Dopamine modulates neural networks involved in effort-based decision-making.
ABSTRACT Recent animal and human studies suggest that the dorsal anterior cingulate cortex (dACC) and its related subcortical structures including nucleus accumbens (NAc) are in the center of a brain network that determines and pursues the best option from available alternatives. Specifically, the involvement of the dACC network in decision-making can be categorized under two broad processes of evaluation and execution. The former aims to determine the most cost-effective option while the latter aims to attain the preferred option. The present article reviews neural and molecular findings to show that the dopamine system might modulate this dACC network at multiple levels to optimize both processes. Several lines of evidence suggest that the dopamine system has a bimodal effect, allows the network to compare different representations in the evaluation phase, and focuses the network on the preferred representation in the execution phase. This is apparently achieved by modulating other neurotransmission systems and by transmitting different signals via D1 vs. D2 receptor subtypes and phasic vs. tonic firing.
Article: Design of activation functions for inference of fuzzy cognitive maps: application to clinical decision making in diagnosis of pulmonary infection.[show abstract] [hide abstract]
ABSTRACT: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.Healthcare informatics research. 06/2012; 18(2):105-14.
[show abstract] [hide abstract]
ABSTRACT: Tyrosine hydroxylase is the rate-limiting enzyme of catecholamine biosynthesis; it uses tetrahydrobiopterin and molecular oxygen to convert tyrosine to DOPA. Its amino terminal 150 amino acids comprise a domain whose structure is involved in regulating the enzyme's activity. Modes of regulation include phosphorylation by multiple kinases at four different serine residues, and dephosphorylation by two phosphatases. The enzyme is inhibited in feedback fashion by the catecholamine neurotransmitters. Dopamine binds to TyrH competitively with tetrahydrobiopterin, and interacts with the R domain. TyrH activity is modulated by protein-protein interactions with enzymes in the same pathway or the tetrahydrobiopterin pathway, structural proteins considered to be chaperones that mediate the neuron's oxidative state, and the protein that transfers dopamine into secretory vesicles. TyrH is modified in the presence of NO, resulting in nitration of tyrosine residues and the glutathionylation of cysteine residues.Archives of Biochemistry and Biophysics 12/2010; 508(1):1-12. · 2.93 Impact Factor
Dopamine modulates neural networks involved in effort-based decision-making
Seyed M. Assadia,b,*, Murat Yu ¨cela,c, Christos Pantelisa
aMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Australia
bPsychiatry and Psychology Research Centre, Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
cORYGEN Research Centre, Department of Psychiatry, The University of Melbourne, Australia
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Neural network of decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.Rats and T-maze studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.Primate and human studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Molecular basis of decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1. Dopamine in cortico-basal ganglia models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.Dopamine’s role in decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusions and implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
All animals continuously face decision-making circumstances
in which they must choose between two or more options. Making
apt decisions is vital as it has a significant impact on key
evolutionary outcomes such as survival and reproduction (Gibson
and Langen, 1996; Krebs, 1978; Mulder, 1990). Decision-making is
a complex process and involves different variables. Recent studies
have started to shed light on the neurobiology of decision-making.
However, such studies vary widely and include investigations
conducted across different species from rats to humans, with
investigative methods ranging from the behavioral through to the
neural and molecular levels. The aim of the present article is to
review the previous findings, using a common framework and
terminology. We focus specifically on the decisions that are made
to solve a current problem and not on those that are aimed at long-
term outcomes. The review also focuses on the decisions that are
made to exploit the available options to greatest benefit, rather
than those that are adventurous and explore novel alternatives.
We provide evidence that decision-making is related to a brain
Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
A R T I C L E I N F O
Received 21 March 2008
Received in revised form 25 October 2008
Accepted 27 October 2008
Dorsal anterior cingulate cortex
A B S T R A C T
Recent animal and human studies suggest that the dorsal anterior cingulate cortex (dACC) and its related
subcortical structures including nucleus accumbens (NAc) are in the center of a brain network that
determines and pursues the best option from available alternatives. Specifically, the involvement of the
dACC network in decision-making can be categorized under two broad processes of evaluation and
execution. The former aims todetermine the most cost-effective optionwhile the latter aims toattain the
preferred option. The present article reviews neural and molecular findings to show that the dopamine
system might modulate this dACC network at multiple levels to optimize both processes. Several lines of
representations in the evaluation phase, and focuses the network on the preferred representation in the
execution phase. This is apparently achieved by modulating other neurotransmission systems and by
transmitting different signals via D1 vs. D2 receptor subtypes and phasic vs. tonic firing.
? 2008 Elsevier Ltd. All rights reserved.
* Corresponding author at: Melbourne Neuropsychiatry Centre, Sunshine
Hospital, 176 Furlong Road, St. Albans, Vic. 3021, Australia.
Tel.: +61 3 83451303; fax: +61 3 83450599.
E-mail address: email@example.com (S.M. Assadi).
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
0149-7634/$ – see front matter ? 2008 Elsevier Ltd. All rights reserved.
network centered on the dorsal anterior cingulate cortex (dACC)
and its related subcortical structures. We also consider how the
dopamine system modulates this network at multiple levels to
facilitate an optimal decision.
2. Neural network of decision-making
There are considerable connections between the anterior
cingulate cortex (ACC) and the striatum including the nucleus
accumbens (NAc) across mammalian species. Reciprocal connec-
tions between these brain regions were described in humans more
than two decades ago (Alexander et al., 1986); the ACC projects to
the NAc, which projects back to the ACC via the mediodorsal
(Ongur and Price, 2000; Ray and Price, 1993; Yeterian and Pandya,
1988) and anteroventral nuclei of the thalamus (Xiaob and Barbas,
2004; Zikopoulos and Barbas, 2007). Both regions are connected
with other limbic structures, namely the amygdala and hippo-
campus, and with the dopamine-enriched area in the ventral
tegmentum (VTA) (Baleydier and Mauguiere, 1980; Floresco and
Ghods-Sharifi, 2007; Morecraft et al., 2007; Morecraft and Van
Hoesen, 1998; O’Donnell and Grace, 1995).
Although the original model of the segregated parallel loops
proposed that the ACC was mainly linked with the NAc (Alexander
et al., 1986), recent studies have shown that the ACC is also
connected with the sensorimotor striatum (Ferry et al., 2000;
Voorn et al., 2004). In addition, it has been shown that the lower
and upper tiers of the ACC have their own specific basal ganglia
connections. The lower tier is linked to the ventromedial striatum
(i.e. the NAc and ventral putamen) while the upper tier to the
dorsolateral striatum (i.e. the dorsal caudate and the putamen)
(Ferry et al., 2000).
We review both animal and human studies to provide evidence
that this brain network is important in decision-making, with the
lower and upper tiers involved in different aspects of decision-
making. We start with rat studies to provide an overall view and
proceed to primate and human studies to examine specific aspects
2.1. Rats and T-maze studies
Rat studies have suggested that damage to or dopamine
depletion in the ACC-NAc circuit leads to an abnormality in
decision-making. In a paradigm developed by Salamone et al.
(1994), rats were placed in a T-maze to choose between two arms
with different amounts of food (benefits) and task difficulties
(costs). In this task, rats had a choice between climbing a barrier to
obtain a large reward in one arm vs. running into the other arm
without any barrier in order to obtain a small reward (Fig. 1A).
Normally, rats preferred the high reward arm even though they
had to exert greater effort. However, manipulations affecting the
ACC, the NAc, and their dopamine system dramatically altered
choice behavior so that rats tended to put less effort and to content
themselves with the small reward in the alternative arm (Fig. 1B).
The manipulations included lesioning the ACC (Schweimer and
Hauber, 2005; Walton et al., 2003) and NAc (Hauber and Sommer,
2007), disrupting ACC-NAc connection (Hauber and Sommer,
2007), and dopamine depletion or blockade in the ACC (Schweimer
and Hauber, 2006; Schweimer et al., 2005; but see also Walton
et al., 2005) or the NAc (Cousins et al., 1996; Salamone et al., 2003,
1994). A recent study also showed that a disconnection between
maze performance (Floresco and Ghods-Sharifi, 2007).
to preserve their ability to appreciate costs and benefits in the T-
maze task because reducing the cost or increasing the reward in
the high-cost arm caused these rats to return to the high-cost,
high-reward option (Salamone et al., 2007; Schweimer et al., 2005;
Walton et al., 2003). Moreover, NAc dopamine depletion does not
generally change hedonic and aversive reactions to rewarding and
aversive stimuli, respectively, which suggests that dopamine-
depleted rats retain their ability to recognize the benefits of
rewarding stimuli (Berridge and Robinson, 1998). It has also been
shown that genetically modified mice that lack dopamine
demonstrate normal preference for sucrose over water, suggesting
that perception of the beneficial aspects of stimuli remains intact
Moreover, motor deficit does not appear to be the cause of
abnormal T-maze performance as it has been found following both
NAc dopamine depletion, which makes animals slow and
hypoactive (Berridge and Robinson, 1998; Cousins et al., 1994),
and ACC dopamine depletion, where animals do not show any
noticeable hypoactivity (Rudebeck et al., 2006b; Schweimer and
Hauber, 2006; Schweimer et al., 2005).
