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    Chapter 7: Relevance of Sign-Tracking to Co-Occurring Psychiatric Disorders

    a Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109

    b Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109

    Corresponding Author:

    Jonathan D. Morrow, MD, PhD, 4250 Plymouth Rd. SPC 5767, Ann Arbor, Michigan 48109–2700; e-mail:; phone: (734) 764–4283; fax: (734) 232–0244.

    Sign-tracking is a behavior that reflects Pavlovian learning. Sign-tracking and its complementary behavior, goal-tracking, are often discussed in terms of their relevance to substance use disorders, but emotional learning is a fundamental part of several other neuropsychiatric disorders. The overlapping neurobiology of sign-tracking, addiction, and other psychiatric disorders suggest that individual variation in incentive-motivational processes may contribute to specific patterns of psychiatric comorbidity. Targeting transdiagnostic traits such as this could lead to more effective treatments than can be achieved by focusing on individual disorders. Here, the phenomenology and neurobiology that links sign-tracking to a wide range of disorders is discussed.
    Keywords: addiction; comorbidity; dual-diagnosis; individual differences; Pavlovian conditioned approach; personality


    Addiction is a complex disorder and is often deeply intertwined with other neuropsychiatric disorders. Studies of patients in treatment for substance use disorders (SUDs) routinely find very high rates of psychiatric comorbidities, typically with >80% of patients meeting criteria for an identified psychiatric disorder. Such studies may overestimate the prevalence of comorbidity in the general population because those who enter treatment tend to be the most functionally impaired (Sackett, 1979). However, epidemiological studies of community samples largely avoid such bias, and they consistently find co-occurring mental health disorders in more than half of people with SUDs (Grant et al., 2016; Kessler et al., 1996; Regier et al., 1990). Understanding and addressing these comorbidities is essential because co-occurring psychiatric illness makes SUDs more severe and difficult to treat, leading to more hospitalizations, more interpersonal problems, and worse physical health (Ritsher, McKellar, Finney, Otilingam, & Moos, 2002; Schaar & Ojehagen, 2001). Patients tend to believe their own disorders are functionally related and prefer concurrent, integrated treatment (Brown, Stout, & Gannon-Rowley, 1998).

    Several potential reasons have been proposed to explain why SUDs are so commonly complicated by the presence of comorbid psychiatric disorders. One category of explanation is essentially that SUDs play a causal role in the development of other psychiatric disorders. For example, the prolonged use of drugs and alcohol can damage sensitive brain structures like the prefrontal cortex, which in turn can contribute to the development of other psychiatric disorders (Lyvers, 2000). This is particularly relevant in the adolescent and teenage years, when substance use can lead to impaired cognitive and social development, resulting in increased risk of psychiatric disorders later in life. Another potential explanation for the link between SUDs and psychiatric comorbidities is that psychiatric disorders in some way cause addiction, and this process is most commonly framed in terms of “self-medication” (Khantzian, 1985). While there is evidence to support both these types of causal relationships, there is also evidence of common factors such as genetic variants or early life experiences that can predispose toward both addiction and other psychiatric disorders (Kendler, Prescott, Myers, & Neale, 2003). This type of shared risk is thought to be largely mediated by personality or behavioral traits such as impulsivity and neuroticism (Kotov, Gamez, Schmidt, & Watson, 2010; Verdejo-Garcia, Lawrence, & Clark, 2008). Sign-tracking behavior may be an index of one such trait that can contribute to multiple psychiatric disorders.

    As Tomie outlined in the introductory chapter of this book, sign-tracking is a type of learned attraction to cues that predict reward. Sign-tracking is often distinguished from goal-tracking, which is a learned, cue-triggered approach toward the location of impending reward delivery. Sign-tracking, as opposed to goal-tracking, is difficult to restrain and may contribute significantly to cue-induced relapse and other problematic features of SUDs. The neurobiology of sign-tracking behavior corresponds almost exactly to the neurobiology of motivated behavior in general. For example, sign-tracking appears largely dependent on dopaminergic activity within the nucleus accumbens (Flagel, Clark, et al., 2011; Fraser & Janak, 2017; Saunders & Robinson, 2012). The nucleus accumbens is part of a larger system, often referred to as the limbic system, whose overall function seems to be translating thoughts, perceptions, and emotions into behavior (Mogenson, Jones, & Yim, 1980; Salamone & Correa, 2012). Because mental illness by definition involves difficulties with generating appropriate emotional, cognitive, and behavioral responses to the environment, it should come as no surprise that limbic circuitry has been implicated in practically all psychiatric disorders. Because sign-tracking behavior reflects a particular bias within this limbic emotional-motivational system, we might expect sign-tracking to be involved in a large number of motivational and emotional abnormalities. In this chapter, we will touch on some of the ways sign-tracking intersects with neuropsychiatric conditions other than drug addiction.

    Impulse Control Disorders

    SUDs are most closely related to a group of diagnoses known as “behavioral addictions,” including both well-accepted disorders such as pathological gambling and more controversial conditions such as compulsive sexual behavior and Internet gaming disorder. The neurobiology of behavioral addictions overlaps considerably with that of SUDs (Leeman & Potenza, 2013). Behavioral addictions and SUDs are highly comorbid, have a shared genetic basis, and cross-sensitize with one another. Though there have been few attempts to measure sign-tracking behavior per se in human subjects, a related trait known as “cue-reactivity” is predictive of problematic substance use (Carter & Tiffany, 1999). Cue-reactivity refers to measurable emotional, motivational, and physiological responses (i.e., cravings) in response to drug-associated versus neutral cues. Several studies have documented heightened cue-reactivity to specific reward-related stimuli among patients with behavioral addictions (Goudriaan, de Ruiter, van den Brink, Oosterlaan, & Veltman, 2010; Jansen et al., 2003; Ko et al., 2009; Thalemann, Wolfling, & Grusser, 2007; Voon et al., 2014). In Chapter 5 of this volume, Patrick Anselme outlined several ways in which gambling games and casinos employ reward-cue configurations that are known to maximize sign-tracking behavior, and many online games use similar tactics to keep users engaged. Given the lack of restraint and seeming irrationality that is so characteristic of sign-tracking, it is possible that behavioral addictions actually represent an extreme, almost pure form of sign-tracking behavior.

