A critical review of impairments in decision-making in Patients with Parkinson’s disease
Agata Ryterska & Magda Osman
Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary
University of London, Mile End Road, London, E1 4NS, UK
Correspondence to: [email protected]
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder which affects both motor and
cognitive functioning. The focus of this review to consider the evidence concerning the deficits
associated with a specific type of cognitive function: decision-making under uncertainty and under
risk. Thus far, the extant evidence paints a complex and often conflicting picture of what types of
decision-making deficits are associated with patients with PD. To explain the inconsistencies in the
findings this review proposes that the deficits result from 1) the medication status of the patient (i.e.
when they are on dopaminergic medication), 2) situations in which patients engage in a cost/benefit
analysis of the decision situation, as well 3) when patients are asked to evaluate the outcomes of the
choices they make.
A critical review of impairments in decision-making behaviour in Patients with Parkinson’s disease
To begin, we refer to decision-making as a process in which an individual selects on some basis of
value (e.g. most rewarding, least painful, least effortful, most likely) the best option from a set of
alternative options. This process also involves considering the costs associated with each option,
such as uncertainty and risk (the probability of obtaining reward), time delay, and effort. The aim of
this review is to discuss the associated deficits in decision-making under uncertainty and under risk
in patients with Parkinson’s disease, and to speculate as to the reasons for these deficits. This review
will briefly start by introducing the various psychological tools used to examine decision-making
under uncertainty and risk, and then consider the factors that contribute most to deficits that are
reported in the neuroscientific domain. As a final point of discussion the review will propose
practical ways in which decision-making under uncertainty and risk can be facilitated in patients with
Parkinson’s disease.
Midbrain Dopamine Neurons and Decision-making
To date, there is considerable work suggesting that neurotransmitter dopamine, alongside
serotonine and noradrenaline, is heavily implicated in the process of making a decision in situations
where the outcome is uncertain and difficult to predict based on the cues available in the
environment (Doya,et al., 2008; Platt, et al., 2008; St Onge, et al., 2008; Tom, et al., 2007; see Trepel
et al. 2005 for a review),. Midbrain dopamine neurons have been implicated in a catalogue of
operations that are thought to be involved in decision-making under uncertainty and risk. For
instance, people are often required to make predictions about what they expect will happen once
they make a decision; this is thought to be mediated by mesencephalic dopamine neurons (Bayer, et
al., 2005; Pessiglione, et al., 2006; Steinberg, et al., 2013). There is a large body of work suggesting
that midbrain dopaminergic neurons play an active role when people engage in thinking about the
values associated with outcome they expect to occur (Enomoto, et al., 2011; Nomoto, et al., 2010;
Parush, et al., 2011; Pasquereau, et al., 2013). Follow this, people must also pay attention to the
consequences of their actions once they make a decision; the evidence from human behavioural and
imaging studies suggest that midbrain dopamine neurons are active when this happens (Palminteri,
et al., 2009; Poldrack et al., 2001; Seger & Cincotta, 2005).
Decision-making in Patients with Parkinson’s Disease
Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting dopaminergic cells in
substantia nigra, which is located in the midbrain region. As a result, the disorder effects the
functioning of the midbrain dopaminergic system including the functioning of other basal ganglia
nuclei, such as the striatum. At the behavioral level, what this means is that patients with PD show
impairments to their motor (bradyknesia (slowness of movement), akinesia (poverty of action),
tremor and rigidity) and cognitive functioning.
As might be predicted from work examining associations between midbrain dopaminergic neurons
and decision-making under risk and uncertainty, the loss of dopamine producing cells typically found
in patients with PD has negative consequences for their decision-making (Brand et al., 2004;
Czernecki et al., 2002; Delazer et al., 2009; Euteneuer et al., 2009; Ibarretxe-Bilbao et al., 2009;
Kobayakawa et al., 2008; Mimura et al., 2006; Pagonabarraga et al., 2007). More to the point, there
are a variety of decision-making tasks, which form a test battery of tools used to uncover
impairments that patients with PD show when making decisions in uncertain and risky environment
(e.g., weather prediction task [Knowlton, et al., 1996; Jahanshahi et al, 2010; Shohamy et al, 2006;
Wilkinson, et al., 2011], Iowa gambling task [Mimura et al, 2006; Poletti, et al, 2010], Game of Dice
[Mimura et al., 2006; Pagonabarraga et al., 2007]).
