NeuroImage 52 (2010) 1514–1520

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Aging and decision making under uncertainty: Behavioral and neural evidence forthe preservation of decision making in the absence of learning in old age

S.M. Hadi Hosseini a,b,c,⁎, Maryam Rostami a, Yukihito Yomogida b,d, Makoto Takahashi a,Takashi Tsukiura b, Ryuta Kawashima b,c

a Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai, Japanb Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japanc Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japand Japan Society for the Promotion of Science (JSPS), Japan

⁎ Corresponding author. Department of ManagemGraduate School of Engineering, Tohoku University, 6-Aoba-ku, Sendai, 980-8579, Japan. Fax: +81 22 795484

E-mail address: hadi.hosseini@most.tohoku.ac.jp (S.

1053-8119/$ – see front matter © 2010 Elsevier Inc. Adoi:10.1016/j.neuroimage.2010.05.008

a b s t r a c t

a r t i c l e i n f o

Article history:Received 27 January 2010Revised 22 April 2010Accepted 4 May 2010Available online 18 May 2010

Keywords:AgingDecision makingUncertaintyProbability matchingMaximizingfMRI

Decision making under uncertainty is an essential component of everyday life. Recent psychological studiessuggest that older adults, despite age-related neurological decline, can make advantageous decisions wheninformation about the contingencies of the outcomes is available. In this study, a two-choice predictionparadigm has been used, in conjunction with functional magnetic resonance imaging (fMRI), to investigatethe effects of normal aging on neural substrates underlying uncertain decision making in the absence oflearning that have not been addressed in previous neuroimaging studies. Neuroimaging results showed thatboth the healthy older and young adults recruited a network of brain regions comprising the rightdorsolateral prefrontal cortex, bilateral inferior parietal lobule, medial frontal cortex, and right lateralorbitofrontal cortex during the prediction task. As was hypothesized, the performance of older adults in theprediction task was not impaired compared to young adults. Although no significant age-related increases inbrain activity have been found, we observed an age-related decrease in activity in the right inferior parietallobule. We speculate that the observed age-related decrease in parietal activity could be explained by age-related differences in decision making behavior revealed by questionnaire results and maximizing scores.Together, this study demonstrates behavioral and neural evidence for the preservation of decision making inolder adults when information about the contingencies of the outcome is available.

ent Science and Technology,6-11-808, Aramaki-Aza-Aoba,7.M.H. Hosseini).

ll rights reserved.

© 2010 Elsevier Inc. All rights reserved.

Introduction

Decision making under uncertainty is an essential component ofeveryday life. Because of age-related neurological decline, one mightexpect that older adults would be more at risk than younger adultswhen they are required to make decisions about medical care,housing, etc. On the other hand, older adults have a lifetime ofexperience that may partially offset age-related cognitive decline.However, evidence regarding the effects of aging on the quality ofdecision making under uncertainty is controversial.

Decision making under uncertainty in humans has been investi-gated in several psychological, neuroimaging and neuropsychologicalstudies (Elliott et al., 1999; Hsu et al., 2005; Krain et al., 2006; Pauluset al., 2001, 2002; Sanfey et al., 2006; Volz et al., 2003, 2004; Yoshidaand Ishii, 2006) (see Platt and Huettel (2008) for a review). In thesestudies, subjects are either given the full information about the

problem situation, e.g. probabilities and associated gains and losses, orrequired to learn the contingencies of stimulus–response associationsin order to make advantageous decisions.

In recent years, a number of neuroimaging studies have investi-gated the effects of aging on the neural correlates of decision makingunder uncertainty in humans (Fera et al., 2005; Lee et al., 2008; Mellet al., 2009; Samanez-Larkin et al., 2010). However, performance inmost of these studies is dependent on learning. To the best of ourknowledge, no previous functional neuroimaging study has yetexamined the effect of aging on the neural substrates underlyingdecision making under uncertainty in the absence of learning.Previous neuroimaging studies examining the neural substratesunderlying this type of decision making on young healthy adultsreported activations in a network of brain regions comprising thelateral prefrontal cortex (right more than left), right orbitofrontalcortex, medial frontal cortex, bilateral inferior parietal lobule, andright thalamus and attributed them to response selection, cross-trialperformance monitoring, and working memory processes (Elliottet al., 1997, 1999). Volumetric brain imaging studies report thatfrontal and parietal lobes show the steepest rate of atrophy duringaging (Raz, 2005; Raz et al., 2005; Resnick et al., 2003). Age-related

Table 1Mean (SD) age, FAB and MMSE scores of the participants.

