiley.
Australian Bureau of Statistics. (2010). Year book Australia, 2009–2010. (ABS Catalogue No. 1301.0). Retrieved from http://www.abs.gov.au/ausstats/abs@.nsf/0/9B34B8C8BF2FDA34CA25773700169C83?opendocument
Australian Bureau of Statistics. (2011). Australian social trends. (ABS Catalogue No. 4102.0). Retrieved from http://www.abs.gov.au/AUSSTATS/abs@.nsf/0/C822971247C9CAD1CA2578BD0013DAEC?opendocument
Australian Bureau of Statistics. (2013). Australian demographic statistics. (ABS Catalogue No. 3101.0). Retrieved from http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/3101.0Main+Features1Mar%202013?OpenDocument
Australian Communications and Media Authority. (2013). Like, post, share: Young Australians’ experiences of social media [Quantitative research report]. Retrieved from http://www.acma.gov.au/~/media/mediacomms/Report/pdf/Like%20post%20share%20Young%20Australians%20experience%20of%20social%20media%20Quantitative%20research%20report.pdf
Black, J. E. (1998). How a child builds its brain: Some lessons from animal studies of neural plasticity. Preventive Medicine, 27, 168–171. doi:10.1006/pmed.1998.0271
Bremner, D. J., Narayan, M., Anderson, E. R., Staib, L. H., Miller, H. L., & Charney, D. S. (2000). Hippocampal volume reduction in major depression. The American Journal of Psychiatry, 157, 115–117.
Bryant, F. B., & Smith, B. D. (2001). Refining the architecture of aggression: A measurement model for the Buss–Perry Aggression Questionnaire. Journal of Research in Personality, 35, 138–167. doi:10.1006/jrpe.2000.2302.
Buss, A. H., & Perry, M. (1992). The aggression questionnaire. Journal of Personality and Social Psychology, 63, 452–459.
Casile, A., Caggiano, V., & Ferrari, P. F. (2011). The mirror neuron system: A fresh view. The Neuroscientist, 17, 524–538. doi:10.1177/1073858410392239
Chen, S-K. (2012). Internet use and psychological well-being among college students: A latent profile approach. Computers in Human Behavior, 28, 2219–2226. doi:0.1016/j.chb.2012.06.029.
Clara, I. P., Cox, B. J., & Enns, M. W. (2001). Confirmatory factor analysis of the depression–anxiety–stress scales in depressed and anxious patients. Journal of Psychopathology and Behavioral Assessment, 23, 61–67. doi:10.1023/A:1011095624717
Cozolino, L. J. (2006). The neuroscience of human relationships: Attachment and the developing social brain. New York, NY: Norton.
Cozolino, L. J. (2010). The neuroscience of psychotherapy: Healing the social brain (2nd ed.). New York, NY: Norton.
European Travel Commission. (2013). Demographics [Web log post]. Retrieved from http://www.newmediatrendwatch.com/markets-by-country/17-usa/123-demographics
Fabbri-Destro, M., & Rizzolatti, G. (2008). Mirror neurons and mirror system in monkeys and humans. Physiology, 23, 171–179. doi:10. 1152/ physiol. 00004.2008
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: SAGE.
Freud, S. (1968). A general introduction to psycho-analysis: A course of twenty-eight lectures delivered at the University of Vienna, by Prof. Sigmund Freud. New York, NY: Liveright.
Frodl, T., Jäger, M., Smajstrlova, I., Born, C., Bottlender, R. Palladino, T., . . . Meisenzahl, E. (2008). Effect of hippocampal and amygdala volumes on clinical outcomes in major depression: A 3-year prospective magnetic resonance imaging study. Journal of Psychiatry and Neuroscience, 33, 423–430.
Grawe, K. (2007). Neuropsychotherapy: How the neurosciences inform effective psychotherapy. New York, NY: Psychology Press.
Guan, S. A., & Subrahmanyam, K. (2009). Youth Internet use: Risks and opportunities. Current Opinion in Psychiatry, 22, 351–356.
Huesmann, L. R., Dubow, E. F., & Boxer, P. (2009). Continuity of aggression from childhood to early adulthood as predictor of life outcomes: Implications for the adolescent-limited and life-course-persistent models. Aggressive Behavior, 35, 136–149. doi:10.1002/ab.20300
Huttenlocher, P. R. (2002). Neural Plasticity: The effects of environment on the development of the cerebral cortex. Cambridge, MA: Harvard University Press.
Iacoboni, M., Molnar-Szakacs, I., Gallese, V., Buccino, G., Mazziotta, J. C., & Rizzolatti, G. (2005). Grasping the intentions of other’s with one’s own mirror neuron system. PLoS Biology, 3, e79. doi:10.1371/journal.pbio.0030079
Jang, K. L., Woodward, T. S., Lang, D., Honer, W. G., & Livesly, J. W. (2005). The genetic and environmental basis of the relationship between schizotypy and personality: A twin study. The Journal of Nervous and Mental Disease, 193, 153–159.
Joseph, R. (1998). Environmental influences on neural plasticity, the limbic system, emotional development and attachment: A review. Child Psychiatry and Human Development, 29, 189–208.
Kandel, E. R. (1998). A new intellectual framework for psychiatry. The American Journal of Psychiatry, 155, 457–469.
Kandel, E. R. (2006). In search of memory: The emergence of a new science of mind. New York, NY: Norton.
Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., & Hudspeth, A. J. (Eds.) (2013). Principles of neural science (5th ed.). New York, NY: McGraw-Hill.
Kang, S. (2007). Disembodiment in online social interaction: Impact of online chat on social support and psychosocial well-being. Cyberpsychology and Behavior, 10, 475–477. doi:10.1089/cpb.2006.0327.
