LACKNER, RYAN J., Ph.D., August 2019 PSYCHOLOGICAL SCIENCES

OUT OF THE MIND AND INTO THE BODY: DOES SWITCHING MODES OF SELF-

REFERENCE REDUCE PERSEVERATIVE COGNITION? (89 PP.)

Dissertation Advisor: David M. Fresco, Ph.D.

The ability to hold one’s “self” in awareness is thought to be a distinguishing feature of human beings, allowing us to mentally reconstruct personal events from the past and construct possible events in the future. Though one of our greatest assets, the wandering mind may also become

“stuck” in negative, evaluative forms of self-referential thinking, or perseverative cognition (PC; e.g., worry and rumination). Many psychological disorders share the transdiagnostic feature of

PC, and worry and rumination are associated with deleterious psychological and physiological outcomes. Thus, it is important and clinically relevant to deliberately target PC and develop methods to “unstick” the mind. Whereas PC represents a mode of thinking that is negative, evaluative, and for that moment, oriented in the past or future, interoception –awareness of one’s body signals – provides a mode of self-reference that is experiential and present-centered.

Interoception is emphasized in many mindfulness-based interventions, and theory suggests the development of interoception facilitates a shift away from neural processes devoted to more abstract, narrative forms of self-reference. Thus, the purpose of the current study was to determine whether engaging interoceptive attention reduces PC. One-hundred fifty-eight participants were randomized to one of three experimental manipulations of attention: (1) interoceptive cues; (2) self-referential cues; or (3) non-self-referential cues. Participants then completed a mind-wandering paradigm interspersed with thought probes, which measured PC. We hypothesized that individuals undergoing the interoception condition would report less PC during mind-wandering, relative to each of the other two conditions. Results indicated no effect of group on reported PC. Limitations and future considerations are discussed.

OUT OF THE MIND AND INTO THE BODY: DOES SWITCHING MODES OF SELF-

REFERENCE REDUCE PERSEVERATIVE COGNITION?

A dissertation submitted

to Kent State University in partial

fulfillment of the requirements for the

degree of Doctor of Philosophy

Ryan J. Lackner

Dissertation written by

Ryan J. Lackner

B.S., Indiana University, 2013

M.A., Kent State University, 2016

Ph.D., Kent State University, 2019

Approved by

______, Chair, Doctoral Dissertation Committee David M. Fresco, Ph.D.

______, Members, Doctoral Dissertation Committee Karin G. Coifman, Ph.D.

______Christopher A. Was, Ph.D.

______Joshua W. Pollock, Ph.D.

Accepted by

______, Chair, Department of Psychological Sciences Maria S. Zaragoza, Ph.D.

______, Dean, College of Arts and Sciences James L. Blank, Ph.D. TABLE OF CONTENTS…………………………………………………………………...... v

LIST OF TABLES……………………………………………………………………………… vi

ACKNOWLEDGEMENTS…………………………………………………………………….. vii

INTRODUCTION……………………………………………………………………………… 1

METHODS……………………………………………………………………………………… 20

RESULTS………………………………………………………………………………..……… 28

DISCUSSION…………………………………………………………………………………... 39

REFERENCES…………………………………………………………………………………. 49

TABLES………………………………………………………………………………………… 72

FIGURES……………………………………………………………………………………….. 79

APPENDICES

A. Mindful Body Breathing Script……………………………………………….…...... 81

B. Attention to Non-negative, Self-referential Information Script …………………...…84

C. Attention to Non-negative, Non-self-referential Information Script …………...…....86

D. Ordinary Least Squares (OLS) Analyses as Originally Proposed………………...….88

LIST OF TABLES

Table 1. Correlations, Means, Standard Deviations, and Internal Consistencies among All

Questionnaires ………………………………………………………………………………….. 72

Table 2. Intercorrelations among Thought Probe Responses ………………………...………… 73

Table 3. Factor Loadings for PSWQ and RSQ Items …………………………….……………. 74

Table 4. Responses to Thought Probes and PC Scores between Groups ………………………. 75

Table 5. Negative Binomial Regression Model Examining Group Differences in PC Thought-

Probes …………………………………………………………………………………………... 76

Table 6. Linear Regression Model Examining Group Differences in Stickiness of Thought …. 77

Table 7. Linear Regression Model Examining Group Differences in Intrusiveness of Thought.. 78

Table D1. Linear Regression Model Examining Group Differences in PC Thought-Probes…....89

Acknowledgements

I wish to thank my dissertation committee for their time and helpful comments. I would especially like to thank Dr. David M. Fresco for his vital contribution toward my development as a critical thinker, researcher, and aspiring clinical psychologist. Finally, I would like to thank my father, Larry, and mother, Luann, for their perpetual love and support.

vii

Out of the Mind and into the Body:

Does Switching Modes of Self-reference Reduce Perseverative Cognition?

As human beings, much of our time is spent contemplating the past, wondering about the future, and engaging in mental busywork aimed at avoiding certain experiences while maximizing the chance that more favorable ones occur. Although the ability to hold one’s “self” in awareness has allowed us to enjoy the benefits associated with imagining and predicting possibilities for reward and threat, considerable suffering may occur when the mind becomes dominated by negative self-referential activity. This quality of thinking (i.e., “negative self- referential processing [NSRP]; Northoff et al., 2006) is an encompassing characteristic of individuals with mood and anxiety disorders (e.g., major depressive disorder and generalized anxiety disorder), which have also been called misery or distress disorders (e.g., Watson, 2005).

Importantly, previous work has established that NSRP is an individual difference factor across many forms of emotional disorders, complicates clinical presentation and treatment, and appears to be a crucial target to address if treatment gains are to be gained and maintained (Mennin &

Fresco, 2013).

Recent developments in affective science have given us new perspectives from which to consider misery and distress disorders, as well as NSRP. For instance, a recent neuroanatomical and processing model of depression and anxiety proposes that dysfunction arises from aberrant self-referential processing (Paulus & Stein, 2010). Specifically, the model incorporates

1 interactions between NSRP and processing related to the “material self” (Craig, 2002), or the awareness of the physiological condition of the body (i.e., interoception). Coinciding with theoretical developments, clinical initiatives deliberately targeting NSRP are increasingly appearing within the treatment literature, many of which employ mindfulness to reduce reliance on self-referentiality in favor of an orientation that is nonjudgmental, nonreactive, and present centered (Mennin & Fresco, 2013). Generally, these treatments target attention and metacognition, which have been theorized to underlie NSRP (Mennin, Ellard, Fresco, & Gross,

2013), by cultivating interoception in response to stress (Farb, Segal, & Anderson, 2012). In doing so, an implicit objective of mindfulness-based treatments is to assist individuals in becoming more unbiased observers of their own internal experience and promote more adaptive ways of relating to self-relevant information (Mennin, Ellard et al., 2013; Teasdale, 1999).

Joining in this objective, we endeavor to better understand NSRP, interoception, and how examining these various aspects of self may improve treatment for disorders characterized by

NSRP. The primary goal of the current study is to investigate whether attending to interoceptive cues may reduce NSRP. First, we give an account of the burden of affective disorders, followed by a contextualization of the current study within a transdiagnostic framework. We then offer a brief history of the self as a construct of psychological inquiry. Next, we elucidate normative aspects of self-reference, including interoception, and describe how psychopathology is linked to aberrations of this normative framework. Subsequently, we characterize NSRP and discuss its psychological and physiological impact. We then examine current treatments and consider why cultivating interoception may counteract NSRP and state objectives and hypotheses of the current study.

The Burden of Misery

A subset of psychiatric conditions, often referred to as misery or distress disorders (e.g.,

Watson, 2005) place widespread burden on society and induce considerable human suffering.

According to recent reports, two of the most common distress disorders are major depressive disorder (MDD) and generalized anxiety disorder (GAD; Grant et al., 2009). Both disorders impair cognitive, affective, and somatic domains of functioning. Specifically, MDD is characterized by sustained depressed mood and/or loss of interest or pleasure in activities of daily living, and symptoms including changes in appetite, sleep disturbance, changes in activity levels, loss of energy, feelings of guilt or worthlessness, difficulties concentrating, and suicidality (Diagnostic and Statistical Manual of Mental Disorders 5th edition; DSM-5;

American Psychiatric Association [APA], 2013). GAD is characterized by excessive worry that the person finds difficult to control, restlessness or feeling keyed up or on edge, being easily fatigued, difficulty concentrating, irritability, muscle tension, and restless or unsatisfying sleep

(APA, 2013). Of note, impairment is more severe when MDD and GAD co-occur (e.g.,

Whisman, Sheldon, Goering, 2000; Henning, Turk, Mennin, Fresco, & Heimberg, 2007).

Although considerable advances in understanding and treating MDD and GAD have occurred, improvements in treatment efficacy are greatly needed, as both conditions are associated with suboptimal long-term treatment response (e.g., Farabaugh et al., 2010; Newman,

Przeworski, Fisher, & Borkovec, 2010). For instance, less than half of patients receiving a combination of medication and psychotherapy for MDD achieve remission (e.g., Casacalenda,

Perry, & Looper, 2002). Similarly, only 50-60% of patients seeking treatment for GAD demonstrate clinically meaningful change (Borkovec & Ruscio, 2001). One possible explanation for these treatment refractory patients is that they reflect a subgroup that is especially marked by

3 destructive forms of NSRP (Watkins, 2008), and that more deliberate efforts are needed to reduce the corrosive effects of NSRP and improve treatment durability (Mennin & Fresco,

2013).

NSRP as a Transdiagnostic Focus

Although the DSM-5 continues to classify MDD and GAD as separate disorders, increasing evidence indicates that they might not be distinguishable biological conditions

(Mennin, Heimberg, Fresco, & Rittter, 2008). In support of this, high levels of comorbidity are found in clinical (Mennin et al., 2008) and community samples (Kessler et al., 2005), and structural equation modeling studies revealed a high degree of association between MDD and

GAD (e.g., Watson, 2005; 2008). As a result, GAD and MDD are often labeled as “distress disorders” (e.g., Watson, 2005), or as conditions grouped under the same factor of “anxious misery” (Lang et al., 2016).

Such findings have led to alternative systems of nosology that emphasize basic and translational findings in neuroscience, such as the Research Domain Criteria (RDoC; Insel et al.,

2010). The approach proposed by RDoC consists of defining a series of bio-behavioral domains

(e.g., negative and positive valence systems, social processes) that are investigated at various levels of inquiry (e.g., molecular, behavioral, clinician observation, self-report). The goal of

RDoC is to first outline normative functioning within these domains and subsequently contrast normative functioning with disordered subgroups to identify potential mechanisms of interest that may serve as targets of treatment, resulting in more personalized, and potentially more efficacious treatment. Although the DSM-III (APA, 1980) and its successors have been effective in increasing diagnostic reliability (Insel et al., 2010), one of the unintended consequences of a nosology that relied on purely descriptive approaches based on clinical observation was that it

4 encouraged research to differentiate between a “pure” manifestation of a single disorder and a healthy comparison group with “ideal” mental health (Lang, McTeague, & Bradley, 2016). Not only is comorbidity often the rule as opposed to the exception (e.g., Brown et al., 2001; Kessler et al., 2005), but focusing on isolating distinct categories may obstruct efforts to identify and treat underlying pathophysiological mechanisms that actually produce the surface level characteristics of emotional disorders (Insel et al., 2010). Accordingly, research programs are progressively placing emphasis on improving clinical outcomes for all patients by identifying aberrant mechanisms that are transdiagnostic in nature (Insel & Cuthbert, 2009).