Group n Age FAB score(out of 18)

MMSE score(out of 30)

Young 14 20.0 (1.77) – –

Older 14 67.5 (3.84) 15.3 (2.27) 28.7 (1.14)

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atrophy of the brain regions involved in decision making under un-certainty as well as age-related decline in the key cognitive com-ponents of the decision making process may adversely affect thequality of decision making in older adults.

However, age-related deficits in decision making may also belimited to decisions that depend on associative learning of thecontingencies from experience. Recent psychological studies suggestthat older adults can make advantageous decisions when informationabout the contingencies of the outcomes is available (Kovalchik et al.,2005; Zamarian et al., 2008) while they show poor performancerelative to young adults when they are required to acquire thecontingencies of stimuli or stimulus–response associations duringtask performance (Hartman et al., 2001; Mell et al., 2005; Ridder-inkhof et al., 2002; Zamarian et al., 2008). Thus, the observed impairedperformance of older adults in the previous decision making para-digms might be related to an age-related deficit in learning stimulusreinforcement associations (Mell et al., 2005, 2009). Presuming thatgiving older adults complete information about the problem situationwill help them preserve their performance, it is quite important tofind out how they compensate for the mentioned age-related cog-nitive and neurological decline.

Moreover, since learning is a prerequisite in the previous studies ofaging, subjects try to test different hypotheses to increase theirperformance in these types of tasks. Giving information about thecontingencies of the outcomes to the subjects will facilitate differen-tiation of the strategies taken by the subjects, in a manner that wouldbe minimally sensitive to other variables such as the rate of learning(Deakin et al., 2004).

In the present study, a revised version of the two-choice predictiontask (Unturbe and Corominas, 2007) has been used, in conjunctionwithfunctionalmagnetic resonance imaging (fMRI), to investigate the effectsof normal aging on neural correlates of decision making under un-certainty when information about the stimuli's probability of occur-rences is given to the subjects in advance. In the two-choice predictiontask, subjects are required to decide which of the two different types ofevents will occur, given the contingencies of the occurrences of theevents in advance. Evidence from previous psychological studies sug-gests that, in this type of task, subjects tend to “match” probabilities:that is, they allocate their responses to the two options in proportion totheir relative likelihood (Koehler and James, 2009; Shanks et al., 2002;Vulkan, 2000). Nonetheless, the optimal strategy is to select the morelikely choice on all of the trials, i.e., “maximizing” (West and Stanovich,2003). Using the two-choice prediction task, we can therefore analyzedifferences in decision making strategies, i.e., probability matching vs.maximizing, between the two groups.

In this type of prediction task, subjects usually use a strategicapproach to the task where they try to monitor their performance onprevious trials to guide their predictions (Elliott et al., 1999). Weexpect that during the prediction task, the right frontoparietalnetwork and medial frontal cortex will be commonly activated inboth groups of older and young adults reflecting working memoryprocesses and action monitoring, respectively (Klingberg et al., 1997;Naghavi and Nyberg, 2005; Rushworth et al., 2004, 2007). Previousneuroimaging studies reported the involvement of the medialorbitofrontal cortex in learning stimulus–reward associations (O'Doh-erty, 2004; O'Doherty et al., 2001; Rolls, 2000). Since learning is not aprerequisite in the implemented two-choice prediction task, weexpect activity in the lateral orbitofrontal regions associated with theevaluation of feedback to signal a shift in responses (Kringelbach andRolls, 2004; Windmann et al., 2006). This shift in response arises fromthe very nature of the prediction task that requires subjects to switchback and forth depending upon the runs of one event or the other; aprocess that is different from shifting in response in learning para-digms that arises from switching between hypotheses and rules.