Keeran, D. (2012). Learn empathy: Building skills for caring. Retrieved from http://www.wellbeingaustralia.com.au/wba/EmpathyProject4.pdf
Kim, E. J., Namkoong, K., Ku, T., & Kim, S. J. (2008). The relationship between online game addiction and aggression, self-control and narcissistic personality traits. European Psychiatry, 23, 212–218.
Kinderman, P., Schwannauer, M., Pontin, E., & Tai, S. (2011). The development and validation of a general measure of well-being: The BBC Well-Being scale. Quality of Life Research, 20, 1035–1042. doi:10.007/s11136-010-9841-z.
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? The American Psychologist, 53, 1017–1031. doi:10.1037//0003-066X.53.9.1017
Lange, C., & Irle, E. (2004). Enlarged amygdala volume and reduced hippocampal volume in young women with major depression. Psychological Medicine, 34, 1059–1064.
Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the depression anxiety stress scales (2nd ed.). Sydney, Australia: Psychology Foundation.
McGuffin, P., Owen, M. J., & Farmer, A. E. (1995). Genetic basis of schizophrenia. The Lancet, 346, 678–682.
Mehroof, M., & Griffiths, M. D. (2010). Online game addiction: The role of sensation seeking, self-control, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychology, Behavior, and Social Networking, 13, 313–316. doi:10.1089/cyber.2009.0229
O’Connor, D. B., Archer, J., & Wu, F. W. C. (2001). Measuring aggression: Self-reports, partner reports, and responses to provoking scenarios. Aggressive Behavior, 27, 79–101. doi:10.1002/ab.2
Orphanides, G., & Reinberg, D. (2002). A unified theory of gene expression. Cell, 108, 439–451.
Public Relations Institute of Australia. (2012, July 12). 13 million Australians spend 18 hours a day online [Web log post]. Retrieved from http://www.pria.com.au/industrynews/13-million-australians-spend-18-hours-a-day-online
Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192.
Rizzolatti, G., Fabbri-Destro, M., & Cattaneo, L. (2009). Mirror neurons and their clinical relevance. Nature Clinical Practice Neurology, 5, 24–34. doi:10.1038/ncpneuro0990
Robinson, G. E., Fernald, R. D., & Clayton, D. F. (2008). Genes and social behavior. Science, 322, 896–900. doi:10.1126/science.1159277
Rossouw, P. J. (2011, September). The world as one: The neuroscience of interconnectedness. Neuropsychotherapy, 7, 2–7. Retrieved from
Rossouw, P. J. (2013, July). The effects of bullying on the developing brain. Strategies for effective interventions. Paper presented at No 2 Bullying Conference, Nerang, Queensland, Australia. Retrieved from: http://no2bullying.org.au/n2bblog/category/effects-of-bullying-on-the-brain/
Shaw, L. H., & Gant, L. M. (2002). In defense of the Internet: The relationship between Internet communication and depression, loneliness, self-esteem, and perceived social support. Cyberpsychology and Behavior, 5, 157–171. doi:10.1089/109493102753770552.
Shen, S., Battersby, S., Weaver, M., Clark, E., Stephens, K., & Harmar, A. J. (2000). Refined mapping of the human serotonin transporter (SLC6A4) gene within 17q11 adjacent to the CPD and NF1 genes. European Journal of Human Genetics, 8, 75–78.
Siegel, D. J. (1999). The developing mind: Toward a neurobiology of interpersonal experience. New York, NY: Guilford.
Siegel, D. J. (2001). Toward an interpersonal neurobiology of the developing mind: Attachment relationships, “mindsight,” and neural integration. Infant Mental Health Journal, 22, 67–94.
Siegel, D. J. (2006). An interpersonal neurobiology approach to psychotherapy. Psychiatric Annals, 36, 248–256.
Sullivan, P. F., Kendler, K. S., & Neale, M. C. (2003). Schizophrenia as a complex trait: Evidence from a meta-analysis of twin studies. Archives of General Psychiatry, 60, 1187–1192. doi: 10.1001/archpsyc.60.12.1187
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Boston, MA: Pearson Education.
Tsuang, M. (2000). Schizophrenia: Genes and environment. Biological Psychiatry, 47, 210–220.
Tsuang, M. T., Stone, W. S., & Faraone, S. V. (2001). Genes, environment and schizophrenia. The British Journal of Psychiatry, 178, 18–24. doi:10.1192/bjp.178.40.s18. Abstract only.
Van den Eijnden, R. J., Meerkerk, G. J., Vermulst, A. A., Spijkerman, R., & Engels, R. C. (2008). Online communication, compulsive Internet use, and psychosocial well-being among adolescents: A longitudinal study. Developmental Psychology, 44, 655–665. doi:10.1037/0012-1618.104.22.1685
Walter, H., Berger, M., & Schnell, K. (2009). Neuropsychotherapy: Conceptual, empirical and neuroethical issues. European Archives of Psychiatry and Clinical Neuroscience, 259, 173–182. doi:10.1007/s00406-009-0058-5.
Yang, S. C. (2012). Paths to bullying in online gaming: The effects of gender, preference for playing violent games, hostility, and aggressive behavior on bullying. Journal of Educational Computing Research, 47, 235–249. doi:10.2190/EC.47.3.a
 The data revealed a limited number of participants who indicated to visiting the other listed websites, therefore further analyses included only Facebook and YouTube as representative of Internet-based interactions.
Eloise O’Reilly, The University of Queensland; Pieter Rossouw, Adjunct Professor, Central Queensland University; Director Mediros; Director Institute for Neuropsychotherapy; Director The Braingro Institute