Brain and Cognition 89 (2014) 118–121
Contents lists available at ScienceDirect
Brain and Cognition journal homepage: www.elsevier.com/locate/b&c
Commentary
Some challenges for the triadic model for the study of adolescent motivated behavior Monica Luciana a,⇑, Sidney J. Segalowitz b a b
Department of Psychology and Center for Neurobehavioral Development, University of Minnesota, United States Department of Psychology, Brock University, Canada
a r t i c l e
i n f o
Article history: Accepted 28 May 2014 Available online 24 June 2014 Keywords: Adolescence Triadic model Reward Regulation Motivation
a b s t r a c t Within this special issue, Ernst has provided a comprehensive overview of the triadic neural systems model and its explanatory power for the conceptualization of adolescent development. Within this commentary, we encourage further consideration of several issues as this valuable model is expanded and articulated. These issues include the extent of functional distinctions among the three proposed neural nodes that comprise the triadic framework, the proposed dichotomy between motivation and emotion as linked to approach versus avoidance, the extent to which approach and avoidance can be dissociated on behavioral and neural levels during adolescent development, and how individual difference factors mechanistically interact with broader age-based developmental trends. Ó 2014 Elsevier Inc. All rights reserved.
Introduction Ernst has provided a comprehensive overview of the triadic neural systems model and its explanatory power for the conceptualization of adolescent brain and behavioral development. The triadic model is commendable given its mapping onto longstanding theories regarding the expression of individual differences through broad personality traits. Three-factor models have been proposed to account for the breadth of human individual differences in emotion and motivation (Tellegen & Waller, 1992), including the expression of such differences early in development (Rothbart, 2007). Like the triadic model, these models distinguish emotional tendencies that are positively- versus negatively-valenced and propose the existence of a third dimension of regulatory control. The value of recognizing the historical reach and breadth of the triadic framework is that decades of behavioral assessment contribute to the question of how emotional tendencies are defined, measured, and biologically represented. Ernst’s goal to provide a general account to explain inter-individual variability from a brain-based perspective links this historical foundation of psychological theory with cutting-edge neuroscience-based methodologies. Here, several issues are mentioned for further consideration as the model is refined and applied to adolescent development. These issues concern (a) the extent of functional distinctions among the three ⇑ Corresponding author. Address: Department of Psychology, University of Minnesota, Minneapolis, MN 55455, United States. E-mail address: [email protected] (M. Luciana). http://dx.doi.org/10.1016/j.bandc.2014.05.004 0278-2626/Ó 2014 Elsevier Inc. All rights reserved.
proposed neural nodes that comprise the triadic framework, (b) a descriptive concern regarding the proposed dichotomy between motivation and emotion with respect to approach versus avoidance, (c) the extent to which the proposed approach and avoidance networks can be functionally distinguished in the context of adolescent decision-making, and (d) the need to consider individual difference factors and how these factors mechanistically interact with broader age-based developmental trends. Functional distinctions among the three proposed primary neural nodes Current studies of the adolescent brain focus intently on structural brain changes (for instance, declines in cortical gray matter volumes and increases in white matter volumes) as well as functional brain activations during performance on decision-making tasks. This functional anatomical perspective has led to a proposed distinction between adolescent changes in regulatory capacities and changes in motivational substrates of behavior. Ernst’s triadic model builds upon this literature, in proposing a critically important distinction between motivational tendencies that are positive and approach-driven versus those that are negative and avoidancebased. Ernst links the regulatory system to prefrontal cortex (PFC) function. As recognized within the literature, this brain region is relatively vast and functionally heterogeneous (Blumenfeld, Nomura, Gratton, & D’Esposito, 2013; Bush, Luu, & Posner, 2000). The approach system is linked to the striatum (particularly the ventral striatal region), which is also relatively expansive, given
M. Luciana, S.J. Segalowitz / Brain and Cognition 89 (2014) 118–121
its anatomical connections (Alexander, DeLong, & Strick, 1986; Depue & Collins, 1999; Haber, 2003). The avoidance system is proposed to be centered in the amygdala, which encompasses several nuclear groups that differentially connect to other basal forebrain structures (Alheid, 2003). Two issues are potentially problematic with respect to functional distinctions among nodes within the triadic framework. First, the extent to which each primary neural node is unique to each behavioral repertoire varies. For instance, there is substantial disagreement regarding whether the amygdala has a limited role in facilitating fear/anxiety responses or whether it more broadly encompasses responses to contextual uncertainty (Hsu, Bhatt, Adolphs, Tranel, & Camerer, 2005; Sarinopoulos et al., 2010; Zaretsky, Mendelsohn, Mintz, & Hendler, 2010) or stimulus salience regardless of valence (Adolphs, 2010). Recent formulations suggest that it is highly responsive to salient positive as well as negative cues to achieve an adaptive coordination of cortical networks (Pessoa & Adolphs, 2010), and