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Applied Neuropsychology: Child Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hapc20
The Misnomer of Attention-Deficit Hyperactivity Disorder a
Theodore Wasserman & Lori Drucker Wasserman
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Private Practice, Boca Raton, Florida Published online: 09 Mar 2015.
Click for updates To cite this article: Theodore Wasserman & Lori Drucker Wasserman (2015): The Misnomer of Attention-Deficit Hyperactivity Disorder, Applied Neuropsychology: Child, DOI: 10.1080/21622965.2015.1005487 To link to this article: http://dx.doi.org/10.1080/21622965.2015.1005487
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APPLIED NEUROPSYCHOLOGY: CHILD, 0: 1–7, 2015 Copyright # Taylor & Francis Group, LLC ISSN: 2162-2965 print=2162-2973 online DOI: 10.1080/21622965.2015.1005487
The Misnomer of Attention-Deficit Hyperactivity Disorder Theodore Wasserman and Lori Drucker Wasserman
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Private Practice, Boca Raton, Florida
We propose that attention-deficit disorder represents an inefficiency of an integrated system designed to allocate working memory to designated tasks rather than the absence or dysfunction of a particular form of attention. A significant portion of this inefficiency in the allocation of working memory represents poor engagement of the reward circuit with distinct circuits of learning and performance that control instrumental conditioning (learning). Efficient attention requires the interaction of these circuits. For a significant percentage of individuals who present with attention-deficit disorder, their problems represent the engagement, or lack thereof, of the motivational and reward circuit as opposed to problems, or disorders of attention traditionally defined as problems with orienting, focusing, and sustaining. We demonstrate that there is an integrated system of working-memory allocation that responds by recruiting relevant aspects of both cortex and subcortex to the demands of the task being encountered. In this model, attention is viewed as a gating function determined by novelty, flight-or-fight response, and reward history=valence affecting motivation. We view the traditional models of attention, rather than describe specific types of attention per se, as representing the description of the behavioral output of this integrated orienting and engagement system designed to allocate working memory to task-specific stimuli.
Key words:
arousal engagement, attention-deficit disorder, motivation
INTRODUCTION Historically, attention disorders were attributed to minimal brain dysfunction associated with regions of the brain that were responsible for specific activities. This led to research to determine the areas of cortical dysfunction involved (Mirsky, 1987) and the effect on subtypes of attentional activity. Toward this end, models of attention have been proposed and extensively researched (Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991; Posner & Rothbart, 2007) and a number of subtypes of attention have been identified. For example, Sohlberg and Mateer (1989) identified five different types of attentional activity—focused, sustained, selective, alternating, and divided—while Mirsky
Address correspondence to Theodore Wasserman, Ph.D., ABPP, ABPdN, 21301 Powerline Rd., Suite 209, Boca Raton, FL 33433. E-mail: [email protected]
proposed four types, including focus, sustain, encode, and shift, each correlated with particular cortical areas. In addition to the subtyping, a number of component cognitive processes have been hypothesized and identified. For example, Knudsen (2007) identified four components that were considered fundamental to attention: working memory, top-down sensitivity control, competitive selection, and automatic bottom-up filtering for salient stimuli. Posner and Rothbart (2007) identified three attentional networks: the alerting, orienting, and executive attention systems. Each system was related to its specific constellation of brain structures and functioned as an integrated network subserved by a specific neurotransmitter. For example, the orienting system (superior parietal, temporal parietal junction, the frontal eye fields, and the superior colliculus) was an acetylcholine-based network; the alerting system (locus coeruleus, right frontal, and parietal cortex) was a norepinephrine-based network, and the executive attention system (anterior cingulate, lateral ventral,
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prefrontal, and basal ganglia) was a dopamine-based network. Neuropsychologically speaking, attention is a construct describing a complex set of underlying cortical and subcortical processes that function to orient an individual to a particular element in the environment. There is little doubt that the utilization and understanding of this construct is necessary for the elucidation of conscious experience (Knudsen, 2007) as well as its role in neurodevelopmental disorders. Recognition of this belief is found in the considerable amount of time and focus that the concept has received in the literatures of neuropsychology and cognitive psychology. However, these models hold little utility for the practitioner. These and other models of attention, like other aspects of executive function, have been developed to respond to a serial-order information-processing paradigm, distinct from sensorimotor and perceptualprocessing systems, wherein an individual detects a stimulus, orients to the selected stimulus, develops a response, and then responds. Attention in this view is an executive action of a cortically based informationprocessing system that first transforms sensory information into perceptual representations and then goes on to use these representations to construct knowledge, make decisions, and translate these decisions to action (Cisek & Kalaska, 2010). There is, however, as of now little empirical evidence to support this model (Cisek & Kalaska, 2010; Koziol & Lutz, 2013). In fact, there is increasing evidence that neural correlates of decision-making processing are widely distributed both cortically and subcortically. Not only are these areas widely distributed, but the circuitry involved appears to be recruited in a number of activities that include both appraisal of reinforcement and the direction of motor responses designed to obtain that reinforcement simultaneously (Cisek & Kalaska, 2010). Not only do the circuits used in decision making recruit widely distributed cortical and subcortical circuits, but there is increasing evidence that these recruited circuits process information in parallel and that they operate at the same time while integrating the results of their operations at key junctures in the human nervous system (Catani, Jones, & Ffytche, 2005; Ploner, Schmitz, & Freund, 1999). We posit that the aforementioned classical models of attention, rather than specific types of attention per se, represent the description of the behavioral output of an integrated orienting and engagement system reacting to categorically task-similar stimuli. In other words, what we have been studying as types of attention were in fact assessments of how the human orienting and engagement system reacts to specific types of task-specific stimuli. Although these traditional models might provide needed information about the systems’
task-related engagement capabilities and therefore are valuable in describing the task-related operation of an integrated system, they are not inherently useful in understanding the principles that underlie the operation of the system as a whole. We therefore posit that these traditional models do not provide useful information in describing the vast majority of attentional problems that manifest themselves in the clinical population. We submit that these problems, when encountered in clinical practice—and often labeled an attention-deficit disorder—actually represent inefficiencies in performing task demands in relation to the specific operation of the system rather than the absence or dysfunction of a particular form of attention. Further, we propose that a significant portion of the problems encountered at the clinical level reflect the engagement, or lack thereof, of the motivational and reward circuit as opposed to problems, or disorders of orienting, focusing, and sustaining. To state it another way, we propose that an individual may have difficulty sustaining attention on a particular task as a result of reward history or lack of perception of novelty rather than because of a difficulty in the mechanism itself of sustaining. We propose that attention is best understood as the processes involved in the operation of an integrated system that allocates working memory to specific tasks (Shell et al., 2010), initially based on novelty, flight-or-fight response, and the reward salience of the task. We will further argue that once initially encoded, task-specific response becomes automatic and is largely the responsibility of subcortically based networks. We further propose that motivation is best understood as the reward saliency and reinforcement history that determines which stimuli is gated to working memory (Wasserman, 2012). We propose that this integrated system reflects the operation of basic cognitive processes such as those described by Knudsen (2007) directed by the reward saliency system, which dictates the bottom-up, largely automatic filtering for salient material. This model has been proposed before in discussing the impact of emotions on cognition (Ledoux, 1989). Ledoux (1989) points out that ‘‘emotion and cognition are mediated by separate but interacting systems of the brain. The core of the emotional system is a network that evaluates (computes) the biological significance of stimuli, including stimuli from the external or internal environment or from within the brain (thoughts, images, memories). The computation of stimulus significance takes place prior to and independent of conscious awareness, with only the computational products reaching awareness, and only in some instances’’ (p. 269). ‘‘We propose then that rather than an isolated descriptor of frontal-lobe dysfunction in attention-deficit hyperactivity disorder (ADHD), reward-driven saliency is involved as the main element at play in most developmental disorders of attention
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and accounts for the notoriously poor motivation reported by significant numbers of individuals with disorders of attention’’ (p. 269). As a result, the global construct of attentional types (hyperactive vs. inattentive) has to be deemphasized in future neuropsychological research in favor of an understanding of the core cognitive elements and subcortical circuitry involved and the role of the reward saliency system in the allocation of working-memory resources.
