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Commentary
Response to commentaries regarding the Triadic Systems Model perspective Monique Ernst ⇑, Elizabeth Hale, Katherine O’Connell National Institute of Mental Health, National Institutes of Health, 15K North Drive, Bethesda, MD 20892, United States
Drs. Segalowitz and Luciana, editors at Brain and Cognition, initiated this special issue that brings together the presentation of a theoretical heuristic model of neural systems function, commentaries criticizing the model, and responses to these commentaries. We would like to thank them for this wonderful opportunity to discuss and re-think the triadic model. We also would like to thank Dr. Somerville and colleagues, Dr. Willoughby and Dr. Sercombe, and Drs. Luciana and Segalowitz for their thoughtful and insightful comments. Dr. Somerville and colleagues address more specifically the nature of the neural systems framework. Drs. Luciana and Segalowitz provide a different perspective on the triadic model that partitions neural systems into motivational approach vs. motivational avoidance systems as a way to address some of the limitations of the model. Finally, the commentaries offered by Dr. Willoughby and Dr. Sercombe emphasize the characterization of adolescence, and the behavioral features of adolescence that the triadic model is mapping onto neural systems. We first address the neural aspects and then the behavioral aspects of the model. Responses to Somerville et al. Commentaries 1. ‘‘Recent advances in cognitive neuroscience warrant reconsideration of the proposed structure–function trichotomy’’ Somerville and colleagues make the point that ‘‘recent advances in cognitive neuroscience warrant reconsideration of the proposed structure–function trichotomy’’. This point is important and welltaken. The past several years have witnessed a concerted effort to refine our understanding of the role of structures, and more recently networks, in the coding of cognitive, emotional and motivational processes. The triadic model has not yet taken full advantage of these advances, partly because of the difficulty in articulating specific discrete processes across a broadly-defined neural systems platform. However, as we try to refine the model by identifying more functionally specific networks within the neural systems triad, we hope to generate a new formulation of the neural mechanisms underlying motivated behavior. For example, we proposed in 2009 a quasi ‘‘fractal’’ dimension to the triadic model through the proposal of mirroring circuits within each module (see Fig. 1) (Ernst & Fudge, 2009). In other ⇑ Corresponding author. Tel.: +1 (301) 675 4525; fax: +1 (301) 402 2010. E-mail address: [email protected] (M. Ernst).
words, the striatal, amygdalar and PFC modules each was shown to hold a triadic network at the image of the full triadic model. Such representation, admittedly not quite matching the definition of ‘‘fractal’’ organization, emphasized the notion of multiple functions within each node. Specifically, each node carries not only their dominant function (e.g., striatum and approach), but also contributes to the other complementary functions (e.g., striatum and avoidance), supported by distinct subregions (see Fig. 1). More recently (Carlisi, Pavletic, & Ernst, 2013), we underscored the notion that neural systems models were incomplete in their formulation and needed to include a dynamic account of how neural systems/networks functionally related to one another. With the recent explosion of functional connectivity studies, we hope to garner the pieces to populate the missing links of the model. For example, a critical and still unclear question is how reward-related information navigates through the different parts of the striatum (e.g., ventral, dorsal striatum), and the different parts of the amygdala (e.g., basolateral, central nuclei). A follow-up question concerns the way in which such collaboration across sub-regions of a specific node differs when the goal is an approach response vs. an avoidant response. 2. ‘‘. . .theoretical shifts away from a modular view of valenced emotion centers of the brain and toward. . . salience and predictability.’’ Somerville and colleagues provide two examples that set the stage for proposing a revision of the model that would move away from a valence-based model (aversive/avoidance vs. appetitive/ approach) to a salience-based and/or predictability-based model. This is quite an interesting proposal. However, we are not sure how such a model can be directly translatable to behavior. For example, simplistically, highly salient stimuli may generate either a strong approach or a strong avoidance behavior. We believe that these two opposite responses have to be coded along separate, although overlapping circuitries (Hardin, Pine, & Ernst, 2009). In addition, the salience information is likely to be carried also by these two circuitries, explaining the difficulty in dissociating valence from salience networks consistently. For example, in a study that examined favorable outcomes (net positive gamble) vs. unfavorable outcome (net negative gamble) in a positive context (gain vs. no-gain) and a negative context (no-loss vs. loss), we reasoned that the positive context would enhance the salience
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Fig. 1. The fractal triadic model (Ernst & Fudge, 2009).
of the favorable outcome, and the negative context would enhance the salience of the negative outcome. We found a dissociation in the structures that were sensitive to ‘‘positive salience’’ (ventral striatum and orbitofrontal cortex) vs. those sensitive to the ‘‘negative salience’’ (amygdala and insula) (see Fig. 2). This finding is consistent with the idea of both valence and salience dimensions being coded within these amygdala and striatal circuits. This notion regarding the difficulty in clearly dissociating valence and salience also emerges in the meta-analysis of amygdala studies by Costafreda, Brammer, David, & Fu, 2008, cited by Somerville and colleagues. This meta-analysis revealed that the amygdala was generally activated by emotions, and seemed to be more responsive to negative emotions like fear and disgust than to positive emotions like happiness (Costafreda et al., 2008). The nature of the meta-analysis made it impossible to estimate a measure of the salience of the emotions, and the authors raised the possibility that the negative emotions were more arousing than the positive emotions. This is a possibility, but, unfortunately, there were no data to back-up this interpretation. Regarding the striatum, Somerville and colleagues note the role of the striatum in prediction error (PE) (Pagnoni, Zink, Montague, & Berns, 2002) and in
Fig. 2. Regional bold activation (y axis) during a Wheel of Fortune task in positive and negative contexts in nucleus accumbens (NAc), orbitofrontal cortex (OFC), medial prefrontal cortex (medPFC), amygdala and insula (Hardin et al., 2009).