that the amygdala functions primarily as an attentional system, not an evaluative one. Pessoa and Adolphs (2010) argue that it is the pulvinar and its associated network with the prefrontal cortex that provides the evaluative function. Similar concerns are evident with respect to the nucleus accumbens (ventral striatal region) in terms of whether it is a core substrate of reward-seeking behavior as our group has suggested (Depue & Collins, 1999; Luciana & Collins, 2012), or whether, despite an apparent bias in linking positive motivation to action, it plays a broader role in the assignment of stimulus salience and learning about aversive cues (Koob, Wall, & Bloom, 1989). The PFC can also be subdivided into limbic versus cortical subregions with topographical distinctions between areas devoted to reward/approach/positive motivation and those devoted to loss/ avoidance/negative motivation (O’Doherty et al., 2003; Rushworth, Noonan, Boorman, Walton, & Behrens, 2011). Accordingly, distinct prefrontal networks may be topographically segregated to facilitate approach versus avoidance-based motivational goals, and maturation may not differentially map onto the modules in a straightforward way. Viewed in this manner, the regulatory system could be construed as a ‘‘servant’’ versus ‘‘master’’ in that it adaptively structures pursuit of goals within each motivational arm rather than acting in a valence-free manner. While the approach and avoidance modules are not capable on their own to self-regulate (by definition), the regulatory system may end up serving the goals of the other modules and thereby engineer high-level risky behaviors. The drag-racing teenager may consciously pick a deserted street; the adventure-seeker plots carefully to obtain new drugs. Thus, a more sophisticated PFC may lead to more cautious choices, but it may also lead to well-planned risky choices as well. The triadic model implies that the regulatory module only reins in the others, but perhaps it also serves them, depending on the context. The question as to which system serves whose goals may need an answer focusing on context and opportunity in addition to maturation of brain regions. Future iterations of the triadic model might benefit from a more nuanced network-based framework, akin to Alexander’s (1986) corticostriatal loops whereby approach and avoidance motivations are each generated within topographically segregated subcortical regions (potentially within the same neural structures), interconnect with topographically distinct regions of the striatum and thalamus, and are then regulated by medial (approach) versus lateral (avoidance) regions of the prefrontal cortex, the cingulate cortex, and insula. This alternative way of viewing the emotion/regulation interface suggests two major motivational systems, each with a regulatory component. These two systems can lead to considerable individual differences (see below).
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From a developmental perspective, the problem then becomes how to integrate data related to structural brain development and functional changes that occur throughout the adolescent period within a framework that is primarily centered around avoidance versus approach motivation. Developmental data suggest, for instance, that regulatory capacities mature in a linear fashion while motivational drives (at least with respect to approach/incentive motivation) peak in adolescence with lower levels observed in childhood and in adulthood. How can these distinct developmental trajectories for regulation and motivation be accommodated if the regulatory arm is inherent to the motivational system? One possibility is that a given motivational system develops such that the regulatory arm of each system is the last intra-network function to mature. Another approach is taken by Steinberg and his colleagues (Chein, Albert, O’Brien, Uckert, & Steinberg, 2011; Steinberg, 2008, pp. 90–100), who assert that adolescence is a period where the balance of approach and avoidance is tipped due to specific hormonal changes and social contexts. These are factors that would need to be integrated into the triadic model. An interesting question concerns whether regulation of approach behavior matures at the same rate as regulation over avoidance behavior. Another important consideration is that as a field of inquiry, those of us who study adolescent brain development may have become side-tracked by the wealth of available structural and functional imaging data, limiting consideration of other possible mechanisms that might explain the observed developmental trends. For instance, given the evidence for pronounced overlap in the structures that contribute to approach and avoidance responding, it may be that distinctions between approach and avoidance circuitry are neurochemical rather than structural or anatomical (Fudge & Haber, 2001). Our group has argued that the approach system is dopaminergically-modulated (Depue & Collins, 1999; Wahlstrom, Collins, White, & Luciana, 2010) and that adolescent risk-taking can be attributed to unique molecular features of the dopamine system during that period of the lifespan; others assert that avoidance-based responding is more strongly grounded in GABA-ergic and noradrenergic mechanisms (cf., Ninan, 1999), although this theorizing has not been comprehensively applied to the study of adolescent behavior outside of the clinical realm. A neurochemical framework allows the same structures to be strongly implicated in both motivational streams but through dissociable molecular mechanisms. Moreover, an emphasis on neurochemistry within a general framework characterized by multiple levels of analysis allows for the possibility of inverted-U-shaped quadratic patterns of behavioral and neurodevelopmental change (Luciana, Wahlstrom, Collins, & Porter, 2012), a trajectory