For instance, NSRP is found throughout a multitude of psychological disorders, including depression and anxiety (Abbott & Rapee, 2004; Michael, Halligan, Clark, & Ehlers, 2007;

Paulus & Stein, 2010), bipolar disorder (Gruber, Eidelman, Johnson, Smith, & Harvey, 2011), and eating disorders (Nolen-Hoeksema, Stice, Wade, & Bohon, 2007). Aside from its ubiquity,

NSRP is linked to substantial deficits in cognitive and behavioral responding (e.g., Lissek, 2012;

Whitmer & Gotlib, 2012), poorer somatic health (Ottaviani et al., 2016), increased health risk behaviors (e.g., substance use, alcohol consumption, unhealthy eating, smoking; Clancy,

Prestwich, Caperon, & O’Connor, 2016), inferior treatment response and greater relapse (Jones,

Siegle, & Thase, 2008; Olantunji, Naragon-Gainey, & Wolitzky-Taylor, 2013), and aberrant neural activation in regions linked to self-referentiality (e.g., Andreescu et al., 2014; Chen &

Etkin, 2013). Thus, in keeping with the principles of RDoC, the current study takes a mechanism-driven approach to investigating the self, which, in turn, may provide critical implications for targeting the pervasiveness and impactful effects of NSRP across a wide range of psychological disorders.

The “Self” in Psychology and Neuroscience

Dating back to the era of ancient Greek philosophy, and perhaps further, the aphorism to

“know thyself” propelled humanity to understand one’s individuality so as to capture the phenomenological sense of the inner “I.” Throughout the years, concepts of the self have shifted and morphed, influenced by culture and the system of thought undertaken by various philosophers. For instance, one of the earliest concepts of self, was the soul, described by Plato as “the initiator of activity, conscious, life-giving and immaterial” (Viney, 1969). Similarly,

Aristotle is often credited as the first thinker to make a systematic inquiry into the nature of the ego; he contended that the soul was the core essence of a human being, but disagreed with

Plato’s idea that it had a separate existence from the being itself. Centuries later, Rene Descartes would famously write “Cogito erg sum. (I think, therefore I am.),” followed by countless expositions by other philosophers to interpret its meaning. Whereas Western thought generally views the self as an unchanging, core aspect of human existence, some Eastern schools of thought, such as Buddhist traditions, argue that our subjective sense of a permanent, autonomous self is merely and illusion and that there is no underlying essence of a person. Although a comprehensive account of the self transcends the objectives of the current investigation, clearly the ripple effects of inquiries of the self have been felt throughout the ages.

Investigating and refining our notion of the self has been a prominent topic of interest throughout the history of philosophy, and more recently, in psychology and neuroscience

(Northoff et al., 2006). Since William James’ (1890/1982) distinction between the physical self, mental self, and spiritual self, many theorists have developed concepts to advance the understanding of self in neuroscience (Northoff et al., 2006). For example, Damasio (1999) proposes a tripartite, hierarchical theory of consciousness, comprised of the protoself (i.e., a

6 preconscious state shared by all organisms representing the domain of the body), core consciousness (i.e., awareness of self-ownership of thoughts), and extended consciousness (i.e., an autobiographical layer of self, reflecting the domain of memory). Various other concepts of self have been proposed, such as the “minimal self” (i.e., the “I” who is experiencing the present moment) and narrative self (i.e., the self constituted with the past and future; Gallagher, 2000), emotional self (Fossati et al., 2003), spatial self (Vogeley & Fink, 2003), and the verbal or interpreting self (Turk, Heatherton, Macrae, Kelly, & Gazzaniga, 2003).

A variety of methods developed to uncover the neural correlates of self-referential processing. Tasks include recognizing one’s own face (Kircher et al., 2000; Platek, Thomson, &

Gallup, 2004), detecting one’s own first name (Perrin et al., 2005), attributing action to oneself

(Farrer et al., 2003), recalling personally relevant information (Maguire & Mummery, 1999), and assessing one’s own personality, physical appearance, attitudes, or feelings (e.g., Craik et al.,

1999; Fossati et al., 2003; Gusnard, Akbudak, Shulman, & Raichle, 2001; Gutchess, Kensinger,

& Schacter, 2007; Kelley et al., 2002; Kircher et al., 2000; Kjaer, Nowak, & Lou, 2002; Lou et al., 2004; Ochsner & Gross, 2005). Although these experimental paradigms interrogate a variety of functional domains (e.g., verbal, spatial, emotional, and facial), meta-analytic results converge on the finding that self-referential processing is mediated by cortical midline structures (e.g., medial prefrontal cortex [mPFC] and posterior cingulate cortex [PCC]) regardless of domain

(Northoff et al., 2006). Thus, as stated by Northoff (2007), one possible interpretation of these findings is that self-referential processing may be at the core of what we call the “self.”

Adaptive Self-reference

The mechanism by which humans are able to mentally reconstruct personal events from the past and construct possible events in the future is thought to separate ourselves from other

7 animals (Suddendorf & Corballis, 1997). Some have referred to this capability as “mental time travel,” or the ability to mentally project the self backwards in time to re-live events or forwards to pre-live events (Suddendorf & Corbalis, 2007). Similarly, the ability to envision one’s mind in a variety of possible futures is referred to as prospection (e.g., Gilbert & Wilson, 2007). In both instances, these forms of self-reference represent “narrative self-reference,” which describes a focus on the self that stiches together temporally disparate experiences into a cohesive storyline (Farb et al., 2007). As humans, we engage in this behavior quite often; in fact, much of our time is spent mind-wandering (e.g., 46.9% in samples collected by Killingsworth &

Gilbert, 2010) and thinking about ourselves. Accordingly, drifting toward inner thoughts and feelings that are unrelated to the present task may reflect the default mode of human cognition

(Mason et al., 2007), which is supported by recent neuroimaging work demonstrating reliable interactions between brain regions when individuals are free from engaging in task-based procedures (Buckner, Andrews-Hanna, & Schacter, 2008). Specifically, neuroimaging researchers have termed this pattern of neural activity the “default mode network” (e.g., Raichle et al., 2001); core brain regions include the mPFC and PCC, and activation in these areas are consistently associated with self-referential thought, as well as autobiographical, self-monitoring, and social cognitive functions (Ho et al., 2015; Raichle et al., 2001). The mPFC has also been implicated in the detection of emotionally salient stimuli (Morris et al., 1998; Phillips, Drevets,

Rauch, & Lane, 2003) as well as evaluating whether beliefs are “acceptable” or “unacceptable”

(Paulus & Stein, 2010).

Benefits of accessing a default, narrative state have received theoretical and empirical attention in relation to adaptive human functioning. For instance, creating a narrative of one’s self can lead to greater accuracy in one’s self-concept (Morin, 2011), greater self-regulation

(Baumeister & Vohs, 2007), as well as enhanced social and empathic relating to others and the ability to infer and respond to the mental states of others (Amodio & Frith, 2006). Furthermore, allowing the mind to wander is important for maintaining optimal levels of arousal, aiding in integrating the past, present, and future (Barrs, 2010), anticipating and planning personally- relevant goals, and facilitating creative thinking (Mooneyham & Schooler, 2013). Reliance on this ability may be particularly useful when environmental cues are ambiguous, or when we are inattentive and distracted; humans naturally “fill in the blanks” by creating internal representations, imagining ourselves engaging in past scenarios or projecting ourselves into future situations, thereby allowing us to determine the optimal behavioral response (e.g., Fresco et al., 2017).

Whereas narrative self-reference is more evaluative and temporally situated in past or future, the capacity for self-reflection also allows for a more experiential awareness of an immediate “material self” and forms the basis of “how I feel,” referred to as “interoception”

(Craig, 2002; Paulus & Stein, 2010). Specifically, interoception provides an integrated sense of the body’s present moment physiological condition (Craig, 2002), including the perception of pain (LaMotte, Thalhammer, Torebjork, & Robinson, 1982), temperature (Craig & Bushnell,

1994), itch (Schmelz, Schmidt, Bickel, Handwerker, & Torebjork, 1997), sensual touch (Vallbo,

Olausson, Wessberg, & Kakuda, 1995), and muscle tension (Light & Perl, 2003). Emerging research demonstrates that interindividual differences in interoceptive capacity are associated with right anterior insula cortical thickness, suggesting potential neuroplasticity effects of interoception (Critchley, Wiens, Rotshtein, Ohman, & Dolan, 2004; Lazar et al., 2005).

Momentary access to internal visceral signals is considered critical to an organism’s maintenance of desired physiological states, self-regulation, and ultimately, survival (Craig,

2013; Farb et al., 2015; Paulus, 2007). For instance, burgeoning research demonstrates that interoception is a key element for better decision-making (Dunn, Evans, Makarova, White, &

Clark, 2012; Dunn et al., 2010; Werner, Jung, Duschek, & Schandry, 2009), affect and emotion regulation (Füstos, Gramann, Herbert, & Pollatos, 2013; Sze, Gyurak, Yuan, & Levenson, 2010), and sense of self (Damasio, 1999; Camerson, 2002). Further research indicates that localized attention to body sensations enables subsequent gains in emotional and cognitive regulation by enhancing sensory information processing in the brain (Kerr, Sacchet, Lazar, Moore, & Jones,

2013). Presumably, increasing awareness of the body’s response to emotional stimuli may lead to greater awareness and understanding of one’s own emotions, which is argued to be a precondition for regulating emotions (Hozel et al., 2011). Enhanced internal awareness of one’s own experience has also been linked to increased engagement in empathetic responses toward others (Dekeyser, Raes, Leijssen, Leysen, & Dewulf, 2008).

In addition to the benefits derived from adaptive reference of the self, previous theory posits that the ability to flexibly switch between aspects of self-reference in response to environmental demands is an essential capacity of mental health (e.g., Teasdale, 1999). Indeed, flexibility in responding is an emerging individual difference factor that is being increasingly examined, for example, in healthy coping and emotion regulation. Findings from this literature indicate that the most efficacious regulatory strategies are likely the ones that are most flexible

(see Bonanno & Burton, 2013). Individuals with greater capacity for flexibility are more able to effectively identify demands specific to the environmental context, select a response from a range options, match the response to the contextual demands, and adjust responses as additional information is obtained or the demands of the environment change (e.g., Stange, Alloy, &

Fresco, 2017). Notably, responding flexibly to environmental demands may also include

10 adaptively, and temporarily, becoming inflexible when it serves a functional and evolutionary purpose. For instance, Wilson and Murrell (2004) propose that, in response to threat, an organism should have a greater chance of survival if it narrows its behavioral repertoire, allocates attentional resources to the threat, and utilizes high autonomic arousal for the purpose of fighting or fleeing. Similarly, in response to an environment devoid of actual or perceived reinforcement, an organism may become behaviorally inactive, which may reflect a conservation of energy until the environment has changed, thus again increasing its chance of survival. In summary, healthy functioning, in part, appears to include adaptive and flexible reference of both narrative and experiential (e.g., material) aspects of self.

Maladaptive Self-reference

Although the human capacity to construct a narrative self can be beneficial, it may also become destructive – especially when mind-wandering becomes repetitive negative thinking

(i.e., NSRP), or perseverative cognition (PC; Ottaviani, Shapiro, & Couyoumdjian, 2013) such as rumination, which describes a focus on negative past events along with a tendency to respond to sad mood by passively and repetitively focusing on the causes and consequences of negative emotions (Nolen-Hoeksema & Morrow, 1993) and worry, which focuses on possible negative events in the future and strategies to prevent such events from occurring (Borkovec & Inz, 1990).

Though distinct in their temporal orientation, rumination and worry appear to share a common process given their high degree of correlation (rs = .60 - .70; e.g., Calmes & Roberts, 2007), as well as structural equation models indicating that rumination and worry load on a common factor

(e.g., Fresco, Frankel, Mennin, Turk, & Heimberg, 2002). Notably, rumination and worry direct one’s attention toward the past or future, shifting focus away from present moment experience

(Farb et al., 2015).