Overall, it seems that the ACC, NAc, and associated dopamine
system are involved in the decision-making process that occurs
between initial sensory perception and final motor performance.
decision-making are processed by these brain structures (Sala-
mone et al., 2007). Decision-making consists of a set of complex
multivariate events such as analyzing different costs and benefits
(Stevens et al., 2005; Maynard Smith, 1982) and their probabilities
(Green and Myerson, 2004; Kacelnik and Bateson, 1997), taking
account of previous outcomes (Kennerley et al., 2006; Nishida,
1997), and processing different motor variables for pursuing the
preferred option (Barnes et al., 2005; Graybiel, 2005; Berridge and
Robinson, 1998). Taken as a whole, however, decision-making can
Fig. 1. (A)Schematic diagram of the T-maze task. Normal ratstend to choose the left
arm (high-cost–high-benefitoption)insteadof therightarm(low-cost–low-benefit
option). (B) Effect of lesion or dopamine depletion on reallocation (R) of the
behavioral response from the left arm to the right arm.
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
be considered as a cycle of two general processes, namely,
evaluation and execution (Fig. 2). Evaluation refers to all events
that lead to a new decision. The process starts with exposure to a
new set of options and ends with an overall preference. This
includes analysis of costs and benefits of different options as well
as appraisal of previous outcomes. Execution refers to all events
that attempt to actualize the preference. Execution starts with the
overall preference and ends with the attainment of the preferred
option. This requires the mobilization of resources and the
planning of a sequence of movements to attain the preferred
option. Mobilizing resources can be thought of as the physiological
aspect of motivation that is aimed at providing the required
resources to pursue the preferred option, while action sequencing
is aimed at using the mobilized resources in the most effective and
efficient manner to achieve the goal.
Both evaluation and execution have been suggested to be
processed by the ACC-NAc circuit and its dopamine system. For
example, a number of studies have proposed that behavioral
reallocation following dopamine abnormality in this circuit is due
to abnormal cost–benefit analysis (Floresco et al., 2008; Phillips
et al., 2007; Rudebeck et al., 2006b). Cost–benefit analysis is a
major component of evaluation and involves assessing and
weighing up the perceived costs and benefits (Maynard Smith,
1982). Phillips et al. (2007) have recently developed a mathema-
tical model for cost–benefit analysis in the ACC-NAc circuit. In this
model, the maximum response cost that the animal would afford
for different rewardmagnitudesfollows a hyperbolic curve(Fig. 3).
Inhibition of dopamine transmission shifts the curve downwards
and reduces the maximum response cost that the animal would
allocate to obtain a reward. Enhancement of dopamine transmis-
sion, onthe other hand, shifts this curve upwards and increasesthe
affordable response cost. In other words, dopamine blockade
biases the cost–benefit analysis towards avoiding excessive
expenditure whereas dopamine enhancement biases it towards
achieving maximum reward.
While this model can satisfactorily explain T-maze findings
with dopamine depletion, the upward shift following dopamine
enhancement is rather hypothetical and does not accord with the
bidirectional and biphasic effects observed with dopamine
enhancement, in which low and high doses of dopamine agonists
have opposite and/or dissimilar effects (Seamans and Yang, 2004).
This biphasic effect was recently reported in a decision-making
task study (Floresco et al., 2008). Therefore, it is probable that
moderate dopamine enhancement shifts the cost–benefit curve
upwards while extreme augmentation shifts it in the opposite
In addition, the hypothesis does not address the complexity of
cost–benefit analysis. Costs and benefits are not unitary para-
meters but, rather, consist of several variables. For example,
benefits are characterized by the quality, quantity, and probability
of gaining a reward (Green and Myerson, 2004; Kacelnik and
Bateson, 1997). Animals prefer certain types of reward over others,
large rewards over small ones, and highly probable rewards over
those that are poorly predictable. The ‘‘net benefit’’ can be defined
as the overall advantage of approached rewards over those of the
ignored rewards in a given set of decisions. In contrast, cost
variables include the hazard of approaching the reward, the energy
and time required for obtaining the given reward (Green and
Myerson, 2004; Stevens et al., 2005), and the probability of cost
occurrence (Kacelnik and Bateson, 1997). Animals prefer costs that
inflict minor or no hazards compared with those with major
hazards. They also consider the energy and/or the time that they
have to spend in order to obtain a reward(Stevens et al., 2005). The
‘‘net cost’’ can be considered as the sum of the purchased costs
minus the sum of the avoided costs in a given set of decisions.
However, previous studies including the above model have not
clarified which variables in the cost–benefit analysis are changed
by dopamine blockade and future studies are needed to explore
Other studies have suggested that the ACC-NAc circuit is
involved in the execution phase of a decision. It has been proposed
that lesioning or dopamine depletion in the ACC-NAc circuit
dampens motivation and impairs the ability to mobilize the
resources needed to pursue a preferred reward. This is usually
Fig. 2. Decision-making cycle, which consists of two general processes. The first
process is evaluation, which is composed of analysis of cost and benefit and
appraisal of pervious outcomes. Evaluation ends in an overall preference for one
option over other competing options. The second process is execution of the
preference and consists of motivation and action. Motivation refers to mobilization
of resources and action to planning a sequence of movements to attain the chosen
option. Execution ends in attaining the preferred option.
Fig. 3. The proposed effect of dopamine on the maximum affordable cost (modified
from Phillips et al., 2007). The maximum response cost as a function of reward
magnitude follows a hyperbolic curve. Enhancement or attenuation of the
dopamine system shifts the curve upwards or downwards, respectively. The
enhancement portion, however, is rather theoretical and is depicted as dopamine
dysregulation. See text for further discussion.
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
et al., 2003). The inference is based on reasoning that rats’
behavioral responses indicate that the energy resource has been
mobilized beforehand. Other studies have suggested that rats’
cortico-basal ganglia circuit is involved in action sequencing
(Barnes et al., 2005; Graybiel, 2005), with the dopamine system
playing a key role in sequence generation (Daberkow et al., 2005).
Involvement of the ACC-NAc circuit and its dopamine system in
motivation and action sequencing has also received empirical
support from recent primate and human studies (reviewed below).
In summary, rat studies have provided some evidence that the
ACC-NAc circuitcontributesto evaluation and executionand to the
analysis, motivation, and action elements of the decision-making
cycle. However, inconsistencies exist and further studies are
needed to verify the above conclusion; for example, ACC dopamine
depletion did not cause abnormal T-maze choice in one study
(Walton et al., 2005), the effect of ACC lesion was transient in
all the effort-based tasks in yet another study (Schweimer and
Hauber, 2005), suggesting that the ACC may not be involved in all
types of effort-related decisions. Further, it has remained unclear
how exactly dopamine depletion or blockade changes behavioral
responses in the T-maze task. In particular, further studies are
required to clearly untangle variables involved in the cost–benefit
analysis. For example, recent rat studies have suggested that the
ACC-NAc circuit is not involved in all aspects of cost–benefit
analysis. Analysis of time-related costs (i.e. waiting for reward and
the phenomenon of delay discounting) is not impaired by NAc
dopamine depletion (Wakabayashi et al., 2004; Winstanley et al.,
2005) or by an ACC lesion (Rudebeck et al., 2006b; Walton et al.,
2007). Instead, delay discounting has been attributed to the
orbitofrontal cortex (Kheramin et al., 2003; Mobini et al., 2002;
Roesch et al., 2007; Walton et al., 2007), the NAc core (Bezzina
et al., 2007; Cardinal et al., 2001), and their dopamine innervations
(Cheung et al., 2007; Kheramin et al., 2004; Mobini et al., 2000;
Wade et al., 2000). Further, recent studies have proposed that the
orbitofrontal cortex is also involved in processing reward
probability (Kheramin et al., 2003; Mobini et al., 2002).
2.2. Primate and human studies
between two options with different benefits and costs, has not
been tested inprimates or humans asyet. However,several studies
have examined different aspects of decision-making in humans
and non-human primates. These studies have generally indicated
that decision-making is dependent on the brain circuit involving
the ventral striatum and the medial wall of the prefrontal cortex,
especially the dACC. A recent functional imaging study used a
probabilistic decision-making task in which normal human
subjects chose between two options with different probabilities
of winning or loosing money (Hampton et al., 2006; Hampton and
O’Doherty, 2007). The study concluded that the combined signals
from three brain regions, i.e. the ACC, the medial prefrontal cortex,
and the ventral striatum provided the information sufficient to
decode the decisions made by subjects. Of these three regions, the
dACC stood out as contributing the most in this regard (Hampton
and O’Doherty, 2007). Another research group has shown that the
activity of the dACC increased as a result of an increase in the
the general involvement of this region in decision-making.
Furthermore, studies suggested that the key step of decision-
making, i.e. choosing one option rather than another, is dependent
2007). Finally,a recentstudy hasshown thatinhibitionof the dACC
using repetitive transcranial magnetic stimulation (rTMS) disrupts
subjects’ response-switching (Rushworth et al., 2002). These
findings have led to a recent hypothesis suggesting that the
primate dACC network is involved in decision-making similar to
that of the rat ACC-NAc circuit (Rushworth et al., 2004).
There is accumulating evidence that dACC and its related
subcortical structures are important in the evaluation process of
decision-making. Studies have suggested that this network is
involved incost–benefitanalysis. A recentstudy showedthat dACC
activity increased when the distance between the reward value of
two options diminished (Blair et al., 2006), suggesting a role of the
dACC in analyzing the benefits of one option over another. There is
also evidence for the involvement of the dACC in analyzing the
costs of options. For example, it has been found that tasks that
contrast a demanding option with a less demanding one generally
activate the dACC (Paus et al., 1998; Raichle et al., 2001).
been studied. For example, the involvement of the dACC in
appraisal has been suggested by studies assessing error detection
and conflict monitoring, where this region is activated by the
occurrence of error or conflict from onetrial to another. Recently, it
has been proposed that the involvement of the dACC in the
monitoring of response conflict could be considered as one
instance of a more general outcome monitoring function
(Botvinick et al., 2004). In line with this suggestion, a recent
study has suggested that the dACC is involved in assessing the
consequences of choices (Walton et al., 2004). Two other studies
found that the reward-related activity of dACC neurons in a given
trial was often modulated by the rewards that were delivered in
previous trials (Kennerley et al., 2006; Seo and Lee, 2007). This
suggests that neurons in the dACC might be involved in the
evaluation ofoutcomesofa decision (Seo and Lee,2007) andadjust
future decisions on the basis of the history of previous decisions
and their outcomes (Kennerley et al., 2006). This may be partly
accomplished by predicting error likelihood (Brown and Braver,
2005; Yu ¨cel et al., 2007b).
