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The debate over dopamine’s role in reward: the case for incentive salience

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Abstract

Introduction

Debate continues over the precise causal contribution made by mesolimbic dopamine systems to reward. There are three competing explanatory categories: ‘liking’, learning, and ‘wanting’. Does dopamine mostly mediate the hedonic impact of reward (‘liking’)? Does it instead mediate learned predictions of future reward, prediction error teaching signals and stamp in associative links (learning)? Or does dopamine motivate the pursuit of rewards by attributing incentive salience to reward-related stimuli (‘wanting’)? Each hypothesis is evaluated here, and it is suggested that the incentive salience or ‘wanting’ hypothesis of dopamine function may be consistent with more evidence than either learning or ‘liking’. In brief, recent evidence indicates that dopamine is neither necessary nor sufficient to mediate changes in hedonic ‘liking’ for sensory pleasures. Other recent evidence indicates that dopamine is not needed for new learning, and not sufficient to directly mediate learning by causing teaching or prediction signals. By contrast, growing evidence indicates that dopamine does contribute causally to incentive salience. Dopamine appears necessary for normal ‘wanting’, and dopamine activation can be sufficient to enhance cue-triggered incentive salience. Drugs of abuse that promote dopamine signals short circuit and sensitize dynamic mesolimbic mechanisms that evolved to attribute incentive salience to rewards. Such drugs interact with incentive salience integrations of Pavlovian associative information with physiological state signals. That interaction sets the stage to cause compulsive ‘wanting’ in addiction, but also provides opportunities for experiments to disentangle ‘wanting’, ‘liking’, and learning hypotheses. Results from studies that exploited those opportunities are described here.

Conclusion

In short, dopamine’s contribution appears to be chiefly to cause ‘wanting’ for hedonic rewards, more than ‘liking’ or learning for those rewards.

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Notes

  1. Preliminary caveats

    Beyond dopamine caveat. In this paper, ‘the role of dopamine in reward’ is taken to be a short-hand term for the dopaminergic component of mesocorticolimbic systems. Dopamine is just one link in that chain of neuronal signals, and of course, we must go beyond dopamine neurons and synapses to understand reward function. Still, many causal manipulations powerfully affect reward by acting directly or indirectly on dopamine neurotransmission, and dopamine neural activation clearly codes reward events. Thus, dopamine deserves the special attention it has received as a crucial node of reward, and its precise role needs to be understood.

    Anatomical caveat. This discussion centers on mesolimbic dopamine projections especially to nucleus accumbens, but in practice, it is often difficult to distinguish the role of mesolimbic dopamine from neostriatal, cortical, and other dopamine systems. That is because many experiments use systemic drug administration, genetic manipulations or neural sensitization to alter reward, and all are bound to impact many dopamine systems simultaneously. Dopamine might well mediate different functions in different targets, even if involving similar cellular and molecular mechanisms in each structure, but the functional dividing lines between structures cannot yet be fully drawn. For that reason, I will de-emphasize specific anatomical targets here and attempt to consider dopamine’s most dominant role in reward. Still, we can, at least, surmise certain points about particular structures by a process of elimination. For example, if a reward function survives unchanged after dopamine is suppressed throughout the entire brain, then that function probably does not need dopamine in any particular brain structure.

    Tonic-phasic caveat. Similarly, phasic vs tonic dopamine signals might well have consequences that differ from each other, but we cannot tell them apart in most experiments that manipulate reward. So although the distinction’s importance is not denied, I will mostly focus on what we can say about the role of dopamine in reward more generally without trying to assign causal responsibility specifically to phasic or tonic signals.

  2. ‘Reinforcing’ terminology is slightly ambiguous: ‘Reinforcement’ often means the positive affective value or hedonic impact of a reward stimulus, as when applied to the hedonia hypothesis. It was long used as a technical term for hedonic impact, and some neuroscientists still use positive reinforcement as their chief synonym for positive affect or emotion today (Rolls 2005). Alternatively, reinforcement can sometimes mean a purely associative strengthening of learned S–S or S–R links without any affective connotations. Yet, a third meaning is radical behaviorist, where it refers simply to an observed strengthening of prior responses on which the reinforcer is contingent, with no explanatory connotations at all of underlying neural or psychological mechanisms. In any case, reinforcement was often used in a hedonic sense by many dopamine-reward papers in the 1980s–1990s and apparently in the hedonia quotes mentioned above.