The material self may also become disordered. For instance, heightened body awareness can lead to somatosensory amplification, worsened symptoms of anxiety and hypochondriasis, and maladaptive clinical outcomes, such as pain (Mehling et al., 2012). For example, studies reveal increased interoception in anxiety disorder patients (Ehlers & Breuer, 1992; Pineles &

Mineka, 2005). In trying to account for these seemingly discrepant findings, recent research suggests that metacognitive processes related to threatening beliefs about body sensations may explain the conditions under which interoception is associated with positive or negative outcomes (Yoris et al., 2015).

In other words, NSRP may moderate the association between psychopathology and interoception. According to one model, which integrates self-referential and interoceptive processes, Paulus and Stein (2010) posit that individuals at risk for anxiety and depression demonstrate 1) a reduced ability to adequately perceive information regarding the physiological condition of the body (i.e., poor interoception) due to amplification of interoceptive signals processed within the insula; 2) overactive self-referential processes (e.g., negative beliefs about interoceptive cues), processed within medial-prefrontal brain areas, which consequently 3) result in enhanced top-down modulation by these medial prefrontal areas to differentially amplify or attenuate interoceptive signals. In support of this model, task-based neuroimaging studies of individuals with MDD and GAD show hyperactivity of the anterior insula coupled with increased connectivity with self-referential brain areas (e.g., Hamilton, Chen, & Gotlib, 2013;

Paulus & Stein, 2010; Yuen et al., 2014). Similarly, findings from Kaiser and colleagues (2015) revealed that MDD patients were distinguished from controls by increased connectivity of the medial prefrontal cortex (mPFC) to the insula, and that depression severity was associated with stronger mPFC-insula connectivity. Also in support of this model, in an unselected sample of

12 college students, previous work revealed that low interoception coupled with high brooding rumination was associated with increased anxiety and depression-related distress (Lackner &

Fresco, 2016).

Deleterious effects of NSRP extend to both psychological and physiological functioning.

The treatment literature reveals that NSRP contributes to inferior treatment responses, as well as higher risk for relapse across many psychological ailments (Farb, Anderson, Bloch, & Segal,

2011). For instance, pretreatment levels of NSRP predict inferior treatment responses in MDD and dysthymic disorder (e.g., Ciesla & Roberts, 2002; Jones et al., 2008; Schmaling, Dimidjian,

Katon, & Sullivan, 2002) in addition to panic disorder (Papageorgiou & Wells, 2003). Similarly,

Watkins and colleagues (2011) found that higher levels of rumination are associated with a greater chance of relapse following acute CBT treatment for MDD. In regards to physical health, a series of meta-analyses conducted by Ottaviani and colleagues (2016) concluded that

NSRP contributes to poorer somatic health due to its ability to elicit prolonged physiological activity in cardiovascular, autonomic, and endocrine systems. For example, PC was associated with higher systolic and diastolic blood pressure, heart rate, and cortisol. Taken together, research investigating the impact of NSRP suggests a psychophysiological pathway by which chronic stress, and one’s response to it, results in long-term disease outcomes and health vulnerability (Ottaviani et al., 2016).

One of the most prominent characteristics of PC is difficulty disengaging from this manner of mentation (Koster, Lissnyder, Derakshan, & De Raedt, 2011) – commonly referred to as “sticky” modes of thinking (Joormann, Levens, & Gotlib, 2011), trains of thought that are difficult to disengage from (van Vugt, Hitchcock, Shahar, & Britton, 2012), or becoming “stuck” in a particular mind-set (Davis & Nolen-Hoeksema, 2000). In essence, these conceptualizations

13 converge on a “stickiness” dimension of PC, and recent research has shown that stickiness of thought can be meaningfully captured during mind-wandering tasks (van Vugt & Broers, 2016) and within validated self-report measures, such as the Perseverative Thinking Questionnaire

(Ehring et al., 2011).

Various theories account for why the mind becomes “stuck,’ in this manner. According to the impaired disengagement hypothesis (Koster et al., 2011), getting “stuck” in perseveration is a consequence of impaired attentional control. In support of this theory, studies have found reduced attentional control in depression (e.g., Marriam, Thase, Haas, Keshavan, & Sweeney,

1999), and other task-based results indicate that individuals presenting with high levels of rumination and worry exhibit greater difficulty disengaging attention from or inhibiting irrelevant information (Beckwe, Deroost, Koster, De Lissnyder, & De Raedt, 2014; Donaldson,

Lam, & Mathews, 2007; Joorman, 2006; Siegle, Ghinassi, & Thase, 2007), especially when the information is negative (Donaldson et al., 2007: Joorman, 2004). Furthermore, neurobehavioral evidence reveals that individuals with psychiatric disorders are characterized by a preponderance of activity within the DMN, at the cost of preventing or delaying purposeful activation of neural regions associated with cognitive control (e.g., Whitfield-Gabrieli & Ford, 2012). Thus, one possible explanation for why individuals become “stuck” is that perseverative cognition reflects a more general tendency toward cognitive inflexibility, making it difficult to switch attention away from negative self-referential information and in turn, generate alternative ways of coping

(e.g., Davis & Nolen-Hoeksema, 2000). Additionally, engaging in repetitive or perseverative reactive cognitive processes such as worry and rumination may serve to reduce unpredictability and intense emotionality in situations of perceived threat (Mennin & Fresco, 2013), which temporarily provides distraction from aversive emotional responses. Subsequently, worry and

14 rumination become reinforced as they reduce intense emotionality (Mennin & Fresco, 2013).

Finally, other factors that may maintain NSRP include that individuals prone to rumination erroneously believe that perseverating will increase their self-awareness, clarify their problems and moods, and prevent future mistakes (Watkins & Baracaia, 2002); that is, remaining “stuck” seems justifiable.

Targeting NSRP

Thus far, we have characterized adaptive functioning as including normative, flexible self-reference, and we have described how this process may also become negative, repetitive, and rigid. A logical next step is to explore methods that facilitate healthy self-reference and potentially restore flexibility to a “stuck” mind. Indeed, many contemporary treatments for emotional disorders, such as Mindfulness-Based Cognitive Therapy (Segal, Williams, &

Teasdale, 2002), Dialectical Behavior Therapy (DBT; Linehan, 1987), Acceptance and

Commitment Therapy (ACT; Hayes, Strosahl, & Wilson, 1999), and Emotion Regulation

Therapy (Mennin & Fresco, 2014), have endeavored to identify and target biobehavioral markers such as attention and metacognition, which have been theorized to underlie transdiagnostic factors common to anxiety and mood disorders (e.g., NSRP; Mennin, Ellard et al., 2013). As noted, interoception is generally incorporated within these treatments as a method to target attention and metacognition, which is thought to assist individuals in cultivating more adaptive ways of relating to self-relevant information (Mennin, Ellard, Fresco, & Gross, 2013; Teasdale,

1999).

In mindfulness practice, the focus of attention is generally placed on sensory experiences of breathing, or other body sensations (Hölzel et al., 2011). Recently, interoception has been considered a possible foundation by which mindfulness practices promote cognitive change

(Farb et al., 2012), via the reduction of evaluative, narrative self-referential processing and enhancement of present-moment experience (Tang, Hölzel, & Posner, 2015). Accordingly, many imaging studies have shown interoceptive-specific functional plasticity in insular regions following attention training (Craig, 2009; Farb, Segal, & Anderson, 2013; Farb et al., 2007), suggesting the possibility that enhanced insula activity reflects an amplified awareness of present-moment experience (Tang et al., 2015). Additionally, longitudinal studies of MBSR indicate structural and grey matter density increases in insular regions (Holzel et al., 2011a;

Holzel et al., 2011b). Furthering the distinction of evaluative, narrative modes of self-focus from experiential forms, a study by Farb and colleagues (2007) revealed a functional dissociation between cortical areas that support a narrative self-focus (i.e., mPFC) verses a more experiential, momentary self-focus (i.e., insula). Importantly, functional connectivity analyses demonstrated a strong coupling between the insula and mPFC that became uncoupled following MBSR training that included sustained attention toward bodily sensation. Thus, one interpretation of this finding is that trained attention to interceptive cues may facilitate a shift away from the narrative self- reference that supports perseverative thinking, and subsequently, worsens psychological and physical well-being.

Whereas abstract, perseverative self-focus is often maladaptive, attention to immediately experienced interoceptive sensations appears to be adaptive (Watkins & Moulds, 2005). This notion originated, in part, from theories proposing that more experimental forms of attention allow the integration of multiple elements of experience, which subsequently facilitates emotional well-being (Teasdale, 1999). From a mechanistic perspective, in the absence of new interoceptive information, perseverative forms of thinking may habitually dominate attention

(Farb et al., 2015); increasing or training one’s capacity for interoceptive experience may aid in

16 the disengagement or de-habituation from perseverative forms of self-reference that underlie maladaptive cognitive reactivity (Farb et al., 2011), which has important implications for treating disorders marked by PC. Clinical research supporting this notion, for example, indicates that experiential forms of self-awareness appear to be more adaptive than analytical forms of self- reference in recovered depressed patients (Watkins & Teasdale, 2004). Other research examining interoceptive attention, mostly in the context of meditation, indicates that it is an important element in stress reduction (Astin, Shapiro, Eisenberg, & Forys, 2003; Barnes, Powell-

Griner, McFann, & Nahin, 2004).

Given the damaging effect of PC on psychological and physiological health, a worthy endeavor is to improve existing treatments by exploring effective techniques for disengaging, or

“unsticking,” from such modes of thinking. Current data suggest promising effects of, for example, distraction (Hilt & Pollak, 2012), worry postponement (Brosschot & van der Doef,

2006), written exposure (Goldman, Dugas, Sexton, & Gervais, 2007), attentional training

(MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002), cognitive control training

(Siegle et al., 2007), and subliminal evaluative conditioning (Dijksterhuis, 2004). Another promising intervention is Mindfulness Based Stress Reduction (MBSR; Kabat-Zinn, 1990), in which individuals develop enhanced attentiveness to moment-to-moment experience through mindfulness meditation over the course of eight weeks. Proposed mechanisms by which mindfulness promotes salutary effects include decentering of experience (e.g., Bernstein et al.,

2015; Fresco et al., 2007), increases in attention and emotion regulation (Hölzel et al., 2011), and the development of focused attention to the body’s internal signals, or interoception (Farb, Segal,

& Anderson, 2012). The purpose of the current study is to investigate the effectiveness of interoceptive attention on the reduction of PC.

Current Study

In summary, the demonstrated detrimental effects of PC provokes a need to specifically target such processing in hopes of optimizing treatment, reducing relapse, and improving overall psychological and physiological well-being. Prior research establishes interoception as a critical capacity for well-being, and more recently, as a potential foundation by which mindfulness practice promotes cognitive change. Although previous studies have demonstrated that worry and rumination can be reduced using various methods (e.g., worry-postponement, written exposure, and subliminal evaluative conditioning), increasing one’s interoceptive ability may not only reduce PC, but may also enhance decision making, emotion and affect regulation, and promote overall greater awareness of one’s own internal experience – benefits which, arguably, may not manifest via distraction or postponement of PC. However, such an inquiry would first require evidence showing that explicit focus of attention on interoceptive cues reduces PC.

Furthermore, although treatments (e.g., MBSR) incorporating aspects of interoception have been successful in reducing perseveration, no study that we are aware of has endeavored to isolate attention to interoceptive cues and examine its effect on PC.

