Behavioral assessment

Parents filled out the Child Behavior Check List (CBCL, Achenbach, 1991) for participants younger than 18years, in order to screen for psychiatric conditions. All participants scored below clinical levels on all subscales of the CBCL, and had scores within 1 SD of the mean of a normative standardized sample.

Participants completed two subscales (similarities and block design) of either the Wechsler Adult Intelligence Scale (WAIS) or the Wechsler Intelligence Scale for Children (WISC) in order to obtain an estimate of their intelligence quotient (Wechsler, 1991, 1997). There were no significant differences in estimated IQ scores between the different age groups, F(2, 66)=1.63, p=0.20 (see Table ​Table11).

Task procedure

Data acquisition

Participants were familiarized with the scanner environment on the day of the fMRI session through the use of a mock scanner, which simulated the sounds and environment of a real MRI scanner. Data were acquired using a 3.0T Philips Achieva scanner at the Leiden University Medical Center. Stimuli were projected onto a screen located at the head of the scanner bore and viewed by participants by means of a mirror mounted to the head coil assembly. First, a localizer scan was obtained for each participant. Subsequently, T2*-weighted Echo-Planar Images (EPI) (TR=2.2s, TE =30ms, 80×80 matrix, FOV=220, 35 2.75mm transverse slices with 0.28mm gap) were obtained during two functional runs of 232 volumes each. The first two scans were discarded to allow for equilibration of T1 saturation effects. A high-resolution T1-weighted anatomical scan and a high-resolution T2-weighted matched-bandwidth high-resolution anatomical scan, with the same slice prescription as the EPIs, were obtained from each participant after the functional runs. Stimulus presentation and the timing of all stimuli and response events were acquired using E-Prime software. Head motion was restricted by using pillow and foam inserts that surrounded the head.

fMRI data analysis

Data were preprocessed using SPM5 (Wellcome Department of Cognitive Neurology, London). The functional time series were realigned to compensate for small head movements. Translational movement parameters never exceeded 1 voxel (<3mm) in any direction for any subject or scan. There were no significant differences in movement parameters between age groups F(2, 65)=0.152, p= 0.85, (see Table ​Table1).1). Functional volumes were spatially smoothed using a 6mm full-width half-maximum Gaussian kernel. Functional volumes were spatially normalized to EPI templates. The normalization algorithm used a 12 parameter affine transformation together with a nonlinear transformation involving cosine basis functions and resampled the volumes to 3mm cubic voxels. The MNI305 template was used for visualization and all results are reported in the MNI305 stereotaxic space (Cosoco et al., 1997), an approximation of Talairach space (Talairach and Tourneaux, 1988).

Statistical analyses were performed on individual participants’ data using the general linear model in SPM5. The fMRI time series data were modeled by a series of events convolved with a canonical haemodynamic response function (HRF). The presentation of the feedback screen was modeled as 0-duration events. The stimuli and responses were not modeled separately as these occurred in one prior or overlapping EPI images as feedbackpresentation.

In the model, feedback was further subdivided into correct vs. alternative rule and positive vs. negative feedback. These trial functions were used as covariates in a general linear model, along with a basic set of cosine functions that high-pass filtered the data, and a covariate for run effects. The least-squares parameter estimates of height of the best-fitting canonical HRF for each condition were used in pair-wise contrasts. The resulting contrast images, computed on a participant-by-participant basis, were submitted to group analyses. At the group level, contrasts between conditions were computed by performing one-tailed t-tests on these images, treating participants as a random effect. We further performed voxelwise ANOVAs to identify regions that showed age-related differences in relation to feedback processing. We tested for linear increases (−1 0 1) and decreases (1 0 −1) in the contrasts specified below.

We applied AlphaSim (Ward, 2000) to calculate the appropriate threshold significance level and cluster size for the whole-brain analyses. A significance threshold of p<0.05, corrected for multiple comparisons was calculated by performing 10.000 Monte Carlo simulations in AlphaSim resulting in an uncorrected threshold of p<0.001, requiring a minimum of 24 voxels in a cluster. This threshold was used for all whole-brain analyses.

fMRI results positive versus negative feedback

Whole-brain comparisons across age groups

First, we identified the neural correlates of feedback processing by comparing the (positive feedback vs. negative feedback) contrast across all participants. This analysis revealed increased BOLD responses for positive feedback >negative feedback in several regions including the left and right caudate, left DLPFC and left parietal cortex (see Figure ​Figure2A).2A). The opposite contrast (negative >positive feedback) resulted in increased activation in the dACC. The coordinates for these comparisons (positive feedback vs. negative feedback) are reported in Table ​Table22.

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Figure 2

(A) Regions from the (positive vs. negative feedback) contrasts across all participants (B) Parameter estimates and standard errors for positive and negative feedback that followed either the correct or the alternative rule displayed for each age group in left DLPFC, left parietal cortex, dACC and left caudate. Significant differences between brain activity in two conditions are indicated with an asterisk (*Bonferroni corrected).