    SUDs and behavioral addictions both fall under a larger umbrella of disorders related to a lack of impulse control. Impulsivity has been more clearly and consistently associated with addiction than any other personality trait. More specifically, the inability to withhold a pre-potent response predicts escalation of drug use in both animals and humans (Belin, Mar, Dalley, Robbins, & Everitt, 2008; Dalley, Everitt, & Robbins, 2011; Verdejo-Garcia et al., 2008). Precisely this type of “impulsive action” has been associated with sign-tracking behavior in rats (Lovic, Saunders, Yager, & Robinson, 2011). Impulsive action results from a lack of prefrontal cortical control over subcortical impulses (Davis et al., 2013; Schmaal, Goudriaan, van der Meer, van den Brink, & Veltman, 2012), and sign-tracking rats show the same pattern of decreased functional connectivity between cortical and subcortical regions (Flagel, Cameron, et al., 2011). Attention-deficit hyperactivity disorder (ADHD) is one example of an impulse control disorder that may have mechanistic commonalities with sign-tracking. As implied by the name, attentional deficits are a defining feature of ADHD, and sign-tracking rats also have difficulty sustaining attention over time and in the presence of distractors (Paolone, Angelakos, Meyer, Robinson, & Sarter, 2013). The attentional deficits in sign-trackers appear to be due to a relatively unresponsive choline transporter and a consequently reduced capacity to mobilize acetylcholine in the prefrontal cortex during attentionally demanding tasks (Koshy Cherian et al., 2017; Pitchers, Kane, Kim, Robinson, & Sarter, 2017). A human genetic polymorphism of the choline transporter gene mimics some of the functional deficits seen in sign-tracking rats, and this polymorphism is associated with ADHD (English et al., 2009). Impulse control disorders can also include abnormalities in patterned motor outputs, for example, Tourette’s disorder. Intriguingly, recent experiments have shown abnormalities in complex motor control tasks among sign-trackers, indicating a possible relevance of the sign-tracking phenotype to a range of neuropsychiatric movement disorders (Kucinski, Kim, Lustig, & Sarter, 2018).

    Personality Disorders

    Impulse control disorders are often placed within an even broader group of “externalizing disorders,” including conduct disorder, antisocial personality disorder, and borderline personality disorder, all of which are associated with high rates of SUDs. In addition to impulsivity, the association with SUDs and externalizing disorders is thought to be mediated by other traits such as sensation-seeking (Dick et al., 2013; Hicks, Foster, Iacono, & McGue, 2013; Pingault et al., 2013). Interestingly, in outbred rats, sensation-seeking does not correlate with sign-tracking behavior in the way impulsivity does (Beckmann, Marusich, Gipson, & Bardo, 2011; Robinson & Flagel, 2009; Vanhille, Belin-Rauscent, Mar, Ducret, & Belin, 2015). The sensation-seeking trait seems to specifically predispose individuals to initiation of drug use and to using high doses of drug but does not directly hasten the transition from casual to dysregulated drug use patterns (Belin et al., 2008; Deroche-Gamonet, Belin, & Piazza, 2004; Ersche et al., 2013; Piazza, Deminiere, Le Moal, & Simon, 1989). Thus, sign-tracking may be more specifically involved in predisposing individuals with externalizing disorders to addiction, as opposed to just drug taking. Borderline personality is a particularly interesting disorder in this regard, because it is characterized in part by a combination of neuroticism, impulsivity, and cue-reactivity, all of which are associated with increased risk for SUDs. Many borderline patients suffer from a particular type of emotional instability characterized by exaggerated emotional responses to seemingly innocuous cues. Though sign-tracking has not been studied in this population, the strong cue-induced motivated behaviors seen in sign trackers are presumably accompanied by equally strong cue-induced emotional responses. Indeed, studies of ultrasonic vocalizations indicate that sign-trackers derive more intense pleasure from Pavlovian conditioned approach procedures than do goal-trackers (Meyer, Ma, & Robinson, 2012). This raises the possibility that sign-tracking, or a closely related psychological process, may be a major contributor to the functional impairment seen in borderline personality disorder.

    Anxiety Disorders

    The affective instability of borderline personality involves disproportionate emotional responses to stimuli associated with positive, rewarding experiences and also to stimuli associated with negative, aversive experiences. This fact raises the question of whether sign-tracking is specific to reward learning, or whether a similar process may be at work in all types of emotional learning. Pavlovian fear conditioning involves generating a fear response to a neutral cue that has been paired with an aversive experience, for example, a mild electrical shock to the foot. The fear conditioning process is very similar to Pavlovian conditioned approach procedures that produce sign-tracking. The main difference is that conditioned fear learning is supported by an aversive outcome, while conditioned approach is supported by an appetitive reward. There are significant individual differences in the amount of fear expressed after a classic Pavlovian fear conditioning paradigm, and sign-trackers express more fear to discrete shock-associated cues than goal-trackers (Morrow, Maren, & Robinson, 2011). This suggests that the increased emotional and motivational value that sign-trackers attribute to cues is not limited to reward. Fear learning is a central part of several anxiety disorders, including post-traumatic stress disorder (PTSD) and specific phobias. Much like addiction, in which craving is triggered by drug-related cues, PTSD and phobias involve excessive fear states that are triggered by trauma-related cues. Indeed, sign-trackers are more susceptible to developing PTSD-like abnormal fear responses that “incubate,” or increase instead of staying stable or decreasing over time (Morrow, Saunders, Maren, & Robinson, 2015). If sign-tracking is a process common to both addiction and PTSD, this may help to explain some of the high rates of co-occurrence of these two disorders found in patient populations (Cottler, Compton, Mager, Spitznagel, & Janca, 1992; Kulka et al., 1990).

    Another important feature held in common between sign-trackers and PTSD patients is difficulty with processing and integrating contextual information. A context is a particular combination of stable cues that together comprise the setting in which an event occurs. In behavioral experiments, distinct contexts are intentionally created by using different configurations of odors, background lighting, background noise, floor textures, and color patterns on the walls of the testing chamber. The brain processes information about contexts differently from discrete cues; for example, contextual learning is highly dependent on hippocampal activity, whereas discrete cue learning depends more on the amygdala (Marschner, Kalisch, Vervliet, Vansteenwegen, & Buchel, 2008; Phillips & LeDoux, 1992; Selden, Everitt, Jarrard, & Robbins, 1991). Interestingly, hippocampal dysfunction is one of the most consistent findings among PTSD patients (Abdallah et al., 2017; Gilbertson et al., 2002; Logue et al., 2018; Smith, 2005), and there is evidence of differential hippocampal involvement in sign- versus goal-tracking (Fitzpatrick, Creeden, Perrine, & Morrow, 2016; Fitzpatrick, Perrine, Ghoddoussi, Galloway, & Morrow, 2016; Ito, Everitt, & Robbins, 2005). Impaired contextual fear learning has been demonstrated in PTSD patients and is now hypothesized to be a central feature of the disorder (Garfinkel et al., 2014; Maren, Phan, & Liberzon, 2013; Rougemont-Bucking et al., 2011). Sign-tracking rats also show deficits in contextual fear learning (Morrow et al., 2011). In addition, contextual cues are less effective at triggering relapse-like drug-seeking behavior in sign trackers as compared to goal-trackers (Pitchers, Phillips, Jones, Robinson, & Sarter, 2017; Saunders, O’Donnell, Aurbach, & Robinson, 2014). Thus, if an individual is predisposed toward sign-tracking behavior, that individual will likely have strong emotional and motivational responses to any cue that is paired with an emotionally salient event. In addition, these cue-triggered responses will be relatively immune from any contextual modulation. A sign-tracker will tend to react strongly to cues regardless of the time, place, or social situation in which the cue is encountered. For example, a military veteran who has experienced a roadside bomb attack may feel intense fear and start driving with a dangerous level of aggression at the sight of a pile of garbage close to the road. That would be an entirely appropriate response in the context of an active military operation in Afghanistan, but it is a pathological response in the context of driving home from a child’s birthday party in a Michigan suburb. In a similar vein, using psychoactive drugs such as alcohol and marijuana is a normal human behavior and can be appropriate in certain social contexts like celebrations or religious ceremonies. However, over the course of addiction drug use begins to occur in increasingly inappropriate contexts, for example, at work, before driving, or while caring for children. A decreased ability to limit the expression of emotional and motivational responses to their appropriate contexts may be a common thread that links sign-tracking, addiction, PTSD, and other related disorders.