Other lines of evidence indicate that the dACC is also important
inthe executionprocess and the motivationand actionelements of
decision-making. Some evidence comes from recent attempts to
unravel the functionalanatomyof the ACC.Thereare currentlytwo
hypotheses about the functional subdivisions of the ACC. Bush
et al. (2000) have proposed that the dACC is mainly involved in the
cognitive aspects of behavior while the ventral part of the anterior
cingulate cortex (vACC) is involved in affective aspects such as
emotion and motivation. This conclusion follows evidence that the
vACC is anatomically connected with brain structures such as the
NAc and the hypothalamus (Devinsky et al., 1995) and that this
region, rather than the dACC, is activated during emotion-related
tasks such as the Emotional Counting Stroop (Whalen et al., 1998).
This conclusion, however, is in sharp contrast with recent findings
that the dACC activation is associated with autonomic responses
(Critchley et al., 2003, 2005). Based on the anatomical and
physiological data, several researchers have proposed that the
supracallosal portion of the ACC can be divided into the lower and
upper tiers (Koski and Paus, 2000; Picard and Strick, 1996; Rahm
et al., 2006; Yu ¨cel et al., 2003). The dACC comprises areas 24a0–c0in
macaque monkey and 24a0–c0and 320in humans (see Fig. 4). The
lower tier consists of 24a0and 24b0and the upper tier of 24c0and
320. Although the specific roles of each tier remains to be clarified,
it seems that the two tiers differ in terms of their association with
affective and motor aspects, with the lower tier related to affective
aspects and the upper tier to motor aspects of decision-making.
Below we provide evidence regarding this functional partitioning.
We believe that the evidence supports the notion that the dACC is
involved not only in the cognitive, evaluative aspects of decision-
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
making (i.e. analysis and appraisal) but also in its affective (i.e.
motivation) and motor (i.e. action) aspects.
Anatomically, the lower tier of the dACC is connected to the
subcortical regions involved in affective processing while the
upper tier is not. As shown in Fig. 4, the lower tier, rather than the
upper tier, has anatomical connections with the NAc and
hypothalamus. Tracer studies in macaque monkeys have shown
that the anterior and posterior hypothalamus (Ongur et al., 1998
and personal communication with J.L. Price) as well as the shell
region of the NAc (Ferry et al., 2000; Kunishio and Haber, 1994)
receive fibers from areas 24a0–b0but scarcely from area 24c0. The
latter, as expected of its executive role, sends fibers to the dorsal
Recent findings indicate that the dACC is involved in the
execution process including its motivation and action elements.
PET studies have shown that the regional blood flow to the dACC
increases along with blood pressure during physical or mental
effort (e.g. isometric exercise and mental arithmetic tasks). This
effect intensifies in patients with pure autonomic failure—a
disorder that results in failure of autonomic response to physical
or mental effort. The compensatory hyperactivity of the dACC in
this disorder strongly suggests that the dACC is important in
provoking autonomic responses during goal-directed behaviors
(see for review Critchley, 2005).
There is also ample evidence that the dACC is involved in action
sequencing and in initiating and maintaining goal-directed
behaviors (Dosenbach et al., 2006). Several primate studies have
identified dACC activation at different stages of goal-directed
behaviors including action selection (Matsumoto et al., 2003;
Shima and Tanji, 1998) and action progression, with the lower
bank of 24c0likely involved in evaluating the progression by
monitoring the reward expectancy in the course of a given trial
(Shidara and Richmond, 2002) and the upper bank of 24c0involved
in directing the progression via a general role in keeping track of
primate findings, a recent human study using rapid event-related
3-T fMRI suggested that the upper tier of the dACC was involved in
integrating the target and arm information to conduct a goal-
directed behavior (Beurze et al., 2007).
Together, it could be concluded that the dACC participates in
and maintenance of goal-directed behaviors. The dACC might do
the latter not only through its extensive connections with the
motor cortex but also by activating the midbrain gray matter. The
dACC sends projections to different columns of midbrain
periaqueductal gray matter (An et al., 1998). This region is well-
known for generating various behavioral patterns such as foraging
(Comoli et al., 2003), active coping strategies (e.g. fight and flight),
passive coping strategies (i.e. immobility and withdrawal) (Keay
and Bandler, 2001), and sexual and nursing behaviors (Lonstein
and Stern, 1998) and is necessary for switching between different
behavioral patterns (Sukikara et al., 2006).
In summary, recent primate and human studies support the
idea that the dACC and its related subcortical structures are
important both in the evaluation and execution processes of
array of cognitive, affective, and motor data to help in making an
apt decision. This view is supported by the monkey electro-
physiological studies that have shown that the dACC neurons are
functionally heterogeneous (Ito et al., 2003; Matsumoto et al.,
2003;Shidaraet al.,2005; Shidaraand Richmond,2002; Shimaand
Tanji, 1998) and have suggested that this region processes various
elements of goal-directed behavior (Bush et al., 2002; Richmond
et al., 2003; Satterthwaite et al., 2007). This conclusion has the
advantage of integrating the diverse roles attributed to this brain
region (see Bush et al., 2000 for a review). We suggest that the
previous findings about the dACC can be better understood within
this framework. The attributed roles might merely reflect the
different paradigms used to evaluate dACC function. Roles related
to analysis include anticipation of reward and punishment
(Knutson et al., 2000), attention for action (Posner et al., 1988),
response competition monitoring (Carter et al., 1998), and motor
response selection (Paus et al., 1998). Roles related to appraisal
include error detection (Kiehl et al., 2000), conflict monitoring
(Botvinick et al., 1999), and outcome monitoring (Ito et al., 2003).
Roles related to motivation could be motivational valence assign-
ment (Mesulam, 1990) and autonomic control (Critchley et al.,
2003). Finally, roles related to action could include reward
expectancy encoding (Shidara and Richmond, 2002) and perfor-
mance adjustment (Ridderinkhof et al., 2004).
Decision-making, however, is not limited to this circuit. The
circuit acts in concert with other brain regions, especially the
dorsolateral, orbitofrontal, and ventromedial prefrontal cortices,
the amygdala, the hippocampus,and the dorsal/ventral striatum.It
has been suggested that the integrity of the whole prefrontal-
striatal circuit is necessary for error detection, which could
emphasize that an intact cortical–subcortical network is crucial in
decision-making (Hogan et al., 2006; Ullsperger and von Cramon,
2006). Moreover, some aspects of cost–benefit analysis are
probably processed in other brain areas. Studies suggested that
reward probability may be processed in the mesial prefrontal
cortex (Amiez et al., 2005) and the anterior insula (Rolls et al.,
2008). The cost related to time (delay discounting) is probably
processed in the posterior insula (Wittmann et al., 2007), the
lateralprefrontalcortex,andthe posteriorparietal cortex (McClure
et al., 2004). Finally, evidence suggests that social cues for making
decisions are processed in the vACC (Somerville et al., 2006) and
the orbitofrontal cortex (Rudebeck et al., 2006a).
It is also noteworthy to mention that the dACC is probably
involved in here-and-now, exploiting decision-making and not in
decisions concerning long-term outcomes and exploring novel
options. Short-term, here-and-know decisions are concerned with
Fig. 4. Relative density of connections between different subregions of the ACC and
anterior/posterior hypothalamus in macaque monkeys. The density is scaled to the
value of Brodmann area 25 (subcallosal cingulate). Similar pattern was observed
with the NAc shell. The pattern of connectivity suggests that the dACC (Brodmann
area 24) can be divided into upper and lower tiers. Drawn based on Ferry et al.
(2000), Ongur et al. (1998).
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
the immediate outcomes of ongoing events. Long-term decisions,
on the other hand, aim at the ultimate outcome of a series of
decisions, irrespective of immediate gains or losses. While the
dACC is important for short-term decisions, long-term decisions
probably involve the orbitofrontal cortex (Wallis, 2007). Studies of
patients with ventromedial prefrontal lesions have shown that
short-term decision-making is preservedin these patients butthey
fail to consider long-term outcomes (Bechara et al., 1999, 2000).
Risk-taking, exploratory decisions aim to identify novel
options, compared to the conservative, exploiting decisions that
aim to exploit the current options as well as possible. It has been
suggested that exploratory decision-making is related to the
frontopolar cortex and the intraparietal sulcus in humans (Daw
has any role in exploratory decisions.
Finally, it is required to assess whether this circuit is related to
conscious aspects of decision-making, to unconscious aspects, or
both. A recent study suggested that dACC activity is related to
conscious aspects of decision-making (Dehaene et al., 2003),
though further studies are needed to examine this finding.
3. Molecular basis of decision-making
Assuming that the dACC-striatum-thalamus circuit and its
dopamine system are involved in decision-making, the next
question is how dopamine modulates this circuit to facilitate an
optimal decision. Below, we first review different cortico-basal
ganglia models to illustrate an overall view of how the dopamine
system modulates the network. Then, we review dopamine studies
to provide clues on how this system can facilitate progression of
different aspects of decision-making at the molecular level.