Previous psychological studies of aging reported that, in probabi-listic decision making tasks, older adults show a smaller response bias

toward the reward-maximizing choice than do young adults (Deakinet al., 2004; Denburg et al., 2005; Kovalchik et al., 2005; Tripp andAlsop, 1999). Such a difference may result from the fact that olderadults may be less likely than young adults to risk an incorrectresponse (Deakin et al., 2004; Dror et al., 1998) and they try harder tobe correct on 100% of the trials (Fantino and Esfandiari, 2002). Recentneuroimaging findings suggest the involvement of parietal region inprobability judgments and evaluation of risk (Huettel et al., 2005,2006; Venkatraman et al., 2009; Vickery and Jiang, 2009). Studies onnon-human primates also indicated that neurons within the posteriorparietal cortex provide codes for decision variables such as expectedvalue and probabilities (Dorris and Glimcher, 2004; McCoy and Platt,2005; Platt and Glimcher, 1999; Sugrue et al., 2004). The results of thisstudy could also offer insights into age-related changes in activity ofthe cortical regions associated with decision making behavior.

Material and methods

Subjects

Sixteen right-handed college students (13males and3 females; aged18–25 years old; mean age 20 years) and 24 older adults (12males and12 females; aged 61–77 years old; mean age 69 years) participated inthis study. All the subjects were healthy with no signs or histories ofneurological diseases. Written informed consent was obtained fromeach subject in accordance with the guidelines approved by theInstitutional Review Board (IRB) of the Medical School of TohokuUniversity and the 1975 Helsinki Declaration of Human Rights.

Frontal assessment battery (FAB) (Dubois et al., 2000) and minimental state examination (MMSE) tests (Tangalos et al., 1996) wereused for screening the older adults before the experiment. FAB is abedside cognitive scale designed to measure frontal lobe dysfunctionswhile MMSE is a test commonly used to screen for dementia.Information regarding themeanage andmean scores of theparticipantsin the FAB and MMSE tests are listed in Table 1. Two subjects of theyoung age group were excluded from the analysis because of excessiveheadmotion (more than2 mminany axis). Five subjects of theolder agegroup were excluded from the analysis because of poor performance(FAB scores of less than 12 or MMSE scores of less than 24) in thescreening tests and three subjects were excluded because of excessivehead motion (more than 2 mm in any axis). In addition, two subjectswere excluded from the analysis since their spatially normalized imagesinclude major artifacts. Totally, data of 14 young adults and 14 olderadults met the above mentioned criteria.

Experimental paradigm

The block design of the experiment is shown in Fig. 1A. The scanningsession consisted of eight runs. The runs themselves were made up of10 trials of the prediction task and five trials of the control task,separated by 13 s of rest blocks. The task order was counterbalancedacross participants.

The prediction task consisted of predicting which of the two whitesquares (left or right) presented on a display would change its color. Atthe beginning of each block of the prediction task, the word “Predict”along with the likelihoods of both events was displayed for 2 s. In eachtrial of the prediction task subjects had 2 s to indicate with a buttonpress which of the two white squares (left or right) presented on the

Fig. 1. (A) Experimental design. (B) Prediction task. (C) Control condition.

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display would change its color. After the subject made his/her pre-diction, one of thewhite squares turned yellow (the outcome) based onthe likelihood already presented to the subjects at the beginning of eachblock (Fig. 1B.). The color of the squares changed randomly, but for 70%of the prediction trials the right square turned yellow while for 30% ofthe prediction trials the left square turned yellow.

In the control task, subjects were asked to indicate with a buttonpress which of the two squares (left or right) already presented on thedisplay was yellow. Each block of the control task started with theword “Report” displayed for 2 s at the center of the screen. In each trialof the control condition, subjects were first shown which of thesquares turned yellow for 1 s and then they had 2 s to report whichsquare had turned yellow (Fig. 1C). The color of the squares in eachblock of the control task changed randomly, but the proportion oftrials in which the left/right square changed color was calculated

based on the subjects' responses in the previous prediction task block.In order to control formotor output, the programwas designed so thatthe proportion of the left and right button presses by the subjectwould be the same for both the prediction and control tasks.

Before scanning, subjects were instructed to read the experimentalinstructions and they practiced how to do the task in the MRI scanner.The total duration of the functional imaging was around 11 min foreach subject.

In order to analyze subjects' strategies during the prediction task,subjects were asked, after the scanning session, to describe theirstrategies during the prediction task aswell as to fill out a questionnaireadapted from West and Stanovich (2003). In the questionnaire, fivetypes of strategies were presented to the subjects:

− Strategy A: Go by intuition, switching when there has been toomany of one event or the other.