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REWARD VALENCE IN THE ALLOCATION OF WORKING MEMORY Cortical-subcortical networks involved in the detection of reward have been clearly elucidated. Haber and Knutson (2010) state that ‘‘the cortical-basal ganglia circuit is at the heart of the reward system. The key structures in this network are the anterior cingulate cortex, the orbital prefrontal cortex, the ventral striatum, the ventral palladium, and the midbrain dopamine neurons. In addition, other structures, including the dorsal prefrontal cortex, amygdala, hippocampus, thalamus, and lateral habenula nucleus, and specific brainstem structures such as the pedunculopontine nucleus, and the raphe nucleus, are key components in regulating the reward circuit. Connectivity between these areas forms a complex neural network that mediates different aspects of reward processing’’ (p. 4). In a review article, O’Doherty (2004) identified a ‘‘highly interconnected network of brain areas that included orbital and medial prefrontal cortex, amygdala, striatum and dopaminergic mid-brain in reward processing’’ (p. 769). His review of the research indicated that ‘‘distinct reward-related functions can be attributed to different components of this network. Orbitofrontal cortex is involved in coding stimulus reward value and in concert with the amygdala and ventral striatum is implicated in representing predicted future reward. Such representations can be used to guide action selection for reward, a process that depends, at least in part, on orbital and medial prefrontal cortex as well as dorsal striatum’’ (O’Doherty, 2004, p. 769). Studies using functional magnetic resonance imaging have identified the nucleus accumbens (NAcc), the ventral striatum, the ventromedial prefrontal=orbital frontal cortex, and medial orbitofrontal cortex as playing a part in the detection and determination of reward (Berns, McClure, Pagnoni, & Montague, 2001; O’Doherty, Dayan, Friston, & Dolan, 2003; Pujara & Koenigs, 2013). It is critical to point out at this juncture that many of these same areas have been identified in diffusion tensor imaging studies as important segments of the white-matter networks involved with the regulation of attention (Tami, Barnea-Goraly, & Reiss, 2012). There is clear evidence that the
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recruitment of elements of this system is both task- and reward history-specific. For example, Yacubian et al. (2006) found that ventral striatal responses coded both reward probability and magnitude during an anticipation-of-reward task. This permitted the local computation of expected value (EV). For our discussion, it is important to note that the ventral striatum only represented the gain-related part of EV. Loss-related EV and the associated prediction error were represented in the amygdala. The authors concluded that the ventral striatum and the amygdala distinctively process the value of a prediction and subsequently compute a prediction error for gains and losses. Berns et al. (2001) found that predictability modulated the human brain response to reward. They found activity for rewarding stimuli in both the NAcc and medial orbitofrontal cortex was greatest when the stimuli were unpredictable. When participants were asked to state their preferences (historically rewarding), activity was noted in the sensorimotor (subcortical) circuits. This implies that as the reward history strengthens, the gating of a reward, or the allocation of attention to a rewarding stimulus, occurs on an automatic level. This in turn allows the organism to allocate mental resources to other decision-making processes. It also establishes the basis for a multitude of neurophysiologically established conditioned rewards, which can be essential in motivating the organism rapidly and efficiently. Finally, there is evidence that motivational bias is predicted by normal variation in asymmetry of the dopamine-based signaling system that underlies it (Tomer et al., 2013). Normal variation implies that there is a normally distributed range of ability to engage the D2=D3 dopamine binding system, which would result in a normally distributed range of ability of individuals to utilize this system to engage in appetitive approach-related behavior (motivation).
GATING Gating is the activity of the human orienting system to select relevant or meaningful stimuli from the vast array of available environmental stimuli that impinge upon the human sensory system. There is evidence for the involvement of both the striatum and the basal ganglia in gating. Seger (2013) stated that open loop projections from the caudate are crucial for gating in visual working memory and noted research showing that the striatum is important for selecting what items should be gated into working memory. Seger also noted that empirical work has supported the idea that the basal ganglia are especially important for selecting which items should enter working memory. Research has demonstrated that sensory gating may be a multistep process, with an early phase subserved by the temporal-parietal and prefrontal
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cortex and a later phase mediated by the hippocampus (Grunwald et al., 2003). Grunwald et al. (2003) point out that ‘‘early, preattentive, stages may involve filtering out irrelevant input and are mediated by neocortical (prefrontal and peri-Sylvian) regions, as measured by P50 auditory evoked potentials spiking, whereas the hippocampus proper contributes more to a later, and possibly attentive, filtering in of relevant input. The stages are separated in time for a period lasting approximately 200 milliseconds. The initial areas showing P50 spiking were discrete whereas the second tier respondency was more generalized’’ (p. 517). We propose that this initial P50 spiking represented the reaction of the orienting system to both novelty and flight-or-fight subcortically based responses, whereas the subsequent recruitment of the hippocampus and related systems represented the gating provided by the reward prediction system.