  3. Even in ordinary people, purely objective or non-subjective affective reactions can be demonstrated under certain conditions in the form of unconscious ‘liking’. For example, a subliminal happy or fearful facial expression, viewed too briefly to be consciously perceived, can produce affective reactions that markedly change a person’s subsequent affective rating and consumption of a subsequent hedonic stimulus (sweet beverage), without ever being felt at the moment the hedonic reaction was caused (Berridge and Winkielman 2003; Winkielman et al. 2005). To become subjectively felt, such ‘unconscious liking’ reactions may require further brain processing, presumably including orbitofrontal and related cortical mechanisms (Kringelbach 2005). But the point here is that if ‘unconscious liking’ reactions ever exist at all, then it means that objective indicators of hedonic reactions can sometimes reveal more about underlying pleasure mechanisms than verbal reports, even in people.

    The probable homology of taste ‘liking’ reactions in humans and rats is indicated by several observations. For example, microfeatures of taste reactivity patterns show taxonomic clustering across species: humans share the greatest number of reaction details with other hominids (great apes such as orangutans and chimpanzees), share moderately with old world monkeys and new world monkeys (which cluster into their own groups), and share lightly with rodents (rats and mice; also cluster together) (Berridge 2000; Steiner et al. 2001). But all primates and rodent species tested so far share at least a half dozen reaction details all in common (e.g., rhythmic tongue protrusions to sweet tastes and negative ‘disliking’ gapes to bitter tastes). The homology of those shared components is further indicated by the fact that those shared components also share the same identical rule for generating certain aspects of expression microstructure, such as allometric timing, in primates (including humans) and rodents alike. For example, the duration of expression components observes the equation:

    $${\text{duration}}{\left( {{\text{in}}\,{\text{ms}}} \right)} = 0.26 \times {\left( {{\text{adult}}\,{\text{species}}\,{\text{weight}}\,{\left[ {{\text{in}}\,{\text{kg}}} \right]}} \right)}^{{0.32}} $$

    That allometry rule means that the human or gorilla tongue protrusion or gape is relatively slow, whereas, the same reaction in a rat or mouse reaction is much faster, yet all have identical timing ‘deep structure’ scaled to their evolved size. Finally, other observations indicate that those timing rules for ‘liking’ and ‘disliking’ reactions for each species are actively programmed by brain circuits For example, infants and adults share the same species timing, despite their different sizes, which further indicates homology of brain mechanisms and that timing is not passively produced by actual size acting on the physics of movement (Berridge 2000; Steiner et al. 2001). The implication of the probable homology of taste ‘liking’ reactions for affective neuroscience studies of hedonic impact is that identification of hedonic hotspots and neurochemical bases of ‘liking’ in rats can provide insights that probably apply also to brain hedonic mechanisms in humans.

    Several demonstrations reveal that hedonic neural hierarchies control the expression of ‘liking’ reactions used in our taste reactivity studies. For example, microinjections of opioid agonists and other neurotransmitter agents in forebrain structures such as the nucleus accumbens and ventral pallidum cause increases in ‘liking’ reactions, whereas, forebrain lesions of the ventral pallidum or ‘thalamic’ ablation of telencephalon cause increases in ‘disliking’ reactions (Cromwell and Berridge 1993; Grill and Norgren 1978b; Peciña and Berridge 2000, 2005; Reynolds and Berridge 2002; Smith and Berridge 2005). Taste reactivity ‘liking’ patterns have also been used to guide positive identification of neural firing patterns in the forebrain that code hedonic impact (e.g., rate codes by neurons in ventral pallidum) (Tindell et al. 2006). Such forebrain-related observations extend traditional notions of taste reactivity as a brainstem response, which were grounded on basic taste reactions elicited from decerebrate rats or cats or from anencephalic humans (Grill and Norgren 1978b; Sherrington 1906; Steiner 1973), by demonstrating that forebrain hedonic circuits normally exert overriding dominance over brainstem circuits in the control of ‘liking’ reactions, and that forebrain hedonic signals are normally reflected in behavioral ‘liking’ reactions.