    Psychotic Disorders

    Dopamine signaling is critical for the transformation of neutral cues into motivationally relevant stimuli. As mentioned previously, blocking dopamine transmission prevents sign-tracking behavior (Danna & Elmer, 2010; Flagel, Clark, et al., 2011). However, increasing dopamine transmission by direct injection of amphetamine can also inhibit sign-tracking under some circumstances (Holden & Peoples, 2010; Simon, Mendez, & Setlow, 2009). This apparent dichotomy may be due to the fact that dopamine’s effects on the motivational properties of a given cue are critically dependent on the relative timing of the cue, the associated outcome, and the dopamine signal (Chang et al., 2016; Sharpe et al., 2017; Steinberg et al., 2013). Reward learning is best supported by transient dopamine signals that occur in close temporal proximity to both the cue and the reward. Dopamine transients outside of that time window may actually reduce the motivational connections between the cue and the reward. This timing effect may help to explain why route of administration can have such a profound effect on the addictive properties of drugs. Generally speaking, the faster a drug causes dopamine levels to spike in the brain, the more efficient that drug will be at inducing and maintaining addictive behavior (Allain, Minogianis, Roberts, & Samaha, 2015; Samaha, Li, & Robinson, 2002). For example, smoking crack cocaine is more addictive than snorting powdered cocaine because inhaling cocaine delivers the drug to the brain faster than absorbing it through the nasal mucosa. The consequent dopamine release will then presumably strengthen associations with cues that are in closer proximity to the act of smoking crack than it will to cues that accompany snorting cocaine.

    Dysfunction within the dopaminergic system is a prominent feature of many psychiatric disorders, some of which have already been discussed in this chapter. In particular, schizophrenia and other psychotic disorders are thought to result in part from excessive, inappropriately timed dopaminergic activity (Kapur, Mizrahi, & Li, 2005). The general applicability of this hypothesis is attested by the efficacy of dopamine receptor blockade in reducing the symptoms of psychosis (Kapur & Mamo, 2003) and by the prominence of psychotic symptoms as side effects of dopaminergic drugs (Cummings, 1991; Goetz, Tanner, & Klawans, 1982). It is specifically the positive symptoms of schizophrenia, such as hallucinations and delusions, that seem to be mediated by aberrant dopamine signals. Psychotic delusions typically start out as “overvalued ideas,” which refers to a thought that feels so important to the individual that it begins to drown out all other considerations (McKenna, 1984). Such ideas become delusional when seemingly random and unconnected perceptions, such as television ads or even cloud formations, start to acquire new, typically self-referential meaning, for example, “the United Nations is altering weather patterns in order to send me personal messages in the clouds.” In the example just given, the delusional thought might have crystallized due to a random surge of dopamine that happened to coincide with looking at a particular cloud. The same process that gives drug-associated cues the motivational value to produce approach behaviors like sign-tracking may be at play in psychotic disorders, driving random thoughts and perceptions to a place of prominence in the mind because of the dopamine-driven sense that they are connected and personally relevant. Once that feeling of importance is established, the details of the delusional content are filled in by “top-down” cognitive processes based on the individual’s unique life experiences. Like sign-tracking behavior, delusional thoughts are typically very difficult to restrain, and depending on the severity of the illness patients may struggle with “knowing” their delusion is not true without being able to reconcile the “feeling” that it is both true and extremely important. Addiction patients have a similar, biologically driven relationship with drug use, and in treatment circles this internal struggle is commonly referred to as “ambivalence.”

    Disorders of Over-Control

    When considering the relevance of sign-tracking to psychopathology, it is important to remember that sign-tracking is a normal behavior. All individuals are capable of both sign-tracking and goal-tracking, but the sensory features of the cue, the proximity of the cue to the reward in both space and time, and other such testing parameters can influence the likelihood that a given individual will employ one strategy or the other (Burns & Domjan, 2001; Christie, 1996; Gallistel & Gibbon, 2000; Meyer, Cogan, & Robinson, 2014). Sign-tracking evidently has a long evolutionary history. A great deal of recent sign-tracking studies have used rats, but sign-tracking behavior has been reported in a wide variety of organisms including primates, horses, birds, and fish (Bullock & Myers, 2009; Burns & Domjan, 1996; Miyashita, Nakajima, & Imada, 1999; Nilsson, Kristiansen, Fosseidengen, Ferno, & van den Bos, 2008). In fact, sign-tracking has been observed among invertebrate species such as insects and cephalopods, whose common ancestors with humans likely did not even have a brain (Purdy, Roberts, & Garcia, 1999; Zhang, Bartsch, & Srinivasan, 1996). The ubiquity of sign-tracking throughout the animal kingdom suggests that it must be an adaptive behavioral strategy and may even be a required feature for organisms that use complex behaviors to navigate the real world. However, if either a propensity toward sign-tracking or a propensity toward goal-tracking were always advantageous, only one of these traits would exist because natural selection pressures would quickly cull the other out of the population. The persistence of both sign- and goal-tracking suggests that there must be an evolutionary trade-off of some kind, such that each trait has advantages over the other depending on the different life circumstances that an individual may face (Wolf, van Doorn, Leimar, & Weissing, 2007). For example, sign-tracking may provide a faster, more stereotyped way to pursue rewards, whereas goal-tracking may be a slower but more flexible response pattern. In that case, sign-tracking would be more advantageous in environments with scarce, unpredictable access to resources. In contrast, goal-tracking would be the more suitable strategy in stable environments with a relative abundance of resources. In modern, civilized societies, and especially those parts of society that value academic achievement, the disadvantages of externalizing traits like sign-tracking are often emphasized, while traits like prudence and patience are almost universally extolled as virtues. However, just as a lack of prefrontal control over subcortical impulses can contribute to psychopathology, an overabundance of prefrontal control can also lead to functional impairment.