coding both appetitive and aversive contexts (Delgado, Li, Schiller, & Phelps, 2008). Prediction error is coded in many other brain regions, including medial PFC, amygdala, posterior cingulate cortex, parietal cortex (e.g., Cohen et al., 2010; Rutledge, Dean, Caplin, & Glimcher, 2010). Dopamine is fundamental to this function (Schultz, 1998). How these different regions collaborate using their computation of PE to influence behavior has not yet been clarified. It would be quite interesting to map PE onto the triadic model. This could be achieved through the design of an activation fMRI study probing PE (positive and negative) and being optimized to examine directional functional connectivity among components of the striatum, amygdala and medial PFC, for example. 3. ‘‘. . .valence-specific theories. . .’’ A common thread through Somerville and colleagues’ comments concerns the issue of ‘‘valence specificity’’ that has been tagged onto the subcortical nodes of the triadic model. The triadic model has been developed to map motivated behavioral responses rather than the mechanisms underlying emotions. In addition, from the inception of the model in 2006 (Ernst, Pine, & Hardin, 2006), we consistently underlined the concept of ‘‘functional dominance,’’ and recognized a multidimensional role for each triadic node, including the processing of both aversive and appetitive information, particularly at the service of learning (associative learning and PE). We still believe in the dominant contribution of the striatum to the coding of reward-related processes that motivate approach behavior, and of the amygdala to the coding of threat information that triggers avoidance. As already mentioned, regarding the former, dopamine is the backbone of reward function, and the striatum is the main subcortical station of dopamine inputs, suggesting a privileged role of the striatum in reward function. Automatic avoidance through flee/freeze responses depends principally on the amygdala projections to hypothalamus and brainstem, also supporting a privileged role of the amygdala in stereotypical responses to threat. These reflections would support a different articulation of the triadic model. For example, it would be less controversial to avoid naming the approach circuit as Striatum-based, the avoidance circuit as Amygdala-based, and the control (arbitrator) circuit as
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Fig. 3. Parallel circuits within the ventral striatum, pre-frontal cortex and amygdala, which interact with features of attention and conditioning.
PFC-based. Instead, the model might feature two parallel and interacting triadic networks across the striatum, amygdala and PFC nodes, supporting approach and avoidant behavior, respectively (see Fig. 3). These two functional networks would process salient information and modulate effector systems (e.g., motor cortex, brainstem nuclei) to determine behavioral responses. With this organizational scheme, the nature of the interactions across both networks would be critical to define. These interactions are expected to vary as a function of a number of factors including type of stimuli, context, and individual characteristics. Of note, the locus (or loci) of balance that would determine the behavioral output is not clear. This type of model adds a new level of complexity that functional connectivity studies could certainly clarify. Finally, we would like to suggest that two other fundamental basic processes, attention and learning, should be included as modulators of the way information is processed across the triadic model. Broadly, attention filters information that is used to guide behavior. Learning confers valence and salience to stimuli, which are the two main features that attention uses to filter information. Therefore, any developmental changes in attention and learning processes would affect the working of the triadic model (e.g., Ernst, Daniele, & Frantz, 2011). Responses to the Luciana and Segalowitz commentary Quite elegantly, Luciana and Segalowitz begin their commentary by placing the triadic model in the historical context of psychological theory. This rich historical perspective, centered on explaining individual behavioral differences was indeed the springboard that led to the formulation of the triadic model. It highlights the goal of the triadic model to provide a mechanistic neural systems model that can explain individual behavioral differences in health and pathology, and also individual changes across development. Luciana and Segalowitz raise three issues, and by doing so, suggest an alternative version to the triadic model. 1. ‘‘Functional distinctions among the three proposed primary neural nodes’’ The first issue, somewhat reminiscent of Somerville commentaries, questions the functional specialization of the three nodes of the triadic model. This comment is important as it reflects the pervasive struggle in the current field of neuroimaging to reconcile anatomy with function. This is indeed a challenge as functional
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neuroimagers try to match cognitive processes with often blurry boundaries to overlapping neural systems, using tools with limited spatial resolution and signal-to-noise ratio. More specifically with regard to the triadic model, the three structures assigned to distinct behavioral dimensions are heterogeneous at all three anatomical, histological and neurochemical levels, and naturally, at the functional level too. At this juncture, we would like to stress two points. First, a model is by definition reductionist, balancing simplicity against accuracy and usefulness. Second, the triadic model is a neural systems model, based on neural networks rather than individual structures per se, although each of these neural networks involves a critical hub (striatum, amygdala, PFC). The critical hubs (i.e., nodes) are used to label the networks, a strategy which, unfortunately, has generated much confusion. A careful delineation of the putative different subregions associated with the networks of the triadic model has been addressed in Ernst and Fudge (2009). As suggested in Ernst and Fudge (2009), sub-circuits (or intranetworks as per Luciana and Segalowitz) may also be important to consider at the regional level (e.g., ventral-dorsal striatum). However, the challenge will be to keep the model simple enough to be useful. Luciana and Segalowitz propose to modify the triadic model into a dual-network model featuring an appetitive/approach motivational and an aversive/avoidance motivational system. There is a large body of animal and human data that informs such model (e.g., see Luciana and Segalowitz commentary), including our own data (Hardin et al., 2009). For us, the issue with such a model is that it makes it difficult to understand where or how these two systems communicate to arrive at a given avoidant or approach behavior. 2. ‘‘Terminology: Are motivation and emotion distinct?’’ The second issue raised by Luciana and Segalowitz is that of terminology. We completely agree with the incongruity of linking approach to ‘‘motivation’’ in contrast to linking avoidance to ‘‘emotion’’. These terms, motivation and emotion, are labels commonly attached to the striatum and amygdala. Again, such short-cut labeling, without qualifiers, is highly detrimental to the field, by fostering confusion. Both approach and avoidance infer motivation and emotion. We recognize that future descriptions of the model would benefit from a more precise usage of terminology. 3. ‘‘High Approach or Diminished Avoidance’’; ‘‘The Impact of pre-adolescent motivational biases and other individual difference factors’’ The third and fourth issues concern the proposed behavioral dissociation of high approach and low avoidance behavioral pattern in adolescents relative to other age groups, and the import of accounting for individual differences. The approach/avoidance formulations have been associated with reward sensitivity and punishment sensitivity. The balance between these two behavioral outputs depends highly on internal (e.g., anxiety) and external (e.g., ambiguity) context, as does the age-related changes in these behavioral manifestations. The triadic model handles this dichotomy by allowing influences from other systems onto the nodes/circuits of the triadic model. For example, the social dimension is a powerful modulator of behavior, particularly in adolescence. Accordingly, the social information processing network (Blakemore, 2008; Nelson, Leibenluft, McClure, & Pine, 2005) is expected to interact and influence nodes/networks of the triadic model, taking into account the overlaps of the social network with the triadic neural systems. A last note stresses inter-individual differences, that depend on a multitude of factors, again making the use of a simplistic model important but also theoretically biased
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(e.g., emphasizing given concepts over others), and incomplete, but heuristic, and helpful in guiding studies. Responses to the Willoughby et al. Commentary 1. Are the increases in mortality and morbidity from childhood to adolescence as dire as often implied?