THE ROLE OF THE REWARD SYSTEM IN ATTENTION AND GATING ‘‘Reward is a central component for driving incentive-based learning, appropriate responses to stimuli, and the development of goal-directed behaviors. One of the main goals of animal and human studies is to understand how the different brain regions in the circuit work together to evaluate environmental stimuli and transform that information into actions’’ (Haber & Knutson, 2010, p. 4). There is substantial recognition of the cortical and subcortical contributions to this complex human behavior (Koziol & Budding, 2009). Motivation, which we define as the contribution of the reward system to the allocation of working memory, involves multiple brain systems operating in concert (McGinty et al., 2011). Specifically, McGinty et al. (2011) identified the subthalamic nucleus (STN) and ventral palladium, the subiculum and related hippocampal areas, the lateral habenula, the mesopontine rostromedial tegmental nucleus, the extended amygdala, the bed nucleus of the stria terminals, and the hypothalamus. They conclude by stating ‘‘one consistent point that became apparent was that brain regions cannot be simply labeled as either contributing, or not contributing, to motivated behavior; rather, it’s necessary to consider the specific circumstances under which the region is being engaged’’ (McGinty et al., 2011, p. 356). Demonstrating the interface between the construct of attention and the construct of motivation, McGinty et al. review the role of the STN in both motivation and the regulation of impulses. They state that evidence also associates the STN with self-control impairment on measures of impulsivity. STN lesions also increase compulsive lever pressing as does functional disconnection of the medial
prefrontal cortex-to-STN pathway. STN lesions also appear to increase willingness to wait for a reward in a delay discounting task. This seemingly paradoxical finding suggests that these measures of impulsivity are served by distinct neuronal mechanisms. These same networks have been identified in other complex human behavior such as the hyperfocused, in terms of attention and early stages of intense love (Aaron et al., 2005). Aaron et al. (2005) concluded that their ‘‘results suggest that romantic love uses subcortical reward and motivation systems to focus on a specific individual, that limbic cortical regions process individual emotion factors, and that there is localization heterogeneity for reward functions in the human brain’’ (p. 327). Haber and Knutson (2010) point out that there are projections from the orbital frontal cortex, ventral medial prefrontal cortex and dorsal anterior cingulate that also converge in specific focal terminal fields within the ventral striatum, thereby providing, by weaving and converging, an anatomical substrate for modulation between these circuits. Finally, Hart, Leung, and Balleine (2014) point out that ‘‘considerable evidence suggests that distinct neural processes mediate the acquisition and performance of goal-directed instrumental actions. Whereas a cortical-dorsomedial striatal circuit appears critical for the acquisition of goal-directed actions, a cortical-ventral striatal circuit appears to mediate instrumental performance, particularly the motivational control of performance’’ (p. 104). While they point out that these distinct circuits of learning and performance constitute two distinct ‘‘streams’’ controlling instrumental conditioning, the interface between these two streams or circuits might represent a juncture for a limbic-motor interface. Hart et al. posit that the basolateral amygdala, which is heavily interconnected with both the dorsal and ventral subregions of the striatum, coordinates this interaction and provides input to the final common path to action. We propose that this interface represents the reward circuitry that creates and maintains motivation and engagement. In support of this model, Pennartz, Ito, Verschure, Battaglia, and Robbins (2011) point out that while the hippocampal formation and striatum subserve declarative and procedural memory, respectively, experimental evidence suggests that the ventral striatum, as opposed to the dorsal striatum, does not lend itself to being part of either system. They posit that the ventral striatum may constitute a system integrating inputs from the amygdala, prefrontal cortex, and hippocampus to generate motivational, outcome-predicting signals that invigorate goal-directed behaviors. They postulate that dorsal and ventral striatum compute outcome predictions largely in parallel, using different types of information as input. There are other subcortical structures that play a significant role in both gating and reward prediction.
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One of these is the pedunculopontine nucleus (PPN) (also referred to as pedunculopontine tegmental nucleus), which is located in the brainstem, to the rear of the substantia nigra and next to the superior cerebellar peduncle. The PPN is historically identified as one of the main components of the reticular activating system (Garcia-Rill, 1991). The PPN projects to a wide variety of cortical and subcortical systems. In addition, the PPN plays a significant role in gating both sensorimotor and reward-related behavior (Diederich & Koch, 2005). Similarly, the NAcc has been identified as critical in the control of goal-directed behavior (Taha & Fields, 2006). Taha and Fields (2006) found that that a subset of NAcc neurons demonstrated a long-lasting inhibition in firing rate when their onset preceded initiation of goal-directed sequences of behavior and terminates at the conclusion of the sequence. This firing pattern suggested that, when active, these neurons inhibited appetitive behaviors and that, when inhibited, these neurons permissively gated those behaviors. The caudate nucleus is commonly active when learning relationships between stimuli and responses or categories (Seger & Cincotta, 2005). Seger and Cincotta (2005) found that activity associated with successful learning has been localized in the body and tail of the caudate and putamen. In contrast, activity in the head of the caudate and ventral striatum was associated most strongly with processing feedback and was decreased across trials. The left superior frontal gyrus was more active for deterministic than probabilistic stimuli; conversely, extrastriate visual areas were more active for probabilistic than deterministic stimuli. Overall, hippocampal activity was associated with receiving positive feedback, but not with correct classification. Successful learning correlated positively with activity in the body and tail of the caudate nucleus and negatively with activity in the hippocampus. Clearly then, there is a role for and, more importantly, actual substantial neural networks that interface reward-determining systems with executive systems. Braver and Cohen (2000) developed a computational model that could serve to describe the interaction of these systems in subserving gating and control functions. They noted, ‘‘An Important aspect of cognitive control is the ability to appropriately select, update, and maintain contextual information related to behavioral goals, and to use this information to coordinate processing over extended periods. The selection, updating, and maintenance of context occur through interactions between the prefrontal cortex (PFC) and dopamine (DA) neurotransmitter system. Phasic D Activity serves two simultaneous and synergistic functions: (1) a gating function, which regulates the access of information to active memory mechanisms subserved by PFC; and (2) a learning function, which allows the system to discover
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which information is relevant for selection as context’’ (p. 713).