− Strategy B: Predict the more likely choice on most of the trials butoccasionally, after a long run of the more likely choice, predict theother one.

− Strategy C: Make predictions according to the frequency ofoccurrence (e.g. 70% right and 30% left).

− Strategy D: Predict the more likely choice on all of the trials.− Strategy E: Predict the more likely choice until you get a wrong

feedback, then switch to the other choice and continue until youget a wrong feedback, then switch and so on.

Subjects were asked to choose the strategy that most fit theirdecision making behavior during the prediction task. This question-naire helped us better identify differences in the strategies taken bythe subjects for decision making. In addition, performance resultswere analyzed to find subjects' maximizing scores (i.e. proportion ofprediction trials in which a subject selected the most frequentstimulus) in order to examine differences in maximizing behaviorbetween the two groups.

Imaging procedure

A 3 T Philips Achieva scanner (Philips Medical System)was used forscanning in this study. For performing the fMRI experiment, the subjectswere asked to lie supine in the MRI scanner. A semi-lucent screenwas placed at the back end of the scanner and visual stimuli weredelivered to the subject via this screen and a mirror suspended in frontof the subject's face. Slices (n=42, slice thickness=3, gap=0 mm)covering the whole brain were acquired by gradient-echo echo-planar(GE-EPI) magnetic resonance imaging (TR=3000 ms, TE=30ms, flipangle=90°, FOV=192×192 mm2, voxel size 3×3×3 mm3). A T1-weighted structural image was also acquired for each subject(matrix=240×240, TR=6.5 ms, TE=3 ms, FOV=240 mm, 162 slices,slice thickness=1 mm).

fMRI data analysis

Image processing and statistical analyses of fMRI data were carriedout using statistical parametric mapping (SPM5, Welcome Depart-ment of Cognitive Neurology, London, UK) software (Friston et al.,1995a,b). The four initial scans of each subject were dummy scans toequilibrate the state of magnetization and were discarded from theanalysis. The differences in the acquisition timing across slices in eachscan were adjusted and the effects of head motion across the scanswere corrected by realigning all the scans to the first scan. Subjectswith excessive head movement (more than 2 mm in any axis) wereexcluded from the analysis. Functional scans were then spatiallynormalized to a standard EPI template and spatially smoothedwith an8-mm full-width at half-maximum (FWHM) Gaussian filter to reducethe effects of noise and normalization errors.

After preprocessing the images, statistical analysis was firstperformed for each subject using a general linear model in SPM5.

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The design was modeled as a boxcar, convolved with a hemodynamicresponse. Themodelfitwasperformed individually for each subject, andcontrast images were generated for each of the two event types in eachgroup: PdY (prediction in young adult group), CtY (control in youngadult group), PdE (prediction in older adult group), and CtE (control inolder adult group). Subtraction images were created for “PdY vs. CtY”(PdY−CtY) and “PdE vs. CtE” (PdE−CtE). Between-group maps werecreated by performing a two-sample t-test on the contrast images asfollows: age-related decrease: “PdY vs. PdE” ((PdY−CtY)−(PdE−CtE)) masked by “PdY vs. CtY”, age-related increase: “PdE vs. PdY”((PdE−CtE)−(PdY−CtY)) masked by “PdE vs. CtE”, and task-related: “PdY and PdE” ((PdY−CtY)+(PdE−CtE)). The statisticalthreshold for task-related activations was set at pb0.05 (FWEcorrected for multiple comparison) and for age-related activationswas set at pb0.001 (cluster level corrected, pb0.05). The statisticalthreshold considered for the mask images was set at pb0.05(uncorrected). This approach provided us brain regions showing(a) significant group difference of activity corrected by the multiplecomparison method (Y vs. E), and (b) significant voxel-by-voxelactivations in one group by the masking method. Thus we couldidentify only brain activations fulfilling a very conservative thresholdof FWE corrections. Masking also excludes voxels related to agedifferences in visuomotor performance. Finally, the resulting activa-tion maps were constructed and superimposed onto the stereo-tactically standardized T1-weighted MRI images.