3.1. Dopamine in cortico-basal ganglia models
Dopamine has a prominent, decision-making-related role in all
the models that have been developed in the past two decades to
explain the function of the cortico-basal ganglia circuits (see Bar-
Gad et al., 2003 for review of these models).
In single pathway models, information flows from the cortex,
through the striatum to the globus pallidus internus and back to
the cortex through the thalamus. Information from multiple
sources can either converge (leading to information sharing;
Percheron et al., 1984) or flow in parallel (leading to segregated
parallel processing; Alexander et al., 1986). Studies in monkeys
and humanshave suggested that the dopaminesystem keeps brain
circuits segregated both in the basal ganglia (Bergman et al., 1998;
Pessiglione et al., 2005) and the cortex (Constantinidis et al., 2002;
Goldman-Rakic et al., 2000; Yang et al., 1999), allowing parallel
processing of cognitive, affective, and motor components. This
could be crucial in decision-making where these different aspects
need to happen almost simultaneously.
In multiple pathway models, each cortico-basal ganglia loop
consists of two competing pathways: the direct pathway mediates
a positive feedback while the indirect pathway a negative
feedback. It has been proposed that the relative activity of the
pathways is controlled by dopaminergic input, which enhances
direct pathway activity through D1 receptors while attenuates
indirect pathway activity through D2 receptors (Alexander et al.,
1986; Gerfen et al., 1990). Dopamine activity,therefore, resultsin a
net positive feedbackand leads to the initiation and continuanceof
loop activity, which is considered essential for goal-directed
behavior (DeLong, 1990; Wichmann and DeLong, 1996).
In action selection models, the role of the dopamine system is
also decisive. These models assume that the basal ganglia choose
an actionout of the numeroussuch actions presented by the cortex
(Mink, 1996; Plenz, 2003; Wickens, 1997). For example, in the
focused selection model, the basal ganglia potentiates a given
activation pattern (promoting a certain response) while inhibiting
other competing activation patterns (preventing alternative
responses). It has been proposed that the dopamine system
triggers a particular activation pattern by strengthening the
efficacy of some cortico-striatal synapses while weakening others
(Calabresi et al., 2007; Reynolds and Wickens, 2002).
Sequence generation models propose that the cortico-basal
ganglia circuits are involved in selecting and/or generating action
sequences (Berns and Sejnowski, 1998). This is crucial to goal-
directed behavior and the action element of decision-making.
Studies have suggested that the dopamine system in the basal
ganglia is essential for sequence learning (Daberkow et al., 2005;
Suri and Schultz, 1998).
Finally, the dimensionality reduction model considers the basal
ganglia as a multi-layered neural network with a dynamic pattern
of activity. The network receives data from various sources,
highlights important data, and neglects unimportant ones. This
model suggests the dopamine activity via cortico-striatal-dopa-
minergic synapse triads acts as a reinforcement signal, indicating
which data are important (Bar-Gad et al., 2003). Therefore, the
dopamine system would be crucial in this model for evaluating the
significance of different options/responses during decision-mak-
Overall, the models can be broadly divided into two groups;
those supporting the role of dopamine in evaluation and those in
execution. The first group assumes that the dopamine system is
involved in the evaluation phase of decision-making. They propose
that dopamine helps to differentiate important signals from
unimportant ones (e.g. dimensionality reduction model) and to
choose a favorable response over alternative responses (e.g. action
selection models). The second group considers the dopamine
system as a facilitator of the execution phase. These models
assume that dopamine manipulates the cortico-basal ganglia
macrocircuit to initiate and maintain an action (e.g. multiple
pathway models) and to generate the motor sequence that is
necessary for that action (e.g. sequence generation models).
3.2. Dopamine’s role in decision-making
Human and animal studies have assessed the role of the
dopamine system in different aspects of decision-making. These
studies, in line with cortico-basal ganglia models, suggest that the
dopamine system has a pervasive effect on different elements of
decision-making (Schultz, 2007a), including analysis (Cohen et al.,
2005; Knutson et al., 2004; Salamone et al., 2007; Vrshek-
Schallhorn et al., 2006), appraisal (de Bruijn et al., 2006; Morris
et al., 2006; Schultz and Dickinson, 2000), motivation (Salamone
et al., 2007; Both et al., 2005; McLean et al., 2004; Satoh et al.,
2003), and action (Cromwell et al., 1998; Daberkow et al., 2005).
This pervasive role of the dopamine system in decision-making
is not surprising, considering that this system acts in a variety of
brain regions, ranging from the midbrain gray matter through to
the prefrontal cortex (Hurd et al., 2001). Furthermore, evidence
suggests that the dopamine system extensively modulates other
neurotransmitter systems (Greengard, 2001), transmits different
signals via phasic vs. tonic firing (Grace et al., 2007; Schultz,
high dopamine concentrations (Trantham-Davidson et al., 2004),
pre- vs. post-synaptic receptors (Adell and Artigas, 2004) and D1
vs. D2 receptor subtypes (Seamans et al., 2001; see for review
Seamans and Yang, 2004).
It has recently been proposed that the dopamine system
modulates the pattern of activity in prefrontal networks (Lapish
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
et al., 2007; Seamans et al., 2001; Trantham-Davidson et al., 2004)
(Fig. 5). The model has taken account of the complex interplay
between dopamine and a variety of ionic and synaptic currents in
the prefrontal cortex, especially GABA and NMDA currents. This
model has suggested that a predominantly D2 receptor activation
(i.e. D2 state) leads to a net reduction of network inhibition. As a
result, multiple inputs can gain access to the prefrontal network.
This allows multiple representations to be processed simulta-
neously in prefrontal networks. On the other hand, the model has
proposed that a predominantly D1 receptor activation (i.e. D1
state) leads to a net increase in network inhibition so that only
strong inputs can persist in the prefrontal network. It has been
suggested that phasic, high concentrations of dopamine (>1 mM)
induce a transient D2 state while tonic, low concentrations
(>500 nM) induce a long-lasting D1 state (Seamans et al., 2001;
Trantham-Davidson et al., 2004). These two states fit well with the
evaluation and execution phases of decision-making. First, the
transient D2 state allows the simultaneous existence of multiple
representations, which is required for cost–benefit analysis and
outcome appraisal. Subsequently, the longer lasting D1 state
stabilizes the selected representation and shuts off the influence of
other representations; this keeps the animal focused on the
selected goal. A recent study has suggested that D2 receptor
reduction is associated with impaired learning from negative
outcomes and with decreased dACC activity during performance
monitoring (Klein et al., 2007); this supports the above hypothesis
that the D2 state of the dACC network is important in outcome
appraisal. The above-mentioned changes, however, occur in
minutes and have been used to explain the role of the dopamine
system in working memory and the dorsolateral prefrontal cortex
(Seamans and Yang, 2004). Therefore, it remains to be determined
whether similar changes occur in the medial prefrontal cortex
within milliseconds to explain the rapid sequences relevant to
Studies of the basal ganglia have also indicated a similar
some evidence that fast, phasic dopamine activity (lasting
<500 ms with >100 nM dopamine concentration) could be
considered as an evaluating signal that encodes a prediction error,
signaling whether the event is better or worse than expected
(Schultz et al., 1997). In contrast, slow, tonic dopamine activity
(lasting for seconds with dopamine concentrations of ?10–30 nM)
seems to be an arousal signal while the animal pursues a
motivationally relevant goal. Tonic activity appears to encode
reward uncertainty in the period between occurrences of
conditioned stimuli to the expected time of reward (Fiorillo
et al., 2003). Therefore, the phasic, evaluating signal might be
related to the evaluation phase, where the significance of stimuli is
analyzed and the tonic, arousal signal might be related to the
execution phase, when the level of arousal could be adjusted
according to the level of reward uncertainty (Salamone, 1996). At
the basal ganglia level, phasic and tonic firings have been
suggested to act via different dopamine receptor subtypes (Bilder
et al., 2004; Floresco et al., 2003; Grace, 1991; Schultz, 2007b).
In addition, studies have indicated that NAc dopamine might
act at the macrocircuit level, modulating inputs from the
extrastriatal regions involved in decision-making. These studies
have suggested that the NAc dopamine system plays a critical role
in response selection and perseverance (see Grace et al., 2007 for
review). Studies have found that dopamine signals, through D1
receptors, potentiate the hippocampal inputs while they attenuate
the inputs from the basolateral amygdala (Floresco et al., 2001).
Dopamine signals also inhibit the medial prefrontal inputs to the
NAc through D2 receptors. The net result would be the acquisition
and maintenance of a new response strategy because the D1-
mediated potentiation of the hippocampal inputs leads to the
acquisition of a response strategy while the D2-mediated
attenuation of the medial prefrontal inputs prevents response-
switching and results in response perseverance (Goto and Grace,
Overall, recent studies provide evidence that the dopamine
system probably has a bimodal effect on the dACC-NAc network. In
the evaluation phase, dopamine modulates this network so that
different variables can be considered, processed, and compared.
This enables the network to appraise previous outcomes and to
analyze existing costs and benefits. In the execution phase, on the
other hand, the dopamine system makes the network focused on
the chosen stimulus and the variables related to its attainment;
therefore, the network concentrates on the planning and execution
of a series of mental and motor actions to attain the chosen option
without interference by irrelevant stimuli.