Table 2

Brain Regions revealed by whole-brain contrasts.

Anatomical region	L/R	voxel volume	Z	MNI coordinates
				x	y	z
POSITIVE>NEGATIVE
Striatum (ventral and dorsal)	L/R	774	7.49	−6	12	−3
Dorsolateral prefrontal cortex	L	71	4.61	−27	24	51
Superior parietal cortex	L	170	4.23	−30	−75	48
Precuneus	L/R	137	4.07	−3	−36	33
Ventral medial PFC	L/R	26	4.03	3	54	−12
Visual cortex	L/R	332	4.50	27	−93	−9
NEGATIVE>POSITIVE
Dorsal anterior cingulate cortex	L/R	63	4.43	9	21	36

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MNI coordinators for main effects, peak voxels reported at p <0.001, at least 24 contiguous voxels (SVC).

fMRI region of interest results for feedback ×rule ×age group interactions

Next, we tested for age differences and rule sensitivity in these regions by performing region of interest (ROI) analyses. The ROI analyses were restricted to the four a priori defined regions which emerged in the (positive vs. negative) contrast across participants: bilateral caudate, left DLPFC, left parietal cortex and dACC. In order to investigate whether there were age differences in how the statistical regularities learned by the participants had an effect on how feedback was processed we performed 3 ×2 ×2 ANOVAs testing for the interaction between valence (positive vs. negative) and rule (correct vs. alternative) as within-subjects factors and age (children, adolescents, adults) as the between-subjects factor for each ROI (see Figure ​Figure22B).

Feedback processing in adults

Our analysis of positive and negative feedback processing in a probabilistic environment demonstrated that feedback-related activity in the DLPFC, dACC and caudate was dependent on valence and information value. We started out with a general whole-brain comparison for positive versus negative feedback and used ROI analyses to explore the areas identified in this contrast. This analysis revealed that especially left DLPFC, dACC and bilateral caudate were sensitive to feedback ×rule context interactions. Before interpreting age differences in these activation patterns, we start out with the interpretation of feedback sensitivity observed in adults, which will set the stage for interpreting the developmental effects.

When exploring the data for adults separately, the results showed increased recruitment of DLPFC after receiving negative feedback following the alternative compared to the correct rule. Given that negative feedback after choosing the alternative, but not the correct, rule indicates the need for a switch in behavior, the adult findings are consistent with previous studies demonstrating negative feedback-related sensitivity in DLPFC for feedback that is important for subsequent behavioral adjustment (Kerns, 2006; van Duijvenvoorde et al., 2008; Zanolie et al., 2008) and not for negative feedback per se.

Besides DLPFC, the parietal cortex has previously been implicated in feedback processing (Crone et al., 2008, van Duijvenvoorde et al., 2008) and implementing cognitive control as part of the fronto-parietal network (Brass et al., 2005; Bunge et al., 2002; Dosenbach et al., 2008). In support of this hypothesis our whole-brain analyses revealed that the left superior parietal cortex was involved in feedback processing. However, in contrast with previous studies (van Duijvenvoorde et al., 2008), our subsequent post hoc analyses could not confirm a strong contribution of the superior parietal cortex. Possibly, the parietal cortex was more engaged in prior studies because these involved trial-to-trial learning, whereas in the current study we investigated feedback processing when rules were already learned. Future research is necessary to elucidate the role of the superior parietal cortex in feedback processing in relation to learning.

The analyses of dACC revealed a very similar activation pattern as DLPFC, however the dACC activation pattern in adults was more supportive of a general increase in activity after negative feedback regardless of rule type. Possibly, this finding indicates that, at least in adults, the dACC has a more general role in processing negative feedback; both in terms of detecting general conflict (Brown and Braver, 2005) and signaling the need for behavior change (Holroyd and Coles, 2008; Rushworth, 2008).

Finally, the caudate nucleus also showed sensitivity to feedback and rule type, but this region was more active after positive compared to negative feedback when participants chose the alternative rule. Given that this effect was specific for positive feedback, and that the probability for positive feedback for the alternative rule was low, the signal in the caudate could reflect a positive prediction error; i.e., signaling that the outcome is better than predicted (for review see Schultz, 2007).

Together, analysis of the adult activation pattern confirms prior findings showing that DLPFC and dACC are sensitive to negative feedback and the caudate is sensitive to positive feedback, but the findings further elucidate that these neural responses are dependent on the extent to which these feedback signals provide a learning signal of future performance. That is, DLPFC and caudate responses were more pronounced after selecting the incorrect rule which had a low probability of resulting in positive feedback, but which may have been important to explore. In contrast, when applying over-learned high probability rules, DLPFC and caudate were less involved, possibly because the informative value was smaller.