Toward this aim, the purpose of the current study was to examine whether PC (assessed via thought probes) during a mind-wandering task may be reduced via a brief interoception attention manipulation. As an initial step in determining the relevancy of this intervention in patient populations, we endeavored to first conduct this manipulation using an analogue sample to potentially establish a basis for further investigation. In line with previous research revealing distinct, though interrelated modes of self-reference (i.e., narrative vs. experiential self-focus;

Farb et al., 2007) we decided to compare attention to interoceptive cues (i.e., experiential self- focus) to a condition in which participants are instructed to direct attention to non-negative, self-

18 referential information (i.e., narrative self-focus). As an additional control, a third condition was introduced in which participants are instructed to direct attention to non-negative, non-self- referential information. Previous research suggests that engaging interoception processes is beneficial, in part, because it is self-focused and thereby increases self-awareness. However, it remains untested whether focusing attention on interoceptive information is relatively more beneficial than simply directing attention to other non-negative, self-referential information.

Hypotheses

1. Individuals assigned to the interoception condition will report less PC during the mind-

wandering task, relative to each of the other two conditions.

2. Participants undergoing the interoception condition (relative to each of the other two

conditions) will report that their thoughts are less intrusive and “sticky” (i.e., less

difficulty in disengaging from thoughts).

Method

Participants

Data were collected from a sample 159 undergraduate students. Participants were recruited using the Kent State SONA student pool, and all participants received compensation in the form of course credit. All interested and willing participants provided written informed consent before undertaking any study procedures, and all procedures complied with the local

Institutional Review Board. Eligible participants were English-speaking individuals between the ages of 18 to 25. In the interest of preserving external validity and ensuring variability within perseverative cognition, participants were not excluded on the basis of self-reported psychiatric disorders or elevated mood or anxiety symptoms. This recruitment strategy has been used successfully in previous work (Lackner & Fresco, 2016).

Procedure

Following consenting procedures, participants completed a series of questionnaires

(described below; approximately 30 minutes), presented using Qualtrics software (Provo, Utah,

USA). Next, participants completed a reading span (RSPAN) task that is widely used as an index of working memory capacity (Daneman & Carpenter, 1980). After these baseline measures, participants underwent random assignment to one of three manipulations of attention:

1) Attention to interoceptive information; 2) Attention to non-negative, self-referential information; or 3) Attention to non-negative, non-self-referential information. Following the

20 experimental manipulation, participants completed a mind-wandering task that included thought probes to measure PC (i.e., rumination and worry). A live-feed camera (without recording) during the experiment was used to note participant compliance. Total study time ranged from

1.5 to 2 hours. Figure 1 depicts a flow diagram of study procedures.

Questionnaires

Participants completed a demographics questionnaire, in addition to questionnaires to assess anxiety (Generalized Anxiety Disorder Questionnaire for DSM-IV; Newman, Kachin,

Constantino, Przeworski, Erickson, & Cashman-McGrath, 2002), three dimensions of the tripartite model (Depression Anxiety Stress Scales 21; Lovibond & Lovibond, 1995), rumination

(Ruminative Response Scale; Nolen-Hoeksema & Morrow, 1991), and worry (Penn State Worry

Questionnaire; Meyer, Miller, Metzger, & Borkovec, 1990). Participants also completed questionnaires that are not included in the present analysis.

The Generalized Anxiety Disorder Questionnaire - IV (GAD-Q-IV; Newman et al., 2002) is a 9-item self-report questionnaire that reflects the criteria for GAD as delineated in DSM-IV-

TR (American Psychiatric Association, 2000). Most items are dichotomous and measure the excessive and uncontrollable nature of worry as experienced by persons with GAD and related physical symptoms. The GAD-Q-IV demonstrates high concordance with a diagnosis of GAD yet is uncorrelated with conceptually unrelated measures (Newman et al., 2002). Findings indicate that a 7.67 represents the optimal cut score when screening individuals likely to meet clinician assessed diagnosis of GAD (Moore, Anderson, Barnes, Haigh, & Fresco, 2013).

The Depression Anxiety Stress Scales 21 (DASS-21; Lovibond & Lovibond, 1995) is a

21-item questionnaire yielding three sub-scales of seven items each: depression, anxiety, and stress. Each item is rated from 0 (did not apply to me at all) to 3 (applied to me much, or most of

21 the time), with a maximum score of 42 (i.e., each scale is multiplied by 2 to make scores comparable to the original 42-item version). The DASS-21 has excellent internal consistency

(Cronbach’s alpha = .93) and exhibits high convergent validity with other measures of anxiety and depression (Henry & Crawford, 2005).

The Ruminative Response Scale (RSS) is a 22-item subscale of the Response Styles

Questionnaire (RSQ; Nolen-Hoeksema & Morrow, 1991) that assesses a person’s tendency to ruminate when distressed. Participants rate their agreement with statements on a Likert-type scale ranging from 1 (almost never) to 4 (almost always). The items describe responses to depressed mood that are self-focused, symptom-focused, and focused on the possible consequences and causes of their mood. The RSS features two subscales that capture distinguishable components of rumination: reflective pondering and brooding. Notably, the brooding scale is less confounded with depression and more useful in gaining a finer understanding of the relationship between rumination and depression (Armey et al., 2009;

Treynor, Gonzalez, & Nolen-Hoeksema, 2003). The RSS has demonstrated high internal consistency (Cronbach’s alpha ~ .90) as well as acceptable convergent and predictive validity

(Butler & Nolen-Hoeksema, 1994; Nolen-Hoeksema & Morrow, 1991).

The Penn State Worry Questionnaire (PSWQ; Meyer et al., 1990) contains 16 items rated on a scale from 1 (Not at all typical of me) to 5 (Very typical of me) and assesses the extent to which worry is excessive, uncontrollable, and pervasive. It has good internal consistency among individuals with GAD, other anxiety disorders, and no diagnosis (Brown, Antony, & Barlow,

1992) and good test-retest reliability across intervals ranging 8-10 weeks (Meyer et al., 1990).

The PSWQ is significantly correlated with other measures of worry (e.g., Davey, 1993) and is sensitive to change following treatment among individuals with GAD (Meyer et al., 1990).

Working Memory Capacity

Participants completed a reading span task (RSPAN) aimed at measuring working memory capacity (Daneman & Carpenter, 1980). For each trial, participants are presented with a string of four to six letters and asked to remember the letters in order. While holding the letters in memory, they are then asked to judge whether sentences make sense (e.g., “The prosecutor’s dish was lost because it was not based on fact.”). Finally, participants are asked to identify the correct string of letters. Participants completed the shortened version (i.e., 30 trials) of the

RSPAN (e.g., Oswald, McAbee, Redick, & Hambrick, 2015), in which partial RSPAN scores indicate the additive number of correctly recalled trials throughout the task. Previous data indicate that scores on the RSPAN correlate with a wide range of higher order cognitive tasks, such as reading and listening comprehension, language comprehension, vocabulary learning from context, note taking in class, writing, reasoning, hypothesis generation, and complex task learning (see Conway et al., 2005 for a review).

Experimental Attentional Manipulation Conditions

Participants were randomly assigned to one of three experimental manipulations of attention:

(1) Attention to interoceptive information (“interoception condition”). In this condition, participants completed a 15-minute mindful body breathing (MBB) exercise, a practice utilized in Emotion Regulation Therapy (ERT; Mennin & Fresco, 2013). The MBB exercise (delivered via audio) aims to train attention as well as a focus and awareness to the body. Notably, ERT treatment has been linked to increases in self-reported interoception, particularly within the first half of treatment following the introduction of MBB and similar exercises. During the exercise, participants first focus on breathing, after which the exercise

23 invites participants to allocate their attention to different parts of their body, imagining that they are breathing from these parts. In doing this, the MBB exercise cultivates an awareness of the breath and body. Appendix A provides the full MBB script.

(2) Attention to non-negative, self-referential information (“self condition”).

Participants completed a task that is used widely throughout the literature to engage self- referential thinking (e.g., Craik et al., 1999; Fossati et al., 2003; Gutchess et al., 2007; Lou et al.,

2004). The task was delivered in auditory format so as to equate it with the interoception condition. In this condition, participants were asked to think about the degree to which a list of neutral personality trait adjectives described them. Words comprising the list were constructed using a pool of normalized personality trait adjectives assembled by previous research

(Anderson, 1968), in which words are ordered according to their likability ratings. Previous studies (e.g., Fossati et al., 2003) used the top and bottom 20% of the list to select positive and negative words, respectively. Therefore, we sampled from the middle 80%. Participants were encouraged to think about each word carefully and reflect on the extent to which it describes them for approximately 15 minutes. Appendix B provides a script.

(3) Attention to non-negative, non-self-referential information (“other condition”).

In this condition, participants were presented with the same materials as condition (2); however, they were asked to rate how much the word describes a famous person that they know (e.g.,

Oprah Winfrey). This task is typically presented in the context of condition (2), as described above, and neuroimaging results indicated that performing this task did not selectively engage cortical midline regions, unlike condition (2) (Craik et al., 1999; Fossati et al., 2003; Gutchess, et al., 2007; Lou et al., 2004). Appendix C provides a script.

Mind-wandering and Perseverative Cognition

Aspects of PC were measured via thought probes interspersed within a go/no-go sustained attention task, which has been used frequently throughout the literature to study mind- wandering (e.g., McVay & Kane, 2013; Smallwood et al., 2004; 2009; van Vugt & Broers,

2016). The Sustained Attention to Response Task (SART; Roberstson, Manly, Andrade,

Baddeley, & Yiend, 1997) requires participants to inhibit a response to a target stimuli while responding to non-target stimuli, and its monotonous nature provides strong external validity

(Smallwood et al., 2009), as errors on the SART task have been associated with absentmindedness (Robertson et al., 1997). Additionally, errors on the SART task corresponded to subjective markers of mind-wandering in electroencephalography studies which used the amplitude of the P3 component to index off-task thinking (Smallwood, Beech, Schooler, &

Handy, 2008).

Behavioral incidences of mind-wandering were measured using a variant of the SART employed by Smallwood and colleagues (2009) and van Vugt and Broers (2016). Throughout the course of 15 minutes, participants are asked to respond as quickly and accurately as possible to non-target stimuli (“O”) by pressing the spacebar and inhibit their response when presented with a target stimulus (“=”). The target is presented on approximately 10% of all trials. Each trial begins with a 250 ms fixation cross, followed by a 250 ms blank screen. Stimuli are presented for 2000 ms, during which participants may indicate their response, with an interstimulus interval of 2000 ms. Notably, McVay and Kane (2013) discovered that longer

(e.g., two second) intervals between stimuli are more likely to induce task-unrelated thought compared to shorter intervals (e.g., 500 ms) typically used in SARTs. Mind-wandering is

25 indicated by errors of commission (i.e., erroneously pressing the spacebar when presented with the target stimulus; (Smallwood et al., 2009).

Subjective incidences of mind-wandering (including PC) were measured by thought probes interspersed during the SART. Derived from previous research (e.g., McVay et al., 2013;

Ottaviani et al., 2013; Smallwood et al., 2009; van Vugt & Broers, 2016), each probe inquires about participants’ thoughts in terms of content, temporal orientation, intrusiveness, and difficulty disengaging from the thoughts (i.e., “stickiness”). Specifically, for each thought probe, participants are asked to characterize their ongoing consciousness just prior to the probe, among the following: (1) focused on the task, (2) worrying about a future event, (3) ruminating about a past stressful event, (4) mind-wandering, or (5) distracted by external stimuli (noise, etc.).