CONCLUSION: MANY DISORDERS OF ATTENTION ARE IN FACT DISORDERS OF WORKING-MEMORY ALLOCATION DEPENDENT ON REWARD SALIENCY AND HISTORY Why do these overlapping, layered, and interlinked networks exist? We propose that distinct circuits of learning and performance interface and posit that the basolateral amygdala, which is heavily interconnected with both the dorsal and ventral subregions of the striatum, coordinates this interaction and provides input to the final integrated common path to action. Specifically, a cortical-ventral striatal circuit mediates instrumental performance, particularly the motivational control of performance. It is this interface that permits reward circuitry to be instrumental in the selection (motivation), orienting to (attention and motivation), and maintenance of (attention and motivation) working memory. The contribution of the reward circuitry is to orient and direct goal-directed behavior and by doing so determine the allocation of working memory to a particular task. These circuits interact in all goal-directed or working-memory allocation situations, and it is the automatized reward history that drives the assignment of working memory. The vast majority of clinically identified ADHD cases are therefore instances where the allocation of working memory is inefficiently driven by the strength of perceived reward to process the required task. This implies that there is nothing wrong with working memory per se and might help explain the failure of programs designed to improve working memory to impact measures of ADHD (Rabner, 2014). This also explains in part the frequent parent claim that their child could not possibly have ADHD because they can pay attention to their computer games for great lengths of time. The significant issue is with working-memory allocation based on the initial reward saliency and, more importantly, the reward history of the target. It is difficulties with the allocation process, driven in part by subcortically based reward circuitry, that cause many of the problem behaviors associated clinically with ADHD. The allocation of orienting and working-memory effort is determined by the initial functioning of a hypoactive dopaminergic circuit and the poorly developed historical reward salience that is created when this circuit interacts with the environment. The role of reward in the development of motivation is clearly established in the learning literature (Deci, Koestner, & Ryan, 1999). It is clear that reward history is critically
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involved in learning and in the maintenance of motivation. We are not the first to propose that the behavioral problems of ADHD are likely secondary to altered reinforcement mechanisms resulting from dysfunction of the mesolimbic and mesocortical dopaminergic systems (Russell, de Villiers, Sagvolden, Lamm, & Taljaard, 1995). Sagvolden, Aase, Johansen, and Russell (2005) describe a dynamic developmental model based on the delay aversion that postulates a hypofunctioning mesolimbic dopamine-based circuit that results in both altered reinforcement of behavior and deficient extinction of previously reinforced behavior. We are not the first to suggest a motivational component in ADHD. Volkow et al. (2011) noted that there is increasing evidence of deficits in motivation in individuals with ADHD. Using positron emission tomography, they demonstrated decreased function in the brain dopamine reward pathway in adults with ADHD, and they hypothesized that this decreased function could underlie the motivation deficits in this disorder. What we are hypothesizing is that it is these motivational and reward circuitry deficits that underlie and define the attentional problems in ADHD and that the existing models of both etiology and treatment need to be reassessed. We should therefore discard as historically interesting but no longer neuropsychologically useful behaviorally based nosologies or task-specific models in favor of one that describes the process reflective of difficulties with the allocation of working memory and the operation of the cortical and subcortical circuitry that is involved. Certainly attempts have been made in this direction (Shaw, Stringaris, Nigg, & Leibenluft, 2014). There is little doubt that attention is a useful construct that has utility when describing a complex interconnected circuit’s reaction to the various internal and environmental tasks imposed upon the system. The situation is analogous to the use of the term data to describe the result of coding, programming, and input. However utilitarian the construct is for use as a shorthand description, it has decreasingly limited clinical utility in actually helping people improve the manner with which they interact, select, and store information from the environment. Saying that the attentional system is disordered is analogous to stating that the data are corrupted. We know it is not working; we just do not know why. We must move beyond the historical understanding of types of attention proposed in systems such as Mirsky’s (1987). These systems represent the natural inclination of the human learning system to classify and categorize. They are in that regard descriptions of task-related system responses. For example, Ashby and Maddox (2005) state that ‘‘much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed and conclude that that human
category learning is mediated by multiple, qualitatively distinct systems’’ (p. 146). We would view the results as identifying the recruitment of specific circuitry of the attentional system as a task-specific response of an integrated system to the unique demands of the target stimuli based upon its novelty and reinforcement history. There are not specific brain systems statically waiting to interact with specific tasks. There is one system reacting as it was designed to react. Using the older categorical models, it is likely, based on grouping variables, that there would be as many types of attention as there would be types of tasks. This is a highly inefficient system with significant circuitry being allocated to infrequently occurring events. There is one system that responds by recruiting relevant aspects of both cortex and subcortex to the demands of the task being encountered. We believe that more time should be spent on understanding the operational principles of the operation of the system as a whole, specifically the process of recruitment. We believe that cognitive psychology has made important contributions in that regard. Although it might be interesting to discuss fractionalized elements of the system, on a functional level, the system is not and was never designed to be fractionalized. Neuropsychology must understand and investigate the underlying operation of this system to help understand and address deficiencies when they occur.
