Frontal networks for learning and executing arbitrary stimulus-response associations.
ABSTRACT Flexible rule learning, a behavior with obvious adaptive value, is known to depend on an intact prefrontal cortex (PFC). One simple, yet powerful, form of such learning consists of forming arbitrary stimulus-response (S-R) associations. A variety of evidence from monkey and human studies suggests that the PFC plays an important role in both forming new S-R associations and in using learned rules to select the contextually appropriate response to a particular stimulus cue. Although monkey lesion studies more strongly implicate the ventrolateral PFC (vlPFC) in S-R learning, clinical data and neurophysiology studies have implicated both the vlPFC and the dorsolateral region (dlPFC) in associative rule learning. Previous human imaging studies of S-R learning tasks, however, have not demonstrated involvement of the dlPFC. This may be because of the design of previous imaging studies, which used few stimuli and used explicitly stated one-to-one S-R mapping rules that were usually practiced before scanning. Humans learn these rules very quickly, limiting the ability of imaging techniques to capture activity related to rule acquisition. To address these issues, we performed functional magnetic resonance imaging while subjects learned by trial and error to associate sets of abstract visual stimuli with arbitrary manual responses. Successful learning of this task required discernment of a categorical type of S-R rule in a block design expected to yield sustained rule representation. Our results show that distinct components of the dorsolateral, ventrolateral, and anterior PFC, lateral premotor cortex, supplementary motor area, and the striatum are involved in learning versus executing categorical S-R rules.
- SourceAvailable from: Rinaldo Livio Perri[Show abstract] [Hide abstract]
ABSTRACT: The study investigates the neurocognitive stages involved in the speed-accuracy trade-off (SAT). Contrary to previous approach, we did not manipulate speed and accuracy instructions: participants were required to be fast and accurate in a go/no-go task, and we selected post-hoc the groups based on the subjects' spontaneous behavioral tendency. Based on the reaction times, we selected the fast and slow groups (Speed-groups), and based on the percentage of false alarms, we selected the accurate and inaccurate groups (Accuracy-groups). The two Speed-groups were accuracy-matched, and the two Accuracy-groups were speed-matched. High density electroencephalographic (EEG) and stimulus-locked analyses allowed us to observe group differences both before and after the stimulus onset. Long before the stimulus appearance, the two Speed-groups showed different amplitude of the Bereitschaftspotential (BP), reflecting the activity of the supplementary motor area (SMA); by contrast, the two Accuracy-groups showed different amplitude of the prefrontal negativity (pN), reflecting the activity of the right prefrontal cortex (rPFC). In addition, the post-stimulus event-related potential (ERP) components showed differences between groups: the P1 component was larger in accurate than inaccurate group; the N1 and N2 components were larger in the fast than slow group; the P3 component started earlier and was larger in the fast than slow group. The go minus no-go subtractive wave enhancing go-related processing revealed a differential prefrontal positivity (dpP) that peaked at about 330 ms; the latency and the amplitude of this peak were associated with the speed of the decision process and the efficiency of the stimulus-response mapping, respectively. Overall, data are consistent with the view that speed and accuracy are processed by two interacting but separate neurocognitive systems, with different features in both the anticipation and the response execution phases.Frontiers in Behavioral Neuroscience 07/2014; 8(251). · 4.16 Impact Factor
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ABSTRACT: The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.Frontiers in Neuroscience 06/2014; 8:132.