4. Conclusions and implications
Decision-making requires processing of a variety of different
variables. These variables can be categorized under two broad
processes of evaluation and execution. The former consists of
outcome appraisal and cost–benefit analysis and aims to
determine the best possible option while the latter consists of
motivation and action sequencing and aims to attain the preferred
option. Recent animal and human studies suggest that the dACC
and its related subcortical structures are at the center of a brain
network responsible for evaluation and execution of decisions. In
addition, accumulating evidence suggests that the dopamine
Fig. 5. Effect of D1 vs. D2 receptor activation on the prefrontal network. Reproduced with permission from Seamans et al. (2001).
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
system modulates this network at multiple levels to optimize both
processes. The dopamine system probably causes this bimodal
effect via D1 vs. D2 receptor subtypes and tonic vs. phasic firing
and by modulating different synaptic inputs and neurotransmis-
sion systems. However, further human studies are needed to verify
these hypotheses and to determine the specific variables that are
processed by the dACC and its related subcortical regions. Future
studies are also needed to tailor the general finding about the
dopamine system to the brain network of decision-making.
One important step for future studies would be to develop a
human decision-making test that specifically assesses the type of
decision-making that the dACC network is involved in, i.e. here-
and-now, exploiting, effort-related decision-making. The test
should incorporate both evaluation and execution phases and
should be versatile enough to be conducted with neuroimaging
techniques. The test can consist of two options with different
amounts of reward that require different levels of effort but should
control for the time that subjects spend on each option to rule out
These new insights into the possible function of the dACC and
its dopamine innervations can have important implications for a
number of psychiatric illnesses, especially schizophrenia and
impulsive–compulsive spectrum disorders. There is ample evi-
dence that these disorders are associated with significant
abnormalities in the dACC (Yu ¨cel et al., 2007a,c,d, 2002) and in
the dopamine system (Toda and Abi-Dargham, 2007; Volkow et al.,
2007). A common feature of these disorders might be that patients
often experiencethe feeling ofbeing forced to pursue deviant ideas
and impulses over socially acceptable ones. A dysfunctional dACC
network, therefore, may underlie some of the clinical presenta-
tions of these disorders. For example, dopamine dysregulation in
this network could impair the evaluation phase and the
individual’s ability to accurately assess the costs of pursuing
deviant ideas and impulses and to learn from previous negative
outcomesof such decisions.In addition,aninefficient networkthat
cannot choose and focus on appropriate options may suffer
simultaneous presence of different mental representations and
frequent intrusion of irrelevant representations, which can lead to
formal thought disorders in extreme conditions. Finally, complete
failure of the network may lead to an inability to choose, initiate,
and maintain goal-directed behavior, resulting in symptoms such
as indecisiveness, avolition, and impersistence. These hypotheses
need to be examined by future studies.
Dr Yu ¨cel is supported by a NH&MRC Clinical Career Develop-
ment Award (ID: 509345). Prof Pantelis’s research is supported by
NHMRC Program Grant (ID: 350241). The authors thank Dr. Alex
Fornito for his helpful comments on the manuscript.
Adell, A., Artigas, F., 2004. The somatodendritic release of dopamine in the ventral
and Biobehavioral Reviews 28, 415–431.
Alexander, G.E., DeLong, M.R., Strick, P.L., 1986. Parallel organization of functionally
segregated circuits linking basal ganglia and cortex. Annual Review of Neu-
roscience 9, 357–381.
Amiez, C., Joseph, J.P., Procyk, E., 2005. Anterior cingulate error-related activity is
modulated by predicted reward. European Journal of Neuroscience 21, 3447–
An, X., Bandler, R., Ongur, D., Price, J.L., 1998. Prefrontal cortical projections to
longitudinal columns in the midbrain periaqueductal gray in macaque mon-
keys. Journal of Comparative Neurology 401, 455–479.
Baleydier, C., Mauguiere, F., 1980. The duality of the cingulate gyrus in monkey.
Neuroanatomical study and functional hypothesis. Brain 103, 525–554.
Bar-Gad, I., Morris, G., Bergman, H., 2003. Information processing, dimensionality
reduction and reinforcement learning in the basal ganglia. Progress in Neuro-
biology 71, 439–473.
Barnes, T.D., Kubota, Y., Hu, D., Jin, D.Z., Graybiel, A.M., 2005. Activity of striatal
neurons reflects dynamic encoding and recoding of procedural memories.
Nature 437, 1158–1161.
Bechara, A., Damasio, H., Damasio, A.R., Lee, G.P., 1999. Different contributions of
the human amygdala and ventromedial prefrontal cortex to decision-making.
Journal of Neuroscience 19, 5473–5481.
Bechara, A., Tranel, D., Damasio, H., 2000. Characterization of the decision-making
deficit ofpatients with ventromedialprefrontal cortexlesions. Brain123, 2189–
Bergman, H., Feingold, A., Nini, A., Raz, A., Slovin, H., Abeles, M., Vaadia, E., 1998.
Physiological aspects of information processing in the basal ganglia of normal
and Parkinsonian primates. Trends in Neurosciences 21, 32–38.
Berns, G.S., Sejnowski, T.J., 1998. A computational model of how the basal ganglia
produce sequences. Journal of Cognitive Neuroscience 10, 108–121.
Berridge, K.C., Robinson,T.E., 1998.Whatis theroleof dopamine in reward:hedonic
impact, reward learning, or incentive salience? Brain Research Brain Research
Reviews 28, 309–369.
Beurze, S.M.,de Lange, F.P.,Toni, I.,Medendorp, W.P.,2007.Integration of target and
effector information in the human brain during reach planning. Journal of
Neurophysiology 97, 188–199.
Bezzina, G., Cheung, T.H., Asgari, K., Hampson, C.L., Body, S., Bradshaw, C.M.,
Szabadi, E., Deakin, J.F., Anderson, I.M., 2007. Effects of quinolinic acid-induced
lesions of the nucleus accumbens core on inter-temporal choice: a quantitative
analysis. Psychopharmacology 193, 423–436.
Blair, K., Marsh, A.A., Morton, J., Vythilingam, M., Jones, M., Mondillo, K., Pine, D.C.,
Drevets, W.C., Blair, J.R., 2006. Choosing the lesser of two evils, the better of two
goods: specifying the roles of ventromedial prefrontal cortex and dorsal ante-
rior cingulate in object choice. Journal of Neuroscience 26, 11379–11386.
Bilder, R.M., Volavka, J., Lachman, H.M., Grace, A.A., 2004. The catechol-O-methyl-
transferase polymorphism: relations to the tonic-phasic dopamine hypothesis
and neuropsychiatric phenotypes. Neuropsychopharmacology 29, 1943–1961.
Both,S.,Everaerd, W.,Laan,E.,Gooren, L.,2005.Effectofasingledoseoflevodopaon
sexual response in men and women. Neuropsychopharmacology 30, 173–183.
Botvinick, M., Nystrom, L.E., Fissell, K., Carter, C.S., Cohen, J.D., 1999. Conflict
monitoring versus selection-for-action in anterior cingulate cortex. Nature
Botvinick, M.M., Cohen, J.D., Carter, C.S., 2004. Conflict monitoring and anterior
cingulate cortex: an update. Trends in Cognitive Sciences 8, 539–546.
cingulate cortex. Science 307, 1118–1121.
Bush, G., Luu, P., Posner, M.I., 2000. Cognitive and emotional influences in anterior
cingulate cortex. Trends in Cognitive Sciences 4, 215–222.
Bush, G., Vogt, B.A., Holmes, J., Dale, A.M., Greve, D., Jenike, M.A., Rosen, B.R., 2002.
Dorsal anterior cingulate cortex: a role in reward-based decision making.
Calabresi, P., Picconi, B., Tozzi, A., Di Filippo, M., 2007. Dopamine-mediated regula-
tion of corticostriatal synaptic plasticity. Trends in Neurosciences 30, 211–219.
Cannon, C.M., Bseikri, M.R., 2004. Is dopamine required for natural reward?
Physiology and Behavior 81, 741–748.
Cardinal, R.N., Pennicott, D.R., Sugathapala, C.L., Robbins, T.W., Everitt, B.J., 2001.
Impulsive choice induced in rats by lesions of the nucleus accumbens core.
Science 292, 2499–2501.
Carter, C.S., Braver, T.S., Barch, D.M., Botvinick, M.M., Noll, D., Cohen, J.D., 1998.
Anterior cingulate cortex, error detection, and the online monitoring of per-
formance. Science 280, 747–749.
Cheung, T.H., Bezzina, G., Hampson, C.L., Body, S., Fone, K.C., Bradshaw, C.M.,
Szabadi, E., 2007. Evidence for the sensitivity of operant timing behaviour to
stimulation of D(1) dopamine receptors. Psychopharmacology 195, 213–222.
Cohen, M.X., Young, J., Baek, J.M., Kessler, C., Ranganath, C., 2005. Individual
differences in extraversion and dopamine genetics predict neural reward
responses. Brain Research Cognitive Brain Research 25, 851–861.
Comoli, E., Ribeiro-Barbosa, E.R., Canteras, N.S., 2003. Predatory hunting and
exposure to a live predator induce opposite patterns of Fos immunoreactivity
in the PAG. Behavioural Brain Research 138, 17–28.
Constantinidis, C., Williams, G.V., Goldman-Rakic, P.S., 2002. A role for inhibition in
shaping the temporal flow of information in prefrontal cortex. Nature Neu-
roscience 5, 175–180.
Cousins, M.S., Atherton, A., Turner, L., Salamone, J.D., 1996. Nucleus accumbens
dopamine depletions alter relative response allocation in a T-maze cost/benefit
task. Behavioural Brain Research 74, 189–197.