Responses (2) and (3) are classified as PC. If participants answer (1), then they are asked to rate their level of focus on a likert scale ranging from 1 (not focused) to 5 (very focused), as well as how engaged they are from 1 (not engaged) to 5 (very engaged). If participants’ answers indicate PC, two follow-up ratings are presented: how intrusive and how difficult it was to disengage from the thought (i.e., “stickiness”), both answered on a likert scale ranging from 1

(very intrusive/difficult) to 5 (not intrusive/easy). If participants select (4), then they are asked whether they were thinking about (a) everyday stuff (i.e., recent or impending life events or tasks), (b) current state of being (i.e., conditions such as hunger or sleepiness), (c) daydreaming

(i.e. having fantasies disconnected from reality), or (d) other (McVay & Kane, 2013). They are then asked to what degree they were preoccupied with the thought, from 1 (not that preoccupied) to 5 (very preoccupied). If participants answer (5), then they are asked to rate their level of distraction from 1 (a little distracted) to 5 (very distracted) and the degree to which the distraction affected their performance from 1 (not at all) to 5 (very much). The follow-up

26 questions for responses (1), (4), and (5), which are not relevant to exploring the hypotheses of the current study, were primarily included to equate the degree of effort between selecting any given response to the thought probe. A total of 20 probes were collected, randomly occurring at either 30, 60, or 90 seconds intervals. For each person, a PC score – the main dependent measure of the current study – is calculated to reflect the number of reported instances of rumination or worry, with a maximum score of twenty.

Prior to the task, the experimenter confirmed that the participant understood which kinds of thoughts qualify as responses (1) through (5) by providing common definitions derived from the literature and asking for examples (e.g., McVay & Kane; Ottaviani et al., 2013).

Results

Power Analysis

In the absence of a priori estimates of a true effect, we calculated the sample size required to detect a medium effect size for our primary hypothesis (i.e., Hypothesis 1), which states that individuals assigned to the interoception condition would report less PC during the SART, as compared to individuals in the self condition and other condition. Optimally, with Power = .80, sensitivity analyses for a linear multiple regression with one tested predictor indicated that detecting a main effect with a medium effect size (f2 = .15; Cohen, 1992) required 54 participants. However, because we did not have a priori estimates of a true effect, in addition to the variability that exists in the current study, we sought to overpower the test of this hypothesis substantially by collecting data from at least 150 participants. Specifically, a sample size of 150 allowed us to detect our main effect of interest with an effect size as small as f2 = .05.

Data Cleaning Procedures

Item level analysis indicated less than 3% missing data across all questionnaires, ranging from 0% to 2.5% on any given questionnaire. Little’s Missing Completely at Random Test

(Little, 1988) confirmed that the data were missing completely at random, χ2 = 3225.69, df =

3185, p = .303; thus, missing values on questionnaires were replaced using Expectation-

Maximization (Allison, 2002). Due to technical failure, data from one participant was lost on the

SART task and this case was excluded from analysis.

Outliers were considered to be Z-scores that exceeded 3.5 SDs from the mean (Hampel,

1985). One participant produced a score on the SART that exceeded 4.5 SDs from the mean.

Although the score was theoretically possible, it was removed to prevent substantial influence on the overall sample mean. Sample distributions were analyzed via skewness [-2, 2] and kurtosis

[-7, 7] values. All questionnaires displayed a normal distribution, and the distribution of PC scores within each group also displayed normal distribution indicated by skewness and kurtosis.

Participants (n = 14) who did not achieve at least 85% accuracy in processing trials on the

RSPAN were removed from all analyses using RSPAN scores as a variable of interest (Oswald et al., 2015).

Further inspection of the dependent measure of interest (i.e., PC scores), indicated abnormal residual plots, a low mean (1.49), a low range of scores [1,7], and a mode of 0.

Specifically, 40.51% of participants reported 0 instances of PC in response to the thought probes.

In such cases where the mean is low and the data consist of mostly low non-negative integer values (i.e., “count data”; Gardner, Mulvey, & Shaw, 1995), a model with a Poisson or negative binomial distribution is preferred to an ordinary least-squares linear (OLS) model (Cameron &

Trivedi, 2013). An additional benefit of analyzing the data using a Poisson or negative binomial distribution is that it can account for excess zeros in the data, which usually cause problems of overdispersion in the variance (Greene, 1994; Long, 1997). Thus, for all subsequent analyses in which PC scores were the dependent measure, we chose to treat the data as count data and use a regression model with a Poisson distribution or negative binomial distribution.1

1 Originally proposed OLS analyses for the main dependent measure are available in Appendix D. Note: Results of the current study remained consistent when comparing the OLS to the Poisson and negative binomial distribution models. 29

After data cleaning procedures, the sample size for all subsequent analyses was N = 158, including 53 participants in each of the self and other conditions and 52 participants in the interoception condition.

Sample Descriptive Statistics

Demographics. Analyses were conducted on data from one hundred fifty-eight participants. One hundred twenty-nine participants identified as female, twenty-seven participants identified as male, and two participants identified as “other.” The average age of participants was 19.42 years (SD = 1.66). Participants described themselves as White (80.5%),

Black (8.2%), Asian (1.9%), Native American (1.3%), Latino(a)/Hispanic (3.8%), Biracial

(2.5%), or Other (1.9%). The sample was predominantly right handed (89.9%). Sixteen participants (10%) self-reported current use of psychiatric medication, and forty-one participants

(25.9%) self-reported a current psychiatric diagnosis, including diagnosed or treated depression

(n = 6), anxiety disorder (n = 17), or comorbid depression and anxiety disorders (n = 18).

Questionnaire data. On average, participants in the current sample evidenced high baseline levels of anxiety, as they scored within the “severe” range (8+; Lovibond & Lovibond,

1995) on the DASS-21 Anxiety Subscale (M = 8.87, SD = 8.46). However, dimensional scores on the GADQ-IV, on average, did not exceed the recommended cut-off score (7.67; Moore,

Anderson, Barnes, Haigh, & Fresco, 2013) for identifying individuals likely to meet a clinician assessed diagnosis of GAD (M = 5.92, SD = 3.54). The number of individual participants who met each cut off score were as follows: n = 58 (GADQ-IV), n = 41 (DASS-21 Depression

Subscale), n = 71 (DASS-21 Anxiety Subscale), n = 69 (DASS-21 Stress Subscale). The characteristics of this sample are comparable to previous data collected from a similar sample of

30 undergraduate students (Lackner & Fresco, 2016). Table 1 reports correlations, descriptive statistics, and internal consistencies (Cronbach’s alpha) among all questionnaires.

Working memory capacity. On average, participants received a partial score of 21.79

(SD = 5.62) on the RSPAN. Although there are no normative data available for the short version of the RSPAN, scores in the current study were similar to previously reported scores of undergraduate students (e.g., Oswald et al., 2015; M = 23.25, SD = 4.78). Scores on the RSPAN did not evidence significant correlations with any of the administered questionnaires.

Thought probe responses. In response to the twenty thought probes delivered during the SART, participants, on average, indicated they were focused on the task (51.01%; M = 10.20;

SD = 5.50), worrying (5.35%; M = 1.07; SD = 1.27), ruminating (2.12%; M = .42; SD = .80), mind-wandering (35.45%; M = 7.09; SD = 4.80), or distracted by external stimuli (6.05%; M =

1.21; SD = 1.52). PC scores were calculated as the total number of times that rumination or worry was selected as a response to the thought probes, with the maximum score being twenty.

PC scores (M = 1.49, SD = 1.70) ranged from 0 to 7, with a mode of 0 for the entire sample and a mode of 1 for those reporting at least one instance of PC. For participants who indicated some form of perseveration (i.e., rumination or worry; 59.49% of sample), on a 5-point scale – ranging from 1 (very intrusive/difficult) to 5 (not intrusive/easy) – the average intrusiveness of thought rating was 2.76 (SD = .87), and the average stickiness of thought rating was 2.68 (SD = 1.08).

Table 2 reports intercorrelations between thought probe responses.

Tests of Baseline Differences between Groups

Groups did not differ with respect to age, sex, handedness, current psychiatric diagnosis, or current psychiatric medication use. Groups did not differ with respect to working memory

31 capacity as measured by the RSPAN. Finally, groups did not differ with respect to scores on the

GADQ-IV or DASS-21.

We also tested whether groups differed with respect to trait rumination and worry. Given previous literature showing that rumination and worry are derivative of a common factor (e.g.,

Fresco et al., 2002), we sought to explore the evidence for creating a perseveration composite score using items from the Brooding Subscale of the RSQ-RSS and PSWQ, which were significantly correlated (r = .62, p = .000).

Previous factor analysis of the PSWQ, originally conceptualized as unifactorial, indicates that using only the 11 positively worded items avoids the problem of extracting a potentially spurious second factor that is instead better characterized as a negative method factor (Hazlett-

Stevens, Ullman, & Craske, 2004). Therefore, we used the abbreviated, positively worded 11- item version in subsequent analysis. Reliability analysis of the 11-item version indicated strong internal consistency in the current sample (Cronbach’s alpha = .94).

The 11 positively-worded items from the PSWQ and the 5 items from the RSQ-RSS

Brooding subscale were submitted to a principle components analysis with direct oblimin rotation to allow for the anticipated correlation between factors (Costello & Osborne, 2005). A clear two-factor solution emerged with eigenvalues values greater than one. All items loaded onto only one factor, and all factor loadings exceeded 0.6. Table 3 reports the pattern matrix with each factor loading. Factor 1 (eigenvalue = 8.82) consisted of the 11 positively coded

PSWQ items and accounted for 55.09% of the variance. Factor 2 (eigenvalue = 1.56) consisted of all 5 items of the RSQ-RSS Brooding subscale and accounted for 9.72% of the variance.

Factor scores were computed from this two factor solution. A zero-order correlation between the two factors indicated a strong positive correlation with each other, r = .65, p = .000.

Using the structural equation modeling software program MPlus (Muthen & Muthen,

2007) a structural model with items from the two factor solution in the previous analysis was tested for the presence of a single latent higher-order factor. This model yielded a significant chi-square statistic [χ2 (102) = 191.11, p < 0.00001], indicating a relatively poor fit. However, the χ2 statistic may overstate the lack of model fit in large sample sizes (Bollen, 1989). More commonly used are the guidelines provided by Hu and Bentler (1999), which suggest using the maximum likelihood (ML)-based standardized root mean squared residual (SRMR) in tandem with either the Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), or root mean squared error of approximation (RMSEA). Specifically, they suggest that, for the ML method, a cutoff value of .95 or above on either the TLI or CFI, in combination with either a cutoff value close to

.08 or below for SRMR, or a cutoff value close to .06 or below for RMSEA are needed to conclude that there is a relatively good fit between the hypothesized model and the observed data. According to the recommendations by Hu and Bentler (1999), the higher-order model of perseveration with two first order factors of rumination and worry indicated a good fit to the overserved data (CFI = .950; TLI = .940; SRMR = .044; RMSEA = .074). Thus, we proceeded to compute a perseveration composite score using items from the Brooding Subscale of the RSQ-

RSS and PSWQ. Notably, groups did not differ with respect to baseline levels of perseveration.

Figure 2 depicts the higher order-model tested.

Hypothesis 1

Hypothesis 1 posited that individuals assigned to the interoception condition would report less PC during the SART, compared to individuals in the self condition and other condition. As noted, PC scores exhibited a low mean and a low range of non-negative integer values; thus, the

PC scores better reflected count data and were more appropriately analyzed using a model with a

Poisson distribution, as opposed to using an OLS model (e.g., Cameron & Trivedi, 2013;

Gardner, Mulvey, & Shaw, 1995).

First, a Poisson regression model (PRM) was computed to evaluate Hypothesis 1, using the interoception group as the reference. Goodness of fit indices revealed a high degree of overdispersion (i.e., a variance lager than predicted by the Poisson distribution; Pearson

Dispersion Statistic = 2.044), indicating a violation of an assumption in Poisson regression in which the mean must equal the variance, known as “eqidispersion” (i.e., a Pearson Dispersion

Statistic = 1; Gardner et al., 1995). One known source of overdispersion is when the dependent measure contains a high frequency of zeros (Gardner et al., 1995), which is reflective in the current data. Thus, to address this issue, we used a negative binomial regression model (NBRM) which employs an extra parameter to model overdispersion in variance (Long, 1997). Goodness of fit statistics revealed that the NBRM adequately addressed the problem of overdispersion

(Pearson Dispersion Statistic = 1.008), indicating a good fit of the model. Furthermore, a comparison of the Akaike’s Information Criterion (AIC; Akaike, 2011) revealed superior fit of the NBRM (533.76) to the PRM (574.29). The omnibus test for the NBRM indicated that the independent variable of condition was not a significant predictor of PC, Likelihood Ratio Chi-

Square = 2.89, df = 2, p = .235, McFadden’s (1974) Pseudo R2 = .005. Table 4 indexes responses to thought probes and PC scores sorted by condition. Table 5 presents the NBRM for

Hypothesis 1.