One of the key features of the tasks is that people are required to make decisions based on
information that does not reliably predict the outcome all the time (i.e. it is probabilistic). Also, the
event generated from a decision made is usually associated with information that helps people to
learn to make better decisions over time; that is, people are told whether their decisions was correct
(positive feedback/positive reward) or incorrect (negative feedback/negative reward). Crucially
though, the major problem with research employing these tasks to examine decision-making under
uncertainty and risk in PD is that the profile of deficits in decision-making in these specific
circumstances is unclear, and in some studies PD patients have been shown to be as good in
performance as healthy age-matched controls (HCs). Why might this be? The next section considers
the three tasks more in depth before speculating as to the reason behind the mixed findings.
Weather prediction task (WPT): The WPT (Knowlton et al., 1994) was originally developed to look at
learning of probabilistic information in uncertain environments in amnesic patients. A double
dissociation study (Knowlton, et al., 1996) comparing performance of amnesic patients and patients
with Parkinson’s disease showed that while amnesic patients had problems remembering the
training episode on the task, they still learned about the cue-outcome relationships within the task.
This was in contrast to PD patients’ performance, who had problems learning about the underlying
structure of the task, even though they had a very good recollection of the training episode.
Considering the dysfunction of the striatum in PD, this was taken as evidence for the importance of
this structure for learning and decision-making in probabilistic and uncertain environments. Imaging
studies that followed confirmed the important role of the striatum during execution of the WPT (e.g.
Moody, et al., 2004; Poldrack, et al., 1999; Poldrack, et al., 2001; Wilkinson, et al., 2014).
Performance on this task has generally been found to be impaired in clinical populations suffering
from dopamine imbalance, such as Parkinson’s Disease and Huntington Disease patients (e.g.
Jahanshahi, et al., 2010; Knowlton, et al. 1996; Holl, et al., 2012; Poldrack, et al., 2001; Shohamy, et
al., 2004; Witt, et al., 2002).
Insert Figure 1 about here
In the WPT participants are presented with 100 or so trials. On every trial they see a combination of
tarot cards (maximum number is four) and from this configuration of cards they are asked to predict
an outcome (i.e. whether it will be rainy or sunny) (see Figure 1). The actual outcome is determined
by a probabilistic rule, and in actual fact each card is only partially an accurate predictor of the
outcome (Gluck et al., 2002). Usually participants are told whether or not their predictions were
correct (i.e. outcome feedback) on a trial-by-trial basis, along with whether or not they won or lost
most (i.e. reward information). In general when PD patients on dopaminergic medication (e.g. LDopa) perform the WPT, they show impair performance. That is, they are unable to accurately learn
to use the probabilistic information to predict the outcome on each trial (e.g. Knowlton et al., 1996;
Wilkinson et al., 2008). However there is conflicting evidence from Shohamy et al. (2004) suggesting
that PD patients can perform the as well as HCs under conditions under which they observed the
associations between the probabilistic information and the outcomes during training.
IOWA Gambling task (IGT): The IGT has been initially designed to examine real-life decision-making
in the laboratory setting (Bechara, et al., 1994). It was first developed to examine risky decisionmaking in patients with orbitofrontal damage (e.g. Bechara, et al., 1996), but its’ use was soon
extended to other clinical populations, such as pathological gamblers (e.g. Brand, et al., 2005),
substance abusers (e.g. Barry, et al., 2008; Bolla, et al., 2003; Verdejo-Garcia, et al., 2007), patients
with obsessive-compulsive disorders (Buelow, et al., 2009; Lawrence, et al., 2006) and PD and HD
patients (e.g.: Kobayakawa, et al., 2008; Mimura, et al., 2006; Pagonabarraga, et al., 2007; Perretta,
et al., 2005; Stout, et al., 2001, Thiel, et al., 2003;). Although, according to fMRI studies, the task
primarily activates areas such as dorsolateral prefrontal cortex, mesial orbitofrontal and
ventromedial prefrontal cortex, striatal activation during the task has been observed as well
(Gescheidt, et al., 2013; Li, et al., 2010; Linnet, et al., 2011; Linnet, et al., 2012; Power, et al., 2012;
Yamamoto, et al., 2014). The IGT is a task made popular because it thought to be a reliable way to
measure risky decision-making (though actually see Ryterska et al, in press for alternative
conceptualisation of the task). On each trial in the IGT participants are told to choose between four
decks of cards (see Figure 2).