Cousins, M.S., Wei, W., Salamone, J.D., 1994. Pharmacological characterization of
performance on a concurrent lever pressing/feeding choice procedure: effects
of dopamine antagonist, cholinomimetic, sedative and stimulant drugs. Psy-
chopharmacology 116, 529–537.
Critchley, H.D., 2005. Neural mechanisms of autonomic, affective, and cognitive
integration. Journal of Comparative Neurology 493, 154–166.
L., Shallice, T., Dolan, R.J., 2003. Human cingulate cortex and autonomic control:
converging neuroimaging and clinical evidence. Brain 126, 2139–2152.
activity during error and autonomic response. Neuroimage 27, 885–895.
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
Cromwell, H.C., Berridge, K.C., Drago, J., Levine, M.S., 1998. Action sequencing is
impaired in D1 A-deficient mutant mice. European Journal of Neuroscience 10,
Daberkow, D.P., Kesner, R.P., Keefe, K.A., 2005. Relation between methampheta-
mine-induced monoamine depletions in the striatum and sequential motor
learning. Pharmacology, Biochemistry and Behavior 81, 198–204.
Daw, N.D., O’Doherty, J.P., Dayan, P., Seymour, B., Dolan, R.J., 2006. Cortical sub-
strates for exploratory decisions in humans. Nature 441, 876–879.
de Bruijn, E.R., Sabbe, B.G., Hulstijn, W., Ruigt, G.S., Verkes, R.J., 2006. Effects of
antipsychotic and antidepressant drugs on action monitoring in healthy volun-
teers. Brain Research 1105, 122–129.
Dehaene, S., Artiges, E., Naccache, L., Martelli, C., Viard, A., Schurhoff, F., Recasens, C.,
Martinot, M.L., Leboyer, M., Martinot, J.L., 2003. Conscious and subliminal
conflicts in normal subjects and patients with schizophrenia: the role of the
anterior cingulate. Proceedings of the National Academy of Sciences of the
United States of America 100, 13722–13727.
DeLong, M.R., 1990. Primate models of movement disorders of basal ganglia origin.
Trends in Neurosciences 13, 281–285.
Devinsky, O., Morrell, M.J., Vogt, B.A., 1995. Contributions of anterior cingulate
cortex to behaviour. Brain 118, 279–306.
Dosenbach, N.U., Visscher, K.M., Palmer, E.D., Miezin, F.M., Wenger, K.K., Kang, H.C.,
the implementation of task sets. Neuron 50, 799–812.
Ernst, M., Nelson, E.E., McClure, E.B., Monk, C.S., Munson, S., Eshel, N., Zarahn, E.,
Leibenluft, E., Zametkin, A., Towbin, K., Blair, J., Charney, D., Pine, D.S., 2004.
Choice selection and reward anticipation: an fMRI study. Neuropsychologia 42,
Eshel, N., Nelson, E.E., Blair, R.J., Pine, D.S., Ernst, M., 2007. Neural substrates of
choice selection in adults and adolescents: development of the ventrolateral
prefrontal and anterior cingulate cortices. Neuropsychologia 45, 1270–1279.
Ferry, A.T., Ongur, D., An, X., Price, J.L., 2000. Prefrontal cortical projections to the
striatum in macaque monkeys: evidence for an organization related to pre-
frontal networks. Journal of Comparative Neurology 425, 447–470.
Fiorillo, C.D., Tobler, P.N., Schultz, W., 2003. Discrete coding of reward probability
and uncertainty by dopamine neurons. Science 299, 1898–1902.
Floresco, S.B., Blaha, C.D., Yang, C.R., Phillips, A.G., 2001. Modulation of hippocampal
and amygdalar-evoked activity of nucleus accumbens neurons by dopamine:
cellular mechanisms of input selection. Journal of Neuroscience 21, 2851–2860.
Floresco, S.B., Ghods-Sharifi, S., 2007. Amygdala-prefrontal cortical circuitry reg-
ulates effort-based decision making. Cerebral Cortex 17, 251–260.
Floresco, S.B., Tse, M.T., Ghods-Sharifi, S., 2008. Dopaminergic and glutamatergic
regulation of effort- and delay-based decision making. Neuropsychopharma-
cology 33, 1966–1979.
Floresco, S.B.,West, A.R.,Ash,B., Moore,H.,Grace, A.A.,2003. Afferentmodulation of
dopamine neuron firing differentially regulates tonic and phasic dopamine
transmission. Nature Neuroscience 6, 968–973.
Gerfen, C.R., Engber, T.M., Mahan, L.C., Susel, Z., Chase, T.N., Monsma Jr., F.J., Sibley,
D.R., 1990. D1 and D2 dopamine receptor-regulated gene expression of stria-
tonigral and striatopallidal neurons. Science 250, 1429–1432.
Gibson, R.M., Langen, T.A., 1996. How do animals choose their mates? Trends in
Ecology & Evolution 11, 468–470.
Goldman-Rakic, P.S., Muly 3rd, E.C., Williams, G.V., 2000. D(1) receptors in pre-
frontal cells and circuits. Brain Research. Brain Research Reviews 31, 295–301.
Goto, Y., Grace, A.A., 2005. Dopaminergic modulation of limbic and cortical drive of
nucleusaccumbens in goal-directedbehavior. NatureNeuroscience8,805–812.
Grace, A.A., 1991. Phasic versus tonic dopamine release and the modulation of
dopamine system responsivity: a hypothesis for the etiology of schizophrenia.
Neuroscience 41, 1–24.
Grace, A.A., Floresco, S.B., Goto, Y., Lodge, D.J., 2007. Regulation of firing of dopa-
minergic neurons and control of goal-directed behaviors. Trends in Neuros-
ciences 30, 220–227.
Graybiel, A.M., 2005. The basal ganglia: learning new tricks and loving it. Current
Opinion in Neurobiology 15, 638–644.
Green, L., Myerson, J., 2004. A discounting framework for choice with delayed and
probabilistic rewards. Psychological Bulletin 130, 769–792.
Greengard, P., 2001. The neurobiology of dopamine signaling. Bioscience Reports
Hampton, A.N., Bossaerts, P., O’Doherty, J.P., 2006. The role of the ventromedial
prefrontal cortex in abstract state-based inference during decision making in
humans. Journal of Neuroscience 26, 8360–8367.
Hampton, A.N., O’Doherty, J.P., 2007. Decoding the neural substrates of reward-
related decision making with functional MRI. Proceedings of the National
Academy of Sciences of the United States of America 104, 1377–1382.
Hauber, W., Sommer, S., 2007.Direct interactions between anterior cingulate cortex
Neuroscience Abstract 741.27.
Hogan, A.M., Vargha-Khadem, F., Saunders, D.E., Kirkham, F.J., Baldeweg, T., 2006.
Impact of frontal white matter lesions on performance monitoring: ERP evi-
dence for cortical disconnection. Brain 129, 2177–2188.
Hoshi, E., Sawamura, H., Tanji, J., 2005. Neurons in the rostral cingulate motor area
monitor multiple phases of visuomotor behavior with modest parametric
selectivity. Journal of Neurophysiology 94, 640–656.
Hurd, Y.L., Suzuki, M., Sedvall, G.C., 2001. D1 and D2 dopamine receptor mRNA
expression in whole hemisphere sections of the human brain. Journal of
Chemical Neuroanatomy 22, 127–137.
Ito, S., Stuphorn, V., Brown, J.W., Schall, J.D., 2003. Performance monitoring by the
anterior cingulatecortexduringsaccade countermanding. Science 302,120–122.
Kacelnik, A., Bateson, M., 1997. Risk-sensitivity: crossroads for theories of decision
making. Trends in Cognitive Sciences 1, 304–309.
Keay, K.A., Bandler, R., 2001. Parallel circuits mediating distinct emotional coping
reactions to different types of stress. Neuroscience and Biobehavioral Reviews
Kennerley, S.W., Walton, M.E., Behrens, T.E., Buckley, M.J., Rushworth, M.F., 2006.
Optimal decision making and the anterior cingulate cortex. Nature Neu-
roscience 9, 940–947.
Kheramin,S.,Body,S.,Ho,M.,Velazquez-Martinez, D.N.,Bradshaw,C.M., Szabadi, E.,
Deakin, J.F., Anderson, I.M., 2003. Role of the orbital prefrontal cortex in choice
between delayed and uncertain reinforcers: a quantitative analysis. Beha-
vioural Processes 64, 239–250.
Kheramin, S., Body, S., Ho, M.Y., Velazquez-Martinez, D.N., Bradshaw, C.M., Szabadi,
E., Deakin, J.F., Anderson, I.M., 2004. Effects of orbital prefrontal cortex dopa-
mine depletion on inter-temporal choice: a quantitative analysis. Psychophar-
macology 175, 206–214.
cingulate: an event-related fMRI study. Psychophysiology 37, 216–223.
Klein, T.A., Neumann, J., Reuter, M., Hennig, J., von Cramon, D.Y., Ullsperger, M.,
2007. Genetically determined differences in learning from errors. Science 318,
Knutson, B., Bjork, J.M., Fong, G.W., Hommer, D., Mattay, V.S., Weinberger, D.R.,
2004. Amphetamine modulates human incentive processing. Neuron 43, 261–
Knutson, B., Westdorp, A., Kaiser, E., Hommer, D., 2000. FMRI visualization of brain
activity during a monetary incentive delay task. Neuroimage 12, 20–27.
Koski, L., Paus, T., 2000. Functional connectivity of the anterior cingulate cortex
within the human frontal lobe: a brain-mapping meta-analysis. Experimental
Brain Research 133, 55–65.