mental illness receive appropriate diagnosis and treatment, and more than 2/3rd of the adults living with mental health problems reported noticing symptoms during their youth (Mental Health Commission of Canada, 2013). In addition, Kessler, Chiu, Demler, Merikangas, and Walters (2005) reported that roughly half of all mental health disorders have begun by age 14 (Kessler et al., 2005). 3. Is risk taking necessarily unregulated?
This is a good point, which echoes in some ways the first comment by Sercombe below. The increase in mortality related to accidents during adolescence is indeed small, fortunately, but not negligible and cannot be dismissed. The young adolescents are protected against dire consequences of their risk-taking proclivity by society (e.g., drinking legal age), parental control, and school. Older, college-age adolescents, who have fewer safeguards, are those who actually ‘‘make the statistics’’ (e.g., see Shulman & Cauffman, 2014). This contextual understanding of the progression of risk-taking, and its consequences in adolescence is relevant for the next point. 2. Does the peak age of involvement in real-world risk taking correspond to predictions based on the triadic systems model of brain development? Willoughby provides examples from the substance use literature to support the discrepancy between the claim of adolescence being a period when risk-taking propensity peaks and a linear progression of risk-taking behavior with age. Much of the research on risk-taking behavior uses alcohol studies as indicators of risk preference. Unfortunately, these data are mostly relevant for young adults (e.g., Hooshmand, Willoughby, & Good, 2012), and the study of adolescents is hindered by the legal drinking age. For example, Jernigan found that the average age of first time alcohol use in the United States, where the drinking age is 21 years old, is about 13 years. However in 23 European countries, with drinking ages ranging from 16 to 18 years, more than half of 11 year olds reported having tasted alcohol (Jernigan, 2001). In a similar analysis, Friese and Grube (2011) found that a majority of European countries have higher intoxication rates in young people than the United States and a higher number of Europeans reported being intoxicated before the age of 13 (Friese & Grube, 2011). These studies suggest that alcohol related behaviors are more a product of the drinking age and the culture than the propensity towards risky decision making. Studies on the use of illegal drugs across adolescence into young adulthood are also affected by a number of factors, including the adult supervision of young adolescents and their lack of financial solvency. Overall, it is difficult to gauge the level of risk-taking of young adolescents based on deleterious consequences because of all the protections that are extended to actually guard these youths against such consequences. However, a large long-standing literature, including the two volumes dedicated to Adolescence by Stanley Hall, 1904, identifies adolescence as a unique period of risk directly linked to changes in behavior. In addition, similar findings are reported in animal models of adolescence (Spear, 2000). The next point raised by Willoughby is the notion of adolescence being a vulnerable period for the development of mental problems. Adolescents have the highest rate of onset of depression and anxiety. Since adults have a much longer period (30–40 years) to develop psychiatric disorders than adolescents (8 years), lifetime prevalence of psychiatric disorders would be expected to be higher in adults than adolescents. In addition, there is a latency between recognizing symptoms and receiving a diagnosis or treatment. By the time adolescents receive a diagnosis they may already have transitioned into adults. The Mental Health Commission of Canada reported that fewer than 20% of the youth affected by
Willoughby argues that adolescents take calculated risks, not necessarily related to impulsivity. We fully agree with this statement. The triadic model does not necessarily conclude that this age group presents a higher level of impulsivity than adults, although high impulsivity is noted. The triadic model supports the idea that adolescents would take more calculated risks than adults because the lure of a reward may be stronger than the avoidance of a potential negative consequence, and the risk/benefit ratio of a decision may be intrinsically different in adolescents than in adults. In addition, there are environmental contexts (e.g., social) that may also influence the value of the ratio, as suggested by Willoughby. 4. What differs between adolescent and adult risk taking?’’ Willoughby refers to a behavioral model of decision-making describing a reflexive (fast, intuitive, automatic system) vs. reflective (slow, controlled) system (Friese, Hofmann, & Wanke, 2008). This model can be quite useful, and organizes decision-making along two dimensions (automaticity vs. control) that are different from those used in the triadic model. It would be interesting to try to integrate these two models. In fact, we used a parallel model, the dual model of attention (automatic vs. goal-directed) as a potential complement to the triadic model (Ernst et al., 2011). The dual attention system is well-described behaviorally and neurally, which provides a nice basis on which to try to link them together (Corbetta & Shulman, 2002). We proposed that, in adolescents, the automatic attention system might predominate over the goal-directed system, leading to decision-making more stimulusdriven than deliberate, and, in turn, potentially more risky. Responses to Sercombe Commentaries 1. ‘‘risk taking among adolescents is far from universal’’ Similarly to Willoughby, Sercombe questions the severity or universality of the ‘‘peak risk-taking behavior’’ in adolescence. Sercombe cites Payne as evidence that many young people are not particularly adventurous (Payne, 2012). Payne mostly argues that current metaphors (e.g., all gas, no breaks) oversimplify the adolescent brain. As with many psychosocial traits, riskiness varies between individuals; however, adolescence has been recognized as a time of elevated risk-taking, not only in humans but also in all mammals (Spear, 2000). A risk-averse child will probably not become a risk-seeking adolescent, but will likely exhibit the highest level of risk-taking across his lifetime during adolescence, even though this level of risk-taking might still be considered minimal in comparison to typical adolescents. To support his argument, Sercombe refers to data on condom usage, number of sex partners and illegal drug use data. These data might in part reflect the relatively constrained environment of adolescents (who are supervised and live with adult caretakers) rather than true indices of risk-taking, the same argument evoked earlier in response to Willoughby comments. In addition, more recent data provided by the Center for Disease Control (CDC) are less optimistic than those cited by Sercombe. For example, the CDC reported that females aged 15–19 were the most likely age group to have 4+ sexual partners in the past year