Hypothesis 2

A linear multiple regression with dummy-coding (using the interoception group as the reference) was calculated to evaluate Hypothesis 2 that participants randomized to the interoception condition would report that their thoughts are less intrusive and “sticky” compared

34 to reports of participants from the self and other conditions. It was appropriate to use OLS for this analysis, as it excluded individuals who did not report PC, and subsequently conformed to a more normal distribution. Furthermore, average intrusiveness and stickiness ratings were not count data (i.e., integers). For participants who reported at least one instance of PC during the

SART [n = 94; interoception condition (n = 37); self condition (n = 27); other condition (n =

30)], there were no significant group differences in reported intrusiveness of thought, F(2,91), =

.732, p = .484, R2 = .016, or reported stickiness of thought, F (2,91), = .250, p = .779, R2 = .005.

Tables 6 and 7 present the regression models for Hypothesis 2.

Exploratory/Post-hoc Analyses

In light of the null findings from a priori hypotheses, we conducted a series of post-hoc analyses to better understand the data. First, to investigate the potential moderating effect of working memory capacity on the effect of interest (i.e., Hypothesis 1 and 2), we used RSPAN scores. A NBRM was used to first test for a moderating effect of RSPAN scores on the relationship between experimental group and PC scores; the model showed good fit (Pearson

Dispersion Statistic = 0.998), but failed to find a moderation effect, Likelihood Ratio Chi-Square

= 1.313, df = 3, p = .726, McFadden’s Pseudo R2 = .007. A linear multiple regression was used to test for a moderating effect of RSPAN scores on the relationship between experimental group and reported thought intrusiveness and stickiness. Scores on the RSPAN did not moderate the association between group membership and reported thought intrusiveness [F(5,81) = .374, p =

.866, R2 = .023] or thought stickiness [F(5,81) = .347, p = .883, R2 = .021].

Second, we examined group differences on errors of commission (i.e., erroneously pressing the spacebar when presented with the target stimulus during the SART), which indicate mind-wandering (Smallwood et al., 2009). Overall, the sample was highly accurate during the

35 task, correctly responding to 95.95% of target stimuli. This behavioral index of mind-wandering did not differ between groups, F (2,157) = .333, p = .717, R2 = .004. Accuracy on the SART was not related to reported PC for the thought probes (r = -.12, p = .179).

Third, we examined differences in mind-wandering for thought probes between groups.

No differences emerged between groups, F(2,157) = .821, p = .442, R2 = .010. We then recoded mind-wandering to include instances of rumination and worry, given that PC has been viewed essentially as a particular type of mind-wandering (e.g., Ottaviani et al., 2013); this recoding would also correct for any mischaracterizations of PC as mind-wandering on behalf of the participants. Using the re-coded mind-wandering variable, there were no group differences for thought probe responses, F(2,157) = .241, p = .786, R2 = .003.

We also formed dichotomous, clinical groups based on self-reported current psychiatric diagnosis as well as clinical groups formed on the basis of cut scores of the GADQ-IV, DASS-

21, and PSWQ to determine whether these groups (regardless of experimental group assignment) were associated with increased PC during the SART. As stated, we used the recommended cut score for the GADQ-IV (7.67; Moore et al., 2013) and DASS-21 depression (11+), anxiety (8+), and stress (13+) scales corresponding to categorization as “severe” (Lovibond & Lovibond,

1995). For the PSWQ, exact cut-off scores are less agreed upon; however, previous reports of

PSWQ scores in GAD analog clinical samples have typically ranged from 65-67 (e.g., Molina &

Borkovec, 1994). Thus, we categorized individuals as likely to receive a diagnoses of GAD if they scored 65 and above on the PSWQ (n = 39).

Independent samples t-tests were used to examine whether clinical groups differed with respect to responses for thought probes during the SART. A NBRM was used in the event that the dependent measure was rumination, worry, or total PC score, which all resembled count data

36 with a Poisson or negative binomial distribution (i.e., low means with low range of non-negative integer scores). PC scores did not differ with respect to current self-reported psychiatric diagnosis or cut-scores on the GADQ-IV, DASS-21 Subscales, or PSWQ. No differences were found with respect to reporting worry during the SART.

Individuals scoring above the cut-off on the GADQ-IV exhibited a trending effect of reporting more mind-wandering overall (including instances of PC) compared to individuals scoring below the cut-off (M = 9.60, SD = 5.14 vs. M = 8.00; SD = 5.26) during the SART, t(156) = -1.86, p = .065. Individuals scoring above the cut-off on the DASS-21 Anxiety

Subscale reported more rumination during the SART, compared to individuals scoring below the cut-off (M = .58; SD = .95 vs. M =.29, SD = .63), Likelihood Ratio Chi-Square = 5.005, df = 1, p

= .025, McFadden’s Pseudo R2 = .018. Specifically, individuals scoring above the cut-off on the

DASS-21 Anxiety Subscale were 1.932 (95% CI, 1.079 to 3.461) times more likely to ruminate during the SART than those scoring below the cut-off. Similarly, individuals scoring above the cut-off on the DASS-21 Stress Subscale were also 2.034 (95% CI, 1.135 to 3.645) times more likely to ruminate during the SART than those scoring below the cut-score, Likelihood Ratio

Chi-Square = 5.814, df = 1, p = .016, McFadden’s Pseudo R2 = .021. Additionally, individuals scoring above the cut-off on the DASS-21 Stress Subscale reported more mind-wandering during the SART (including PC) than those scoring below the cut-off (M = 10.04; SD = 5.02 vs. M =

7.46, SD = 5.19), t(156) = -3.16, p = .002, d = .51. Finally, individuals scoring above the cut-off on the PSWQ reported more mind-wandering (including PC) during the SART, compared to individuals scoring below the cut-off (M = 9.92, SD = 4.45 vs. M = 8.15, SD = 5.44), t(156) = -

2.04, p = .045, d = .36.

Of the participants who reported at least one instance of PC, individuals scoring above the GADQ-IV cut-off rated their thoughts as more intrusive compared to their below cut-off counterparts (M = 2.98, SD = .89 vs. M = 2.61, SD = .82), t(92) = -2.03, p = .046, d = .43. This effect was trending for participants who scored above the PSWQ cut-off, t(92) = -1.80, p = .076.

Finally, of interest, PC composite scores were significantly related to both rumination (r = .20, p

= .011) and mind-wandering (r = .17, p = .032) during the SART.

Discussion

The purpose of the current study was to examine whether PC could be reduced via a brief interoception attention manipulation. Although established treatments (e.g., MBSR) that focus on increasing interoceptive awareness have been successful in reducing PC, studies to date have not endeavored to isolate attention to interoceptive cues and examine its effect on PC. We hypothesized that: 1) Individuals randomized to the interoception condition would report less PC during the SART, compared to individuals in the self condition and other condition; and 2)

Individuals randomized to the interoception condition would report that their thoughts were less intrusive and “sticky” compared to reports of participants from the self and other conditions.

Results of the current study did not support either hypothesis.

The novelty of the current study was that it sought to experimentally manipulate attention to interoceptive cues and compare this instruction to other forms of non-negative, self- and non- self-referential processing to determine whether attention to interceptive cues was associated with reduced perseverative cognition in an un-cued mind-wandering paradigm. On account of the null findings from a priori hypotheses, a series of post-hoc analyses were conducted to better understand the data and possibly provide an explanation for the null results. These analyses were conducted in a theoretically motivated manner. First, we examined any influence working memory capacity on the a priori hypothesized effects due to research linking attentional control, or aspects of executive functioning with depression and perseveration (e.g., Koster et al., 2011).

Follow-up analyses indicated no influence of working memory capacity (indexed by RSPAN scores). Next, we created post-hoc clinical groups based on cut scores of commonly used psychopathology measures, as scoring above these cut scores is associated with clinically relevant symptoms, including perseveration (Lovibond & Lovibond, 1995; Meyer et al., 1990;

Newman et al., 2002; Nolen-Hoeksema & Morrow, 1991). Compared to the randomized experimental groups, creating post-hoc groups according to clinical cut scores on commonly used measures proved to be somewhat more predictive of PC and mind-wandering reported for thought probes. As noted, individuals scoring above the cut-off on the DASS-21 Anxiety

Subscale and Stress Subscale (relative to those scoring below the cut-off), reported significantly more rumination and mind-wandering for thought probes, respectively, and individuals scoring above the cut-off on the PSWQ reported more mind-wandering (including PC) for thoughts probes. Notably, individuals scoring above the GADQ-IV cut-off rated their thoughts as more intrusive compared to their below cut-off counterparts, which corresponds to previous research pointing to GAD as a condition especially marked by disruptive forms of NSRP (Mennin &

Fresco, 2013; Watkins, 2008). However, these analyses were exploratory and findings should be interpreted with caution.

Measurement and Task Considerations

Several possible explanations may account for the null findings from our a priori hypotheses. One point of inquiry is to consider various aspects of our thought probe task, which was the primary dependent measure modeled closely from previous research (Ottaviani et al.,

2013). In a sample of 73 healthy (i.e., no current/past psychopathology) university students,

Ottaviani and colleagues (2013) found that, on average, participants reported being focused on the task for 55.2% of probes (current study = 51%), distracted by external stimuli for 6.1% of

40 probes (current study = 6%), mind-wandering for 26.6% of probes (current study = 35.45%), and ruminating or worrying for 12.1% of probes (current study = 7.47%). Furthermore, Ottaviani and colleagues (2013) note that every participant (100%) reported at least one instance of PC in their study (current study = 59.5%). Thus, the main difference in results between the current study and that of Ottaviani and colleagues (2013) is higher rates of mind-wandering and lower rates of PC in the current study. Indeed, results of the current study showed that PC scores, for participants who reported PC, ranged from 1 to 7 (out of a possible maximum of 20) and the PC score mode was 1. Although we did not exclude for current or past psychopathology, one notable difference from Ottaviani and colleagues (2013) is that they experimentally induced PC, which may have resulted in higher rates of PC as opposed to mind-wandering. Another noteworthy consideration is that recent research has shown that people modulate their own mind- wandering in anticipation of changes in task demands (Seli et al., 2018). Thus, the intervals between thought probes in the current study (randomized to present every 30, 60, or 90 seconds) may have become too predictable, causing participants to modulate (i.e., reduce) their mind- wandering in anticipation of the next probe. Taken together, one explanation for our null results is that our main dependent measure evidenced a relatively restricted range of scores, which may have attenuated associations between variables (Sackett, Lievens, Berry, & Landers, 2007).

Another aspect to consider is the construct validity of our dependent measure (i.e., PC as measured by thought probes), particularly the intercorrelations among thought probe responses and the convergent validity with other measures of PC in the current study. With respect to intercorrelations among probe responses, ruminating and worrying evidenced a theoretically predicable, significant relationship (r = .30), as did mind-wandering and focused on the task (r =

-.89). With respect to convergent validity with other measures of PC, we found minimal

41 evidence that PC, as measured by thought probes, corresponded to other measures of PC in the current study. Specifically, the perseveration composite score (i.e., standardized scores on the

PSWQ and RSQ-RSS Brooding Subscale) was significantly related to both rumination (r = .20, p

= .011) and mind-wandering (r = .17, p = .032) thought probes. Additionally, individuals scoring above the cut score on the DASS-21 Anxiety Subscale and Stress Subscale, reported significantly more rumination and mind-wandering for thought probes, respectively. Finally, individuals scoring above the cut-off on the PSWQ reported more mind-wandering (including

PC) for thoughts probes. These findings provide some indication that PC, as measured by the thought probes, correlated with other measures of PC or psychopathology in a theoretically predictable manner.