Results

Behavioral results

Fig. 2A shows the mean accuracy (percentage) of the young andolder adults in the prediction task. No significant difference in meanaccuracy has been observed between groups (t=1.38, p=0.19).Fig. 2B shows the mean reaction time of the young and older adults inthe prediction and control conditions. Subjects' reaction times were

Fig. 2. (A)Mean accuracy of older and young adults in the prediction task. (B) Mean reactionscore of older and young groups. (D) Percent accuracy of both age groups across blocks of

subjected to a two-way analysis of variance (Old vs. Young×Task vs.Control). The results showed a significant main effect of the condition(F1,52=29.2, pb0.001) and a significant main effect of the group(F1,52=16.2, pb0.001), but no significant group by condition inter-action (F1,52=2.04, p=0.16).

The results of the questionnaire survey showed that the majorityof older adults (79%) exploited the “strategy C” (a pure “probabilitymatching” strategy) to make their choices in the prediction task whilethe majority of young adults (93%) reported the “strategy B”(“probability matching with gambling fallacy”) (see West andStanovich (2003) for strategy classification). In addition, subjects'maximizing scores (i.e. proportion of prediction trials in which subjectselected the most frequent stimulus) were also analyzed in order toexamine age-related differences in maximizing behavior between thetwo groups. The results revealed a significant difference between themaximizing scores of older and young adults (t=2.1, pb0.05) asshown in Fig. 2C.

In order to verify that learning is not a prerequisite in theprediction task, we compared mean subjects' accuracy in the last twoblocks of the prediction task with the mean accuracy in the first twoblocks. No significant difference has been found (t=1.05, p=0.3).Fig. 2D. shows mean subjects' accuracy across blocks of the predictiontask for older and young groups.

Neuroimaging results

Task-related activationsComparison of brain activities in the prediction and control

conditions for both groups revealed activations within the rightdorsolateral prefrontal cortex, bilateral inferior parietal lobule, medialfrontal cortex, right lateral orbitofrontal region, bilateral middleoccipital gyrus, right inferior temporal gyrus, and the left cerebellum.The coordinates of activations and their extent are listed in Table 2and depicted in Fig. 3A.

time of older and young adults in the prediction and control tasks. (C) Meanmaximizingthe prediction task.

Table 3Age-related changes of activations in the prediction task.

Brain area Coordinates t-value Cluster-size

x y z

Age-related decreasesR inferior parietal lobulea 48 −32 40 4.91 190

Abbreviations: R, right.a Cluster level corrected, pb0.05.

Table 2Activations in the prediction task which were common in both the older and youngadults.

Brain area Coordinates t-value

Cluster-size

x y z

R superior frontal gyrus 46 36 32 12.3 217338 24 52 9.95

R medial frontal cortex 6 30 42 9.38R inferior parietal lobule 50 −54 48 11.1 2229

42 −72 40 9.0246 −38 40 8.12

R middle frontal gyrus 38 52 −4 10.4 173R lateral orbitofrontal 44 46 −10 8.25L middle occipital gyrus −14 −98 −10 8.76 79L inferior parietal lobule −52 −54 44 8.65 207

−46 −42 42 7.29L cerebellum −32 −66 −38 8.48 115

−18 −88 −26 7.91R middle occipital gyrus 16 −88 −12 8.14 196

16 −100 0 7.85R insula 30 22 −2 8.09 101R inferior temporal gyrus 62 −26 −22 7.51 14R precuneus 8 −68 42 6.90 64L middle frontal gyrus −38 52 10 6.45 13

Abbreviations: L, left; R, right. Corrected for multiple comparison, pb0.05, extent of atleast 10 voxels.

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Age-related activationsThe right inferior parietal lobule was the only brain region that

showeda significant age-relateddecrease inactivitywhile no significantage-related increases in brain activity were observed. The extent of theage-related decrease in activity of the right inferior parietal lobule isdepicted in Fig. 3B. Fig. 3C shows the beta estimates of activity of theright inferior parietal lobule in the prediction task compared to thecontrol condition for young and older adults. The coordinates of age-related activations and their extents are listed in Table 3. The results ofcorrelation analysis showed no significant correlation between theobserved parietal activity and subjects' maximizing scores neither inolder (p=0.26) nor in the young group (p=0.38).