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(Chandra, Mosher, Copen, & Sionean, 2011). Sercombe cites illegal drug use statistics from the National Drug Use Survey (2007) noting that 10% of 12–17 year olds had used illegal drugs in the previous month, which is less than the number of 18–25 year olds (Aldworth, 2009). However, the CDC recently reported that more than 39.9% of high school students had tried marijuana, 11.4% for inhalants, 8.2% for ecstasy and 6.8% for cocaine (Eaton et al., 2012). It should be noted that most adult drug users began experimenting with illegal substances in their adolescence (CASA., 2011). Columbia University reported that 9 out of 10 people who meet criteria for substance use disorders began smoking, drinking or using drugs before they turned 18 (CASA, 2011). It was also reported that 75% of high school students have used addictive substances, including tobacco, alcohol, marijuana or cocaine (CASA, 2011). Taken together, these data are worrisome, and argue for a non-negligible rate of risky behaviors in adolescents. Moreover, these behaviors have the potential for negative consequences in adulthood. 2. ‘‘In the process, it hooked into deficit models of youth as pathology’’ We appreciate this remark, but respectfully disagree with it. In no way does the triadic model consider adolescent development as ‘‘pathological’’. The triadic model presents a dynamic interpretation of brain function, which is set at a unique equilibrium during adolescence in a way that permits the expression of behaviors facilitating the passage from childhood into adulthood (Spear, 2000). Risk-taking is necessary to enter a new unfamiliar world, as do adolescents when they step into adulthood, leaving the safe familial nest to enter the new adult-emerging world which is peer-based, hierarchically ordered and mostly based on self-reliance. The triadic model can map elevated reward seeking (strong approach system vs. avoidant system), elevated risk avoidance (strong avoidant system relative to the approach system), and alternative balances that depend on the context (internal as anxious states, or external as social vs. non-social environment). The flexibility of the triadic model can be used not only to explain age-related changes in behavior, but also behavioral disruptions in mental disorders. 3. Unlikely that risk-taking ‘‘is down to a simple two-way tug of war between reward seeking and regulatory function’’ We fully agree with Sercombe. The brain is of course influenced by environment (e.g., social), and both environment and brain function contribute to risk-taking. The triadic model is only a heuristic neuroscience tool to help formulate hypotheses and study motivated behavior in an organized and logical fashion. As a model, it is simplistic and does not account for the many factors that contribute to risk-taking. But the modulation of the neural triad by these other factors, such as social conditions, is integral to the research questions that we query. The neurobiological basis of the propensity for risk-taking, including impulsivity, is well-established. Behavior, by definition, is highly variable, inter-individually and intra-individually. Thus, to address questions of neural mechanisms, behavior needs to be studied in well-controlled situations. Accordingly, the identification of neural mechanisms of specific facets of behavior, such as risk-taking, is being done in highly controlled environment, such that all the myriad of potentially contributing factors, as described by Sercombe, cannot interfere with the main goal of identifying seminal neural substrates. It is in these well-controlled conditions that basic changes in elemental behaviors can be identified and studied. We fully agree that the neuroscientific approach captures only slices of the decision-making operation, but we believe that these slices are foundational to any decision-making process. In other words, we believe that we do escape the fate of the Blind Men exploring the Elephant. Finally,
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we wish to stress the need to study equally comprehensively each node of the triadic model, in relation with one another, and to particularly highlight the gaps in knowledge about the avoidance module as part of the balance that generates behavioral outputs, as also asserted by Luciana and Segalowitz. References Aldworth, J. (2009). Results from the 2007 national survey on drug use and health: National findings. DIANE Publishing. Blakemore, S. J. (2008). The social brain in adolescence. Nature Reviews Neuroscience, 9(4), 267–277. Carlisi, C. O., Pavletic, N., & Ernst, M. (2013). New perspectives on neural systems models of adolescent behavior: Functional brain connectivity. Neuropsychiatrie de l’Enfance et de l’Adolescence, 61(4), 209–218. CASA. (2011). Adolescent substance use: America’s #1 health problem. National Center of Alcohol and Substance Abuse at Columbia University (Ed.). New York (NY): CASA. Chandra, A., Mosher, W. D., Copen, C., & Sionean, C. (2011). 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Commentary
Response to commentaries regarding the Triadic Systems Model perspective Monique Ernst ⇑, Elizabeth Hale, Katherine O’Connell National Institute of Mental Health, National Institutes of Health, 15K North Drive, Bethesda, MD 20892, United States