N.B. (Eds.), Behavioral Ecology: An Evolutionary Approach. Blackwell Scientific
Publications, London, pp. 23–63.
Kunishio, K., Haber, S.N., 1994. Primate cingulostriatal projection: limbic striatal
versus sensorimotor striatal input. Journal of Comparative Neurology 350, 337–
Lapish, C.C., Kroener, S., Durstewitz, D., Lavin, A., Seamans, J.K., 2007. The ability of
the mesocortical dopamine system to operate in distinct temporal modes.
Psychopharmacology 191, 609–625.
Lonstein, J.S., Stern, J.M., 1998. Site and behavioral specificity of periaqueductal gray
lesions on postpartum sexual, maternal, and aggressive behaviors in rats. Brain
Research 804, 21–35.
Marsh, A.A., Blair, K.S., Vythilingam, M., Busis, S., Blair, R.J., 2007. Response options
and expectations of reward in decision-making: the differential roles of dorsal
and rostral anterior cingulate cortex. Neuroimage 35, 979–988.
Matsumoto, K., Suzuki, W., Tanaka, K., 2003. Neuronal correlates of goal-based
motor selection in the prefrontal cortex. Science 301, 229–232.
Maynard Smith, J., 1982. Evolution and the Theory of Games. Cambridge University
McClure, S.M., Laibson, D.I., Loewenstein, G., Cohen, J.D., 2004. Separate neural
systems value immediate and delayed monetary rewards. Science 306, 503–507.
McLean, A., Rubinsztein, J.S., Robbins, T.W., Sahakian, B.J., 2004. The effects of
tyrosine depletion in normal healthy volunteers: implications for unipolar
depression. Psychopharmacology 171, 286–297.
Mesulam, M.M., 1990. Large-scale neurocognitive networks and distributed pro-
cessing for attention, language, and memory. Annals of Neurology 28, 597–613.
Mink, J.W., 1996. The basal ganglia: focused selection and inhibition of competing
motor programs. Progress in Neurobiology 50, 381–425.
Mobini, S., Body,S., Ho, M.Y., Bradshaw, C.M., Szabadi, E., Deakin, J.F., Anderson, I.M.,
2002. Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and
probabilistic reinforcement. Psychopharmacology 160, 290–298.
Mobini, S., Chiang, T.J., Ho, M.Y., Bradshaw, C.M., Szabadi, E., 2000. Effects of central
5-hydroxytryptamine depletion on sensitivity to delayed and probabilistic
reinforcement. Psychopharmacology 152, 390–397.
Morecraft, R.J., McNeal, D.W., Stilwell-Morecraft, K.S., Gedney, M., Ge, J., Schroeder,
C.M., van Hoesen, G.W., 2007. Amygdala interconnections with the cingulate
motor cortex in the rhesus monkey. Journal of Comparative Neurology 500,
Morecraft, R.J., Van Hoesen, G.W., 1998. Convergence of limbic input to the
cingulate motor cortex in the rhesus monkey. Brain Research Bulletin 45,
Morris, G., Nevet, A., Arkadir, D., Vaadia, E., Bergman, H., 2006. Midbrain dopamine
neurons encode decisions for future action. Nature Neuroscience 9, 1057–1063.
Mulder, M.B., 1990. Kipsigis women’s preferences for wealthy men: evidence for
female choice in mammals? Behavioral Ecology and Sociobiology 27, 255–264.
Nishida, T., 1997. Sexual behavior of adult male chimpanzees of the Mahale
Mountains National Park, Tanzania. Primates 38, 379–398.
Nowend, K.L., Arizzi, M., Carlson, B.B., Salamone, J.D., 2001. D1 or D2 antagonism in
nucleusaccumbens core ordorsomedial shellsuppresses lever pressingforfood
but leads to compensatory increases in chow consumption. Pharmacology,
Biochemistry and Behavior 69, 373–382.
O’Donnell, P., Grace, A.A., 1995. Synaptic interactions among excitatory afferents to
nucleus accumbens neurons: hippocampal gating of prefrontal cortical input.
Journal of Neuroscience 15, 3622–3639.
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
Ongur, D., An, X., Price, J.L., 1998. Prefrontal cortical projections to the hypotha-
lamus in macaque monkeys. Journal of Comparative Neurology 401, 480–505.
Ongur, D., Price, J.L., 2000. The organization of networks within the orbital and
Paus, T., Koski, L., Caramanos, Z., Westbury, C., 1998. Regional differences in the
effects of task difficulty and motor output on blood flow response in the human
anterior cingulate cortex: a review of 107 PET activation studies. Neuroreport 9,
Percheron, G., Yelnik, J., Francois, C., 1984. A golgi analysis of the primate globus
pallidus. III. Spatial organization of the striato-pallidal complex. Journal of
Comparative Neurology 227, 214–227.
Pessiglione, M., Czernecki, V., Pillon, B., Dubois, B., Schupbach, M., Agid, Y., Trem-
blay, L., 2005. An effect of dopamine depletion on decision-making: the tem-
poral coupling of deliberation and execution. Journal of Cognitive Neuroscience
Phillips, P.E., Walton, M.E., Jhou, T.C., 2007. Calculating utility: preclinical evidence
for cost–benefit analysis by mesolimbic dopamine. Psychopharmacology (Ber-
lin) 191, 483–495.
Picard, N., Strick, P.L., 1996. Motor areas of the medial wall: a review of their
location and functional activation. Cerebral Cortex 6, 342–353.
Plenz, D., 2003. When inhibition goes incognito: feedback interaction between spiny
projection neurons in striatal function. Trends in Neurosciences 26, 436–443.
Posner, M.I., Petersen, S.E., Fox, P.T., Raichle, M.E., 1988. Localization of cognitive
operations in the human brain. Science 240, 1627–1631.
Rahm, B., Opwis, K., Kaller, C.P., Spreer, J., Schwarzwald, R., Seifritz, E., Halsband, U.,
Unterrainer, J.M., 2006. Tracking the subprocesses of decision-based action in
the human frontal lobes. Neuroimage 30, 656–667.
2001. A default mode of brain function. Proceedings of the National Academy of
Sciences of the United States of America 98, 676–682.
Ray, J.P., Price, J.L., 1993. The organization of projections from the mediodorsal
nucleus of the thalamus to orbital and medial prefrontal cortex in macaque
monkeys. Journal of Comparative Neurology 337, 1–31.
Reynolds, J.N., Wickens, J.R., 2002. Dopamine-dependent plasticity of corticostriatal
synapses. Neural Networks 15, 507–521.
Richmond, B.J., Liu, Z., Shidara, M., 2003. Neuroscience. Predicting future rewards.
Science 301, 179–180.
Ridderinkhof, K.R., Ullsperger, M., Crone, E.A., Nieuwenhuis, S., 2004. The role of the
medial frontal cortex in cognitive control. Science 306, 443–447.
Roesch, M.R., Calu, D.J., Burke, K.A., Schoenbaum, G., 2007. Should I stay or should I
go? Transformation of time-discounted rewards in orbitofrontal cortex and
associated brain circuits. Annals of the New York Academy of Sciences 1104,
Rolls, E.T., McCabe, C., Redoute, J., 2008. Expected value, reward outcome, and
temporal difference error representations in a probabilistic decision task.
Cerebral Cortex 18, 652–663.
Rudebeck, P.H., Buckley, M.J., Walton, M.E., Rushworth, M.F., 2006a. A role for the
macaque anterior cingulate gyrus in social valuation. Science 313, 1310–1312.
Rudebeck, P.H., Walton, M.E., Smyth, A.N., Bannerman, D.M., Rushworth, M.F.,
2006b. Separate neural pathways process different decision costs. Nature
Neuroscience 9, 1161–1168.
Rushworth, M.F., Hadland, K.A., Paus, T., Sipila, P.K., 2002. Role of the human medial
frontal cortex in task switching: a combined fMRI and TMS study. Journal of
Neurophysiology 87, 2577–2592.
Rushworth, M.F., Walton, M.E., Kennerley, S.W., Bannerman, D.M., 2004. Action sets
and decisions in the medial frontal cortex. Trends in Cognitive Sciences 8, 410–
and conceptual issues in studies of the dynamic activity of nucleus accumbens
dopamine. Journal of Neuroscience Methods 64, 137–149.
Salamone, J.D., Correa, M., Farrar, A., Mingote, S.M., 2007. Effort-related functions of
nucleus accumbens dopamine and associated forebrain circuits. Psychophar-
macology 191, 461–482.
Salamone, J.D., Correa, M., Mingote, S., Weber, S.M., 2003. Nucleus accumbens
dopamine and the regulation of effort in food-seeking behavior: implications
for studies of natural motivation, psychiatry, and drug abuse. Journal of
Pharmacology and Experimental Therapeutics 305, 1–8.
Salamone, J.D., Cousins, M.S., Bucher, S., 1994. Anhedonia or anergia? Effects of
haloperidol and nucleus accumbens dopamine depletion on instrumental
response selection in a T-maze cost/benefit procedure.
Research 65, 221–229.
Satoh, T., Nakai, S., Sato, T., Kimura, M., 2003. Correlated coding of motivation and
outcome of decision by dopamine neurons. Journal of Neuroscience 23, 9913–
Satterthwaite, T.D., Green, L., Myerson, J., Parker, J., Ramaratnam, M., Buckner, R.L.,
2007. Dissociable but inter-related systems of cognitive control and reward
during decision making: evidence from pupillometry and event-related fMRI.
Neuroimage 37, 1017–1031.
Schultz, W., 2007a. Behavioral dopamine signals. Trends in Neurosciences 30, 203–
Schultz, W., 2007b. Multiple dopamine functions at different time courses. Annual
Review of Neuroscience 30, 259–288.