  4. In fact, many of the dopamine activations described that caused ‘wanting’-without-‘liking’ in our taste reactivity studies slightly reduced the number of ‘liking’ reactions to sweet taste while simultaneously stimulating ‘wanting’ for food reward, a potential hedonic suppression that is opposite from what the hedonia hypothesis should predict (dopamine-mediated suppression of ‘liking’ appears to be independent of incentive salience attribution; the mechanism of hedonic suppression is not fully understood but might conceivably involve interaction with known opioid or gamma-aminobutyric acid (GABA) hedonic mechanisms in nucleus accumbens).

  5. Direct vs indirect roles in learning: Clear evidence for indirect roles of dopamine

    It can be useful to distinguish between potential direct causal roles of dopamine, as part of an associative mechanism that learns associative links between S–S or S–R events (teaching signal δ(t), engram stamping-in, prediction V), and indirect roles on other extrinsic mechanisms separate from learning that feed back secondarily to modulate learning or later use of learned information.

    Dopamine and other catecholamine activation may facilitate the capacity to extract new information from training trials, facilitate consolidation after learning, and facilitate learned performance later. For example, dopamine manipulations before training can modulate learning features such as latent inhibition for reward or fear CSs (Gray et al. 1999; Phillips et al. 2003a; Schmajuk et al. 2001), dopamine agonists given before performance tests enhance the motivational value of CSs in conditioned reinforcement and other tasks (and the enhancement can be blocked by accumbens 6-OHDA lesions) (Robbins and Everitt 1996; Taylor and Robbins 1984, 1986). In addition, elegant recent studies have demonstrated that dopamine may contribute to consolidation processes that continue for many minutes after a S–S or S–R learning trial has ended and that help make an already learned association more readily available for later use (Dalley et al. 2005). These consolidation effects appear related to the consolidation effects that have been well documented for norepinephrine, stress hormones, and certain other neurochemical modulators (Dalley et al. 2005; Everitt and Robbins 2005; McGaugh 2002; Smith-Roe and Kelley 2000). Thus, dopamine may indirectly affect the extraction of information from environments or the later use of learned information in many ways. Those roles may remain, even if dopamine in not the primary teaching signal that directly causes new learning.

  6. Why do addicts ‘want’ just drugs? An extension of salience specificity

    Dopamine drugs that activate mesolimbic systems short circuit normal physiological-learning interaction, by plugging directly into the neurobiological mechanism that ordinarily adjusts learned incentive salience in accordance with physiological states. Drugs that activate dopamine neurotransmission or induce neural sensitization may thus directly elevate ‘wanting’ for rewards in a manner that will still be cue-sensitive and reward-specific. Similarly, more enduring effects of addictive drugs, such as neural sensitization, may permanently elevate mesolimbic neural responsiveness to certain motivational stimuli, and increase incentive salience or ‘wanting’ for those rewards, especially drug rewards. This is the basis for the incentive-sensitization theory of addiction, the development of which was led by my colleague Terry Robinson (Robinson and Berridge 1993). It combines the incentive salience hypothesis of what dopamine-related mesolimbic systems contribute to reward with the idea that drugs of abuse may sensitize the same mesolimbic systems in susceptible human addicts.

    It is sometimes objected that incentive-sensitization could not possibly be specific enough to make drugs ‘wanted’ more than other stimuli. For example, Vanderschuren and Everitt engagingly proposed that “incentive sensitization caused by repeated drug exposure can explain the exaggerated motivation for drugs associated with addiction, but not the fact that drug-related activities prevail at the expense of previously important social and professional activities” (Vanderschuren and Everitt 2005). That proposal seems to suppose that incentive-sensitization must necessarily make all things equally more ‘wanted’: drugs and social or professional success alike, similar to the adage that ‘a rising tide floats all boats’. But recent evidence indicates that it is probably more accurate to say that sensitization amplifies ‘wanting’ in ways that can be quite specific to one motivational target rather than another. For example, sensitization may make drugs more ‘wanted’ than natural rewards for some individuals but for others make food or sex more ‘wanted’ than drug (Nocjar and Panksepp 2002). In other experiments described under incentive salience, sensitization can more than triple the ability of some particular cues to trigger ‘wanting’ for their reward, while leaving other cues and baseline motivation in the absence of cues, essentially unchanged (e.g., CS+2 vs CS+1 for incentive coding by ventral pallidum neuronal firing; CS+ vs CS− for behavioral cue-triggered ‘wanting’ in PIT (Tindell et al. 2005; Wyvell and Berridge 2001). Thus, incentive-sensitization can often enhance ‘wants’ for some rewards much more than other rewards, and at some moments, much more than other moments.