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ABSTRACT: Cognitive deficits are recognized in Parkinson's disease. Understanding cognitive functions mediated by the striatum can clarify some of these impairments and inform treatment strategies. The dorsal striatum, a region impaired in Parkinson's disease, has been implicated in stimulus-response learning. However, most investigations combine acquisition of associations between stimuli, responses, or outcomes (i.e., learning) and expression of learning through response selection and decision enactment, confounding these separate processes. Using neuroimaging, we provide evidence that dorsal striatum does not mediate stimulus-response learning from feedback but rather underlies decision making once associations between stimuli and responses are learned. In the experiment, 11 males and 5 females (mean age 22) learned to associate abstract images to specific button-press responses through feedback in Session 1. In Session 2, they were asked to provide responses learned in Session 1. Feedback was omitted, precluding further feedback-based learning in this session. Using functional magnetic resonance imaging, dorsal striatum activation in healthy young participants was observed at the time of response selection and not during feedback, when greatest learning presumably occurs. Moreover, dorsal striatum activity increased across the duration of Session 1, peaking after most associations were well learned and was significant during Session 2 where no feedback was provided, and therefore no feedback-based learning occurred. Preferential ventral striatum activity occurred during feedback and was maximal early in Session 1. Taken together, the results suggest that the ventral striatum underlies learning associations between stimuli and responses via feedback whereas the dorsal striatum mediates enacting decisions.NeuroImage 07/2014; · 6.13 Impact Factor
S-R mapping rules that were usually practiced before scanning. Humans learn these rules very quickly, limiting the ability of imaging
Humans possess a marked aptitude for learning arbitrary stimu-
lus–response (S-R) associations, one hallmark of our cognitive
ioral repertoire in responding to stimuli we encounter and to
retain knowledge of contextually optimal responses to particular
stimuli. Successfully making S-R associations depends on keep-
ing in mind contextual details relevant to stimulus perception or
response choice, as well as salient previous experiences. The use
of this ability is vastly expanded by our capacity for grouping
stimuli that require the same response, a form of categorical
tal lobes, especially the dorsolateral prefrontal cortex (dlPFC)
associative “rules” depends on intact prefrontal and premotor
cortices in both humans (Petrides, 1985, 1990, 1997; Halsband
and Freund, 1990), and monkeys (Halsband and Passingham,
see Murray et al., 2000). Electrophysiological recordings in the
monkey PFC indicate that such associations are highly plastic,
facilitating flexible behavioral change with context (Chen and
Wise, 1995, 1996; Asaad et al., 1998, 2000). Mechanistically, the
PFC is hypothesized to form representations of new S-R associa-
tions and to use existing representations to bias response selec-
tion, given a particular stimulus (Murray et al., 2000; Miller and
Despite the fact that several of the clinical and non-human
primate studies noted above implicate both dorsal and ventral
(vlPFC) regions of the lateral PFC in S-R learning, previous hu-
man functional neuroimaging studies of such tasks have not
found reliable dlPFC activation (Deiber et al., 1997; Toni and
Passingham, 1999; Toni et al., 2001). However, in two of these
the study by Deiber et al., received no feedback during scanning.
scanning but to strengthen newly established associations. Toni
et al. (2001) examined activity during a task identical to that in
their original study (with minimal prescan practice), and al-
though they identified a site in the dlPFC showing decreasing
activity over the scan session only for the new rule condition,
this effect could be attributed to novelty adaptation. More-
over, all previous imaging studies of S-R learning have been
limited to simple one-to-one mapping of a handful of stimuli
onto an equal number of possible manual responses. Humans
learn these associations very quickly, limiting their use for
examining rule acquisition.
This work was supported by National Institutes of Health Grants MH63901 and NS40813 (M.D.), the Wheeler
Correspondence should be addressed to Dr. Charlotte A. Boettiger, Ernest Gallo Clinic and Research Center,
University of California, San Francisco, 5858 Horton Street, Suite 200, Emeryville, CA 94608. E-mail:
TheJournalofNeuroscience,March9,2005 • 25(10):2723–2732 • 2723
To focus on the learning process, we designed the present
functional magnetic resonance imaging (fMRI) study, in which
subjects learned higher-order S-R rules. Abstract visual stimuli
were grouped into sets by non-obvious principles that were dif-
arbitrarily assigned manual response. With this paradigm, we
subjects learn some rules before the scan session and others dur-
ing the scan session. Furthermore, we controlled for stimulus
novelty effects in the learning condition by including an addi-
tional pair of sets that covertly lacked a higher-order grouping
higher-order S-R rule acquisition and utilization.
Participants. Fourteen healthy right-handed subjects (six females; mean
age, 27 years old; range, 19–35 years old) from the San Francisco Bay
Area volunteered and were paid for their participation. All subjects gave
written, informed consent before participation in the study, in accor-
for medical, neurological, and psychiatric illness and for psychoactive
Behavioral task. The two-fold aim of the behavioral task was to (1)
identify neural circuits recruited for learning a demanding categorical
S-R association task and (2) to identify circuits engaged by executing
previously learned rules of this type. Subjects performed an associative
S-R task in which sets of visual stimuli mapped onto one of four manual
response buttons. Individual abstract visual stimuli were presented for
750 ms (visual angle, ?4°), and the subject had 1 s from the stimulus
onset to make a manual button press response. After 1 s, a feedback
screen appeared for 300 ms, indicating “correct,” “incorrect,” or “no
response.” Incorrect and no-response trials were both counted as errors.
Responding was monitored on-line, and all subjects were compliant,
with minimal response omissions. Stimuli were presented serially in 20 s
block consisted of 15 stimuli selected randomly from a pair of stimulus
sets (set size: 10 each, for a total of 20 possible stimuli per block).