    Obsessive-compulsive personality disorder (OCPD) is a relatively straightforward example of how excessive cognitive control can become almost paralyzing and interfere with daily life. These patients spend so much time planning, checking, and attending to every little detail that it is very difficult for them to complete even simple tasks. Because very few data exist that address the functional neuroanatomy of OCPD or goal-tracking, it is difficult to draw parallels between them based on pathophysiology. However, based on phenomenology and their inverse correlations with externalizing disorders and sign-tracking, we could reasonably speculate that OCPD patients and extreme goal-trackers may have some biological similarities. Obsessive-compulsive disorder (OCD) is closely related but distinct from OCPD. Whereas OCPD is characterized by an intense need for control and orderliness, OCD is defined by intrusive thoughts or action patterns that are repeated over and over, such as counting or excessive handwashing, even though they serve no practical purpose. Researchers often highlight phenomenological similarities between addiction and OCD because drug-taking often involves stereotyped rituals, and cravings can be described as intrusive, repetitive thoughts. A multitude of evidence, much of it derived from animal studies, suggests that over time drug use becomes more “habitual” and concurrently shifts from being dependent on ventral striatal structures to more dorsolateral areas of the striatum (Everitt & Robbins, 2016). However, some of this evidence has recently been challenged on the grounds that it could be an artifact of the stereotyped way in which animal subjects are required to use drugs in most controlled studies (Singer, Fadanelli, Kawa, & Robinson, 2018). The preponderance of neuroimaging evidence from OCD patients indicates hyperactivity of prefrontal cortical structures that participate in cortico-striatal loops, a pattern that is largely opposite of that observed in addiction patients (Menzies et al., 2008; Nakao, Okada, & Kanba, 2014). OCPD shares some traits with anorexia, particularly perfectionism, behavioral rigidity, high impulse control, and emotional restraint (Halmi et al., 2005; Young, Rhodes, Touyz, & Hay, 2013). The imaging data that are available for anorexia are not always consistent from one study to the next, but overall they also paint a picture of hyperconnectivity between cortical and subcortical regions of the limbic system (Frank, Shott, Riederer, & Pryor, 2016). The functional circuitry of anorexia might therefore be expected to reduce sign-tracking responses and presumably increase goal-tracking, though a specific test of this hypothesis has not yet been conducted. Clinically, comorbid anorexia and addiction can be particularly challenging because psychotherapies for addiction are generally designed to increase cognitive control over subcortical urges, while psychotherapies for anorexia essentially aim to do the opposite. It is common for symptoms of one disorder to worsen while the other improves, making it essential to monitor both disordered eating behaviors and substance use throughout the course of treatment.


    Despite very high rates of comorbidity between addiction and mood disorders, there have been almost no attempts to relate sign-tracking behavior to either depression or bipolar disorder. Depression comes in many forms, and the clinical definition of depression may in fact encompass more than one biological disorder (Ostergaard, Jensen, & Bech, 2011; Zimmerman, Ellison, Young, Chelminski, & Dalrymple, 2015). Much like the related personality trait of neuroticism, depression can include features consistent with cognitive undercontrol, such as impulsivity, as well as features that might indicate cognitive overcontrol such as the repetitive negative thoughts or worries known as “ruminations.” Because of this heterogeneity, sign- and goal-tracking behavior might not correlate with depression in a straightforward way. However, sign-tracking might prove useful for understanding anhedonia, which is a deficit in reward-related behavior common in depression and several other psychiatric disorders. Anhedonia is typically conceptualized as a reduced capacity for pleasure, but several lines of evidence have suggested that “anhedonia” in many cases is actually a lack of motivation that is mistakenly interpreted by both patients and clinicians as a lack of pleasure (Myin-Germeys, Delespaul, & deVries, 2000; Treadway & Zald, 2013). Based on the known neurobiology of sign-tracking behavior, a deficit in dopaminergic activity might be expected to result in a specific deficit in cue-directed motivation, which might in turn be interpreted as anhedonia. Indeed some recent experiments have suggested that severe stressors can cause deficits in both dopamine-signaling and sign-tracking behavior (Fitzpatrick et al., 2018).


    As might be expected based on the strong relationship between addiction and other psychiatric disorders, sign-tracking has relevance to many conditions beyond just substance use disorders. It fits within a rubric of externalizing disorders that seem to share common neurobiological features including diminished capacity for prefrontal cortical regions to provide contextual modulation and cognitive control over subcortical motivational impulses. This general trait of unrestrained emotional and motivational responses to cues can apply not only to rewards but also to aversive experiences. Disorders of excessive cognitive control such as eating disorders and OCPD may also relate to sign- and goal-tracking behavior, though little research has been done to explore such a hypothesis. Disrupted timing of dopamine signaling in the limbic system can lead to both a loss of motivation toward natural rewards and to pathological increases in responding to irrelevant stimuli. Because multiple psychological functions are coordinated and interconnected within the limbic system, symptoms resulting from disruption to this system can take the form of emotional, motivational, and/or cognitive preoccupation. Current psychiatric classification systems might give a patient with such symptoms multiple diagnoses, even though the fundamental neurobiology is the same. If sign-tracking more directly reflects the neurobiology common to all these disorders, it might be used in the future to more efficiently diagnose and treat patients with multiple psychiatric comorbidities, rather than focusing on each individual disorder.


    I would like to acknowledge Rachel Atkinson for her contributions to an early draft of this manuscript. This work was made possible in part by financial support from the National Institute on Drug Abuse (K08 DA037912–04).