Schultz, W., Dayan, P., Montague, P.R., 1997. A neural substrate of prediction and
reward. Science 275, 1593–1599.
Schultz, W., Dickinson, A., 2000. Neuronal coding of prediction errors. Annual
Review of Neuroscience 23, 473–500.
Schweimer, J., Hauber, W., 2005. Involvement of the rat anterior cingulate cortex in
control of instrumental responses guided by reward expectancy. Learning and
Memory 12, 334–342.
Schweimer, J., Hauber, W., 2006. Dopamine D1 receptors in the anterior cingulate
cortex regulate effort-based decision making. Learning and Memory 13, 777–
Schweimer, J., Saft, S., Hauber, W., 2005. Involvement of catecholamine neuro-
transmission in the rat anterior cingulate in effort-related decision making.
Behavioral Neuroscience 119, 1687–1692.
Seamans, J.K., Gorelova, N., Durstewitz, D., Yang, C.R., 2001. Bidirectional dopamine
modulation of GABAergic inhibition in prefrontal cortical pyramidal neurons.
Journal of Neuroscience 21, 3628–3638.
Seamans, J.K., Yang, C.R., 2004. The principal features and mechanisms of dopamine
modulation in the prefrontal cortex. Progress in Neurobiology 74, 1–58.
Seo, H., Lee, D., 2007. Temporal filtering of reward signals in the dorsal anterior
cingulate cortex during a mixed-strategy game. Journal of Neuroscience 27,
Shidara, M., Mizuhiki, T., Richmond, B.J., 2005. Neuronal firing in anterior cingulate
neurons changes modes across trials in single states of multitrial reward
schedules. Experimental Brain Research 163, 242–245.
Shidara, M., Richmond, B.J., 2002. Anteriorcingulate: single neuronalsignals related
to degree of reward expectancy. Science 296, 1709–1711.
Shima, K., Tanji, J., 1998. Role for cingulate motor area cells in voluntary movement
selection based on reward. Science 282, 1335–1338.
Somerville, L.H., Heatherton, T.F., Kelley, W.M., 2006. Anterior cingulate cortex
responds differentially to expectancy violation and social rejection. Nature
Neuroscience 9, 1007–1008.
Stevens, J.R., Rosati, A.G., Ross, K.R., Hauser, M.D., 2005. Will travel for food: spatial
discounting in two new world monkeys. Current Biology 15, 1855–1860.
Sukikara, M.H., Mota-Ortiz, S.R., Baldo, M.V., Felicio, L.F., Canteras, N.S., 2006. A role
for the periaqueductal gray in switching adaptive behavioral responses. Journal
of Neuroscience 26, 2583–2589.
Suri, R.E., Schultz, W., 1998. Learning of sequential movements by neural network
model with dopamine-like reinforcement signal. Experimental Brain Research
Toda, M., Abi-Dargham, A., 2007. Dopamine hypothesis of schizophrenia: making
sense of it all. Current Psychiatry Reports 9, 329–336.
Trantham-Davidson, H., Neely, L.C., Lavin, A., Seamans, J.K., 2004. Mechanisms
in prefrontal cortex. Journal of Neuroscience 24, 10652–10659.
Ullsperger, M., von Cramon, D.Y., 2006. The role of intact frontostriatal circuits in
error processing. Journal of Cognitive Neuroscience 18, 651–664.
Volkow, N.D., Fowler, J.S., Wang, G.J., Swanson, J.M., Telang, F., 2007. Dopamine in
drug abuse and addiction: results of imaging studies and treatment implica-
tions. Archives of Neurology 64, 1575–1579.
Voorn, P., Vanderschuren, L.J., Groenewegen, H.J., Robbins, T.W., Pennartz, C.M.,
2004. Putting a spin on the dorsal-ventral divide of the striatum. Trends in
Neurosciences 27, 468–474.
Vrshek-Schallhorn, S., Wahlstrom, D., Benolkin, K., White, T., Luciana, M., 2006.
Affective bias and response modulation following tyrosine depletion in healthy
adults. Neuropsychopharmacology 31, 2523–2536.
Wade, T.R., de Wit, H., Richards, J.B., 2000. Effects of dopaminergic drugs on delayed
reward as a measure of impulsive behavior in rats. Psychopharmacology 150,
Wakabayashi, K.T., Fields, H.L., Nicola, S.M., 2004. Dissociation of the role of nucleus
accumbens dopamine in responding to reward-predictive cues and waiting for
reward. Behavioural Brain Research 154, 19–30.
Wallis, J.D., 2007. Orbitofrontal cortex and its contribution to decision-making.
Annual Review of Neuroscience 30, 31–56.
Walton, M.E., Bannerman, D.M., Alterescu, K., Rushworth, M.F., 2003. Functional
specialization within medial frontal cortex of the anterior cingulate for eval-
uating effort-related decisions. Journal of Neuroscience 23, 6475–6479.
Walton, M.E., Croxson, P.L., Rushworth, M.F., Bannerman, D.M., 2005. The meso-
cortical dopamine projection to anterior cingulate cortex plays no role in
guiding effort-related decisions. Behavioral Neuroscience 119, 323–328.
Walton, M.E., Devlin, J.T., Rushworth, M.F., 2004. Interactions between decision
making and performance monitoring within prefrontal cortex. Nature Neu-
roscience 7, 1259–1265.
Walton, M.E., Rudebeck, P.H., Bannerman, D.M., Rushworth, M.F., 2007. Calculating
the cost of acting in frontal cortex. Annals of the New York Academy of Sciences
Whalen, P.J., Bush, G., McNally, R.J., Wilhelm, S., McInerney, S.C., Jenike, M.A., Rauch,
S.L., 1998. The emotional counting Stroop paradigm: a functional magnetic
resonance imaging probe of the anterior cingulate affective division. Biological
Psychiatry 44, 1219–1228.
Wichmann,T., DeLong,M.R., 1996.Functional and pathophysiologicalmodels ofthe
basal ganglia. Current Opinion in Neurobiology 6, 751–758.
Wickens, J., 1997. Basal ganglia: structure and computations. Network: Computa-
tion in Neural Systems 8, R77–R109.
Winstanley, C.A., Theobald, D.E., Dalley, J.W., Robbins, T.W., 2005. Interactions
between serotonin and dopamine in the control of impulsive choice in rats:
therapeutic implications for impulse control disorders. Neuropsychopharma-
cology 30, 669–682.
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393
Wittmann, M., Leland, D.S., Paulus, M.P., 2007. Time and decision making: differ-
ential contribution of the posterior insular cortex and the striatum during a
delay discounting task. Experimental Brain Research 179, 643–653.
Xiaob, D., Barbas, H., 2004. Circuits through prefrontal cortex, basal ganglia, and
ventral anterior nucleus map pathways beyond motor control. Thalamus &
Related Systems 2, 325–343.
Yang, C.R., Seamans, J.K., Gorelova, N., 1999. Developing a neuronal model for the
pathophysiology of schizophrenia based on the nature of electrophysiological
actions of dopamine in the prefrontal cortex. Neuropsychopharmacology 21,
Yeterian, E.H., Pandya, D.N., 1988. Corticothalamic connections of paralimbic
regions in the rhesus monkey. Journal of Comparative Neurology 269, 130–146.
Yu ¨cel, M., Brewer, W.J., Harrison, B.J., Fornito, A., O’Keefe, G.J., Olver, J., Scott, A.M.,
Egan, G.F., Velakoulis, D., McGorry, P.D., Pantelis, C., 2007a. Anterior cingulate
activation in antipsychotic-naive first-episode schizophrenia. Acta Psychiatrica
Scandinavica 115, 155–158.
Yu ¨cel, M., Harrison, B.J., Wood, S.J., Fornito, A., Clarke, K., Wellard, R.M., Cotton, S.,
Pantelis, C., 2007b. State, trait and biochemical influences on human anterior
cingulate function. Neuroimage 34, 1766–1773.
Yu ¨cel, M., Harrison, B.J., Wood, S.J., Fornito, A., Wellard, R.M., Pujol, J., Clarke, K.,
Phillips, M.L., Kyrios, M., Velakoulis, D., Pantelis, C., 2007c. Functional and
biochemical alterations of the medial frontal cortex in obsessive–compulsive
disorder. Archives of General Psychiatry 64, 946–955.
Yu ¨cel, M., Lubman, D.I., Harrison, B.J., Fornito, A., Allen, N.B., Wellard, R.M.,
Roffel, K., Clarke, K., Wood, S.J., Forman, S.D., Pantelis, C., 2007d. A combined
spectroscopic and functional MRI investigation of the dorsal anterior
cingulate region in opiate addiction. Molecular Psychiatry 12 (611), 691–
Yu ¨cel, M., Pantelis, C., Stuart, G.W., Wood, S.J., Maruff, P., Velakoulis, D., Pipingas,
A., Crowe, S.F., Tochon-Danguy, H.J., Egan, G.F., 2002. Anterior cingulate
activation during Stroop task performance: a PET to MRI coregistration study
Yu ¨cel, M., Wood, S.J., Fornito, A., Riffkin, J., Velakoulis, D., Pantelis, C., 2003. Anterior
cingulate dysfunction: implications for psychiatric disorders? Journal of Psy-
chiatry and Neuroscience 28, 350–354.
Zikopoulos, B., Barbas, H., 2007. Parallel driving and modulatory pathways link the
prefrontal cortex and thalamus. PLoS ONE 2, e848.
S.M. Assadi et al./Neuroscience and Biobehavioral Reviews 33 (2009) 383–393