Insert Figure 2 about here
What participants don’t know is that two of the decks are advantageous (small gains, and small
losses- consistent selection from this deck over 100 trials leading to net a profit) and two are
disadvantageous (large gains, and larger losses- consistent selection from this deck over 100 trials
leading to net a loss). Here again, the pattern of findings is mixed. In general, PD patients tend to
select the disadvantageous decks more often than the HCs (Czernecki et al., 2002; Delazer et al.,
2009; Mimura et al., 2006; Pagonabarraga et al., 2007; Perretta et al., 2005), which has been
interpreted as indicating that PDs are risk seeking. Again conflicting findings suggest that when the
task is modified, in this case the disadvantageous deck was associated with small frequent losses,
but even smaller and infrequent gains, PD patients performed just as well as HCs, choosing from the
advantageous deck as often as HCs (Euteneuer et al. 2009; Kobayakawa et al. 2008; Poletti et al.
2010; Thiel et al., 2003).
Game of Dice (GDT): GDT was first developed by Brand et al. (2005) to examine the relationship
between executive functions and risk-taking behavior in an explicit decision-making situation in
patients with frontal lobe dysfunction. Since executive functions in patients with PD are also thought
to be impaired, Brand et al. (2004) examined performance on the task in this population as well.
They showed that PD patients are impaired on this task and that performance of patients was
correlated with both executive functions and feedback processing, relying on fronto-striatal loops.
The importance of the fronto-striatal loops for successful performance on the task was also
demonstrated using brain imaging techniques (e.g. Labudda, et al., 2010; Wilbertz, et al., 2012).
In the GDT task, on each trial, participants need to predict which number will finish face upwards
after a dice is rolled. Participants can nominate up to 4 numbers that they expect will likely occur on
a dice roll (see Figure 3).
Insert Figure 3 about here
The amount that can be won or lost depends on the choice made on each trial (i.e. the riskiest but
most highly rewarding option is to make a single prediction, the least riskiest and least rewarding is
to predict 4 possible numbers). Studies examining PD patients report that they consistently choose
the riskier and higher reward option (i.e. choosing one or two numbers) more often than HCs (e.g.
Brand et al., 2004; Euteneuer et al., 2009). Nevertheless, there is also conflicting evidence suggesting
that PD patients can perform as well as healthy controls in a modified version in which on every trial
participants were not told about their wins and losses (Labudda et al., 2010). In this variation PD
patients showed a preference for the less risky less rewarding option, consistent with HCs.
Why is there such variability in the findings?
One clue to understanding why there is such variability in the findings concerning deficits in
decision-making performance under uncertainty and risk in patients with PD is the precise
manipulations which are introduced into the experimental studies. It is important to locate the
source of possible variation because to date it isn’t clear whether the deficits are driven by
neurological insult or methodological factors concerning the tools examining decision-making
behaviour under uncertainty and risk. We know from various reviews (Osman, 2011; Ryterska et al,
in press) and a recent meta-analysis (Ryterska et al, 2013) conducted on studies of decision-making
in PD that decrements in performance are the result of: 1) patients with PD performing decisionmaking tasks while medicated, 2) using a set up in which outcome feedback (i.e. correct/incorrect) is
present on a trial by trial basis, and 3) the presentation of information that requires patients to
process the negative outcomes (i.e. loss of reward, delay of reward, effort in obtaining reward)
associated with the options.
Let’s consider the first factor – medication status. Dopamine replacement therapy through
dopaminergic medication Levadopa (L-dopa) is often prescribed to patients with PD to alleviate the
motor symptoms. One might predict that increasing dopamine levels in areas of the brain in which
they are depleted, and which have been associated with decision-making, would in turn lead to
improved performance in decision-making tasks. However, while L-dopa successfully attenuates
motor dysfunction, it has reliably been shown to impair cognitive functions including decisionmaking (Gotham et al, 1988; Jahanshahi et al, 2010; Shohamy et al, 2006).