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sharpbrains.com/blog/2014/03/27/is-working-memory-training-thepriority-for-adhd-patients Russell, V., de Villiers, A., Sagvolden, T., Lamm, M., & Taljaard, J. (1995). Altered dopaminergic function in the prefrontal cortex, nucleus accumbens and caudate-putamen of an animal model of attention-deficit hyperactivity disorder—the spontaneously hypertensive rat. Brain Research, 676, 343–351. Sagvolden, T., Aase, H., Johansen, E., & Russell, V. (2005). A dynamic developmental theory of attention deficit hyperactivity disorder Predominantly Hyperactive=Impulsive and Combined types. Behavioral and Brain Science, 28(3), 397–468. Seger, C. (2013). The visual corticostriatal loop through the tail of the caudate: Circuitry and function. Frontiers in Systems Neuroscience, 7, 104. doi:10.3389=fnsys.2013.00104 Seger, C., & Cincotta, C. (2005). The roles of the caudate nucleus in human classification learning. Journal of Neuroscience, 25, 2941–2951. doi:10.1523=JNEUROSCI.3401-04.2005 Shaw, P., Stringaris, A., Nigg, J., & Leibenluft, E. (2014). Emotion dysregulation in attention deficit hyperactivity disorder. American Journal of Psychiatry, 171, 276–293. doi:10.1176=appi.ajp. 2013.13070966 Shell, D., Brooks, D., Trainin, G., Wilson, K., Kauffman, D., & Herr, L. (2010). The unified learning model: How motivational, cognitive, and neurobiological sciences inform best teaching practices. New York, NY: Springer. Sohlberg, M., & Mateer, C. (1989). Introduction to cognitive rehabilitation: Theory and practice. New York, NY: Guilford. Taha, S., & Fields, H. (2006). Inhibitions of nucleus accumbens neurons encode a gating signal for reward-directed behavior. Journal of Neuroscience, 26, 217–222. Tami, L., Barnea-Goraly, N., & Reiss, A. (2012). Diffusion tensor imaging reveals white matter abnormalities in attention-deficit= hyperactivity disorder. Psychiatry Research, 202, 150–154. doi:10.1016=j.pscychresns.2012.04.001 Tomer, R., Slagter, H., Christian, B., Fox, A., King, C., Murali, D., . . . Davidson, R. (2013). Love to win or hate to lose? Asymmetry of dopamine D2 receptor binding predicts sensitivity to reward versus punishment. Journal of Cognitive Neuroscience, 26, 1039–1048. Volkow, N. W., Telang, F., Fowler, J., Goldstein, R., Klein, N. L., Wong, C., & Swanson, J. (2011). Motivation deficit in ADHD is associated with dysfunction of the dopamine reward pathway. Molecular Psychiatry, 16, 1147–1154. doi:10.1038=mp.2010.97 Wasserman, T. (2012). Attention, motivation and reading coherence failure: A neuropsychological perspective. Applied Neuropsychology: Adult, 19, 42–52. Yacubian, J., Glasher, J., Schroeder, K., Sommer, T., Braus, D., & Buchel, C. (2006). Dissociable systems for gain- and loss-related value predictions and errors of prediction in the human brain. Journal of Neuroscience, 26, 9530–9537. doi:10.1523= JNEUROSCI.2915-06.2006
Applied Neuropsychology: Child Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hapc20
The Misnomer of Attention-Deficit Hyperactivity Disorder a
Theodore Wasserman & Lori Drucker Wasserman
a
a
Private Practice, Boca Raton, Florida Published online: 09 Mar 2015.
Click for updates To cite this article: Theodore Wasserman & Lori Drucker Wasserman (2015): The Misnomer of Attention-Deficit Hyperactivity Disorder, Applied Neuropsychology: Child, DOI: 10.1080/21622965.2015.1005487 To link to this article: http://dx.doi.org/10.1080/21622965.2015.1005487
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APPLIED NEUROPSYCHOLOGY: CHILD, 0: 1–7, 2015 Copyright # Taylor & Francis Group, LLC ISSN: 2162-2965 print=2162-2973 online DOI: 10.1080/21622965.2015.1005487
The Misnomer of Attention-Deficit Hyperactivity Disorder Theodore Wasserman and Lori Drucker Wasserman
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Private Practice, Boca Raton, Florida
We propose that attention-deficit disorder represents an inefficiency of an integrated system designed to allocate working memory to designated tasks rather than the absence or dysfunction of a particular form of attention. A significant portion of this inefficiency in the allocation of working memory represents poor engagement of the reward circuit with distinct circuits of learning and performance that control instrumental conditioning (learning). Efficient attention requires the interaction of these circuits. For a significant percentage of individuals who present with attention-deficit disorder, their problems represent the engagement, or lack thereof, of the motivational and reward circuit as opposed to problems, or disorders of attention traditionally defined as problems with orienting, focusing, and sustaining. We demonstrate that there is an integrated system of working-memory allocation that responds by recruiting relevant aspects of both cortex and subcortex to the demands of the task being encountered. In this model, attention is viewed as a gating function determined by novelty, flight-or-fight response, and reward history=valence affecting motivation. We view the traditional models of attention, rather than describe specific types of attention per se, as representing the description of the behavioral output of this integrated orienting and engagement system designed to allocate working memory to task-specific stimuli.
Key words:
arousal engagement, attention-deficit disorder, motivation
INTRODUCTION Historically, attention disorders were attributed to minimal brain dysfunction associated with regions of the brain that were responsible for specific activities. This led to research to determine the areas of cortical dysfunction involved (Mirsky, 1987) and the effect on subtypes of attentional activity. Toward this end, models of attention have been proposed and extensively researched (Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991; Posner & Rothbart, 2007) and a number of subtypes of attention have been identified. For example, Sohlberg and Mateer (1989) identified five different types of attentional activity—focused, sustained, selective, alternating, and divided—while Mirsky
Address correspondence to Theodore Wasserman, Ph.D., ABPP, ABPdN, 21301 Powerline Rd., Suite 209, Boca Raton, FL 33433. E-mail: [email protected]
proposed four types, including focus, sustain, encode, and shift, each correlated with particular cortical areas. In addition to the subtyping, a number of component cognitive processes have been hypothesized and identified. For example, Knudsen (2007) identified four components that were considered fundamental to attention: working memory, top-down sensitivity control, competitive selection, and automatic bottom-up filtering for salient stimuli. Posner and Rothbart (2007) identified three attentional networks: the alerting, orienting, and executive attention systems. Each system was related to its specific constellation of brain structures and functioned as an integrated network subserved by a specific neurotransmitter. For example, the orienting system (superior parietal, temporal parietal junction, the frontal eye fields, and the superior colliculus) was an acetylcholine-based network; the alerting system (locus coeruleus, right frontal, and parietal cortex) was a norepinephrine-based network, and the executive attention system (anterior cingulate, lateral ventral,
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prefrontal, and basal ganglia) was a dopamine-based network. Neuropsychologically speaking, attention is a construct describing a complex set of underlying cortical and subcortical processes that function to orient an individual to a particular element in the environment. There is little doubt that the utilization and understanding of this construct is necessary for the elucidation of conscious experience (Knudsen, 2007) as well as its role in neurodevelopmental disorders. Recognition of this belief is found in the considerable amount of time and focus that the concept has received in the literatures of neuropsychology and cognitive psychology. However, these models hold little utility for the practitioner. These and other models of attention, like other aspects of executive function, have been developed to respond to a serial-order information-processing paradigm, distinct from sensorimotor and perceptualprocessing systems, wherein an individual detects a stimulus, orients to the selected stimulus, develops a response, and then responds. Attention in this view is an executive action of a cortically based informationprocessing system that first transforms sensory information into perceptual representations and then goes on to use these representations to construct knowledge, make decisions, and translate these decisions to action (Cisek & Kalaska, 2010). There is, however, as of now little empirical evidence to support this model (Cisek & Kalaska, 2010; Koziol & Lutz, 2013). In fact, there is increasing evidence that neural correlates of decision-making processing are widely distributed both cortically and subcortically. Not only are these areas widely distributed, but the circuitry involved appears to be recruited in a number of activities that include both appraisal of reinforcement and the direction of motor responses designed to obtain that reinforcement simultaneously (Cisek & Kalaska, 2010). Not only do the circuits used in decision making recruit widely distributed cortical and subcortical circuits, but there is increasing evidence that these recruited circuits process information in parallel and that they operate at the same time while integrating the results of their operations at key junctures in the human nervous system (Catani, Jones, & Ffytche, 2005; Ploner, Schmitz, & Freund, 1999). We posit that the aforementioned classical models of attention, rather than specific types of attention per se, represent the description of the behavioral output of an integrated orienting and engagement system reacting to categorically task-similar stimuli. In other words, what we have been studying as types of attention were in fact assessments of how the human orienting and engagement system reacts to specific types of task-specific stimuli. Although these traditional models might provide needed information about the systems’