    Still, in accordance with Vanderschuren and Everitt’s proposal of broad motivational ‘wanting’, sensitized incentive salience can sometimes spillover, too, in humans and animals at least under some conditions. For example, Fiorino and Phillips observed that “As many as 70% of patients admitted to a New York cocaine addiction treatment program were also reported to suffer from compulsive sexuality” in a study showing that amphetamine sensitization also amplified sexual behavior and dopamine release in rats (Fiorino and Phillips 1999; Washton and Stone-Washton 1993). Parkinson’s patients with dopamine dysregulation who become addicted to over-consuming l-DOPA, may also show other motivational compulsions including gambling, sexual behavior, and obsessive desire to repeat trivial pursuits like sorting drawers (punding) (Dodd et al. 2005; Evans et al. 2006). But even in such cases, some motivational targets are ‘wanted’ much more than others. Thus, target specificity, more than generality, probably is the guiding rule for dopamine-enhanced ‘wanting’, and there might even be cases where ‘winner takes all’.

    In addiction, drugs might be specifically enhanced as targets for sensitization of incentive salience because they have a privileged Bindra–Toates associative relationship as UCS to predictive drug-related CSs, in addition to being strong stimuli for activating and sensitizing dopamine systems directly. In short, activating mesolimbic systems by dopamine agonist drug or by sensitization may amplify and distort the normal specificity by which some stimuli become ‘wanted’ much more than others, but the specificity is not abolished. That may be why addicts ‘want’ their drugs more than other rewards or social success.

  7. The test situation occurred too soon—that is, before new relearning of dopamine-augmented reward value was possible—for any existing prediction error model to produce an increment in CS-triggered V, the associative prediction of future reward, in the studies of Tindell et al. (2005) or Wyvell and Berridge (2000, 2001). V increments require retraining with an elevated UCS teaching signal. Because mesolimbic activation (sensitization and/or acute amphetamine) was delayed until after training finished, there were no opportunities for prediction error to enhance a teaching signal for V before the first test trial (even if dopamine activation had increased the prediction error UCS signal). Thus, V could not possibly have been enhanced on the first test trial without doing serious violence to the right side of the V equation of the temporal difference model. However, conceivably future computational learning models will escape the ‘need-another-UCS-experience’ constraint of cache-based models and become better able to cope with sudden shifts in value that are not gradually relearned. For example, recent tree-search models have been proposed that exhaustively examine all potential outcomes, pulling up each one for a thorough reevaluation of its utility values—but only so far applied to cortex function and explicitly not to mesolimbic dopamine function (Daw et al. 2005). Still, perhaps a related future model, if applied to mesolimbic dopamine function, might be able to allow ‘instant increases’ in CS predicted utility produced by post-learning sensitization or drug administration. Even if so, though, such future ‘prescient-V-increment’ models still will encounter a major obstacle in the finding by that dopamine activation enhanced the strength of the CS incentive code (CS+2) at the expense of the CS prediction V code strength (CS+1) in the computational profile analysis of neuronal coding in ventral pallidum in Tindell et al. (2005).

  8. Remaining difficulties with the incentive salience hypothesis. Many readers may have noted explanatory gaps that were skipped over in the section above. Though it means momentarily stepping aside from my debate mission here, my colleagues and I readily acknowledge that incentive salience is by no means a complete theory, but only an interim and skeletal hypothesis of dopamine and mesocorticolimbic function that needs additional development on many points. It is based on data available to date, but that is incomplete on several points. The gaps are real and need to be plugged by further research.