The reason that increasing dopamine levels through medication adversely affects decision-making
has been explained by the dopamine ‘overdosing’ hypothesis (Cools, et al., 2001; Gotham et al.,
1988; Jahanshahi et al, 2010; Swainson et al., 2000; Torta, et al., 2009). Patients with PD show
improved cognitive functioning associated with brain areas which have depleted levels of dopamine
(e.g., putamen and dorsal caudate). But, L-dopa also increases levels of dopamine in less affected
brain areas (e.g., ventral striatum), hence overdosing them with dopamine which leads to
dysfunction of cognitive processes (e.g., Cools et al, 2001; Gotham et al, 1988). As discussed earlier,
the midbrain dopaminergic system is thought to play a crucial role in a number of aspects of decision
making behaviour (e.g. Schultz, 2002; Shohamy, et al., 2004); therefore disruption to levels of
dopamine in relatively intact brain regions, such as the ventral striatum, are likely to impair decision
behaviour. However, given that not all studies of decision-making in PD while patients are medicated
(Czernecki et al., 2002; Wilkinson et al., 2008) show impairments in performance, medication alone
can’t account all the conflicting findings.
To understand the impact of feedback and processing the costs associated with decision-making, we
first need to consider the various stages associated with decision-making. According to Rangel, et al.,
(2008) and others (Osman, 2014; Ryterska et al, in press) there are at least four core stages involved
in the decision-making process.
First of all, people need to encode the decision problem in terms of the choices they have and
actions that they need to take to achieve a desirable outcome from one of the choices (stage 1). This
is followed by a valuation stage in which people assign personal (subjective) values and consider the
costs of the options they face (stage 2). If they follow the operations rationally, they will then select
the option which they have assigned the highest subjective value to (Stage 3). Once the choice has
been made the action associated with the choice is taken, and the outcome of the choice is
evaluated for future possible decisions faced in similar types of decision problems (stage 4).
If we return to the literature that we have reviewed here, what is clear is that when frequent
discreet feedback was presented in the tasks in which the outcome was difficult to predict, patients
with PD showed impairments in decision-making performance (e.g., Foerde, et al., 2013; Seo, et al.,
2010; Knowlton et al., 1996). When outcome feedback in the tasks was removed, or different forms
of feedback were presented then patients with PD perform as well as HCs (Minati et al., 2011;
Shohamy et al., 2008; Schmitt-Eliassen et al, 2007). When tasks are devised in such a way that
patients no longer need to process outcome feedback, this seems to bypass a stage in the decisionmaking process which appears to be impaired, which is why performance in particular variants of
decision-making tasks is equivalent to HCs.
In addition, the reviewed evidence also suggests that patients with PD show a greater tendency to
opt for risky over non-risky choices which in often is not considered to be rational (e.g., Djamshidian
et al., 2010; Kapogiannis, et al., 2011; Simioni et al.2012). The implication here is that when the
decision-making task is set up so that risky options are made available, then they prefer to opt for
the risky option. In turn this suggests that the stage of the decision-making process in which the cost
of the choice alternatives (i.e. the risk associated with each option) is evaluated is impaired in PD. As
a result, this affects the cost/benefit analysis stage in which the valuation of options is weighed up,
and this is why PD patients show preferences for risky returns.
Overall, based on the speculations we have made, the deficits in decision-making under uncertainty
and risk in patients with PD appear to be located in the latter stages of the decision-making process.
Moreover, the findings here also suggest that cost, values and feedback are factors in the decisionmaking problem that can be manipulated to facilitate performance, but if they are an integral part of
performing the task, then deficits in performance in PDs will be found.