task-related engagement capabilities and therefore are valuable in describing the task-related operation of an integrated system, they are not inherently useful in understanding the principles that underlie the operation of the system as a whole. We therefore posit that these traditional models do not provide useful information in describing the vast majority of attentional problems that manifest themselves in the clinical population. We submit that these problems, when encountered in clinical practice—and often labeled an attention-deficit disorder—actually represent inefficiencies in performing task demands in relation to the specific operation of the system rather than the absence or dysfunction of a particular form of attention. Further, we propose that a significant portion of the problems encountered at the clinical level reflect the engagement, or lack thereof, of the motivational and reward circuit as opposed to problems, or disorders of orienting, focusing, and sustaining. To state it another way, we propose that an individual may have difficulty sustaining attention on a particular task as a result of reward history or lack of perception of novelty rather than because of a difficulty in the mechanism itself of sustaining. We propose that attention is best understood as the processes involved in the operation of an integrated system that allocates working memory to specific tasks (Shell et al., 2010), initially based on novelty, flight-or-fight response, and the reward salience of the task. We will further argue that once initially encoded, task-specific response becomes automatic and is largely the responsibility of subcortically based networks. We further propose that motivation is best understood as the reward saliency and reinforcement history that determines which stimuli is gated to working memory (Wasserman, 2012). We propose that this integrated system reflects the operation of basic cognitive processes such as those described by Knudsen (2007) directed by the reward saliency system, which dictates the bottom-up, largely automatic filtering for salient material. This model has been proposed before in discussing the impact of emotions on cognition (Ledoux, 1989). Ledoux (1989) points out that ‘‘emotion and cognition are mediated by separate but interacting systems of the brain. The core of the emotional system is a network that evaluates (computes) the biological significance of stimuli, including stimuli from the external or internal environment or from within the brain (thoughts, images, memories). The computation of stimulus significance takes place prior to and independent of conscious awareness, with only the computational products reaching awareness, and only in some instances’’ (p. 269). ‘‘We propose then that rather than an isolated descriptor of frontal-lobe dysfunction in attention-deficit hyperactivity disorder (ADHD), reward-driven saliency is involved as the main element at play in most developmental disorders of attention
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and accounts for the notoriously poor motivation reported by significant numbers of individuals with disorders of attention’’ (p. 269). As a result, the global construct of attentional types (hyperactive vs. inattentive) has to be deemphasized in future neuropsychological research in favor of an understanding of the core cognitive elements and subcortical circuitry involved and the role of the reward saliency system in the allocation of working-memory resources.
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REWARD VALENCE IN THE ALLOCATION OF WORKING MEMORY Cortical-subcortical networks involved in the detection of reward have been clearly elucidated. Haber and Knutson (2010) state that ‘‘the cortical-basal ganglia circuit is at the heart of the reward system. The key structures in this network are the anterior cingulate cortex, the orbital prefrontal cortex, the ventral striatum, the ventral palladium, and the midbrain dopamine neurons. In addition, other structures, including the dorsal prefrontal cortex, amygdala, hippocampus, thalamus, and lateral habenula nucleus, and specific brainstem structures such as the pedunculopontine nucleus, and the raphe nucleus, are key components in regulating the reward circuit. Connectivity between these areas forms a complex neural network that mediates different aspects of reward processing’’ (p. 4). In a review article, O’Doherty (2004) identified a ‘‘highly interconnected network of brain areas that included orbital and medial prefrontal cortex, amygdala, striatum and dopaminergic mid-brain in reward processing’’ (p. 769). His review of the research indicated that ‘‘distinct reward-related functions can be attributed to different components of this network. Orbitofrontal cortex is involved in coding stimulus reward value and in concert with the amygdala and ventral striatum is implicated in representing predicted future reward. Such representations can be used to guide action selection for reward, a process that depends, at least in part, on orbital and medial prefrontal cortex as well as dorsal striatum’’ (O’Doherty, 2004, p. 769). Studies using functional magnetic resonance imaging have identified the nucleus accumbens (NAcc), the ventral striatum, the ventromedial prefrontal=orbital frontal cortex, and medial orbitofrontal cortex as playing a part in the detection and determination of reward (Berns, McClure, Pagnoni, & Montague, 2001; O’Doherty, Dayan, Friston, & Dolan, 2003; Pujara & Koenigs, 2013). It is critical to point out at this juncture that many of these same areas have been identified in diffusion tensor imaging studies as important segments of the white-matter networks involved with the regulation of attention (Tami, Barnea-Goraly, & Reiss, 2012). There is clear evidence that the
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recruitment of elements of this system is both task- and reward history-specific. For example, Yacubian et al. (2006) found that ventral striatal responses coded both reward probability and magnitude during an anticipation-of-reward task. This permitted the local computation of expected value (EV). For our discussion, it is important to note that the ventral striatum only represented the gain-related part of EV. Loss-related EV and the associated prediction error were represented in the amygdala. The authors concluded that the ventral striatum and the amygdala distinctively process the value of a prediction and subsequently compute a prediction error for gains and losses. Berns et al. (2001) found that predictability modulated the human brain response to reward. They found activity for rewarding stimuli in both the NAcc and medial orbitofrontal cortex was greatest when the stimuli were unpredictable. When participants were asked to state their preferences (historically rewarding), activity was noted in the sensorimotor (subcortical) circuits. This implies that as the reward history strengthens, the gating of a reward, or the allocation of attention to a rewarding stimulus, occurs on an automatic level. This in turn allows the organism to allocate mental resources to other decision-making processes. It also establishes the basis for a multitude of neurophysiologically established conditioned rewards, which can be essential in motivating the organism rapidly and efficiently. Finally, there is evidence that motivational bias is predicted by normal variation in asymmetry of the dopamine-based signaling system that underlies it (Tomer et al., 2013). Normal variation implies that there is a normally distributed range of ability to engage the D2=D3 dopamine binding system, which would result in a normally distributed range of ability of individuals to utilize this system to engage in appetitive approach-related behavior (motivation).