    For example, one gap needing attention concerns the relative roles of stage 2 reboosting and stage 3 dynamic generation of incentive salience attributions to a CS. Reboosting is the one feature of the incentive salience hypothesis that was added as a purely post hoc postulate to explain hedonia-type dopamine phenomena from other laboratories. It was added purely to explain why dopamine antagonist drugs sometimes produced what looked like anhedonia effects on instrumental reward tasks, such as the ‘extinction mimicry’ effects described by Wise and others (Ettenberg and McFarland 2003; Wise 1985, 2004a; though compare Salamone et al. 1997). My colleagues and I were quite familiar with extinction mimicry reports by the late 1980s. Indeed, I had been convinced by them that dopamine did mediate hedonic impact, at least, until we began to find ourselves that basic hedonic ‘liking’ reactions were not at all suppressed by dopamine reduction. We devised reboosting as a postulate specifically to reconcile extinction mimicry effects with preserved hedonic impact, in an effort to explain why dopamine could look as though it mediated pleasure when it actually did not (Berridge and Valenstein 1991; Robinson and Berridge 1993).

    As a consequence, reboosting is an add-on feature, somewhat messy though still quite necessary. It operates to influence incentive salience attributions to CSs during pairing with UCSs in stage 2, in addition to the stage 3 integration of prior UCS value and relevant physiological state that occurs when the CS is next encountered. But this degree of messiness may be an acceptable theoretical price that must be paid to buy the most data. In addition, reboosting might prove important in explaining some cases of resistance to goal devaluation, cases in which a reward CS remains ‘wanted’ even after its UCS goal is suddenly devalued and becomes no longer attractive (e.g., by pairing UCS food reward with LiCl illness). In those cases, the incentive salience of the CS may become independent of its UCS, so the CS may be no longer dynamically adjusted in stage 3 based strictly on current UCS value (perhaps persisting especially when additional associative layers such as aversion conditioning or sensory-specific habituation, rather than a direct physiological state shift such as hunger, are used to revalue the UCS). One possible explanation is that repeated reboosting of incentive salience to CS, before the devaluation, sometimes builds up ‘wanting’ for the cue in a way that to some degree becomes independent from stage 3 integration with the current state. In that case, the CS might remain attractive even after the UCS incentive value is gone. Of course, this account of resistance to devaluation is purely speculative, but it could be evaluated empirically that the relation between stage 2 reboosting and stage 3 dynamic integration become clarified by future results. To sum up reboosting, the evidence available suggests that dopamine influences incentive salience both via reboosting (during UCS training) and via dynamic mesolimbic generation (later at moment of CS reexposure). Both routes can be modeled computationally and studied experimentally. Together, they may cover much of the dopamine-related evidence on reward that gave rise originally to hedonia and stamping-in reinforcement hypotheses and motivation ‘wanting’ effects.

    Another difficulty that needs addressing in the future is to develop a more complete account of how dopamine effects on CS incentive salience are translated into UCS-directed instrumental actions beyond simple approach behaviors. The puzzle to be explained is how incentive salience becomes attributed to reward representation targets of instrumental responses or even sometimes to instrumental acts themselves. The evidence shows it does. One clear example is cue-triggered ‘wanting’ based on Pavlovian-instrumental transfer (chosen because it strips away alternative explanations) (Dickinson et al. 2000; Peciña et al. 2006; Wyvell and Berridge 2000, 2001). Cue-triggered ‘wanting’ is arguably potent in many human situations, such as addictive cue-triggered relapse. Instrumental application of incentive salience might also contribute to conditioned instrumental reinforcement situations, where individual work simply to gain a reward cue. Dopamine activation potently magnifies conditioned reinforcement (Everitt et al. 1999; Everitt and Robbins 2005). In such cases, animals must use a central neural representation of the CS incentive to guide their action because the physical cue does not occur until after the action (though contextual cues likely serve as occasion setters to activate the cue representation and incentive salience attribution). A similar logic might also apply the role of cues in seeking–taking situations or cases where earning a cue (in addition to drug reward) contributes an increment to motivation for earning the unconditioned reward by itself (Nicola et al. 2005; Vanderschuren and Everitt 2005). But a good theoretical account of how incentive salience is attributed by dopamine-related mechanisms precisely to motivate instrumental actions will need future work (Dickinson and Balleine 2002).