Future directions
There is amassing evidence showing that neurodegeneration in the basal ganglia structures of the
brain can impair decision-making performance of PD patients on various decision-making tasks in
which the outcome of the decision-making scenario is difficult to predict. The pattern of results from
the studies reviewed here suggests that the source of this impairment lies in the medication status
of the patient, as well as the way in which feedback information is presented, and also whether
highly risky options are presented. This in turn means that lowering the cost of options and giving
feedback which is richer than correct/incorrect can have a profound impact on decision-making in
PD, facilitating performance. More to the point, this has important implications for improving
decision-making for PD patients in clinical setting, and for future investigations of decision-making in
PD, which should take into consideration the costs, values and feedback inherent to the decisionmaking scenario. Such insights can be used to increase the possibility for patients with PD to make
accurate decisions in their day to day lives.
References
1. Barry, D., Petry, N.M. 2008. Predictors of decision-making on the Iowa Gambling Task:
Independent effects of lifetime history of substance use disorders and performance on the
Trail Making Test. Brain Cogn. 66(3), 243-252.
2. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. 1994. Insensitivity to future
consequences following damage to human prefrontal cortex. Cognition 50(1–3), 7–15.
3. Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. 1996. Failure to respond autonomically
to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex 6,
215–225.
4. Bolla, K.I., Eldreth, D.A., London, E.D., Kiehl, K.A., Mouratidis, M., Contoreggi, C., Matochik,
J.A., Kurian, V., Cadet, J.L., Kimes, A.S., Funderburk, F.R., Ernst, M. 2003. Orbitofrontal cortex
dysfunction in abstinent cocaine abusers performing a decision-making task. NeuroImage
19(3), 1085-1094.
5. Brand, M., Fujiwara, E., Borsutzky, S., Kalbe, E., Kessler, J., Markowitsch, H.J. 2005. Decisionmaking deficits of Kkorsakoff patients in a new gambling task with explicit rules: associations
with executive functions. Neuropsychology 19(3), 267-77.
6. Brand, M., Labudda, K., and Kalbe, E., 2004. Decision-making impairments in patients with
Parkinson’s disease. Behav. Neurol. 15, 77-85.
7. Buelow, M.T., Suhr, J.A. 2009. Construct validity of the Iowa Gambling Task. Neuropsychol
Rev. 19(1), 102-14.
8. Cools, R., 2001. Enhanced or impaired cognitive function in Parkinson’s disease as a function
of dopaminergic medication and task demands. Cereb. Cortex 11(12), 1136-1143.
9. Cools, R., Barker, R.A., Sahakian, B.J., Robbins, T.W., 2003. L-Dopa medication remediates
cognitive inflexibility, but increases impulsivity in patients with Parkinson's disease.
Neuropsychologia 41(11), 1431-1441.
10. Czernecki, V., Pillon, B., Houeto, J.L., Pochon, J.B., Levy, R., Dubois, B., 2002. Motivation,
reward and Parkinson’s disease: influence of dopatherapy. Neuropsychologia 40(13), 22572267.
11. Djamshidian, A., Jha, A., O'Sullivan, S.S., Silveira-Moriyama, L., Jacobson, C., Brown, P., Lees,
A., Averbeck, B.B. 2010. Risk and learning in impulsive and nonimpulsive patients with
Parkinson's disease. Mov. Disord. 25(13), 2203-2210.
12. Doya, K. 2008. Modulators of decision making. Nature Neurosci. 11, 410 - 416
13. Euteneuer, F., Schaefer, F., Stuermer, R., Boucsein, W., Timmermann, L., Barbe, M.T.,
Ebersbach, G., Otto, J., Kessler, J., Kalbe, E., 2009. Dissociation of decision-making under
ambiguity and decision-making under risk in patients with Parkinson’s disease: A
neuropsychological and psychophysiological study. Neuropsychologia 47, 2882-2890.
14. Foerde, K., Braun, E.K., Shohamy, D. 2013. A trade-off between feedback-based learning and
episodic memory for feedback events: evidence from Parkinson's disease. Neurodegener.
Dis. 11(2), 93-101.
15. Gescheidt, T., Mareček, R., Mikl, M., Czekóová, K., Urbánek, T., Vaníček, J., Shaw, D.J., Bareš,
M. 2013. Functional anatomy of outcome evaluation during Iowa Gambling Task
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
performance in patients with Parkinson's disease: an fMRI study. Neurol Sci. 34(12), 215966.