that cannot otherwise be readily explained through structural refinements or increases in functional connectivity (Cho et al., 2013; Di Martino et al., 2008) that are hypothesized to occur between critical nodes that comprise the triadic framework with advancing development. Terminology: Are motivation and emotion distinct? Within the triadic framework, motivation is the term descriptively linked to approach; while ‘‘emotion’’ is the term linked to avoidance. Motivation is described as ‘‘the energy that fuels behavior’’ with the assumption that such energy will be positively valenced. In contrast, Ernst asserts that ‘‘Emotion defines internal subjective states that influence the direction of subjects’ actions’’. A more parsimonious organizational scheme might assign distinct subjective emotional states to each system, describe the action tendencies of each system as reflective of approach versus avoidance motivations, and then describe the neural substrates of the subjective (emotional) versus objective (motor) indices of each system. In other words, both approach and avoidance are mediated
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by action-based motivational systems with distinct subjective emotional features, with some potential complex interactions moderated by intensity of arousal that becomes especially rewarding during adolescence (e.g., horror films, scary rides). The crosssystem segregation of ‘‘motivation’’ (limited to approach) from ‘‘emotion’’ (limited to avoidance) has the potential to generate confusion in relation to the descriptive utility of the model as well as the nature of the neural systems involved. High approach or diminished avoidance—A developmental conundrum Recent dual systems models of adolescent development have focused on the linear development of regulatory control and an apparent quadratic increase in approach behavior in mid-to-late adolescence (Luciana et al., 2012; Urosevic, Collins, Muetzel, & Luciana, 2012). As Ernst indicates, much of the evidence in support of the increase in approach motivation is derived from functional neuroimaging studies of adolescents as they perform risk-taking tasks. While many (including our own group: Luciana et al., 2012) have emphasized the implications of the findings for positive motivation and its development during adolescence, a neglected series of findings relates to the adolescent’s apparently diminished level of risk-aversion or what might be construed as a decreased sensitivity of the avoidance system. For instance, van Leijenhorst et al. (2010) and Ernst et al. (2005) have both observed that, relative to adults, adolescents demonstrated decreased risk aversion. van Leijenhorst et al. (2010) reported that adults relative to adolescents were more risk-averse in contexts characterized by the highest ambiguity in relation to outcome. In general, more cautious behavioral choices were associated with distinct patterns of prefrontal activation as compared with risky choices. Using a different gambling paradigm, Eshel, Nelson, Blair, Pine, and Ernst (2007) found that adults, relative to adolescents, showed more activation in the dorsal ACC and VLPFC when they made risky choices together with a general tendency to avoid risky behaviors. Interpretive challenges are posed by the observation of increased approach/risk-taking and heighted ventral striatal activations coincident with decreased avoidance/sensitivity to punishment within adolescents during the same decision-making task. An intertwining of the two systems is likely to be ecologically valid in the context of real-world behavior, given the contextual ambiguity that characterizes many decision-making contexts. This ambiguity raises questions. Do approach and avoidance systems function independently or are they necessarily functionally interconnected? The avoidance system has been sorely neglected in current studies of adolescent neurodevelopment. The impact of pre-adolescent motivational biases and other individual difference factors Finally, just as more work is needed to determine the extent to which the approach and avoidance systems are interdependent, the impact of inherent trait dispositions and other individual difference factors might be more comprehensively considered as predictors of adolescent behavior. For instance, van Leijenhorst et al. (2010) found that individual differences in risk-taking biases moderated brain responses to high versus low risk conditions. We have found, that individual differences in behavioral activation predict the magnitude of later increases during the adolescent period (Urosevic et al., 2012) and that structural variations in subcortical brain volumes also contribute to these effects. Similarly, we have observed that some adolescents react to the presence of peers with reduced risk-taking rather than the converse (Segalowitz et al., 2012). To the extent that adolescence has been associated with problematic risk-taking behaviors, it is important to clarify that