Drs. Segalowitz and Luciana, editors at Brain and Cognition, initiated this special issue that brings together the presentation of a theoretical heuristic model of neural systems function, commentaries criticizing the model, and responses to these commentaries. We would like to thank them for this wonderful opportunity to discuss and re-think the triadic model. We also would like to thank Dr. Somerville and colleagues, Dr. Willoughby and Dr. Sercombe, and Drs. Luciana and Segalowitz for their thoughtful and insightful comments. Dr. Somerville and colleagues address more specifically the nature of the neural systems framework. Drs. Luciana and Segalowitz provide a different perspective on the triadic model that partitions neural systems into motivational approach vs. motivational avoidance systems as a way to address some of the limitations of the model. Finally, the commentaries offered by Dr. Willoughby and Dr. Sercombe emphasize the characterization of adolescence, and the behavioral features of adolescence that the triadic model is mapping onto neural systems. We first address the neural aspects and then the behavioral aspects of the model. Responses to Somerville et al. Commentaries 1. ‘‘Recent advances in cognitive neuroscience warrant reconsideration of the proposed structure–function trichotomy’’ Somerville and colleagues make the point that ‘‘recent advances in cognitive neuroscience warrant reconsideration of the proposed structure–function trichotomy’’. This point is important and welltaken. The past several years have witnessed a concerted effort to refine our understanding of the role of structures, and more recently networks, in the coding of cognitive, emotional and motivational processes. The triadic model has not yet taken full advantage of these advances, partly because of the difficulty in articulating specific discrete processes across a broadly-defined neural systems platform. However, as we try to refine the model by identifying more functionally specific networks within the neural systems triad, we hope to generate a new formulation of the neural mechanisms underlying motivated behavior. For example, we proposed in 2009 a quasi ‘‘fractal’’ dimension to the triadic model through the proposal of mirroring circuits within each module (see Fig. 1) (Ernst & Fudge, 2009). In other ⇑ Corresponding author. Tel.: +1 (301) 675 4525; fax: +1 (301) 402 2010. E-mail address: [email protected] (M. Ernst).
words, the striatal, amygdalar and PFC modules each was shown to hold a triadic network at the image of the full triadic model. Such representation, admittedly not quite matching the definition of ‘‘fractal’’ organization, emphasized the notion of multiple functions within each node. Specifically, each node carries not only their dominant function (e.g., striatum and approach), but also contributes to the other complementary functions (e.g., striatum and avoidance), supported by distinct subregions (see Fig. 1). More recently (Carlisi, Pavletic, & Ernst, 2013), we underscored the notion that neural systems models were incomplete in their formulation and needed to include a dynamic account of how neural systems/networks functionally related to one another. With the recent explosion of functional connectivity studies, we hope to garner the pieces to populate the missing links of the model. For example, a critical and still unclear question is how reward-related information navigates through the different parts of the striatum (e.g., ventral, dorsal striatum), and the different parts of the amygdala (e.g., basolateral, central nuclei). A follow-up question concerns the way in which such collaboration across sub-regions of a specific node differs when the goal is an approach response vs. an avoidant response. 2. ‘‘. . .theoretical shifts away from a modular view of valenced emotion centers of the brain and toward. . . salience and predictability.’’ Somerville and colleagues provide two examples that set the stage for proposing a revision of the model that would move away from a valence-based model (aversive/avoidance vs. appetitive/ approach) to a salience-based and/or predictability-based model. This is quite an interesting proposal. However, we are not sure how such a model can be directly translatable to behavior. For example, simplistically, highly salient stimuli may generate either a strong approach or a strong avoidance behavior. We believe that these two opposite responses have to be coded along separate, although overlapping circuitries (Hardin, Pine, & Ernst, 2009). In addition, the salience information is likely to be carried also by these two circuitries, explaining the difficulty in dissociating valence from salience networks consistently. For example, in a study that examined favorable outcomes (net positive gamble) vs. unfavorable outcome (net negative gamble) in a positive context (gain vs. no-gain) and a negative context (no-loss vs. loss), we reasoned that the positive context would enhance the salience
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Fig. 1. The fractal triadic model (Ernst & Fudge, 2009).
of the favorable outcome, and the negative context would enhance the salience of the negative outcome. We found a dissociation in the structures that were sensitive to ‘‘positive salience’’ (ventral striatum and orbitofrontal cortex) vs. those sensitive to the ‘‘negative salience’’ (amygdala and insula) (see Fig. 2). This finding is consistent with the idea of both valence and salience dimensions being coded within these amygdala and striatal circuits. This notion regarding the difficulty in clearly dissociating valence and salience also emerges in the meta-analysis of amygdala studies by Costafreda, Brammer, David, & Fu, 2008, cited by Somerville and colleagues. This meta-analysis revealed that the amygdala was generally activated by emotions, and seemed to be more responsive to negative emotions like fear and disgust than to positive emotions like happiness (Costafreda et al., 2008). The nature of the meta-analysis made it impossible to estimate a measure of the salience of the emotions, and the authors raised the possibility that the negative emotions were more arousing than the positive emotions. This is a possibility, but, unfortunately, there were no data to back-up this interpretation. Regarding the striatum, Somerville and colleagues note the role of the striatum in prediction error (PE) (Pagnoni, Zink, Montague, & Berns, 2002) and in
Fig. 2. Regional bold activation (y axis) during a Wheel of Fortune task in positive and negative contexts in nucleus accumbens (NAc), orbitofrontal cortex (OFC), medial prefrontal cortex (medPFC), amygdala and insula (Hardin et al., 2009).