Gluck, M.A., Shohamy, D., Myers, C., 2002. How do people solve the "weather prediction"
task?: individual variability in strategies for probabilistic category learning. Learn. Mem. 9(6),
408-418.
Gotham, A.M., Brown, R.G., Marsden, C.D., 1988. Frontal cognitive function in patients with
Parkinson’s disease on and off levodopa. Brain 111(2), 299-321.
Holl, A.K., Wilkinson, L., Tabrizi, S.J., Painold, A., Jahanshahi, M. 2012. Probabilistic
classification learning with corrective feedback is selectively impaired in early Huntington's
disease--evidence for the role of the striatum in learning with feedback. Neuropsychologia
50(9), 2176-86.
Housden, C.R., O'Sullivan, S.S., Joyce, E.M., Lees, A.J., Roiser, J.P. 2010. Intact reward
learning but elevated delay discounting in Parkinson's disease patients with impulsivecompulsive spectrum behaviors. Neuropsychopharmacology 35(11), 2155-2164
Jahanshahi, M., Wilkinson, L., Gahir, H., Dharminda, A., Lagnado, D.A., 2010. Medication
impairs probabilistic classification learning in Parkinson's disease. Neuropsychologia 48(4),
1096-1103.
Kapogiannis, B.G., Mooshagian, E., Campion, P., Grafman, J., Zimmermann, T.J., Ladt, K.C.,
Wassermann, E. M., 2011. Reward processing abnormalities in Parkinson’s disease. Mov.
Disord. 26(8), 1451-1457.
Knowlton, B.J., Squire, L.R., Gluck, M.A., 1994. Probabilistic classification learning in amnesia.
Learn. Mem. 1, 106-120.
Knowlton, B.J., Mangels, J.A., Squire, L.R., 1996. A neostriatal habit learning system in
humans, Science 273(5280), 1399-1402.
Kobayakawa, M., Koyama, S., Mimura, M., Kawamura, M., 2008. Decision-making in
Parkinson’s disease: Analysis of behavioral and physiological patterns in the Iowa Gambling
Task. Mov. Disord. 23(4), 547-552.
Labudda, K., Brand, M., Mertens, M., Ollech, I., Markowitsch, H.J., Woermann, F.G., 2010.
Decision-making under risk condition in patients with Parkinson’s disease: A behavioural and
fMRI study. Behav. Neurol. 23, 131-143.
Lawrence, N.S., Wooderson, S., Mataix-Cols, D., David, R., Speckens, A., Phillips, M.L. 2006.
Decision making and set shifting impairments are associated with distinct symptom
dimensions in obsessive-compulsive disorder. Neuropsychology, 20(4), 409-419.
Li, X., Lu, Z., D'Argembeau, A., Ng, M., Bechara, A. 2010. The Iowa Gambling Task in fMRI
images. Hum. Brain Mapp. 31(3), 410–423.
Linnet, J., Mouridsen, K., Peterson, E., Møller, A., Doudet, D.J., Gjedde, A. 2012. Striatal
dopamine release codes uncertainty in pathological gambling. Psychiatry Res. 204(1), 55-60.
Linnet, J., Møller, A., Peterson, E., Gjedde, A., Doudet, D. 2011. Inverse association between
dopaminergic neurotransmission and Iowa Gambling Task performance in pathological
gamblers and healthy controls. Scand. J. Psychol. 52(1), 28-34.
Majsak, M.J., Kaminski, T., Gentile, A.M., Flanagan, J.R. 1998. The reaching movements of
patients with Parkinson's disease under self-determined maximal speed and visually cued
conditions. Brain 121(4), 755-766.
Mazzoni, P., Hristova, A., Krakauer, J.W. 2007. Why don't we move faster? Parkinson's
disease, movement vigor, and implicit motivation. J. Neurosci. 27(27), 7105-7116.
32. Mimura, M., Oeda, R., Kawamura, M., 2006. Impaired decision-making in Parkinson’s
disease. Parkinsonism Relat. Disord. 12, 169-175.
33. Minati, L., Piacentini, S., Ferre, F., Nanetti, L., Romito, L., Mariotti, C., Grisoli, M., Medford,
N., Critchley, H.D., Albanese, A., 2011. Choice-option evaluation is preserved in early
Huntington and Parkinson’s disease. Neuroreport. 22(15), 753-757.