GATING Gating is the activity of the human orienting system to select relevant or meaningful stimuli from the vast array of available environmental stimuli that impinge upon the human sensory system. There is evidence for the involvement of both the striatum and the basal ganglia in gating. Seger (2013) stated that open loop projections from the caudate are crucial for gating in visual working memory and noted research showing that the striatum is important for selecting what items should be gated into working memory. Seger also noted that empirical work has supported the idea that the basal ganglia are especially important for selecting which items should enter working memory. Research has demonstrated that sensory gating may be a multistep process, with an early phase subserved by the temporal-parietal and prefrontal
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cortex and a later phase mediated by the hippocampus (Grunwald et al., 2003). Grunwald et al. (2003) point out that ‘‘early, preattentive, stages may involve filtering out irrelevant input and are mediated by neocortical (prefrontal and peri-Sylvian) regions, as measured by P50 auditory evoked potentials spiking, whereas the hippocampus proper contributes more to a later, and possibly attentive, filtering in of relevant input. The stages are separated in time for a period lasting approximately 200 milliseconds. The initial areas showing P50 spiking were discrete whereas the second tier respondency was more generalized’’ (p. 517). We propose that this initial P50 spiking represented the reaction of the orienting system to both novelty and flight-or-fight subcortically based responses, whereas the subsequent recruitment of the hippocampus and related systems represented the gating provided by the reward prediction system.
THE ROLE OF THE REWARD SYSTEM IN ATTENTION AND GATING ‘‘Reward is a central component for driving incentive-based learning, appropriate responses to stimuli, and the development of goal-directed behaviors. One of the main goals of animal and human studies is to understand how the different brain regions in the circuit work together to evaluate environmental stimuli and transform that information into actions’’ (Haber & Knutson, 2010, p. 4). There is substantial recognition of the cortical and subcortical contributions to this complex human behavior (Koziol & Budding, 2009). Motivation, which we define as the contribution of the reward system to the allocation of working memory, involves multiple brain systems operating in concert (McGinty et al., 2011). Specifically, McGinty et al. (2011) identified the subthalamic nucleus (STN) and ventral palladium, the subiculum and related hippocampal areas, the lateral habenula, the mesopontine rostromedial tegmental nucleus, the extended amygdala, the bed nucleus of the stria terminals, and the hypothalamus. They conclude by stating ‘‘one consistent point that became apparent was that brain regions cannot be simply labeled as either contributing, or not contributing, to motivated behavior; rather, it’s necessary to consider the specific circumstances under which the region is being engaged’’ (McGinty et al., 2011, p. 356). Demonstrating the interface between the construct of attention and the construct of motivation, McGinty et al. review the role of the STN in both motivation and the regulation of impulses. They state that evidence also associates the STN with self-control impairment on measures of impulsivity. STN lesions also increase compulsive lever pressing as does functional disconnection of the medial
prefrontal cortex-to-STN pathway. STN lesions also appear to increase willingness to wait for a reward in a delay discounting task. This seemingly paradoxical finding suggests that these measures of impulsivity are served by distinct neuronal mechanisms. These same networks have been identified in other complex human behavior such as the hyperfocused, in terms of attention and early stages of intense love (Aaron et al., 2005). Aaron et al. (2005) concluded that their ‘‘results suggest that romantic love uses subcortical reward and motivation systems to focus on a specific individual, that limbic cortical regions process individual emotion factors, and that there is localization heterogeneity for reward functions in the human brain’’ (p. 327). Haber and Knutson (2010) point out that there are projections from the orbital frontal cortex, ventral medial prefrontal cortex and dorsal anterior cingulate that also converge in specific focal terminal fields within the ventral striatum, thereby providing, by weaving and converging, an anatomical substrate for modulation between these circuits. Finally, Hart, Leung, and Balleine (2014) point out that ‘‘considerable evidence suggests that distinct neural processes mediate the acquisition and performance of goal-directed instrumental actions. Whereas a cortical-dorsomedial striatal circuit appears critical for the acquisition of goal-directed actions, a cortical-ventral striatal circuit appears to mediate instrumental performance, particularly the motivational control of performance’’ (p. 104). While they point out that these distinct circuits of learning and performance constitute two distinct ‘‘streams’’ controlling instrumental conditioning, the interface between these two streams or circuits might represent a juncture for a limbic-motor interface. Hart et al. posit that the basolateral amygdala, which is heavily interconnected with both the dorsal and ventral subregions of the striatum, coordinates this interaction and provides input to the final common path to action. We propose that this interface represents the reward circuitry that creates and maintains motivation and engagement. In support of this model, Pennartz, Ito, Verschure, Battaglia, and Robbins (2011) point out that while the hippocampal formation and striatum subserve declarative and procedural memory, respectively, experimental evidence suggests that the ventral striatum, as opposed to the dorsal striatum, does not lend itself to being part of either system. They posit that the ventral striatum may constitute a system integrating inputs from the amygdala, prefrontal cortex, and hippocampus to generate motivational, outcome-predicting signals that invigorate goal-directed behaviors. They postulate that dorsal and ventral striatum compute outcome predictions largely in parallel, using different types of information as input. There are other subcortical structures that play a significant role in both gating and reward prediction.