    • Abdallah, C. G., Wrocklage, K. M., Averill, C. L., Akiki, T., Schweinsburg, B., Roy, A., . . . Scott, J. C. (2017). Anterior hippocampal dysconnectivity in posttraumatic stress disorder: A dimensional and multimodal approach. Translational Psychiatry, 7(2), e1045. doi:10.1038/tp.2017.12.
    • Allain, F., Minogianis, E. A., Roberts, D. C., & Samaha, A. N. (2015). How fast and how often: The pharmacokinetics of drug use are decisive in addiction. Neuroscience & Biobehavioral Review, 56, 166–179. doi:10.1016/j.neubiorev.2015.06.012.
    • Beckmann, J. S., Marusich, J. A., Gipson, C. D., & Bardo, M. T. (2011). Novelty seeking, incentive salience and acquisition of cocaine self-administration in the rat. Behavioral Brain Research, 216(1), 159–165. doi:10.1016/j.bbr.2010.07.022.
    • Belin, D., Mar, A. C., Dalley, J. W., Robbins, T. W., & Everitt, B. J. (2008). High impulsivity predicts the switch to compulsive cocaine-taking. Science, 320(5881), 1352–1355. doi:10.1126/science.1158136.
    • Brown, P. J., Stout, R. L., & Gannon-Rowley, J. (1998). Substance use disorder-PTSD comorbidity. Patients’ perceptions of symptom interplay and treatment issues. Journal of Substance Abuse Treat, 15(5), 445–448. doi:S0740547297002869 [pii].
    • Bullock, C. E., & Myers, T. M. (2009). Stimulus-food pairings produce stimulus-directed touch-screen responding in cynomolgus monkeys (macaca fascicularis) with or without a positive response contingency. Journal of the Experimental Analysis of Behavior, 92(1), 41–55. doi:10.1901/jeab.2009.92–41.
    • Burns, M., & Domjan, M. (1996). Sign tracking versus goal tracking in the sexual conditioning of male Japanese quail (Coturnix japonica). Journal of Experimental Psychology Animal Behavior Process, 22(3), 297–306.
    • Burns, M., & Domjan, M. (2001). Topography of spatially directed conditioned responding: Effects of context and trial duration. Journal of Experimental Psychology Animal Behavior Process, 27(3), 269–278.
    • Carter, B. L., & Tiffany, S. T. (1999). Meta-analysis of cue-reactivity in addiction research. Addiction, 94(3), 327–340. doi:10.1046/j.1360–0443.1999.9433273.x.
    • Chang, C. Y., Esber, G. R., Marrero-Garcia, Y., Yau, H. J., Bonci, A., & Schoenbaum, G. (2016). Brief optogenetic inhibition of dopamine neurons mimics endogenous negative reward prediction errors. Nature Neuroscience, 19(1), 111–116. doi:10.1038/nn.4191.
    • Christie, J. (1996). Spatial contiguity facilitates Pavlovian conditioning. Psychonomic Bulletin and Review, 3(3), 357–359. doi:10.3758/BF03210760.
    • Cottler, L. B., Compton, W. M., 3rd, Mager, D., Spitznagel, E. L., & Janca, A. (1992). Posttraumatic stress disorder among substance users from the general population. American Journal of Psychiatry, 149(5), 664–670.
    • Cummings, J. L. (1991). Behavioral complications of drug treatment of Parkinson’s disease. Journal of American Geriatrics Society, 39(7), 708–716.
    • Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron, 69(4), 680–694. doi:10.1016/j.neuron.2011.01.020.
    • Danna, C. L., & Elmer, G. I. (2010). Disruption of conditioned reward association by typical and atypical antipsychotics. Pharmacology Biochemistry Behavior, 96(1), 40–47. doi:S0091–3057(10)00099–7 [pii] 10.1016/j.pbb.2010.04.004.
    • Davis, F. C., Knodt, A. R., Sporns, O., Lahey, B. B., Zald, D. H., Brigidi, B. D., & Hariri, A. R. (2013). Impulsivity and the modular organization of resting-state neural networks. Cerebral Cortex, 23(6), 1444–1452. doi:10.1093/cercor/bhs126.
    • Deroche-Gamonet, V., Belin, D., & Piazza, P. V. (2004). Evidence for addiction-like behavior in the rat. Science, 305(5686), 1014–1017. doi:10.1126/science.1099020.
    • Dick, D. M., Aliev, F., Latendresse, S. J., Hickman, M., Heron, J., Macleod, J., . . . Kendler, K. S. (2013). Adolescent alcohol use is predicted by childhood temperament factors before age 5, with mediation through personality and peers. Alcohol Clinical and Experimental Research, 37(12), 2108–2117. doi:10.1111/acer.12206.
    • English, B. A., Hahn, M. K., Gizer, I. R., Mazei-Robison, M., Steele, A., Kurnik, D. M., . . . Blakely, R. D. (2009). Choline transporter gene variation is associated with attention-deficit hyperactivity disorder. Journal of Neurodevelopmental Disorder, 1(4), 252–263. doi:10.1007/s11689-009-9033-8.
    • Ersche, K. D., Jones, P. S., Williams, G. B., Smith, D. G., Bullmore, E. T., & Robbins, T. W. (2013). Distinctive personality traits and neural correlates associated with stimulant drug use versus familial risk of stimulant dependence. Biological Psychiatry, 74(2), 137–144. doi:10.1016/j.biopsych.2012.11.016.
    • Everitt, B. J., & Robbins, T. W. (2016). Drug addiction: Updating actions to habits to compulsions ten years on. Annual Review of Psychology, 67, 23–50. doi:10.1146/annurev-psych-122414–033457.
    • Fitzpatrick, C. J., Creeden, J. F., Perrine, S. A., & Morrow, J. D. (2016). Lesions of the ventral hippocampus attenuate the acquisition but not expression of sign-tracking behavior in rats. Hippocampus, 26(11), 1424–1434. doi:10.1002/hipo.22619.
    • Fitzpatrick, C. J., Jagannathan, L., Lowenstein, E., Robinson, T. E., Becker, J. B., & Morrow, J. D. (2018). Single prolonged stress decreases sign-tracking and cue-induced reinstatement of cocaine-seeking. Manuscript submitted for publication.
    • Fitzpatrick, C. J., Perrine, S. A., Ghoddoussi, F., Galloway, M. P., & Morrow, J. D. (2016). Sign-trackers have elevated myo-inositol in the nucleus accumbens and ventral hippocampus following Pavlovian conditioned approach. Journal of Neurochemistry, 136(6), 1196–1203. doi:10.1111/jnc.13524.
    • Flagel, S. B., Cameron, C. M., Pickup, K. N., Watson, S. J., Akil, H., & Robinson, T. E. (2011). A food predictive cue must be attributed with incentive salience for it to induce c-fos mRNA expression in cortico-striatal-thalamic brain regions. Neuroscience, 196, 80–96. doi:10.1016/j.neuroscience.2011.09.004.