coding both appetitive and aversive contexts (Delgado, Li, Schiller, & Phelps, 2008). Prediction error is coded in many other brain regions, including medial PFC, amygdala, posterior cingulate cortex, parietal cortex (e.g., Cohen et al., 2010; Rutledge, Dean, Caplin, & Glimcher, 2010). Dopamine is fundamental to this function (Schultz, 1998). How these different regions collaborate using their computation of PE to influence behavior has not yet been clarified. It would be quite interesting to map PE onto the triadic model. This could be achieved through the design of an activation fMRI study probing PE (positive and negative) and being optimized to examine directional functional connectivity among components of the striatum, amygdala and medial PFC, for example. 3. ‘‘. . .valence-specific theories. . .’’ A common thread through Somerville and colleagues’ comments concerns the issue of ‘‘valence specificity’’ that has been tagged onto the subcortical nodes of the triadic model. The triadic model has been developed to map motivated behavioral responses rather than the mechanisms underlying emotions. In addition, from the inception of the model in 2006 (Ernst, Pine, & Hardin, 2006), we consistently underlined the concept of ‘‘functional dominance,’’ and recognized a multidimensional role for each triadic node, including the processing of both aversive and appetitive information, particularly at the service of learning (associative learning and PE). We still believe in the dominant contribution of the striatum to the coding of reward-related processes that motivate approach behavior, and of the amygdala to the coding of threat information that triggers avoidance. As already mentioned, regarding the former, dopamine is the backbone of reward function, and the striatum is the main subcortical station of dopamine inputs, suggesting a privileged role of the striatum in reward function. Automatic avoidance through flee/freeze responses depends principally on the amygdala projections to hypothalamus and brainstem, also supporting a privileged role of the amygdala in stereotypical responses to threat. These reflections would support a different articulation of the triadic model. For example, it would be less controversial to avoid naming the approach circuit as Striatum-based, the avoidance circuit as Amygdala-based, and the control (arbitrator) circuit as
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Fig. 3. Parallel circuits within the ventral striatum, pre-frontal cortex and amygdala, which interact with features of attention and conditioning.
PFC-based. Instead, the model might feature two parallel and interacting triadic networks across the striatum, amygdala and PFC nodes, supporting approach and avoidant behavior, respectively (see Fig. 3). These two functional networks would process salient information and modulate effector systems (e.g., motor cortex, brainstem nuclei) to determine behavioral responses. With this organizational scheme, the nature of the interactions across both networks would be critical to define. These interactions are expected to vary as a function of a number of factors including type of stimuli, context, and individual characteristics. Of note, the locus (or loci) of balance that would determine the behavioral output is not clear. This type of model adds a new level of complexity that functional connectivity studies could certainly clarify. Finally, we would like to suggest that two other fundamental basic processes, attention and learning, should be included as modulators of the way information is processed across the triadic model. Broadly, attention filters information that is used to guide behavior. Learning confers valence and salience to stimuli, which are the two main features that attention uses to filter information. Therefore, any developmental changes in attention and learning processes would affect the working of the triadic model (e.g., Ernst, Daniele, & Frantz, 2011). Responses to the Luciana and Segalowitz commentary Quite elegantly, Luciana and Segalowitz begin their commentary by placing the triadic model in the historical context of psychological theory. This rich historical perspective, centered on explaining individual behavioral differences was indeed the springboard that led to the formulation of the triadic model. It highlights the goal of the triadic model to provide a mechanistic neural systems model that can explain individual behavioral differences in health and pathology, and also individual changes across development. Luciana and Segalowitz raise three issues, and by doing so, suggest an alternative version to the triadic model. 1. ‘‘Functional distinctions among the three proposed primary neural nodes’’ The first issue, somewhat reminiscent of Somerville commentaries, questions the functional specialization of the three nodes of the triadic model. This comment is important as it reflects the pervasive struggle in the current field of neuroimaging to reconcile anatomy with function. This is indeed a challenge as functional
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neuroimagers try to match cognitive processes with often blurry boundaries to overlapping neural systems, using tools with limited spatial resolution and signal-to-noise ratio. More specifically with regard to the triadic model, the three structures assigned to distinct behavioral dimensions are heterogeneous at all three anatomical, histological and neurochemical levels, and naturally, at the functional level too. At this juncture, we would like to stress two points. First, a model is by definition reductionist, balancing simplicity against accuracy and usefulness. Second, the triadic model is a neural systems model, based on neural networks rather than individual structures per se, although each of these neural networks involves a critical hub (striatum, amygdala, PFC). The critical hubs (i.e., nodes) are used to label the networks, a strategy which, unfortunately, has generated much confusion. A careful delineation of the putative different subregions associated with the networks of the triadic model has been addressed in Ernst and Fudge (2009). As suggested in Ernst and Fudge (2009), sub-circuits (or intranetworks as per Luciana and Segalowitz) may also be important to consider at the regional level (e.g., ventral-dorsal striatum). However, the challenge will be to keep the model simple enough to be useful. Luciana and Segalowitz propose to modify the triadic model into a dual-network model featuring an appetitive/approach motivational and an aversive/avoidance motivational system. There is a large body of animal and human data that informs such model (e.g., see Luciana and Segalowitz commentary), including our own data (Hardin et al., 2009). For us, the issue with such a model is that it makes it difficult to understand where or how these two systems communicate to arrive at a given avoidant or approach behavior. 2. ‘‘Terminology: Are motivation and emotion distinct?’’ The second issue raised by Luciana and Segalowitz is that of terminology. We completely agree with the incongruity of linking approach to ‘‘motivation’’ in contrast to linking avoidance to ‘‘emotion’’. These terms, motivation and emotion, are labels commonly attached to the striatum and amygdala. Again, such short-cut labeling, without qualifiers, is highly detrimental to the field, by fostering confusion. Both approach and avoidance infer motivation and emotion. We recognize that future descriptions of the model would benefit from a more precise usage of terminology. 3. ‘‘High Approach or Diminished Avoidance’’; ‘‘The Impact of pre-adolescent motivational biases and other individual difference factors’’ The third and fourth issues concern the proposed behavioral dissociation of high approach and low avoidance behavioral pattern in adolescents relative to other age groups, and the import of accounting for individual differences. The approach/avoidance formulations have been associated with reward sensitivity and punishment sensitivity. The balance between these two behavioral outputs depends highly on internal (e.g., anxiety) and external (e.g., ambiguity) context, as does the age-related changes in these behavioral manifestations. The triadic model handles this dichotomy by allowing influences from other systems onto the nodes/circuits of the triadic model. For example, the social dimension is a powerful modulator of behavior, particularly in adolescence. Accordingly, the social information processing network (Blakemore, 2008; Nelson, Leibenluft, McClure, & Pine, 2005) is expected to interact and influence nodes/networks of the triadic model, taking into account the overlaps of the social network with the triadic neural systems. A last note stresses inter-individual differences, that depend on a multitude of factors, again making the use of a simplistic model important but also theoretically biased
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(e.g., emphasizing given concepts over others), and incomplete, but heuristic, and helpful in guiding studies. Responses to the Willoughby et al. Commentary 1. Are the increases in mortality and morbidity from childhood to adolescence as dire as often implied?