34. Moisello, C., Perfetti, B., Marinelli, L., Sanguineti, V., Bove, M., Feigin, A., Di Rocco, A.,
Eidelberg, D., Ghilardi, M.F. 2011. Basal ganglia and kinematics modulation: insights from
Parkinson's and Huntington's diseases. Parkinsonism Relat. Disord. 17(8), 642-644.
35. Moody, T.D., Bookheimer, S.Y., Vanek, Z., Knowlton, B.J. 2004. An implicit learning task
activates medial temporal lobe in patients with Parkinson's disease. Behav. Neurosci. 118(2),
438-42.
36. Osman, M., 2011. The Role of Feedback in Decision-making (Chapter 3). Diagnosis and
Treatment of Parkinson’s Disease, 117. InTech Publishers.
37. Osman, M. (2014). Future-minded: The psychology of Agency and Control . PalgraveMacMillan
38. Pagonabarraga, J., Garcia-Sanchez, C., Llebaria, G., Pascual-Sedano, B., Gironell, A.,
Kulisevsky, J., 2007. Controlled study of Decision-making and cognitive impairment in
Parkinson’s disease. Mov. Disord. 22(10), 1430-1435.
39. Palminteri, S., Lebreton, M., Worbe, Y., Grabli, D., Hartmann, A., Passiglione, M. 2009.
Pharmacological modulation of subliminal learning in Parkinson’s disease and Tourette’s
syndromes. PNAS 106(45), 19179-19184.
40. Perretta, J.G., Pari, G., Beninger, R.J., 2005. Effects of Parkinson’s disease on two putative
nondeclarative learning tasks: probabilistic classification and gambling. Cogn. Behav. Neurol.
18(4), 185-192.
41. Platt, M.L., Huettel, S.A. 2008. Risky business: the neuroeconomics of decision making under
uncertainty. Nature Neurosci. 11, 398 – 403.
42. Poldrack, R.A., Clark, J., Paré-Blagoev, E.J., Shohamy, D., Creso Moyano, J., Myers, C., Gluck,
M.A., 2001. Interactive memory systems in the human brain. Nature 414(6863), 546-50.
43. Poldrack, R.A., Prabhakaran, V., Seger, C.A., Gabriel, J.D.E. 1999. Striatal activation during
acquisition of a cognitive skill. Neuropsychology 13( 4), 564-574.
44. Poletti, M., Frosini, D., Lucetti, C., Del Dotto, P., Ceravolo, R., Bonuccelli, U., 2010. Decisionmaking in de novo Parkinson’s disease. Mov. Disord. 25(10), 1432-1436.
45. Power, Y., Goodyear, B., Crockford, D. 2012. Neural correlates of pathological gamblers
preference for immediate rewards during the iowa gambling task: an fMRI study. J. Gambl.
Stud. 28(4), 623-36.
46. Schmitt-Eliassen, J., Ferstl, R., Wiesner, C., Deuschl, G., Witt, K., 2007. Feedback-based
versus observational classification learning in healthy aging and Parkinson’s disease. Brain
Res. 1142, 178-188.
47. Seo, M., Beigi, M., Jahanshahi, M., Averbeck, B.B. 2010. Effects of dopamine medication on
sequence learning with stochastic feedback in Parkinson's disease. Front. Syst. Neurosci.
4(36), 1-9.
48. Shohamy, D., Myers, C.E., Geghman, K.D., Sage, J., Gluck, M.A., 2006. L-dopa impairs
learning, but spares generalization, in Parkinson's disease. Neuropsychologia 44(5), 774-784.
49. Shohamy, D., Myers, C.E., Grossman, S., Sage, J., Gluck, M.A., Poldrack, R.A., 2004. Corticostriatal contributions to feedback-based learning: converging data from neuroimaging and
neuropsychology. Brain 127(4), 851-859.
50. Shohamy, D., Myers, C.E., Kalanithi, J., Gluck, M.A., 2008. Basal ganglia and dopamine
contributions to probabilistic category learning. Neurosci. Biobehav. Rev. 32(2), 219-36.