    • Flagel, S. B., Clark, J. J., Robinson, T. E., Mayo, L., Czuj, A., Willuhn, I., . . . Akil, H. (2011). A selective role for dopamine in stimulus-reward learning. Nature, 469(7328), 53–57. doi:nature09588 [pii] 10.1038/nature09588.
    • Frank, G. K., Shott, M. E., Riederer, J., & Pryor, T. L. (2016). Altered structural and effective connectivity in anorexia and bulimia nervosa in circuits that regulate energy and reward homeostasis. Translational Psychiatry, 6(11), e932. doi:10.1038/tp.2016.199.
    • Fraser, K. M., & Janak, P. H. (2017). Long-lasting contribution of dopamine in the nucleus accumbens core, but not dorsal lateral striatum, to sign-tracking. European Journal of Neuroscience, 46(4), 2047–2055. doi:10.1111/ejn.13642.
    • Gallistel, C. R., & Gibbon, J. (2000). Time, rate, and conditioning. Psychological Review, 107(2), 289–344.
    • Garfinkel, S. N., Abelson, J. L., King, A. P., Sripada, R. K., Wang, X., Gaines, L. M., & Liberzon, I. (2014). Impaired contextual modulation of memories in PTSD: An fMRI and psychophysiological study of extinction retention and fear renewal. Journal of Neuroscience, 34(40), 13435–13443. doi:10.1523/JNEUROSCI.4287–13.2014.
    • Gilbertson, M. W., Shenton, M. E., Ciszewski, A., Kasai, K., Lasko, N. B., Orr, S. P., & Pitman, R. K. (2002). Smaller hippocampal volume predicts pathologic vulnerability to psychological trauma. Nature Neuroscience, 5(11), 1242–1247. doi:10.1038/nn958.
    • Goetz, C. G., Tanner, C. M., & Klawans, H. L. (1982). Pharmacology of hallucinations induced by long-term drug therapy. American Journal of Psychiatry, 139(4), 494–497. doi:10.1176/ajp.139.4.494.
    • Goudriaan, A. E., de Ruiter, M. B., van den Brink, W., Oosterlaan, J., & Veltman, D. J. (2010). Brain activation patterns associated with cue reactivity and craving in abstinent problem gamblers, heavy smokers and healthy controls: An fMRI study. Addiction Biology, 15(4), 491–503. doi:10.1111/j.1369–1600.2010.00242.x.
    • Grant, B. F., Saha, T. D., Ruan, W. J., Goldstein, R. B., Chou, S. P., Jung, J., . . . Hasin, D. S. (2016). Epidemiology of DSM-5 drug use disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions-III. JAMA Psychiatry, 73(1), 39–47. doi:10.1001/jamapsychiatry.2015.2132.
    • Halmi, K. A., Tozzi, F., Thornton, L. M., Crow, S., Fichter, M. M., Kaplan, A. S., . . . Bulik, C. M. (2005). The relation among perfectionism, obsessive-compulsive personality disorder and obsessive-compulsive disorder in individuals with eating disorders. International Journal of Eating Disorder, 38(4), 371–374. doi:10.1002/eat.20190.
    • Hicks, B. M., Foster, K. T., Iacono, W. G., & McGue, M. (2013). Genetic and environmental influences on the familial transmission of externalizing disorders in adoptive and twin offspring. JAMA Psychiatry, 70(10), 1076–1083. doi:10.1001/jamapsychiatry.2013.258.
    • Holden, J. M., & Peoples, L. L. (2010). Effects of acute amphetamine exposure on two kinds of Pavlovian approach behavior. Behavioural Brain Research, 208(1), 270–273. doi:10.1016/j.bbr.2009.11.014.
    • Ito, R., Everitt, B. J., & Robbins, T. W. (2005). The hippocampus and appetitive Pavlovian conditioning: Effects of excitotoxic hippocampal lesions on conditioned locomotor activity and autoshaping. Hippocampus, 15(6), 713–721. doi:10.1002/hipo.20094.
    • Jansen, A., Theunissen, N., Slechten, K., Nederkoorn, C., Boon, B., Mulkens, S., & Roefs, A. (2003). Overweight children overeat after exposure to food cues. Eating Behavior, 4(2), 197–209. doi:10.1016/S1471–0153(03)00011–4.
    • Kapur, S., & Mamo, D. (2003). Half a century of antipsychotics and still a central role for dopamine D2 receptors. Progress in Neuropsychopharmacol Biological Psychiatry, 27(7), 1081–1090. doi:10.1016/j.pnpbp.2003.09.004.
    • Kapur, S., Mizrahi, R., & Li, M. (2005). From dopamine to salience to psychosis—linking biology, pharmacology and phenomenology of psychosis. Schizophrenia Research, 79(1), 59–68. doi:10.1016/j.schres.2005.01.003.
    • Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archive of General Psychiatry, 60(9), 929–937. doi:10.1001/archpsyc.60.9.929.
    • Kessler, R. C., Nelson, C. B., McGonagle, K. A., Liu, J., Swartz, M., & Blazer, D. G. (1996). Comorbidity of DSM-III-R major depressive disorder in the general population: Results from the US National Comorbidity Survey. British Journal of Psychiatry Supplement (30), 17–30.
    • Khantzian, E. J. (1985). The self-medication hypothesis of addictive disorders: Focus on heroin and cocaine dependence. American Journal of Psychiatry, 142(11), 1259–1264.
    • Ko, C. H., Liu, G. C., Hsiao, S., Yen, J. Y., Yang, M. J., Lin, W. C., . . . Chen, C. S. (2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43(7), 739–747. doi:10.1016/j.jpsychires.2008.09.012.
    • Koshy Cherian, A., Kucinski, A., Pitchers, K., Yegla, B., Parikh, V., Kim, Y., . . . Sarter, M. (2017). Unresponsive choline transporter as a trait neuromarker and a causal mediator of bottom-up attentional biases. Journal of Neuroscience, 37(11), 2947–2959. doi:10.1523/JNEUROSCI.3499–16.2017.
    • Kotov, R., Gamez, W., Schmidt, F., & Watson, D. (2010). Linking “big” personality traits to anxiety, depressive, and substance use disorders: a meta-analysis. Psychological Bulletin, 136(5), 768–821. doi:10.1037/a0020327.
    • Kucinski, A., Lustig, C., & Sarter, M. (2018). Addiction vulnerability trait impacts complex movement control: Evidence from sign-trackers. Behavioural Brain Research, 350, 139–148. doi: 10.1016/j.bbr.2018.04.045.
    • Kulka, R. A., Schlenger, W. E., Fairbank, J. A., Hough, R. L., Jordan, B. K., Marmar, C. R.. . . . Grady, D. A. (1990). Trauma and the Vietnam War generation: Report of findings from the National Vietnam Veterans Readjustment Study. New York, NY: Brunner/Mazel.
    • Leeman, R. F., & Potenza, M. N. (2013). A targeted review of the neurobiology and genetics of behavioural addictions: An emerging area of research. Canadian Journal of Psychiatry, 58(5), 260–273. doi:10.1177/070674371305800503.