Stimulus set construction. To ensure that the stimulus properties were
as closely matched as possible, we designed stimulus sets that were iden-
tically sized and shaped and used identical subcomponents but that var-
ied in the arrangement of these components across sets. Thus, sensory
familiar conditions. As can be seen in Figure 1B, no set members shared
any subcomponents, but rather for each stimulus set, a unique constel-
lation of subcomponents varied together in color across set members.
Grouping or categorizing the members of each stimulus set required
detecting and remembering the unique constellation of covarying ele-
ments. Stimulus sets were constructed as follows. For each set, a ran-
were generated from this matrix using 10 different color maps of the
same 10 isoluminant hues (Matlab 6.1; Mathworks, Natick, MA). Thus,
each member of the set shared no commonly colored squares but rather
sets are shown in Figure 1B.
Task training. Subjects learned arbitrary S-R mappings for two block
the scan session, with all subjects reporting at least one full night of sleep
between training and testing, ensuring adequate consolidation of learn-
sets and the S-R rules acquired during training as “familiar” (FAM). Each
set was assigned randomly to one of four possible manual response but-
tons. The categorical rules were not stated explicitly and were instead
learned by trial and error. Subjects were instructed to learn the correct
response associated with each stimulus and were told that some of the
stimuli were related by rules that, if acquired, would help them learn the
associations more quickly.
Subjects were required to reach a performance criterion of 90% accu-
racy for both blocks; all subjects tested were able to do so. Training to
criterion took an average of seven blocks of 40 trials each for these sub-
jects, corresponding to a total training duration of ?15 min. A few sub-
block type (order was counter-balanced across subjects), but once a suc-
cessful strategy was established, learning the associative rule for the sec-
ond block type was always more rapid. All subjects were required to
demonstrate retention (accuracy, ?90%) of the learned rules immedi-
ately before the functional scanning session. Subjects practiced the pre-
viously learned sets in the magnet during shimming and anatomical
scans, which lasted 15–20 min, immediately before functional scanning.
time to reach the performance criterion of 90% accuracy of two practice
blocks per block type.
Task conditions. During the scan session, there were three types of
experimental blocks, each composed of two stimulus sets. FAM blocks
ticed on the day of the scan. “Novel” (NOV) blocks were identical in
blockswereincludedineachscansession).Although NOVand FAMblocks
differed in terms of learning, they also differed in degree of stimulus
novelty. Thus, detected differences in the blood oxygenation level-
dependent (BOLD) signal between these two conditions could simply
al., 2000). To control for this possibility, we included a condition with
two stimulus sets composed of novel but unrelated stimuli. In these
2724 • J.Neurosci.,March9,2005 • 25(10):2723–2732 BoettigerandD’Esposito•RuleLearningintheFrontalCortex
blocks, no rule bound the set members together, prohibiting subjects
from acquiring a rule to facilitate accurate performance. Subjects were
not warned in advance that such a set was included. “No rule” (NR)
blocks consisted of 20 independent stimuli that were each generated
from a different underlying matrix, with 10 arbitrarily assigned to one
given block type, individual members of the two sets were intermixed
randomly. Each scanning run consisted of 18 blocks (for a total of ?6
min), including three of each experimental block type and 3 fixation/
baseline blocks, for a total of 18 blocks per run, presented in random
order. At the end of each run, the subject was provided with the overall
accuracy for that run. Subjects were awarded increasing monetary bo-
nuses for exceeding each of three percentage levels (60, 70, and 80%).
This served to motivate accurate performance in the FAM blocks, to spur
rapid learning of the NOV blocks, and to discourage giving up during the
36 min of functional scan time. To maximize the chances of detecting
of learning across several scanning runs. The behavioral results from the
scanning session indicate that our design served the intended purpose.
The majority of subjects participated in a second (later) experiment
within the same scan session, the results of which will be reported
Data acquisition. MRI was performed on a Varian/Inova (Palo Alto,
CA) whole-body 4T scanner that was equipped with echo-planar imag-
ing. For all experiments, a standard radiofrequency (RF) head coil was
subject responses via a magnet-compatible fiber-optic keypad. An LCD
projector (Epson, Long Beach, CA) projected stimuli onto a backlit pro-
subjects viewed via a mirror mounted within the head coil.
time, 22 ms; flip angle, 20°) was used to detect BOLD contrast. The
correction to reduce Nyquist ghosts. Three-millimeter coronal slices
were acquired to facilitate coverage of the ventral/orbital PFC. In-plane
resolution was 3 ? 3 mm, yielding isotropic voxels. Twenty-two slices
with a 0.5 mm interslice gap were acquired (field of view, 19.2 cm2),
which accommodated complete coverage of the frontal lobes. In most
postcentral gyrus. Each run was preceded by 20 s of dummy gradient RF
pulses to achieve steady-state tissue magnetization and to minimize
startle-induced motion in the functional data. Coplanar T1-weighted
anatomical images were acquired for each participant. In addition, an
weighted image was acquired for use in spatial normalization.