Although participants verbally conferred their understanding of the differences between rumination, worry, and mind-wandering, it is possible that conflating these definitions during the

SART task led to spurious results. The differences between mind-wandering and PC may be subtle, and previous studies have generally viewed PC as a subset of task unrelated thought (e.g.,

Smallwood et al., 2009), or as existing on a “mind-wandering-perseverative cognition” continuum (Ottaviani et al., 2013). Consequently, participants may be unaware of, and unable to report, the exact point at which mind-wandering becomes “stuck” in a maladaptive pattern.

Assuming that participants may have mistakenly categorized PC as mind-wandering in the current study, experimental groups still did not differ with respect to mind-wandering in a broader sense (i.e., when PC was coded as mind-wandering). To better distinguish between instances of PC and mind-wandering, more rigorous, objective measures may be needed. For instance, emerging evidence indicates that PC is associated with a particular autonomic signature, mostly notably increased heart rate and decreased heart rate variability (see Ottaviani

42 et al., 2016 for a review and meta-analysis), which may be useful indicators to corroborate self- reported PC.

Relatedly, responses to thought probes are essentially self-reports, and thus are subject to the same limitations as any other questionnaire with a specific prompt and constrained responses.

Biases in responses may be influenced by features of the research instrument, including the wording of the question, format, and context, in addition to social desirability and demand characteristics (Schwarz, 1999). In a comprehensive discussion of the science of mind- wandering, Smallwood and Schooler (2015) catalog several approaches to experience sampling

(ES) and note that the probe-caught method (used in the current study) is the most common method, with studies showing greater reports of off-task thought with larger gaps between probes. Although participants in the current study reported mind-wandering for 35.45% of probes, a longer duration between probes may serve to increase this percentage, thereby increasing the opportunity for higher rates of PC. Other methods of ES include participants spontaneous providing ES reports (“self-caught” method), gathering ES data at the end of a task

(“retrospective method”), or asking participants to describe in their own words what they experienced (“open-ended” method). Exploring other methods of ES may be fruitful in better uncovering the covert nature of mind-wandering. Smallwood and Schooler (2015) note that external measures may be necessary to ensure that results are not simply a consequence of the limitations of self-report. Thus, advances in capturing and understanding task-unrelated activity of the brain (i.e., default mode network; Buckner et al., 2008; Raichle et al., 2001) may eventually provide an objective taxonomy for categorizing inner experience.

Conceptual and Design Considerations

Differences in PC scores between experimental groups unexpectedly showed that individuals in the interoception condition evidenced the highest levels of PC, though the comparison did not reach the level of statistical significance. Although interpretations of null results are unwarranted, a reconsideration of the potential effects of increased (trained) interoception may be instructive. Previous literature suggests that interoception assists individuals in cultivating more adaptive ways of relating to self-relevant information (Mennin et al., 2013; Teasdale, 1999), amplifies awareness of present moment experience (Tang et al.,

2015), and enhances affect and emotion regulation (Füstos et al., 2013; Sze et al., 2010) and information processing in the brain (Kerr et al., 2013). Furthermore, proposed frameworks of mindfulness suggest that it develops meta-awareness of self (i.e., self-awareness) along with an ability to effectively manage or alter one’s responses and impulses (i.e., self-regulation; Vago &

Silbersweig, 2012). In light of these considerations, it is possible that training individuals to increase awareness of interoceptive signals may also increase awareness of other self-related processes, which could include awareness of thought processes characterized as PC. In other words, interoceptive training may not necessarily cause individuals to experience more PC, but may increase the ability to recognize PC in the moment, thereby increasing one’s ability to report it, while simultaneously viewing it as less intrusive and engaging. However, the current study failed to find support for the hypothesis that individuals in the interoception condition would experience PC as less intrusive and engaging, and the current study is unable to address the larger issue of causality.

Another factor potentially responsible for our null results is our experimental design of a single session, brief intervention. Prior research has established effects of brief interventions on

44 various cognitive processes. For instance, one study found that an 8-minute mindful-breathing training session reduced subsequent mind-wandering, when compared to control conditions of passive-relaxation or a reading task (Mrazek, Smallwood, & Schooler, 2012). Another study found that a 15-minute period of meditation practice reduced negative reactivity to repetitive thoughts during the session (Feldman, Greeson, & Senville, 2010). In other areas of research, brief meditation trainings have also been shown to reduce sunk-cost bias (i.e., the tendency to continue an endeavor once an investment has been made; Hafenbrack, Kinias, & Barsade, 2014) and reduce implicit race and age bias on an implicit association test (Lueke & Gibson, 2014).

However, studies focusing more explicitly on training interoception are limited. One study found no differences between a training and control group when feedback on a mental tracking task was used to improve interoception via heartbeat perception over the course of two sessions

(Schaefer, Egloff, Gerlach, & Witthöft, 2014). Conversely, successful efforts in training interoception have been more intensive, such as 20 minutes of body scanning daily for 8 weeks

(Flscher, Messner, & Pollatos, 2017), 3 months of contemplative training (Bornemann, Herbert,

Mehling, & Singer, 2015), or daily practice of 4 different kinds of medication across a 9 month training period (Kok & Singer, 2017). Thus, the intensity and length of training may be critical to increasing interoceptive capacity and subsequently affecting cognitive processes.

Additional Limitations

Taken together, limitations of the current study include a low occurrence, and thereby restricted range of PC in the sample, concerns of convergent validity of PC indexed by thought probes compared with other measures of PC, potential mischaracterization of thoughts by participants, the covert nature of the intended object of study, and the experimental design of a single session. Another limitation is the inability to truly detect fixed or random responding for

45 the thought probes, as any pattern of responses could be considered theoretically plausible. In terms of adherence to the study protocol, accuracy on the SART may represent overall engagement with the study. However, as noted, the sample was highly accurate during the task, correctly responding to 95.95% of target stimuli, which is approximately 3% less accurate compared to other samples (e.g., Ottaviani et al., 2013). Moreover, we did not find a significant correlation between SART accuracy and PC measured by thought probes. Another limitation of the current study is that the sample consisted of an overwhelmingly white (80.5%), female

(81.6%) sample of university students, 25.9% of which self-reported a current psychiatric diagnosis. Such sample characteristics restrict generalizability. Furthermore, the current research aim may be better explored in a clinical sample in which rates of PC are relatively high.

Future Directions

In light of our null results, implications of the current study are primarily research-based in nature and offer a wealth of future directions. Considering the aforementioned limitations, future work may want to develop and advance experience sampling methods in tandem with more objective indicators of mind-wandering, perhaps using either physiological (Ottaviani et al., 2016) or neuroimaging indicators (Smallwood & Schooler, 2015). Task-unrelated thought may also be indexed by electroencephalography using the amplitude of the P3 component to

(Smallwood et al., 2008). With respect to experience sampling to capture PC, an open-ended methodology may be better suited to detect the subtle differences between mind-wandering and

PC, while preserving the experience of the participant for later categorization by researchers. As noted by McVay and Kane (2013) longer intervals between stimuli on the SART are more likely to induce task-unrelated thought compared to shorter intervals (e.g., 500 ms) typically used in

SARTs. Although we chose to use a 2000 ms interstimulus interval, this interval may not

46 represent the most optimal interval to produce mind-wandering. Furthermore, although our decision to use a maximum of 20 thought probes was made, in part, to conform to a single- session design, longer sessions with more thought probes may serve to increase range of scores for PC thought probes. Future studies will also want to consider recent research showing that participants are able to decrease their levels of mind-wandering as they approach predictable upcoming targets (Seli et al., 2018) and perhaps introduce more variability or unpredictability in thought prove delivery.

Aside from aspects of the SART, future research will want to conduct more longitudinal studies to examine the trainability and time course of interoception increases. Current literature

(including the present study) suggests that a single session format may be inadequate, whereas longer training intervals of 8-weeks, 3-months, and 9-months have been shown to produce effects. One possible clinical implication of the current study, in light of these findings, is that body awareness may not be easily increased in a brief duration. Finally, longitudinal studies using clinical samples evidencing high rates of PC may provide a better examination of how PC responds to trained interoception.

Conclusion

In summary, the current study aimed to experimentally test whether perseverative cognition could be reduced during a mind-wandering paradigm via a brief interoception attention manipulation in a sample of undergraduate students. Compared to directing attention to other non-negative aspects of self- and other-referential processing, our hypothesis that the interoception condition would evidence the lowest levels of PC was not supported. Similarly, our hypothesis that individuals in the interoception condition would report that their thoughts were less intrusive and engaging was not supported. Our null findings offer several research

47 directions for future investigations of interoception, mind-wandering, and perseverative cognition.

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Table 1. Correlations, Means, Standard Deviations, and Internal Consistencies among All Questionnaires

1 2 3 4 5 6 7 M SD α

1. DASS-21 Depression -- 7.95 8.79 .89

2. DASS-21 Anxiety .70 -- 8.87 8.46 .81

3. DASS-21 Stress .76 .76 -- 12.39 9.80 .88

4. RSQ-RSS Brooding .60 .57 .72 -- 10.88 4.16 .86

5. PSWQ .47 .49 .64 .62 -- 53.38 13.59 .93

6. QIDS-SR16 .74 .62 .73 .60 .59 -- 7.38 4.03 .75

7. GADQ-IV .60 .63 .73 .76 .76 .71 -- 5.92 3.54 .91 Note. All correlations are significant at the .01 level; DASS-21 = Depression Anxiety Stress Scales 21; RSQ-RSS = Response Styles Questionnaire - Ruminative Response Scale; PSWQ = Penn State Worry Questionnaire; QIDS-SR16 = Quick Inventory of Depressive Symptomology Self Report 16; GADQ-IV = Generalized Anxiety Disorder Questionnaire for DSM-IV; M = Mean; SD = Standard Deviation; α = Cronbach’s Alpha

Table 2. Intercorrelations among Thought Probe Responses

1 2 3 4 5

1. Focused on Task --

2. Ruminating -.33** --

3. Worrying -.41** .30** --

4. Mind-wandering -.89** .07 .10 --

5. Distracted by External Stimuli -.30** .19* .18* -.06 --

Note. ** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.