    • Logue, M. W., van Rooij, S. J. H., Dennis, E. L., Davis, S. L., Hayes, J. P., Stevens, J. S., . . . Morey, R. A. (2018). Smaller hippocampal volume in posttraumatic stress disorder: A multisite ENIGMA-PGC study: subcortical volumetry results from posttraumatic stress disorder consortia. Biological Psychiatry, 83(3), 244–253. doi:10.1016/j.biopsych.2017.09.006.
    • Lovic, V., Saunders, B. T., Yager, L. M., & Robinson, T. E. (2011). Rats prone to attribute incentive salience to reward cues are also prone to impulsive action. Behavioural Brain Research, 223(2), 255–261. doi:S0166–4328(11)00293–2 [pii] 10.1016/j.bbr.2011.04.006.
    • Lyvers, M. (2000). “Loss of control” in alcoholism and drug addiction: a neuroscientific interpretation. Experimental and Clinical Psychopharmacology, 8(2), 225–249.
    • Maren, S., Phan, K. L., & Liberzon, I. (2013). The contextual brain: Implications for fear conditioning, extinction and psychopathology. Nature Reviews Neuroscience, 14(6), 417–428. doi:10.1038/nrn3492.
    • Marschner, A., Kalisch, R., Vervliet, B., Vansteenwegen, D., & Buchel, C. (2008). Dissociable roles for the hippocampus and the amygdala in human cued versus context fear conditioning. Journal of Neuroscience, 28(36), 9030–9036. doi:10.1523/JNEUROSCI.1651–08.2008.
    • McKenna, P. J. (1984). Disorders with overvalued ideas. British Journal of Psychiatry, 145, 579–585.
    • Menzies, L., Chamberlain, S. R., Laird, A. R., Thelen, S. M., Sahakian, B. J., & Bullmore, E. T. (2008). Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: The orbitofronto-striatal model revisited. Neuroscience and Biobehavioral Reviews, 32(3), 525–549. doi:10.1016/j.neubiorev.2007.09.005.
    • Meyer, P. J., Cogan, E. S., & Robinson, T. E. (2014). The form of a conditioned stimulus can influence the degree to which it acquires incentive motivational properties. PLoS One, 9(6), e98163. doi:10.1371/journal.pone.0098163.
    • Meyer, P. J., Ma, S. T., & Robinson, T. E. (2012). A cocaine cue is more preferred and evokes more frequency-modulated 50-kHz ultrasonic vocalizations in rats prone to attribute incentive salience to a food cue. Psychopharmacology (Berl), 219(4), 999–1009. doi:10.1007/s00213-011-2429-7.
    • Miyashita, Y., Nakajima, S., & Imada, H. (1999). Panel-touch behavior of horses established by an autoshaping procedure. Psychological Reports, 85(3 Pt 1), 867–868. doi:10.2466/pr0.1999.85.3.867.
    • Mogenson, G. J., Jones, D. L., & Yim, C. Y. (1980). From motivation to action: functional interface between the limbic system and the motor system. Progress in Neurobiology, 14(2–3), 69–97. doi:0301–0082(80)90018–0 [pii].
    • Morrow, J. D., Maren, S., & Robinson, T. E. (2011). Individual variation in the propensity to attribute incentive salience to an appetitive cue predicts the propensity to attribute motivational salience to an aversive cue. Behavioral Brain Research, 220(1), 238–243. doi:S0166–4328(11)00117–3 [pii] 10.1016/j.bbr.2011.02.013.
    • Morrow, J. D., Saunders, B. T., Maren, S., & Robinson, T. E. (2015). Sign-tracking to an appetitive cue predicts incubation of conditioned fear in rats. Behavioral Brain Research, 276, 59–66. doi:10.1016/j.bbr.2014.04.002.
    • Myin-Germeys, I., Delespaul, P. A., & deVries, M. W. (2000). Schizophrenia patients are more emotionally active than is assumed based on their behavior. Schizophrenia Bulletin, 26(4), 847–854.
    • Nakao, T., Okada, K., & Kanba, S. (2014). Neurobiological model of obsessive-compulsive disorder: Evidence from recent neuropsychological and neuroimaging findings. Psychiatry Clinical Neuroscience, 68(8), 587–605. doi:10.1111/pcn.12195.
    • Nilsson, J., Kristiansen, T. S., Fosseidengen, J. E., Ferno, A., & van den Bos, R. (2008). Sign- and goal-tracking in Atlantic cod (Gadus morhua). Animal Cognition, 11(4), 651–659. doi:10.1007/s10071-008-0155-2.
    • Ostergaard, S. D., Jensen, S. O., & Bech, P. (2011). The heterogeneity of the depressive syndrome: When numbers get serious. Acta Psychiatrica Scandinavica, 124(6), 495–496. doi:10.1111/j.1600–0447.2011.01744.x.
    • Paolone, G., Angelakos, C. C., Meyer, P. J., Robinson, T. E., & Sarter, M. (2013). Cholinergic control over attention in rats prone to attribute incentive salience to reward cues. Journal of Neuroscience, 33(19), 8321–8335. doi:10.1523/JNEUROSCI.0709–13.2013.
    • Phillips, R. G., & LeDoux, J. E. (1992). Differential contribution of amygdala and hippocampus to cued and contextual fear conditioning. Behavioral Neuroscience, 106(2), 274–285.
    • Piazza, P. V., Deminiere, J. M., Le Moal, M., & Simon, H. (1989). Factors that predict individual vulnerability to amphetamine self-administration. Science, 245(4925), 1511–1513.
    • Pingault, J. B., Cote, S. M., Galera, C., Genolini, C., Falissard, B., Vitaro, F., & Tremblay, R. E. (2013). Childhood trajectories of inattention, hyperactivity and oppositional behaviors and prediction of substance abuse/dependence: a 15-year longitudinal population-based study. Molecular Psychiatry, 18(7), 806–812. doi:10.1038/mp.2012.87.
    • Pitchers, K. K., Kane, L. F., Kim, Y., Robinson, T. E., & Sarter, M. (2017). ‘Hot’ vs. ‘cold’ behavioural-cognitive styles: motivational-dopaminergic vs. cognitive-cholinergic processing of a Pavlovian cocaine cue in sign- and goal-tracking rats. European Journal of Neuroscience, 46(11), 2768–2781. doi:10.1111/ejn.13741.
    • Pitchers, K. K., Phillips, K. B., Jones, J. L., Robinson, T. E., & Sarter, M. (2017). Diverse roads to relapse: A discriminative cue signaling cocaine availability is more effective in renewing cocaine seeking in goal trackers than sign trackers and depends on basal forebrain cholinergic activity. Journal of Neuroscience, 37(30), 7198–7208. doi:10.1523/JNEUROSCI.0990–17.2017.
    • Purdy, J. E., Roberts, A. C., & Garcia, C. A. (1999). Sign tracking in cuttlefish (Sepia officinalis). Journal of Comparative Psychology, 113(4), 443–449.