not all individuals are equivalently vulnerable. Such individual differences could conceivably contradict the characterization of there being a general increase in risk for risky behavior in adolescence. The reasons for this inter-individual variation, at genetic, neurochemical, and environmental levels are critical to ascertain. Accordingly, the triadic model could be expanded to accommodate such wide individual differences. To conclude, the triadic framework proposed by Ernst provides a provocative and necessary challenge to the field of adolescent brain development, prompting us to consider how behavioral tendencies toward approach versus avoidance, as representative of the brain’s two major motivational systems, are dissociated in real-world decision-making contexts, whether their neural substrates are more overlapping than distinct, and whether other neurodynamic mechanisms and individual difference factors contribute to group-based developmental trends. References Adolphs, R. (2010). What does the amygdala contribute to social cognition? Annals of the New York Academy of Sciences, 1191, 42–61. http://dx.doi.org/10.1111/ j.1749-6632.2010.05445. Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. Alheid, G. F. (2003). Extended amygdala and basal forebrain. Annals of the New York Academy of Sciences, 985, 185–205. Blumenfeld, R. S., Nomura, E. M., Gratton, C., & D’Esposito, M. (2013). Lateral prefrontal cortex is organized into parallel dorsal and ventral streams along the rostro-caudal axis. Cerebral Cortex, 10, 2457–2466. Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Science, 4(6), 215–222. Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Developmental Science, 14(2), F1–F10. http://dx.doi.org/10.1111/j.14677687.2010.01035.x. Cho, Y. T., Fromm, S., Guyer, A. E., Detloff, A., Pine, D. S., Fudge, J. L., et al. (2013). Nucleus accumbens, thalamus and insula connectivity during incentive anticipation in typical adults and adolescents. Neuroimage, 66, 508–521. Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of personality: Dopamine, facilitation of incentive motivation, and extraversion. Behavioral and Brain Sciences, 22, 491–517. Di Martino, A., Scheres, A., Margulies, D. S., Kelly, A. M. C., Uddin, L. Q., Shehzad, Z., et al. (2008). Functional connectivity of human striatum: A resting state fMRI study. Cerebral Cortex, 18, 2735–2747. Ernst, M., Nelson, E. E., Jazbec, S. P., McClure, E. B., Monk, C. S., & Leibenluft, E. (2005). Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. NeuroImage, 25, 1279–1291. Eshel, N., Nelson, E. E., Blair, R. J., Pine, D. S., & Ernst, M. (2007). Neural substrates of choices selection in adults and adolescents: Development of the ventrolateral prefrontal and anterior cingulate cortices. Neuropsychologia, 45, 1270–1279. Fudge, J. L., & Haber, S. N. (2001). Bed nucleus of the stria terminalis and extended amygdala inputs to dopamine subpopulations in primates. Neuroscience, 104(3), 807–827. Haber, S. (2003). The primate basal ganglia: Parallel and integrative networks. Journal of Chemical Neuroanatomy, 26(4), 317–330. Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. (2005). Neural systems responding to degrees of uncertainty in human decision-making. Science, 310, 1680–1683. Koob, G. F., Wall, T. L., & Bloom, F. E. (1989). Nucleus accumbens as a substrate for the aversive stimulus effects of opiate withdrawal. Psychopharmacology (Berlin), 98, 530–534. Luciana, M., & Collins, P. F. (2012). Incentive motivation, cognitive control, and the adolescent brain: Is it time for a paradigm shift? Child Development Perspectives, 6(4), 392–399. Luciana, M., Wahlstrom, D., Collins, P. F., & Porter, J. N. (2012). Dopaminergic modulation of incentive motivation in adolescence: Age-related changes in signaling, individual differences, and implications for the development of selfregulation. Developmental Psychology, 48(3), 844–861. Ninan, P. T. (1999). The functional anatomy, neurochemistry, and pharmacology of anxiety. Journal of Clinical Psychiatry, 60(Suppl. 22), 12–17. O’Doherty, J., Critchley, H., Deichmann, R., & Dolan, R. J. (2003). Dissociating valence of outcome from behavioral control in human ventral and orbital prefrontal cortices. Journal of Neuroscience, 23(21), 7931–7939. Pessoa, L., & Adolphs, R. (2010). Emotion processing and the amygdala: From a ‘low road’ to ‘many roads’ of evaluating biological significance. Nature Reviews Neuroscience, 11, 773–783. Rothbart, M. K. (2007). Temperament, development and personality. Current Directions in Psychological Science, 16(4), 207–212. http://dx.doi.org/10.1111/ j.1467-8721.2007.00505.x.
M. Luciana, S.J. Segalowitz / Brain and Cognition 89 (2014) 118–121 Rushworth, M. F. S., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E. (2011). Frontal cortex and reward-guided learning and decision-making. Neuron, 70, 1054–1069. Sarinopoulos, I., Grupe, D. W., Mackiewicz, K. L., Herrington, J. D., Lor, M., Steege, E. E., et al. (2010). Uncertainty during anticipation modulates neural responses to aversion in human insula and amygdala. Cerebral Cortex, 20, 929–940. http:// dx.doi.org/10.1093/cercor/bhp155. Segalowitz, S. J., Santesso, D. L., Willoughby, T., Reker, D. L., Campbell, K., Chalmers, H., et al. (2012). Adolescent peer interaction and trait surgency weaken medial prefrontal cortex responses to failure. Social, Cognitive and Affective Neuroscience, 7, 115–124. Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28(1), 78–106. Tellegen, A., & Waller, N. G. (1992). Exploring personality through test construction: Development of the Multi-dimensional Personality Questionnaire (MPQ). Department of Psychology, University of Minnesota, unpublished manuscript.
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Urosevic, S., Collins, P. F., Muetzel, R., & Luciana, M. (2012). Longitudinal changes in adolescent behavioral approach system and behavioral inhibition system sensitivities: Associations with OFC and nucleus accumbens volumes. Developmental Psychology, 48(5), 1488–1500. van Leijenhorst, L., Gunther Moor, B., Op de Macks, Z. A., Rombouts, S. A. R. B., Westenberg, P. M., & Crone, E. A. (2010). Adolescent risky decision making: Neurocognitive development of reward and control regions. Neuroimage, 51, 345–355. Wahlstrom, D., Collins, P. F., White, T., & Luciana, M. (2010). Developmental changes in dopamine neurotransmission in adolescence: Behavioral implications and issues in assessment. Brain and Cognition, 72(1), 146–159. Zaretsky, M., Mendelsohn, A., Mintz, M., & Hendler, T. (2010). In the eye of the beholder: Internally driven uncertainty of danger recruits the amygdala and dorsomedial prefrontal cortex. Journal of Cognitive Neuroscience, 22(10), 2263–2275.