Table 3. Factor Loadings for PSWQ and RSQ Items Component Item Number 1 2 PSWQ_2 .760 PSWQ_4 .802 PSWQ_5 .868 PSWQ_6 .923 PSWQ_7 .739 PSWQ_9 .605 PSWQ_12 .827 PSWQ_13 .786 PSWQ_14 .727 PSWQ_15 .781 PSWQ_16 .730 RSQ_9 .649 RSQ_12 .716 RSQ_13 .765 RSQ_23 .898 RSQ_24 .829 Note. PSWQ = Penn State Worry Questionnaire; RSQ = Response Styles Questionnaire

Table 4. Responses to Thought Probes and PC Scores between Groups Response to Thought Probe (M, SD) Experimental Focused on Worrying Ruminating Mind- Distracted PC Score Group Task wandering by External Stimuli Interoception 10.00 (4.72) 1.37 (1.40) .48 (.78) 6.40 (3.91) 1.75 (1.64) 1.85 (1.79)

Self 10.52 (5.66) .83 (1.16) .38 (.77) 7.34 (5.07) .92 (1.37) 1.20 (1.56)

Other 10.20 (6.12) 1.01 (1.22) .42 (.86) 7.53 (5.31) .96 (1.41) 1.43 (1.70) Note. M = Mean; SD = Standard Deviation; PC = Perseverative Cognition. The maximum score in response to any probe is 20 (e.g., On Average, individuals in the Interoception group indicated they were focused on the task 10/20 times)

Table 5. Negative Binomial Regression Model Examining Group Differences in PC Thought- Probes

Parameter Estimates Regression Model Predictor B IRR CI p R2 df LR χ2 p

Self -0.425 0.654 .276, .951 .309

Other -0.253 0.777 -.739, .234 .094 .005 2, 155 2.90 .235

Note. Self and Other groups are dummy-coded, with the Interoception group as the reference group. B = unstandardized regression coefficient; R2 = McFadden’s (1974) Pseudo R2 which approximates variance explained by the model; IRR = Incidence rate ratio; CI = 95 % Confidence Internal; LR χ2 = Liklihood Ratio Chi-Squared

Table 6. Linear Regression Model Examining Group Differences in Stickiness of Thought

Regression Coefficients Regression Model F Cohen's Predictor B pr t p R2 change df p f2

Self group -0.10 -0.04 -0.348 .728

Other group 0.11 0.04 0.403 .688 0.01 0.25 2, 93 .779 0.01

Note. Self and Other groups are dummy-coded, with the Interoception group as the reference group. B = unstandardized regression coefficient; pr = partial correlation between predictor and dependent measure; R2 = Percent of variance explained by the model.

Table 7. Linear Regression Model Examining Group Differences in Intrusiveness of Thought

Regression Coefficients Regression Model F Cohen's Predictor B pr t p R2 change df p f2

Self group -0.12 -0.06 -0.538 .592

Other group 0.16 0.08 0.742 .460 0.02 .732 2, 93 .484 0.02

Figure 1. Experimental Flow Diagram

Note. All participants completed questionnaires followed by the reading span test (RSPAN). Participants were then randomized into one of three experimental manipulations of attention: (1) interoception; (2) self-referential; or (3) non-self-referential. Finally, all participants completed a mind-wandering task (SART) which included thought probes to measure perseverative cognition (e.g., rumination and worry).

Figure 2. Higher-order Latent Model of Perseverative Cognition

Note. HOPC = Higher-order Perseverative Cognition; PSWQ = Penn State Worry Questionnaire; RSQ = Response Styles Questionnaire

Appendix A. Mindful Body Breathing Script (Mennin & Fresco, 2014)

“When you are ready, sitting upright, with a dignified posture in a chair or couch with a firm back, and allowing your eyes to close gently if you feel comfortable, letting your arms fold comfortably in your lap or on the arms of the chair, and placing your feet flat against the floor, if possible. And slowly bringing your attention to the fact that you are breathing. Not manipulating your breath in any way, but simply experiencing it as the air moves in and out of your body and directing your attention to whatever place in your body where your breathing is most vivid for you … perhaps it’s the sensation of the air as it passes by your nostrils or perhaps it’s noticing the sensations in your abdomen as the breath comes in and out of your body. And with each breath, just letting go, letting your body become heavy as it sinks a little deeper into your chair.

And as you are sitting here, noticing how, from time to time, your mind wanders away from your breath. And every time you notice that your attention is moving away and is no longer on your breathing, do not give yourself a hard time, just intentionally escorting your attention back to your breathing and pick up wherever it happens to be, on the in-breath or the out-breath. With each breath, noticing how each one is unique, how no two breaths are the same.

Noticing the texture, the quality, and the duration of each breath [Pause]

And as you are reconnecting with the breath, seeing if you can make it your companion, as you bring this same quality of attention to various points in your body. First, seeing if you can gently shift your attention to your feet. Noticing how your feet are grounding you, connecting you to the floor. Remaining in contact with your feet as the focus of your attention. [Pause]

And with your feet serving as the solid base for your attention, imagining as best you can, that you are breathing with your toes. Noticing the sensations in the spaces between your toes as

81 your breath passes by each toe. Becoming aware how the air is filling you from the bottom and up … circulating in your body, nourishing your body … and leaving your feet again. Sitting here, breathing with your feet for a little while. [Pause]

And when you are ready, gently moving your attention away from your feet and centering your attention to sensations in your back. Noticing how your back supports your adopting of a dignified posture and how you are from breathing from every pore along the surface of your back – how your back inhales the air … and slowly exhales. [Pause]

And now, bringing your attention to your face ... noticing your mouth and lower parts of your face as you continue to breathe in and out, perhaps noticing the ever so slight pressure exerted upon your lips as they gently touch to keep your mouth closed ... now focusing on your nose and eyes … noticing the sensations of air giving a slight tickle to your nostrils on each in breath and each out breath … and now positioning your mind’s eye as close to the center of your forehead as possible … and imagining, as best as you can that this position on your forehead is in fact the point of entry of air into the body, almost as if it were a blow hole as you have likely seen on dolphins or whales … drinking in the air with your blow hole to nourish your entire body

… expelling the air from your blow hole to make room in your body for the next breath. [Pause]

Now, broadening your attention to your whole body. Include a sense of your body as a whole, sitting here breathing. Feel how your body is grounded on the chair or where ever you’re lying down. As best as you can, imagine that you are breathing with your entire body. Almost as if you’re breathing with your skin. With every in-breath, noticing how every part of your body fills up with air. And with every exhale, noticing the air evacuating your body. [Pause]

And as you continue sitting here, now, moving the breath and the sense of your body into the background of your attention and begin noticing the sounds of your surroundings. Sounds

82 near, and sounds far. Or giving notice to the absence of sounds. Allowing the sounds or silence to be just as they are, keeping them in your attention. Bringing the mind back to hearing, over and over again, when it gets carried away. [Pause]

And when you are ready to do so, opening your eyes, and bringing this very deliberate, focused, well-tuned attentional quality to the remaining activities of the day … remembering that

– wherever you are - your breathing is always available to help you sustain or regain this quality of focused attention.”

Appendix B. Attention to Non-negative, Self-referential Information Script

For this task, you’ll be asked to think about your “self.” I’m going to read to you a list of adjectives, and I want you to think deeply about the degree to which each word describes you. Some words may almost never describe you. Some words might rarely describe you.

Some words might sometimes describe you. And finally, some words might almost always describe you. So instead of thinking in terms of a yes or no, your task is to think carefully about how well each word describes you. And when you hear each word, take a few seconds to think about how well it describes you. Let’s begin. artistic precise scientific orderly social direct careful candid comical self-critical fashionable religious soft-hearted dignified philosophical idealistic soft-spoken disciplined serious definite convincing persuasive obedient quick sophisticated thrifty sentimental objective nonconforming righteous mathematical fearless systematic subtle normal daring middleclass lucky proud sensitive moralistic talkative excited moderate satirical

How well do these words describe you? Remember when you hear each word think about whether it describes you almost never, rarely, sometimes, or maybe the word describes you almost always. Let’s continue. prudent reserved persistent meticulous unconventional deliberate painstaking bold suave cautious innocent inoffensive shrewd methodical nonchalant self-contented perfectionistic forward excitable outspoken prideful quiet impulsive aggressive changeable conservative shy hesitant unpredictable solemn blunt self-righteous average discriminating emotional unlucky bashful self-concerned authoritative lonesome restless choosy

How well do these words describe you? Remember when you hear each word think about whether it describes you almost never, rarely, sometimes, or maybe the word describes you almost always. Let’s continue. naïve opportunist theatrical unsophisticated impressionable ordinary strict skeptical extravagant forceful cunning inexperienced unmethodical daredevil wordy daydreamer conventional materialistic self-satisfied rebellious eccentric opinionated stern lonely dependent unsystematic self-conscious undecided resigned clownish anxious conformingcritical conformist radical dissatisfied old-fashioned meek frivolous discontented troubled irreligious overcautious silent tough ungraceful argumentative withdrawing uninquisitive forgetful inhibited unskilled crafty passive immodest unpopular timid

You have now completed the task.

Appendix C. Attention to Non-negative, Non-self-referential Information Script

For this task, you’ll be asked to think about the famous person you discussed with the experimenter. I’m going to read to you a list of adjectives, and I want you to think deeply about the degree to which each word describes the famous person. Some words may almost never describe this person. Some words might rarely describe this person. Some words might sometimes describe this person. And finally, some words might almost always describe this famous person. So instead of thinking in terms of a yes or no, your task is to think carefully about how well each word describes the famous person that you discussed with the experimenter. And when you hear each word, take a few seconds to think about how well it describes that person. Let’s begin. artistic precise scientific orderly social direct careful candid comical self-critical fashionable religious soft-hearted dignified philosophical idealistic soft-spoken disciplined serious definite convincing persuasive obedient quick sophisticated thrifty sentimental objective nonconforming righteous mathematical fearless systematic subtle normal daring middleclass lucky proud sensitive moralistic talkative excited moderate satirical

How well do these words describe the famous person you discussed with the experimenter?

Remember when you hear each word think about whether it describes that person almost never, rarely, sometimes, or maybe the word describes that famous person almost always.

Let’s continue. prudent reserved persistent meticulous unconventional deliberate painstaking bold suave cautious innocent inoffensive shrewd methodical nonchalant self-contented perfectionistic forward excitable outspoken prideful quiet impulsive aggressive changeable conservative

86 shy hesitant unpredictable solemn blunt self-righteous average discriminating emotional unlucky bashful self-concerned authoritative lonesome restless choosy

How well do these words describe the famous person you discussed with the experimenter?

Remember when you hear each word think about whether it describes that person almost never, rarely, sometimes, or maybe the word describes that famous person almost always.

Let’s continue. naïve opportunist theatrical unsophisticated impressionable ordinary strict skeptical extravagant forceful cunning inexperienced unmethodical daredevil wordy daydreamer conventional materialistic self-satisfied rebellious eccentric opinionated stern lonely dependent unsystematic self-conscious undecided resigned clownish anxious conformingcritical conformist radical dissatisfied old-fashioned meek frivolous discontented troubled irreligious overcautious silent tough ungraceful argumentative withdrawing uninquisitive forgetful inhibited unskilled crafty passive immodest unpopular timid

You have now completed the task.

Appendix D. Ordinary Least Squares (OLS) Analyses as Originally Proposed Hypothesis 1 using OLS A linear multiple regression with dummy-coding (using the Interoception group as the reference group) was calculated to evaluate Hypothesis 1 positing that individuals assigned to the

Interoception group will report less perseverative cognition during the SART, compared to individuals in the Self group and Other group. For each participant, the PC score was calculated as the total number of times that rumination or worry was selected as a response to the thought probes, with the maximum score being twenty. Results indicated no significant differences in PC scores between groups, F(2, 157), = 1.93, p = .148, R2 = .024. Table 4 indexes responses to thought probes and PC scores sorted by group. Table D1, below, presents the regression model for Hypothesis 1.

Exploratory/Post-hoc Analyses using OLS

RSPAN. Partial scores on the RSPAN did not moderate the relationship between group assignment and PC scores, F(5, 152) = 1.44, p = .211, R2 = .034

Questionnaire Cut-score Groups. Differences in reported PC did not differ with respect to current self-reported psychiatric diagnosis or scores on the DASS-21 Depression

Subscale. Individuals scoring above the cut-off on the DASS-21 Anxiety Subscale (M = .58; SD

= .95) indicted more rumination during the SART, compared to individuals scoring below the cut-off (M=.29, SD = .63), t(156) = -2.12, p =.036, d = .36.

Table D1. Linear Regression Model Examining Group Differences in PC Thought-Probes

Regression Coefficients Regression Model F Cohen's Predictor B pr t p R2 change df p f2

Self group -0.64 -0.15 -1.94 .054

Other group -0.41 -0.10 -1.25 .212 0.02 1.93 2, 157 .148 0.02