mental illness receive appropriate diagnosis and treatment, and more than 2/3rd of the adults living with mental health problems reported noticing symptoms during their youth (Mental Health Commission of Canada, 2013). In addition, Kessler, Chiu, Demler, Merikangas, and Walters (2005) reported that roughly half of all mental health disorders have begun by age 14 (Kessler et al., 2005). 3. Is risk taking necessarily unregulated?
This is a good point, which echoes in some ways the first comment by Sercombe below. The increase in mortality related to accidents during adolescence is indeed small, fortunately, but not negligible and cannot be dismissed. The young adolescents are protected against dire consequences of their risk-taking proclivity by society (e.g., drinking legal age), parental control, and school. Older, college-age adolescents, who have fewer safeguards, are those who actually ‘‘make the statistics’’ (e.g., see Shulman & Cauffman, 2014). This contextual understanding of the progression of risk-taking, and its consequences in adolescence is relevant for the next point. 2. Does the peak age of involvement in real-world risk taking correspond to predictions based on the triadic systems model of brain development? Willoughby provides examples from the substance use literature to support the discrepancy between the claim of adolescence being a period when risk-taking propensity peaks and a linear progression of risk-taking behavior with age. Much of the research on risk-taking behavior uses alcohol studies as indicators of risk preference. Unfortunately, these data are mostly relevant for young adults (e.g., Hooshmand, Willoughby, & Good, 2012), and the study of adolescents is hindered by the legal drinking age. For example, Jernigan found that the average age of first time alcohol use in the United States, where the drinking age is 21 years old, is about 13 years. However in 23 European countries, with drinking ages ranging from 16 to 18 years, more than half of 11 year olds reported having tasted alcohol (Jernigan, 2001). In a similar analysis, Friese and Grube (2011) found that a majority of European countries have higher intoxication rates in young people than the United States and a higher number of Europeans reported being intoxicated before the age of 13 (Friese & Grube, 2011). These studies suggest that alcohol related behaviors are more a product of the drinking age and the culture than the propensity towards risky decision making. Studies on the use of illegal drugs across adolescence into young adulthood are also affected by a number of factors, including the adult supervision of young adolescents and their lack of financial solvency. Overall, it is difficult to gauge the level of risk-taking of young adolescents based on deleterious consequences because of all the protections that are extended to actually guard these youths against such consequences. However, a large long-standing literature, including the two volumes dedicated to Adolescence by Stanley Hall, 1904, identifies adolescence as a unique period of risk directly linked to changes in behavior. In addition, similar findings are reported in animal models of adolescence (Spear, 2000). The next point raised by Willoughby is the notion of adolescence being a vulnerable period for the development of mental problems. Adolescents have the highest rate of onset of depression and anxiety. Since adults have a much longer period (30–40 years) to develop psychiatric disorders than adolescents (8 years), lifetime prevalence of psychiatric disorders would be expected to be higher in adults than adolescents. In addition, there is a latency between recognizing symptoms and receiving a diagnosis or treatment. By the time adolescents receive a diagnosis they may already have transitioned into adults. The Mental Health Commission of Canada reported that fewer than 20% of the youth affected by
Willoughby argues that adolescents take calculated risks, not necessarily related to impulsivity. We fully agree with this statement. The triadic model does not necessarily conclude that this age group presents a higher level of impulsivity than adults, although high impulsivity is noted. The triadic model supports the idea that adolescents would take more calculated risks than adults because the lure of a reward may be stronger than the avoidance of a potential negative consequence, and the risk/benefit ratio of a decision may be intrinsically different in adolescents than in adults. In addition, there are environmental contexts (e.g., social) that may also influence the value of the ratio, as suggested by Willoughby. 4. What differs between adolescent and adult risk taking?’’ Willoughby refers to a behavioral model of decision-making describing a reflexive (fast, intuitive, automatic system) vs. reflective (slow, controlled) system (Friese, Hofmann, & Wanke, 2008). This model can be quite useful, and organizes decision-making along two dimensions (automaticity vs. control) that are different from those used in the triadic model. It would be interesting to try to integrate these two models. In fact, we used a parallel model, the dual model of attention (automatic vs. goal-directed) as a potential complement to the triadic model (Ernst et al., 2011). The dual attention system is well-described behaviorally and neurally, which provides a nice basis on which to try to link them together (Corbetta & Shulman, 2002). We proposed that, in adolescents, the automatic attention system might predominate over the goal-directed system, leading to decision-making more stimulusdriven than deliberate, and, in turn, potentially more risky. Responses to Sercombe Commentaries 1. ‘‘risk taking among adolescents is far from universal’’ Similarly to Willoughby, Sercombe questions the severity or universality of the ‘‘peak risk-taking behavior’’ in adolescence. Sercombe cites Payne as evidence that many young people are not particularly adventurous (Payne, 2012). Payne mostly argues that current metaphors (e.g., all gas, no breaks) oversimplify the adolescent brain. As with many psychosocial traits, riskiness varies between