Contents lists available at ScienceDirect
Brain and Cognition journal homepage: www.elsevier.com/locate/b&c
Commentary
Some challenges for the triadic model for the study of adolescent motivated behavior Monica Luciana a,⇑, Sidney J. Segalowitz b a b
Department of Psychology and Center for Neurobehavioral Development, University of Minnesota, United States Department of Psychology, Brock University, Canada
a r t i c l e
i n f o
Article history: Accepted 28 May 2014 Available online 24 June 2014 Keywords: Adolescence Triadic model Reward Regulation Motivation
a b s t r a c t Within this special issue, Ernst has provided a comprehensive overview of the triadic neural systems model and its explanatory power for the conceptualization of adolescent development. Within this commentary, we encourage further consideration of several issues as this valuable model is expanded and articulated. These issues include the extent of functional distinctions among the three proposed neural nodes that comprise the triadic framework, the proposed dichotomy between motivation and emotion as linked to approach versus avoidance, the extent to which approach and avoidance can be dissociated on behavioral and neural levels during adolescent development, and how individual difference factors mechanistically interact with broader age-based developmental trends. Ó 2014 Elsevier Inc. All rights reserved.
Introduction Ernst has provided a comprehensive overview of the triadic neural systems model and its explanatory power for the conceptualization of adolescent brain and behavioral development. The triadic model is commendable given its mapping onto longstanding theories regarding the expression of individual differences through broad personality traits. Three-factor models have been proposed to account for the breadth of human individual differences in emotion and motivation (Tellegen & Waller, 1992), including the expression of such differences early in development (Rothbart, 2007). Like the triadic model, these models distinguish emotional tendencies that are positively- versus negatively-valenced and propose the existence of a third dimension of regulatory control. The value of recognizing the historical reach and breadth of the triadic framework is that decades of behavioral assessment contribute to the question of how emotional tendencies are defined, measured, and biologically represented. Ernst’s goal to provide a general account to explain inter-individual variability from a brain-based perspective links this historical foundation of psychological theory with cutting-edge neuroscience-based methodologies. Here, several issues are mentioned for further consideration as the model is refined and applied to adolescent development. These issues concern (a) the extent of functional distinctions among the three ⇑ Corresponding author. Address: Department of Psychology, University of Minnesota, Minneapolis, MN 55455, United States. E-mail address: [email protected] (M. Luciana). http://dx.doi.org/10.1016/j.bandc.2014.05.004 0278-2626/Ó 2014 Elsevier Inc. All rights reserved.
proposed neural nodes that comprise the triadic framework, (b) a descriptive concern regarding the proposed dichotomy between motivation and emotion with respect to approach versus avoidance, (c) the extent to which the proposed approach and avoidance networks can be functionally distinguished in the context of adolescent decision-making, and (d) the need to consider individual difference factors and how these factors mechanistically interact with broader age-based developmental trends. Functional distinctions among the three proposed primary neural nodes Current studies of the adolescent brain focus intently on structural brain changes (for instance, declines in cortical gray matter volumes and increases in white matter volumes) as well as functional brain activations during performance on decision-making tasks. This functional anatomical perspective has led to a proposed distinction between adolescent changes in regulatory capacities and changes in motivational substrates of behavior. Ernst’s triadic model builds upon this literature, in proposing a critically important distinction between motivational tendencies that are positive and approach-driven versus those that are negative and avoidancebased. Ernst links the regulatory system to prefrontal cortex (PFC) function. As recognized within the literature, this brain region is relatively vast and functionally heterogeneous (Blumenfeld, Nomura, Gratton, & D’Esposito, 2013; Bush, Luu, & Posner, 2000). The approach system is linked to the striatum (particularly the ventral striatal region), which is also relatively expansive, given
M. Luciana, S.J. Segalowitz / Brain and Cognition 89 (2014) 118–121
its anatomical connections (Alexander, DeLong, & Strick, 1986; Depue & Collins, 1999; Haber, 2003). The avoidance system is proposed to be centered in the amygdala, which encompasses several nuclear groups that differentially connect to other basal forebrain structures (Alheid, 2003). Two issues are potentially problematic with respect to functional distinctions among nodes within the triadic framework. First, the extent to which each primary neural node is unique to each behavioral repertoire varies. For instance, there is substantial disagreement regarding whether the amygdala has a limited role in facilitating fear/anxiety responses or whether it more broadly encompasses responses to contextual uncertainty (Hsu, Bhatt, Adolphs, Tranel, & Camerer, 2005; Sarinopoulos et al., 2010; Zaretsky, Mendelsohn, Mintz, & Hendler, 2010) or stimulus salience regardless of valence (Adolphs, 2010). Recent formulations suggest that it is highly responsive to salient positive as well as negative cues to achieve an adaptive coordination of cortical networks (Pessoa & Adolphs, 2010), and that the amygdala