    An additional difficulty is how to reconcile the apparent failure of dopamine to directly cause learning with other evidence that dopamine indirectly modulates learning. As noted above, numerous studies have indicated a role for dopamine neurotransmission in modulating cellular plasticity (e.g., long-term potentiation) and in memory consolidation after learning and modulating attention and other functions that act during training and during test performance based on learned information (Dalley et al. 2005; Everitt and Robbins 2005; McGaugh 2002; Smith-Roe and Kelley 2000). Yet at the same time, recent evidence suggests that dopamine is not serving as a prediction error δ(t) to stamp-in new S–S or S–R associations or to generate learned predictions as V (e.g., ability of mutant mice to learn without dopamine; merely normal learning in other mutant mice with excessive dopamine; failure of dopamine activation to elevate limbic neural coded signal for learning δ(t) or V in recorded mesolimbic outputs in ventral pallidum). Clearly, it is of great importance to understand better exactly what dopamine does to indirectly modulate learning-related mechanisms.

    There are other deficiencies too: for example, there is a pressing need for computational models that better capture dynamic integrative features of incentive salience described above (Zhang et al. 2005). But these difficulties generally seem to be challenges that can be reasonably expected to be met in time and are not insurmountable obstructions. Most important, to return to the central theme of dopamine function, the incentive salience hypothesis is sufficiently developed at present that it can be empirically tested, as in experiments above. It makes specific predictions that can be quite feasibly pitted against learning and ‘liking’ hypotheses of dopamine function in reward. In the cases above where that has been done, the data, so far, support the hypothesis that dopamine causes ‘wanting’ more directly than either learning or ‘liking’ for reward.

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Acknowledgements

I am grateful to many colleagues who have participated in developing these ideas especially my long-term Michigan collaborators Terry Robinson, who co-developed the incentive salience hypothesis at every step and developed the incentive-sensitization theory, and J. Wayne Aldridge, who has led investigations into its neural coding. I am grateful also to Jill Becker, who arranged the Gordon Conference 2005 debate, and to the editors of this special issue, who arranged for it to be put to paper. Talented colleagues conducted the experiments in our labs that produced the data mentioned here, especially Susana Peciña, Cindy Wyvell, Amy Tindell, Jun Zhang, Casey Cromwell, Sheila Reynolds, Kyle Smith, Stephen Mahler, and Alexis Faure. Xiaoxi Zhuang and Barbara Cagniard also collaborated at a distance on the hyperdopaminergic mice project. Our experiments were supported by NIH (DA015188, DA017752, and MH63649).

This essay was written while on leave at the University of Cambridge, supported as a J.S. Guggenheim Fellow. I am deeply indebted to the kind generosity of Barry Everitt, Anthony Dickinson, Trevor Robbins, Wolfram Schultz, Jeff Dalley, Nicky Clayton, Paul Fletcher, Barbara Sahakian, Angela Roberts, Andrew Calder, Andrew Lawrence, Graham Murray, Todd Braver, Deanna Barch, Anthony Marcel, Susan Jones, Phil Corlett, and many other Cambridge colleagues and students in the Department of Experimental Psychology, Downing College, the Behavioral and Clinical Neuroscience Institute, and the MRC Cognition and Brain Sciences Unit for stimulating discussions and hospitality during the academic year in Cambridge.

Finally, I especially thank Barry Everitt, Terry Robinson, Wolfram Schultz, Trevor Robbins, J. Wayne Aldridge, Jill Becker, Martin Sarter, Anthony Dickinson, Joshua Berke, Jeff Dalley, Jaak Panksepp, John Salamone, Susana Pecina, Kyle Smith, Steve Mahler and anonymous reviewers for enormously helpful comments on an earlier draft of this essay.

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Movie 1

Hedonic taste reactions. Examples of positive facial ‘liking’ reactions elicited by sweet taste of sucrose solution from newborn human infants (via oral dropper) and adult rats (via oral cannula). Negative ‘disliking’ reactions elicited by bitter taste of quinine solution. Human infant reactions from Steiner et al. (2001); Rat reactions from Berridge (2000) (MPG 12 mb)

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Berridge, K.C. The debate over dopamine’s role in reward: the case for incentive salience. Psychopharmacology 191, 391–431 (2007). https://doi.org/10.1007/s00213-006-0578-x

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