individuals; however, adolescence has been recognized as a time of elevated risk-taking, not only in humans but also in all mammals (Spear, 2000). A risk-averse child will probably not become a risk-seeking adolescent, but will likely exhibit the highest level of risk-taking across his lifetime during adolescence, even though this level of risk-taking might still be considered minimal in comparison to typical adolescents. To support his argument, Sercombe refers to data on condom usage, number of sex partners and illegal drug use data. These data might in part reflect the relatively constrained environment of adolescents (who are supervised and live with adult caretakers) rather than true indices of risk-taking, the same argument evoked earlier in response to Willoughby comments. In addition, more recent data provided by the Center for Disease Control (CDC) are less optimistic than those cited by Sercombe. For example, the CDC reported that females aged 15–19 were the most likely age group to have 4+ sexual partners in the past year
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(Chandra, Mosher, Copen, & Sionean, 2011). Sercombe cites illegal drug use statistics from the National Drug Use Survey (2007) noting that 10% of 12–17 year olds had used illegal drugs in the previous month, which is less than the number of 18–25 year olds (Aldworth, 2009). However, the CDC recently reported that more than 39.9% of high school students had tried marijuana, 11.4% for inhalants, 8.2% for ecstasy and 6.8% for cocaine (Eaton et al., 2012). It should be noted that most adult drug users began experimenting with illegal substances in their adolescence (CASA., 2011). Columbia University reported that 9 out of 10 people who meet criteria for substance use disorders began smoking, drinking or using drugs before they turned 18 (CASA, 2011). It was also reported that 75% of high school students have used addictive substances, including tobacco, alcohol, marijuana or cocaine (CASA, 2011). Taken together, these data are worrisome, and argue for a non-negligible rate of risky behaviors in adolescents. Moreover, these behaviors have the potential for negative consequences in adulthood. 2. ‘‘In the process, it hooked into deficit models of youth as pathology’’ We appreciate this remark, but respectfully disagree with it. In no way does the triadic model consider adolescent development as ‘‘pathological’’. The triadic model presents a dynamic interpretation of brain function, which is set at a unique equilibrium during adolescence in a way that permits the expression of behaviors facilitating the passage from childhood into adulthood (Spear, 2000). Risk-taking is necessary to enter a new unfamiliar world, as do adolescents when they step into adulthood, leaving the safe familial nest to enter the new adult-emerging world which is peer-based, hierarchically ordered and mostly based on self-reliance. The triadic model can map elevated reward seeking (strong approach system vs. avoidant system), elevated risk avoidance (strong avoidant system relative to the approach system), and alternative balances that depend on the context (internal as anxious states, or external as social vs. non-social environment). The flexibility of the triadic model can be used not only to explain age-related changes in behavior, but also behavioral disruptions in mental disorders. 3. Unlikely that risk-taking ‘‘is down to a simple two-way tug of war between reward seeking and regulatory function’’ We fully agree with Sercombe. The brain is of course influenced by environment (e.g., social), and both environment and brain function contribute to risk-taking. The triadic model is only a heuristic neuroscience tool to help formulate hypotheses and study motivated behavior in an organized and logical fashion. As a model, it is simplistic and does not account for the many factors that contribute to risk-taking. But the modulation of the neural triad by these other factors, such as social conditions, is integral to the research questions that we query. The neurobiological basis of the propensity for risk-taking, including impulsivity, is well-established. Behavior, by definition, is highly variable, inter-individually and intra-individually. Thus, to address questions of neural mechanisms, behavior needs to be studied in well-controlled situations. Accordingly, the identification of neural mechanisms of specific facets of behavior, such as risk-taking, is being done in highly controlled environment, such that all the myriad of potentially contributing factors, as described by Sercombe, cannot interfere with the main goal of identifying seminal neural substrates. It is in these well-controlled conditions that basic changes in elemental behaviors can be identified and studied. We fully agree that the neuroscientific approach captures only slices of the decision-making operation, but we believe that these slices are foundational to any decision-making process. In other words, we believe that we do escape the fate of the Blind Men exploring the Elephant. Finally,
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we wish to stress the need to study equally comprehensively each node of the triadic model, in relation with one another, and to particularly highlight the gaps in knowledge about the avoidance module as part of the balance that generates behavioral outputs, as also asserted by Luciana and Segalowitz. References Aldworth, J. (2009). Results from the 2007 national survey on drug use and health: National findings. DIANE Publishing. Blakemore, S. J. (2008). The social brain in adolescence. Nature Reviews Neuroscience, 9(4), 267–277. Carlisi, C. O., Pavletic, N., & Ernst, M. (2013). New perspectives on neural systems models of adolescent behavior: Functional brain connectivity. Neuropsychiatrie de l’Enfance et de l’Adolescence, 61(4), 209–218. CASA. (2011). Adolescent substance use: America’s #1 health problem. National Center of Alcohol and Substance Abuse at Columbia University (Ed.). New York (NY): CASA. Chandra, A., Mosher, W. D., Copen, C., & Sionean, C. (2011). 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