functions primarily as an attentional system, not an evaluative one. Pessoa and Adolphs (2010) argue that it is the pulvinar and its associated network with the prefrontal cortex that provides the evaluative function. Similar concerns are evident with respect to the nucleus accumbens (ventral striatal region) in terms of whether it is a core substrate of reward-seeking behavior as our group has suggested (Depue & Collins, 1999; Luciana & Collins, 2012), or whether, despite an apparent bias in linking positive motivation to action, it plays a broader role in the assignment of stimulus salience and learning about aversive cues (Koob, Wall, & Bloom, 1989). The PFC can also be subdivided into limbic versus cortical subregions with topographical distinctions between areas devoted to reward/approach/positive motivation and those devoted to loss/ avoidance/negative motivation (O’Doherty et al., 2003; Rushworth, Noonan, Boorman, Walton, & Behrens, 2011). Accordingly, distinct prefrontal networks may be topographically segregated to facilitate approach versus avoidance-based motivational goals, and maturation may not differentially map onto the modules in a straightforward way. Viewed in this manner, the regulatory system could be construed as a ‘‘servant’’ versus ‘‘master’’ in that it adaptively structures pursuit of goals within each motivational arm rather than acting in a valence-free manner. While the approach and avoidance modules are not capable on their own to self-regulate (by definition), the regulatory system may end up serving the goals of the other modules and thereby engineer high-level risky behaviors. The drag-racing teenager may consciously pick a deserted street; the adventure-seeker plots carefully to obtain new drugs. Thus, a more sophisticated PFC may lead to more cautious choices, but it may also lead to well-planned risky choices as well. The triadic model implies that the regulatory module only reins in the others, but perhaps it also serves them, depending on the context. The question as to which system serves whose goals may need an answer focusing on context and opportunity in addition to maturation of brain regions. Future iterations of the triadic model might benefit from a more nuanced network-based framework, akin to Alexander’s (1986) corticostriatal loops whereby approach and avoidance motivations are each generated within topographically segregated subcortical regions (potentially within the same neural structures), interconnect with topographically distinct regions of the striatum and thalamus, and are then regulated by medial (approach) versus lateral (avoidance) regions of the prefrontal cortex, the cingulate cortex, and insula. This alternative way of viewing the emotion/regulation interface suggests two major motivational systems, each with a regulatory component. These two systems can lead to considerable individual differences (see below).
119
From a developmental perspective, the problem then becomes how to integrate data related to structural brain development and functional changes that occur throughout the adolescent period within a framework that is primarily centered around avoidance versus approach motivation. Developmental data suggest, for instance, that regulatory capacities mature in a linear fashion while motivational drives (at least with respect to approach/incentive motivation) peak in adolescence with lower levels observed in childhood and in adulthood. How can these distinct developmental trajectories for regulation and motivation be accommodated if the regulatory arm is inherent to the motivational system? One possibility is that a given motivational system develops such that the regulatory arm of each system is the last intra-network function to mature. Another approach is taken by Steinberg and his colleagues (Chein, Albert, O’Brien, Uckert, & Steinberg, 2011; Steinberg, 2008, pp. 90–100), who assert that adolescence is a period where the balance of approach and avoidance is tipped due to specific hormonal changes and social contexts. These are factors that would need to be integrated into the triadic model. An interesting question concerns whether regulation of approach behavior matures at the same rate as regulation over avoidance behavior. Another important consideration is that as a field of inquiry, those of us who study adolescent brain development may have become side-tracked by the wealth of available structural and functional imaging data, limiting consideration of other possible mechanisms that might explain the observed developmental trends. For instance, given the evidence for pronounced overlap in the structures that contribute to approach and avoidance responding, it may be that distinctions between approach and avoidance circuitry are neurochemical rather than structural or anatomical (Fudge & Haber, 2001). Our group has argued that the approach system is dopaminergically-modulated (Depue & Collins, 1999; Wahlstrom, Collins, White, & Luciana, 2010) and that adolescent risk-taking can be attributed to unique molecular features of the dopamine system during that period of the lifespan; others assert that avoidance-based responding is more strongly grounded in GABA-ergic and noradrenergic mechanisms (cf., Ninan, 1999), although this theorizing has not been comprehensively applied to the study of adolescent behavior outside of the clinical realm. A neurochemical framework allows the same structures to be strongly implicated in both motivational streams but through dissociable molecular mechanisms. Moreover, an emphasis on neurochemistry within a general framework characterized by multiple levels of analysis allows for the possibility of inverted-U-shaped quadratic patterns of behavioral and neurodevelopmental change (Luciana, Wahlstrom, Collins, & Porter, 2012), a trajectory that cannot otherwise be readily explained