Feedback processing: Developmental comparisons

The neural activation patterns described above were differentially sensitive to age modulations. The first notable finding is that of differential activation patterns in the DLPFC. All participants, regardless of age, showed increased recruitment of DLPFC when choosing the alternative rule compared to the correct rule. However, children, but not adults, showed more activation in DLPFC after positive feedback when choosing the alternative rule. In contrast, adults, but not children, showed more activation in DLPFC after negative feedback when choosing the alternative rule. Adolescents seemed to be in a transition phase, because their neural response to positive feedback was similar to that observed in children, but their neural response to negative feedback was similar to that observed in adults. Thus, consistent with prior studies, these developmental differences indicate a shift from focus on positive to a focus on negative feedback with age (Somsen, 2007; Crone et al., 2008; van Duijvenvoorde et al., 2008), which appears to continue across adolescence. In addition, the current results extend previous findings by showing that developmental differences in neural responses to feedback are not related to valence per se, but suggest an age-related change in processing learning signals with different informative value.

In contrast, for all age groups the caudate nucleus was more active for positive compared to negative feedback, in particular when participants chose the alternative rule. This finding indicates that part of the feedback processing network, which is implicated in processing statistical regularities of reward (Schultz, 2007) matures already at an early age, whereas the part of the network that is involved in processing negative feedback and the subsequent control of behavior has a more protracted developmental time course. These findings are consistent with prior reports using cognitive tasks, as these studies have also reported early maturation of subcortical regions and protracted development of cortical brain areas (Casey et al., 2004; van Duijvenvoorde et al., 2008; Velanova et al., 2008). It should be noted that other developmental studies have reported increased sensitivity of the striatum in early adolescence, however, these studies have employed paradigms with a more affective content, such as gambling tasks with real monetary rewards or emotion recognition (Ernst et al., 2005; Galvan et al., 2006; McClure-Tone et al., 2008; van Leijenhorst et al., 2009). In future studies, it will be of interest to examine whether the caudate activation can be modulated by the use of affective task modulations when learning rules or processing performance feedback.

Adaptive behavior and brain activation across development

One of the challenging questions for future studies is how the neural activation is associated with trial-to-trial learning. For example, we did not observe age differences in general learning performance, despite differences in neural activation. This was unexpected, and again demonstrates that differences in neural activation can be present without differences in observable behavior (Ladouceur et al., 2004). However, consistent with prior studies, the sequential analyses revealed that with age, participants became better at using the negative feedback signals to adjust their behavior on subsequent trials (Crone and van der Molen, 2004). As expected, when receiving positive feedback after having applied the correct rule, participants were more likely to stay and select the same stimulus on the subsequent trial. Likewise, when receiving negative feedback after having applied the incorrect alternative rule, participants were more likely to shift and select the correct stimulus on the subsequent trial. Overall, adults appeared better at optimizing than adolescents, and adolescents performed better than children. Based on these findings, in combination with the developmental differences in neural activation, the data are supportive of a linear increase across adolescence. Although these findings differ from earlier reports which have showed larger differences in early adolescence than in later adolescence (e.g. Ladouceur et al. 2004) the findings are consistent with prior fMRI results showing late changes in brain activation and behavior (e.g. Scherf et al., 2006; van Duijvenvoorde et al., 2008).

Intriguingly, even though children were more likely than adults to shift after receiving negative feedback when applying the correct rule, they were also more likely to stay after receiving negative feedback when applying the incorrect alternative rule. The reason for this behavioral pattern is still unclear, but it is possible that children waited with shifting when applying the incorrect alternative rule until they received positive feedback (20%). Future research should use task manipulations that allow for further investigation of this hypothesis.

We performed exploratory analyses to investigate the relation between brain activity and win-stay, lose-shift behavior, although it should be noted that these analyses are preliminary as our study design was not optimized to test for these differences. The analyses on the ROIs identified in the main analyses revealed that, consistent with prior research, dACC and left DLPFC activity predicted behavioral adjustment on the subsequent trial in adults (Kerns et al., 2004; Jocham et al., 2009). However, this pattern was observed for both rule types and appeared independent of feedback valence. Possibly, the dACC and left DLPFC were important for trial-by-trial adjustment (Kerns et al., 2004). We found a similar pattern in adolescents, but only when applying the correct rule. We failed to find similar relations in children, which may indicate that the neural mechanisms that facilitate future behavioral adjustment are still immature or that they employed different strategies to perform the task. These interpretations are consistent with an ERP study showing increased error related negativity across adolescence (Ladouceur et al., 2007). Furthermore, the same study showed that only in adults the ERN amplitude was related to task performance.

The current study is limited by the relatively small number of trials for some of the contrasts examining the neural correlates of shifting behavior. Future studies should make use of tasks that are optimized for studying these developmental differences in more detail.

In addition, a challenging direction for future research will be to investigate the developmental differences in the learning phase. The combined use of computational reinforcement learning models (Klein et al., 2007) with imaging techniques could be a promising endeavor to parse out the developmental changes in different phases of learning (e.g. learning rate) and their neural correlates. These methods could be combined with trial-to-trial data categorization to understand how the observed developmental change in sensitivity from positive to negative feedback hinders or facilitates learning locally versus oriented towards future goals.