Fig. 3. (A) Task-related activations which were common in both the older and young adults (decreases in brain activity (cluster level corrected, pb0.05). (C) Beta estimates of activity in thyoung and older adults.

Discussion

In this study, we tried to investigate the effects of normal aging onthe neural correlates of decision making under uncertainty wheninformation about the stimuli's probability of occurrence was given tothe subjects in advance. Comparison of the mean accuracy in the lasttwoblocks of theprediction taskwith that in thefirst twoblocks showedno significant difference; confirming our hypothesis that learning is nota prerequisite in the implemented prediction task. The behavioralresults showed that themean reaction time was significantly greater inthe prediction task than in the control condition in both groups of olderand young adults; implying that both groups of subjects were engagedin decision making and reasoning in the prediction task in order toimprove their performance.

As was predicted, the performance of older adults in the predictiontask was not impaired compared to young adults; a finding thatcorroborates the results of recent psychological studies suggesting thatolder adults preserve their decision making performance wheninformation about the contingencies of the outcomes is available(Kovalchik et al., 2005; Zamarian et al., 2008). In addition, the results ofthe questionnaire survey showed that the majority of young adultsexploited a “probability matchingwith gambling fallacy” strategywhilethemajority of older adults took a different strategy (a pure “probabilitymatching” strategy) during decision making in the prediction task. Theformer strategy implies that younger adults predicted the more likelychoice on most of the trials (i.e. maximizing behavior) but after a longrun of the more likely choice, they committed the gambler's fallacy and

corrected for multiple comparison, pb0.05, extent of at least 10 voxels). (B) Age-relatede right inferior parietal lobule in the prediction task (compared to control condition) for

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chose the lower probability one. While the latter strategy suggest thatolder adults made predictions according to the frequency of occurrenceof the events; a behavior that is a pure probability matching strategy.Moreover, the performance results revealed a significant difference inmaximizing scores between older and young groups. The latter findingis consistent with the results of previous psychological studies of agingthat suggest older adults show a smaller response bias toward thereward-maximizing choice than do young adults in probabilisticdecision making tasks (Deakin et al., 2004; Denburg et al., 2005;Kovalchik et al., 2005; Tripp and Alsop, 1999).

The neuroimaging results showed that a network of brain regionscomprising the dorsolateral prefrontal cortex, inferior parietal lobule,medial frontal cortex, and right lateral orbitofrontal cortex areinvolved in decision making under uncertainty in both groups ofolder and young adults. While both groups had equal performance inthe prediction task, neuroimaging results revealed a significant age-related decrease in the activity of right inferior parietal lobule.Although we did not observe any significant correlation between theright inferior parietal activity and subjects' maximizing scores, wespeculate that the observed age-related decrease in parietal activitymay arise from differences in decision making behavior between thetwo groups. Since we did not observe any significant compensatoryneural mechanism elsewhere in the brain, we speculate that thestrategy exploited by older adults seems to be more efficient than theone used by young adults. However, further evidence is required tojustify this claim. Together, the observed results provide behavioraland neural evidence for preservation of decision making in theabsence of learning in old age.

Task-related activations

The observed activities in the dorsolateral prefrontal cortex, inferiorparietal lobule, medial frontal cortex, and right lateral orbitofrontalcortex, associated with decision making under uncertainty in bothgroups of older and young adults, are consistent with findings of theprevious neuroimaging study on young healthy adults (Elliott et al.,1999). Previous neuroimaging studies of decision making suggest thatdorsolateral prefrontal cortex is involved in response selection (Elliottet al., 1999; Paulus et al., 2001) as well as in complex/manipulativeworking memory operations (Elliott and Dolan, 1998; Lie et al., 2006).The observed activation in themedial frontal cortex could be attributedto action monitoring and the selection of action sets (Rushworth et al.,2004, 2007). In a prediction task inwhich subjectswere concernedwiththe outcome of their predictions, the medial frontal cortex provideddynamic monitoring of their responses and corresponding effects (Luuet al., 2000). We have also observed activity in the right lateralorbitofrontal cortex in the prediction task. Previous neuroimagingstudies suggest that the medial OFC maintains steady stimulus–outcome associations, whereas the lateral OFC represents unsteadyoutcomes to prepare for response shifts (Elliott et al., 2000; McClureet al., 2004; Windmann et al., 2006). Since the very nature of theprediction task requires subjects to switch back and forth dependingupon runs of one event or the other, the observed activity in the rightlateral orbitofrontal cortex may reflect signaling the shift in response.