through structural refinements or increases in functional connectivity (Cho et al., 2013; Di Martino et al., 2008) that are hypothesized to occur between critical nodes that comprise the triadic framework with advancing development. Terminology: Are motivation and emotion distinct? Within the triadic framework, motivation is the term descriptively linked to approach; while ‘‘emotion’’ is the term linked to avoidance. Motivation is described as ‘‘the energy that fuels behavior’’ with the assumption that such energy will be positively valenced. In contrast, Ernst asserts that ‘‘Emotion defines internal subjective states that influence the direction of subjects’ actions’’. A more parsimonious organizational scheme might assign distinct subjective emotional states to each system, describe the action tendencies of each system as reflective of approach versus avoidance motivations, and then describe the neural substrates of the subjective (emotional) versus objective (motor) indices of each system. In other words, both approach and avoidance are mediated
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by action-based motivational systems with distinct subjective emotional features, with some potential complex interactions moderated by intensity of arousal that becomes especially rewarding during adolescence (e.g., horror films, scary rides). The crosssystem segregation of ‘‘motivation’’ (limited to approach) from ‘‘emotion’’ (limited to avoidance) has the potential to generate confusion in relation to the descriptive utility of the model as well as the nature of the neural systems involved. High approach or diminished avoidance—A developmental conundrum Recent dual systems models of adolescent development have focused on the linear development of regulatory control and an apparent quadratic increase in approach behavior in mid-to-late adolescence (Luciana et al., 2012; Urosevic, Collins, Muetzel, & Luciana, 2012). As Ernst indicates, much of the evidence in support of the increase in approach motivation is derived from functional neuroimaging studies of adolescents as they perform risk-taking tasks. While many (including our own group: Luciana et al., 2012) have emphasized the implications of the findings for positive motivation and its development during adolescence, a neglected series of findings relates to the adolescent’s apparently diminished level of risk-aversion or what might be construed as a decreased sensitivity of the avoidance system. For instance, van Leijenhorst et al. (2010) and Ernst et al. (2005) have both observed that, relative to adults, adolescents demonstrated decreased risk aversion. van Leijenhorst et al. (2010) reported that adults relative to adolescents were more risk-averse in contexts characterized by the highest ambiguity in relation to outcome. In general, more cautious behavioral choices were associated with distinct patterns of prefrontal activation as compared with risky choices. Using a different gambling paradigm, Eshel, Nelson, Blair, Pine, and Ernst (2007) found that adults, relative to adolescents, showed more activation in the dorsal ACC and VLPFC when they made risky choices together with a general tendency to avoid risky behaviors. Interpretive challenges are posed by the observation of increased approach/risk-taking and heighted ventral striatal activations coincident with decreased avoidance/sensitivity to punishment within adolescents during the same decision-making task. An intertwining of the two systems is likely to be ecologically valid in the context of real-world behavior, given the contextual ambiguity that characterizes many decision-making contexts. This ambiguity raises questions. Do approach and avoidance systems function independently or are they necessarily functionally interconnected? The avoidance system has been sorely neglected in current studies of adolescent neurodevelopment. The impact of pre-adolescent motivational biases and other individual difference factors Finally, just as more work is needed to determine the extent to which the approach and avoidance systems are interdependent, the impact of inherent trait dispositions and other individual difference factors might be more comprehensively considered as predictors of adolescent behavior. For instance, van Leijenhorst et al. (2010) found that individual differences in risk-taking biases moderated brain responses to high versus low risk conditions. We have found, that individual differences in behavioral activation predict the magnitude of later increases during the adolescent period (Urosevic et al., 2012) and that structural variations in subcortical brain volumes also contribute to these effects. Similarly, we have observed that some adolescents react to the presence of peers with reduced risk-taking rather than the converse (Segalowitz et al., 2012). To the extent that adolescence has been associated with problematic risk-taking behaviors, it is important to clarify that
not all individuals are equivalently vulnerable. Such individual differences could conceivably contradict the characterization of there being a general increase in risk for risky behavior in adolescence. The reasons for this inter-individual variation, at genetic, neurochemical, and environmental levels are critical to ascertain. Accordingly, the triadic model could be expanded to accommodate such wide individual differences. To conclude, the triadic framework proposed by Ernst provides a provocative and necessary challenge to the field of adolescent brain development, prompting us to consider how behavioral tendencies toward approach versus avoidance, as representative of the brain’s two major motivational systems, are dissociated in real-world decision-making contexts, whether their neural substrates are more overlapping than distinct, and whether other neurodynamic mechanisms and individual difference factors contribute to group-based developmental trends. References Adolphs, R. (2010). What does the amygdala contribute to social cognition? Annals of the New York Academy of Sciences, 1191, 42–61. http://dx.doi.org/10.1111/ j.1749-6632.2010.05445. Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. Alheid, G. F. (2003). Extended amygdala and basal forebrain. Annals of the New York Academy of Sciences, 985, 185–205. Blumenfeld, R. S., Nomura, E. M., Gratton, C., & D’Esposito, M. (2013). Lateral prefrontal cortex is organized into parallel dorsal and ventral streams along the rostro-caudal axis. Cerebral Cortex, 10, 2457–2466. Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Science, 4(6), 215–222. Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Developmental Science, 14(2), F1–F10. http://dx.doi.org/10.1111/j.14677687.2010.01035.x. Cho, Y. T., Fromm, S., Guyer, A. E., Detloff, A., Pine, D. S., Fudge, J. L., et al. (2013). Nucleus accumbens, thalamus and insula connectivity during incentive anticipation in typical adults and adolescents. Neuroimage, 66, 508–521. Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of personality: Dopamine, facilitation of incentive motivation, and extraversion. Behavioral and Brain Sciences, 22, 491–517. Di Martino, A., Scheres, A., Margulies, D. S., Kelly, A. M. C., Uddin, L. Q., Shehzad, Z., et al. (2008). Functional connectivity of human striatum: A resting state fMRI study. Cerebral Cortex, 18, 2735–2747. Ernst, M., Nelson, E. E., Jazbec, S. P., McClure, E. B., Monk, C. S., & Leibenluft, E. (2005). Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. NeuroImage, 25, 1279–1291. Eshel, N., Nelson, E. E., Blair, R. J., Pine, D. S., & Ernst, M. (2007). Neural substrates of choices selection in adults and adolescents: Development of the ventrolateral prefrontal and anterior cingulate cortices. Neuropsychologia, 45, 1270–1279. Fudge, J. L., & Haber, S. N. (2001). Bed nucleus of the stria terminalis and extended amygdala inputs to dopamine subpopulations in primates. Neuroscience, 104(3), 807–827. Haber, S. (2003). The primate basal ganglia: Parallel and integrative networks. Journal of Chemical Neuroanatomy, 26(4), 317–330. Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. (2005). Neural systems responding to degrees of uncertainty in human decision-making. Science, 310, 1680–1683. Koob, G. F., Wall, T. L., & Bloom, F. E. (1989). Nucleus accumbens as a substrate for the aversive stimulus effects of opiate withdrawal. Psychopharmacology (Berlin), 98, 530–534. Luciana, M., & Collins, P. F. (2012). Incentive motivation, cognitive control, and the adolescent brain: Is it time for a paradigm shift? Child Development Perspectives, 6(4), 392–399. Luciana, M., Wahlstrom, D., Collins, P. F., & Porter, J. N. (2012). Dopaminergic modulation of incentive motivation in adolescence: Age-related changes in signaling, individual differences, and implications for the development of selfregulation. Developmental Psychology, 48(3), 844–861. Ninan, P. T. (1999). The functional anatomy, neurochemistry, and pharmacology of anxiety. Journal of Clinical Psychiatry, 60(Suppl. 22), 12–17. O’Doherty, J., Critchley, H., Deichmann, R., & Dolan, R. J. (2003). Dissociating valence of outcome from behavioral control in human ventral and orbital prefrontal cortices. Journal of Neuroscience, 23(21), 7931–7939. Pessoa, L., & Adolphs, R. (2010). Emotion processing and the amygdala: From a ‘low road’ to ‘many roads’ of evaluating biological significance. Nature Reviews Neuroscience, 11, 773–783. Rothbart, M. K. (2007). Temperament, development and personality. Current Directions in Psychological Science, 16(4), 207–212. http://dx.doi.org/10.1111/ j.1467-8721.2007.00505.x.
M. Luciana, S.J. Segalowitz / Brain and Cognition 89 (2014) 118–121 Rushworth, M. F. S., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E. (2011). Frontal cortex and reward-guided learning and decision-making. Neuron, 70, 1054–1069. Sarinopoulos, I., Grupe, D. W., Mackiewicz, K. L., Herrington, J. D., Lor, M., Steege, E. E., et al. (2010). Uncertainty during anticipation modulates neural responses to aversion in human insula and amygdala. Cerebral Cortex, 20, 929–940. http:// dx.doi.org/10.1093/cercor/bhp155. Segalowitz, S. J., Santesso, D. L., Willoughby, T., Reker, D. L., Campbell, K., Chalmers, H., et al. (2012). Adolescent peer interaction and trait surgency weaken medial prefrontal cortex responses to failure. Social, Cognitive and Affective Neuroscience, 7, 115–124. Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28(1), 78–106. Tellegen, A., & Waller, N. G. (1992). Exploring personality through test construction: Development of the Multi-dimensional Personality Questionnaire (MPQ). Department of Psychology, University of Minnesota, unpublished manuscript.
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Urosevic, S., Collins, P. F., Muetzel, R., & Luciana, M. (2012). Longitudinal changes in adolescent behavioral approach system and behavioral inhibition system sensitivities: Associations with OFC and nucleus accumbens volumes. Developmental Psychology, 48(5), 1488–1500. van Leijenhorst, L., Gunther Moor, B., Op de Macks, Z. A., Rombouts, S. A. R. B., Westenberg, P. M., & Crone, E. A. (2010). Adolescent risky decision making: Neurocognitive development of reward and control regions. Neuroimage, 51, 345–355. Wahlstrom, D., Collins, P. F., White, T., & Luciana, M. (2010). Developmental changes in dopamine neurotransmission in adolescence: Behavioral implications and issues in assessment. Brain and Cognition, 72(1), 146–159. Zaretsky, M., Mendelsohn, A., Mintz, M., & Hendler, T. (2010). In the eye of the beholder: Internally driven uncertainty of danger recruits the amygdala and dorsomedial prefrontal cortex. Journal of Cognitive Neuroscience, 22(10), 2263–2275.