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One of these is the pedunculopontine nucleus (PPN) (also referred to as pedunculopontine tegmental nucleus), which is located in the brainstem, to the rear of the substantia nigra and next to the superior cerebellar peduncle. The PPN is historically identified as one of the main components of the reticular activating system (Garcia-Rill, 1991). The PPN projects to a wide variety of cortical and subcortical systems. In addition, the PPN plays a significant role in gating both sensorimotor and reward-related behavior (Diederich & Koch, 2005). Similarly, the NAcc has been identified as critical in the control of goal-directed behavior (Taha & Fields, 2006). Taha and Fields (2006) found that that a subset of NAcc neurons demonstrated a long-lasting inhibition in firing rate when their onset preceded initiation of goal-directed sequences of behavior and terminates at the conclusion of the sequence. This firing pattern suggested that, when active, these neurons inhibited appetitive behaviors and that, when inhibited, these neurons permissively gated those behaviors. The caudate nucleus is commonly active when learning relationships between stimuli and responses or categories (Seger & Cincotta, 2005). Seger and Cincotta (2005) found that activity associated with successful learning has been localized in the body and tail of the caudate and putamen. In contrast, activity in the head of the caudate and ventral striatum was associated most strongly with processing feedback and was decreased across trials. The left superior frontal gyrus was more active for deterministic than probabilistic stimuli; conversely, extrastriate visual areas were more active for probabilistic than deterministic stimuli. Overall, hippocampal activity was associated with receiving positive feedback, but not with correct classification. Successful learning correlated positively with activity in the body and tail of the caudate nucleus and negatively with activity in the hippocampus. Clearly then, there is a role for and, more importantly, actual substantial neural networks that interface reward-determining systems with executive systems. Braver and Cohen (2000) developed a computational model that could serve to describe the interaction of these systems in subserving gating and control functions. They noted, ‘‘An Important aspect of cognitive control is the ability to appropriately select, update, and maintain contextual information related to behavioral goals, and to use this information to coordinate processing over extended periods. The selection, updating, and maintenance of context occur through interactions between the prefrontal cortex (PFC) and dopamine (DA) neurotransmitter system. Phasic D Activity serves two simultaneous and synergistic functions: (1) a gating function, which regulates the access of information to active memory mechanisms subserved by PFC; and (2) a learning function, which allows the system to discover
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which information is relevant for selection as context’’ (p. 713).
CONCLUSION: MANY DISORDERS OF ATTENTION ARE IN FACT DISORDERS OF WORKING-MEMORY ALLOCATION DEPENDENT ON REWARD SALIENCY AND HISTORY Why do these overlapping, layered, and interlinked networks exist? We propose that distinct circuits of learning and performance interface and posit that the basolateral amygdala, which is heavily interconnected with both the dorsal and ventral subregions of the striatum, coordinates this interaction and provides input to the final integrated common path to action. Specifically, a cortical-ventral striatal circuit mediates instrumental performance, particularly the motivational control of performance. It is this interface that permits reward circuitry to be instrumental in the selection (motivation), orienting to (attention and motivation), and maintenance of (attention and motivation) working memory. The contribution of the reward circuitry is to orient and direct goal-directed behavior and by doing so determine the allocation of working memory to a particular task. These circuits interact in all goal-directed or working-memory allocation situations, and it is the automatized reward history that drives the assignment of working memory. The vast majority of clinically identified ADHD cases are therefore instances where the allocation of working memory is inefficiently driven by the strength of perceived reward to process the required task. This implies that there is nothing wrong with working memory per se and might help explain the failure of programs designed to improve working memory to impact measures of ADHD (Rabner, 2014). This also explains in part the frequent parent claim that their child could not possibly have ADHD because they can pay attention to their computer games for great lengths of time. The significant issue is with working-memory allocation based on the initial reward saliency and, more importantly, the reward history of the target. It is difficulties with the allocation process, driven in part by subcortically based reward circuitry, that cause many of the problem behaviors associated clinically with ADHD. The allocation of orienting and working-memory effort is determined by the initial functioning of a hypoactive dopaminergic circuit and the poorly developed historical reward salience that is created when this circuit interacts with the environment. The role of reward in the development of motivation is clearly established in the learning literature (Deci, Koestner, & Ryan, 1999). It is clear that reward history is critically
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involved in learning and in the maintenance of motivation. We are not the first to propose that the behavioral problems of ADHD are likely secondary to altered reinforcement mechanisms resulting from dysfunction of the mesolimbic and mesocortical dopaminergic systems (Russell, de Villiers, Sagvolden, Lamm, & Taljaard, 1995). Sagvolden, Aase, Johansen, and Russell (2005) describe a dynamic developmental model based on the delay aversion that postulates a hypofunctioning mesolimbic dopamine-based circuit that results in both altered reinforcement of behavior and deficient extinction of previously reinforced behavior. We are not the first to suggest a motivational component in ADHD. Volkow et al. (2011) noted that there is increasing evidence of deficits in motivation in individuals with ADHD. Using positron emission tomography, they demonstrated decreased function in the brain dopamine reward pathway in adults with ADHD, and they hypothesized that this decreased function could underlie the motivation deficits in this disorder. What we are hypothesizing is that it is these motivational and reward circuitry deficits that underlie and define the attentional problems in ADHD and that the existing models of both etiology and treatment need to be reassessed. We should therefore discard as historically interesting but no longer neuropsychologically useful behaviorally based nosologies or task-specific models in favor of one that describes the process reflective of difficulties with the allocation of working memory and the operation of the cortical and subcortical circuitry that is involved. Certainly attempts have been made in this direction (Shaw, Stringaris, Nigg, & Leibenluft, 2014). There is little doubt that attention is a useful construct that has utility when describing a complex interconnected circuit’s reaction to the various internal and environmental tasks imposed upon the system. The situation is analogous to the use of the term data to describe the result of coding, programming, and input. However utilitarian the construct is for use as a shorthand description, it has decreasingly limited clinical utility in actually helping people improve the manner with which they interact, select, and store information from the environment. Saying that the attentional system is disordered is analogous to stating that the data are corrupted. We know it is not working; we just do not know why. We must move beyond the historical understanding of types of attention proposed in systems such as Mirsky’s (1987). These systems represent the natural inclination of the human learning system to classify and categorize. They are in that regard descriptions of task-related system responses. For example, Ashby and Maddox (2005) state that ‘‘much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed and conclude that that human
category learning is mediated by multiple, qualitatively distinct systems’’ (p. 146). We would view the results as identifying the recruitment of specific circuitry of the attentional system as a task-specific response of an integrated system to the unique demands of the target stimuli based upon its novelty and reinforcement history. There are not specific brain systems statically waiting to interact with specific tasks. There is one system reacting as it was designed to react. Using the older categorical models, it is likely, based on grouping variables, that there would be as many types of attention as there would be types of tasks. This is a highly inefficient system with significant circuitry being allocated to infrequently occurring events. There is one system that responds by recruiting relevant aspects of both cortex and subcortex to the demands of the task being encountered. We believe that more time should be spent on understanding the operational principles of the operation of the system as a whole, specifically the process of recruitment. We believe that cognitive psychology has made important contributions in that regard. Although it might be interesting to discuss fractionalized elements of the system, on a functional level, the system is not and was never designed to be fractionalized. Neuropsychology must understand and investigate the underlying operation of this system to help understand and address deficiencies when they occur.
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