51. Simioni, A.C., Dagher, A., Fellows, L.K., 2012. Dissecting the effects of disease and treatment
on impulsivity in Parkinson's disease. J. Int. Neuropsychol. Soc. 18(6), 942-951.
52. St Onge, J.R., Floresco, S.B. 2009. Dopaminergic Modulation of Risk-Based Decision Making.
Neuropsychopharmacology 34, 681–697.
53. Stout, J.C., Rodawalt, W.C., Siemers, E.R., 2001. Risky decision-making in Huntington’s
disease. J. Int. Neuropsychol. Soc. 7, 92-101.
54. Swainson, R., Rogers, R.D., Sahakian, B.J., Summers, B.A., Polkey, C.E., Robbins, T.W., 2000.
Probabilistic learning and reversal deficits in patients with Parkinson’s disease or frontal or
temporal lobe lesions: possible adverse effects of dopaminergic medication.
Neuropsychologia 38, 596-612.
55. Thiel, A., Hilker, R., Kessler, J., Habedank, B., Herholz, K., Heiss, W.-D., 2003. Activation of
basal ganglia loops in idiopathic Parkinson’s disease: a PET study. J. Neural Transm. 110(11),
1289-1301.
56. Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. 2007. The Neural Basis of Loss Aversion in
Decision-Making Under Risk. Science 315(5811), 515-518.
57. Torta, D.M., Castelli, L., Zibetti, M., Lopiano, L., Geminiani, G. 2009. On the role of dopamine
replacement therapy in decision-making, working memory, and reward in Parkinson's
disease: does the therapy-dose matter? Brain Cogn. 71(2), 84-91.
58. Trepel, C., Fox, C.R., Poldrack, R.A. 2005. Prospect theory on the brain? Toward a cognitive
neuroscience of decision under risk. Brain. Res. Cogn. Brain. Res. 23(1), 34-50.
59. Verdejo-Garcia, A., Benbrook, A., Funderburk, F., David, P., Cadet, J.L., Bolla, K.I. 2007. The
differential relationship between cocaine use and marijuana use on decision-making
performance over repeat testing with the Iowa Gambling Task. Drug Alcohol Depend. 90(1),
2-11.
60. Wilbertz, G., van Elst, L.T., Delgado, M.R., Maier, S., Feige, B., Philipsen, A., Blechert, J. 2012.
Orbitofrontal reward sensitivity and impulsivity in adult attention deficit hyperactivity
disorder. Neuroimage 60(1), 353-61.
61. Wilkinson, L., Lagnado, D.A., Quallo, M., Jahanshahi, M., 2008. The effect of feedback on
non-motor probabilistic classification learning in Parkinson's disease. Neuropsychologia
46(11), 2683-2695.
62. Wilkinson, L., Beigi, M., Lagnado, D.A., Jahanshahi, M., 2011. Deep brain stimulation of the
subthalamic nucleus selectively improves learning of weakly associated cue combinations
during probabilistic classification learning in Parkinson’s disease. Neuropsychology 25(3),
286-294.
63. Wilkinson, L., Tai, Y.F., Lin, C.S., Lagnado, D.A., Brooks, D.J., Piccini, P., Jahanshahi, M. 2014.
Probabilistic classification learning with corrective feedback is associated with in vivo striatal
dopamine release in the ventral striatum, while learning without feedback is not. Hum.
Brain. Mapp. 2014. doi: 10.1002/hbm.22536. [Epub ahead of print]
64. Witt, K., Nuhsman, A., Deuschl, G. 2002. Dissociation of habit-learning in Parkinson’s and
cerebellar disease. J. Cogn. Neurosci. 14(3), 493-499.
65. Yamamoto, D.J., Reynolds, J., Krmpotich, T., Banich, M.T., Thompson, L., Tanabe, J. 2014.
Temporal profile of fronto-striatal-limbic activity during implicit decisions in drug
dependence. Drug Alcohol Depend.136, 108–114.
Figure 1.
Figure 1 presents the underlying stimulus information that features in the Weather prediction task
Figure 2.
Figure 2 presents the full information associated with the stimuli that are presented in the Iowa
Gambling Task
Figure 3.
Figure 3 presents the different stages associated with one trial in the Game of Dice Task