    • Regier, D. A., Farmer, M. E., Rae, D. S., Locke, B. Z., Keith, S. J., Judd, L. L., & Goodwin, F. K. (1990). Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA, 264(19), 2511–2518.
    • Ritsher, J. B., McKellar, J. D., Finney, J. W., Otilingam, P. G., & Moos, R. H. (2002). Psychiatric comorbidity, continuing care and mutual help as predictors of five-year remission from substance use disorders. Journal of Studies on Alcohol, 63(6), 709–715.
    • Robinson, T. E., & Flagel, S. B. (2009). Dissociating the predictive and incentive motivational properties of reward-related cues through the study of individual differences. Biological Psychiatry, 65(10), 869–873. doi:S0006–3223(08)01094–9 [pii] 10.1016/j.biopsych.2008.09.006.
    • Rougemont-Bucking, A., Linnman, C., Zeffiro, T. A., Zeidan, M. A., Lebron-Milad, K., Rodriguez-Romaguera, J., . . . Milad, M. R. (2011). Altered processing of contextual information during fear extinction in PTSD: an fMRI study. CNS Neuroscience Therapeutics, 17(4), 227–236. doi:10.1111/j.1755–5949.2010.00152.x.
    • Sackett, D. L. (1979). Bias in analytic research. Journal of Chronic Diseases, 32(1–2), 51–63.
    • Salamone, J. D., & Correa, M. (2012). The mysterious motivational functions of mesolimbic dopamine. Neuron, 76(3), 470–485. doi:10.1016/j.neuron.2012.10.021.
    • Samaha, A. N., Li, Y., & Robinson, T. E. (2002). The rate of intravenous cocaine administration determines susceptibility to sensitization. Journal of Neuroscience, 22(8), 3244–3250. doi:20026273.
    • Saunders, B. T., O’Donnell, E. G., Aurbach, E. L., & Robinson, T. E. (2014). A cocaine context renews drug seeking preferentially in a subset of individuals. Neuropsychopharmacology, 39(12), 2816–2823. doi:10.1038/npp.2014.131.
    • Saunders, B. T., & Robinson, T. E. (2012). The role of dopamine in the accumbens core in the expression of Pavlovian-conditioned responses. European Journal of Neuroscience, 36(4), 2521–2532. doi:10.1111/j.1460–9568.2012.08217.x.
    • Schaar, I., & Ojehagen, A. (2001). Severely mentally ill substance abusers: An 18-month follow-up study. Social Psychiatry Psychiatric Epidemiology, 36(2), 70–78.
    • Schmaal, L., Goudriaan, A. E., van der Meer, J., van den Brink, W., & Veltman, D. J. (2012). The association between cingulate cortex glutamate concentration and delay discounting is mediated by resting state functional connectivity. Brain Behavior, 2(5), 553–562. doi:10.1002/brb3.74.
    • Selden, N. R., Everitt, B. J., Jarrard, L. E., & Robbins, T. W. (1991). Complementary roles for the amygdala and hippocampus in aversive conditioning to explicit and contextual cues. Neuroscience, 42(2), 335–350.
    • Sharpe, M. J., Chang, C. Y., Liu, M. A., Batchelor, H. M., Mueller, L. E., Jones, J. L., . . . Schoenbaum, G. (2017). Dopamine transients are sufficient and necessary for acquisition of model-based associations. Nature Neuroscience, 20(5), 735–742. doi:10.1038/nn.4538.
    • Simon, N. W., Mendez, I. A., & Setlow, B. (2009). Effects of prior amphetamine exposure on approach strategy in appetitive Pavlovian conditioning in rats. Psychopharmacology (Berl), 202(4), 699–709. doi:10.1007/s00213–008–1353-y.
    • Singer, B. F., Fadanelli, M., Kawa, A. B., & Robinson, T. E. (2018). Are cocaine-seeking “habits” necessary for the development of addiction-like behavior in rats? Journal of Neuroscience, 38(1), 60–73. doi:10.1523/JNEUROSCI.2458–17.2017.
    • Smith, M. E. (2005). Bilateral hippocampal volume reduction in adults with post-traumatic stress disorder: a meta-analysis of structural MRI studies. Hippocampus, 15(6), 798–807. doi:10.1002/hipo.20102.
    • Steinberg, E. E., Keiflin, R., Boivin, J. R., Witten, I. B., Deisseroth, K., & Janak, P. H. (2013). A causal link between prediction errors, dopamine neurons and learning. Nature Neuroscience, 16(7), 966–973. doi:10.1038/nn.3413.
    • Thalemann, R., Wolfling, K., & Grusser, S. M. (2007). Specific cue reactivity on computer game-related cues in excessive gamers. Behavioral Neuroscience, 121(3), 614–618. doi:10.1037/0735–7044.121.3.614.
    • Treadway, M. T., & Zald, D. H. (2013). Parsing Anhedonia: Translational Models of Reward-Processing Deficits in Psychopathology. Current Directions in Psychological Science, 22(3), 244–249. doi:10.1177/0963721412474460.
    • Vanhille, N., Belin-Rauscent, A., Mar, A. C., Ducret, E., & Belin, D. (2015). High locomotor reactivity to novelty is associated with an increased propensity to choose saccharin over cocaine: New insights into the vulnerability to addiction. Neuropsychopharmacology, 40(3), 577–589. doi:10.1038/npp.2014.204.
    • Verdejo-Garcia, A., Lawrence, A. J., & Clark, L. (2008). Impulsivity as a vulnerability marker for substance-use disorders: Review of findings from high-risk research, problem gamblers and genetic association studies. Neuroscience & Biobehavioral Reviews, 32(4), 777–810. doi:10.1016/j.neubiorev.2007.11.003.
    • Voon, V., Mole, T. B., Banca, P., Porter, L., Morris, L., Mitchell, S., . . . Irvine, M. (2014). Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours. PLoS One, 9(7), e102419. doi:10.1371/journal.pone.0102419.
    • Wolf, M., van Doorn, G. S., Leimar, O., & Weissing, F. J. (2007). Life-history trade-offs favour the evolution of animal personalities. Nature, 447(7144), 581–584. doi:10.1038/nature05835.
    • Young, S., Rhodes, P., Touyz, S., & Hay, P. (2013). The relationship between obsessive-compulsive personality disorder traits, obsessive-compulsive disorder and excessive exercise in patients with anorexia nervosa: a systematic review. Journal of Eating Disorder, 1, 16. doi:10.1186/2050-2974-1-16.
    • Zhang, S. W., Bartsch, K., & Srinivasan, M. V. (1996). Maze learning by honeybees. Neurobiology of Learning and Memory, 66(3), 267–282. doi:10.1006/nlme.1996.0069.
    • Zimmerman, M., Ellison, W., Young, D., Chelminski, I., & Dalrymple, K. (2015). How many different ways do patients meet the diagnostic criteria for major depressive disorder? Comprehensive Psychiatry, 56, 29–34. doi:10.1016/j.comppsych.2014.09.007.