Age-related activations

While performance of the older group was not impaired in theprediction task, a significant age-related decrease in the activity of theright inferior parietal lobule has been observed.

Volumetric brain imaging studies reported that the parietal lobeshows the second steepest rate of atrophy with an average declinerate of between 0.34 and 0.90% per year (Resnick et al., 2003; Razet al., 2005). There is also evidence that within the parietal cortex, theinferior parietal lobule shows the steepest rate of decline (Raz, 2005).In spite of the age-related atrophy, evidence regarding age-related

changes in the activity of inferior parietal regions is controversial(Cabeza et al., 2004; McEvoy et al., 2001; Milham et al., 2002). Cabezaet al. (2004) reported an age-related increase in right inferior parietalactivity in a working memory task. However, their study focused onverbal working memory processes and they reported weaker age-related activity in visual occipital regions along with the observedincreased parietal activity. Therefore, the increased right parietalrecruitment in older adults in a verbal working memory task couldreflect a compensatory mechanism, as they suggested. Milham et al.(2002) in a neuroimaging study of attentional control reported anage-related decrease in activity in the same brain region along withage-related increases in the activity of ventral visual processingregions and attributed the age-related decline in parietal activity topossible impairments in the implementation of attentional control inolder participants. In the present study, we did not find any significantage-related increase in activity in other brain regions. Considering thepreserved performance of older adults, the observed age-relateddecrease in inferior parietal activity is unlikely to be related to age-related decline in attentional control. On the other hand, analysis ofsubjects' reaction time in the prediction task revealed a significantmain effect for age. However, since we did not observe any significantgroup by condition interaction, the observed age-related decrease inparietal activity may not be related to age differences in reaction time.

Previous studies on monkeys indicated that neurons within theposterior parietal cortex provide codes for decision variables such asexpected value and probabilities (Dorris and Glimcher, 2004; McCoyand Platt, 2005; Platt and Glimcher, 1999; Sugrue et al., 2004).Neuroimaging studies on humans also reported the involvement ofinferior parietal regions in decision making under uncertainty(Huettel et al., 2005, 2006; Vickery and Jiang, 2009). Huettel et al.(2005) suggest that parietal regions support the generation andmodification of a set of context-appropriate responses. Otherneuroimaging findings suggest that activity in this brain region isassociated with exploratory decision making, relative subjectivedesirability of action, and confidence associated with a decision(Daw et al., 2006; Dorris and Glimcher, 2004; Kiani and Shadlen,2009). These findings suggest that the observed age-related decreasein inferior parietal activity could reflect differences in decisionmakingbehavior between the two groups. This idea is also supported by theresults of a previous neurophysiological study that reported an age-related decrease in P300 amplitude over parietal regions duringperformance of a sustained attention working memory task (McEvoyet al., 2001). They suggested, without showing any behavioralevidence, that younger adults use a strategy that relies more onparietal regions, whereas older subjects appear to use a strategy thatrelies more on frontal regions. This idea is in line with the result of arecent neuroimaging study that reported an association betweenactivity in parietal control regions and the strategy that maximizes theoverall probability of winning in an economic decision making task(Venkatraman et al., 2009).

In summary, a two-choice prediction paradigm in conjunctionwith fMRI has been used to examine the effects of healthy aging on theneural mechanisms underlying decision making under uncertaintywhen information about the contingencies of each possible outcomewas available. As was hypothesized, performance of older adults inthe prediction task was not impaired, compared to young adults.Neuroimaging results showed that both older and young groupsrecruited a common network during decision making under uncer-tainty in the absence of learning. The only brain region that showedsignificant age-related difference in activity was the right inferiorparietal lobule. We speculate that the observed age-related decreasein parietal activity could be explained by age-related differences indecision making behavior revealed by questionnaire results andmaximizing scores. Together, the results provide behavioral andneural evidence for preservation of decision making in the absence oflearning in old age. However, further evidence is required to clarify

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the linkage between age-related differences in parietal activity anddecision making behavior.

Acknowledgments

This study was supported by a joint research program betweenTohoku University and Toyota Motor Corporation.

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