fMRI data processing. MRI data were processed off-line using the
VoxBo analysis package (http://www.voxbo.org/download.html). First,
images were reconstructed into Cartesian space and sinc interpolated in
time to correct for differences in slice time acquisition (Aguirre et al.,
1998). Next, we motion-corrected the data using a six-parameter, rigid-
slicewise motion compensation that removed spatially coherent signal
changes by applying a partial correlation method to each slice in time
(Zarahn, 1997). Next, an empirically derived threshold was applied to
remove extremely low-intensity voxels, a mask was applied to exclude
regions located outside the brain, and finally, a Gaussian smoothing
fMRI data analyses. Statistical analyses were performed within the
framework of the modified general linear model (GLM) (Worsley and
Friston, 1995) and included a 1/f model of temporal autocorrelation,
derived empirically for each subject (Zarahn et al., 1997). The model
included a design matrix with covariates for each block type convolved
with an empirically derived hemodynamic response function. A notch
filter removed frequencies below 0.03 Hz and above the Nyquist fre-
quency (0.227 Hz). For the brain-behavior and inter-region of interest
done using a model that specified a unique covariate for each block.
Filtering and convolution were as for the main analysis.
ROI analyses. Within subjects, we defined functional ROIs (fROIs)
within each of eight a priori defined anatomical regions, identified from
the T1-weighted anatomical images obtained from each subject, with
reference to standard brain atlases ( p ? 0.05; small volume corrected)
(Duvernoy, 1991; Tzourio-Mazoyer et al., 2002). These ROIs were cho-
sen based on previously published data and included the dlPFC [middle
cortex (FP), striatum, and three regions of the premotor cortex: medial
[supplementary motor area (SMA)/pre-SMA], dorsolateral premotor
area [dlPM; dorsal aspect of the superior frontal gyrus (SFG), including
portions of Brodmann areas (BA) 6 and 8], and ventrolateral premotor
area [vPM; precentral gyrus (PCG), proximal to the caudal terminus of
activation cluster in a contrast of NOV versus FAM blocks to identify areas
tively correlated with the acquisition of S-R rules, and FAM?NOV voxels,
putatively correlated with the execution or application of learned rules.
Robust Nov?Fam activations were found for all subjects in the right
dlPFC, midline SMA/pre-SMA, left vPM, and right striatum. Robust
FAM?NOV activations were found for all subjects in the left dlPM and left
FP ROIs. For each subject, we extracted the average response magnitude
foreachcovariatefromeachthesesixfROIs,and FAMand NOVblocktypes
were compared with a paired t test. By also comparing the NOV and NR
conditions, we were able to rule out simple stimulus novelty effects.
Because learning evolved over the course of a scan session, we predicted
magnitude, exclusively in the NOV blocks, across runs. Therefore, we
comparedtheparameterestimateswithin NOV?FAMand FAM?NOVvoxels
between the first two runs and the last two runs for each block type.
Average parameter estimates for each block were also extracted from
these six fROIs from the second GLM analysis described above.
Brain–behavior correlation analyses. Correlation of activations be-
tween fROIs on a block-by-block basis was calculated as Pearson’s cor-
form. Correlations between behavioral performance and fROI activity,
on a block-by-block basis, were calculated using the transformed accu-
racy scores (see below) for each block. These correlations were deter-
used to determine whether these correlations predicted the ability to
learn new S-R associations in this task, measured as overall accuracy on
Map-wise group analysis. Group statistical parametric maps were gen-
erated for the same contrasts of interest, using the results from the indi-
vidual subject analyses. Each subject’s response magnitude maps were
normalized into the standard space of the Montreal Neurological Insti-
tute (MNI) using SPM99 (http://www.fil.ion.ucl.ac.uk/spm), resampled
to a resolution of 2 mm2and additionally smoothed to yield a total
treating each subject as a random variable. Statistical significance was
defined by meeting a height threshold of p ? 0.005 (uncorrected; t
?3.01), with a minimum cluster size of eight contiguous voxels. This
relatively lenient threshold was chosen to allow direct comparison with
previous studies. As can be seen in Table 1, all but a few activations
actually meet the more stringent threshold of p ? 0.001 (t ? 3.85).
Analysis of behavioral data. Total accuracy was calculated for block,
condition, and experiment. For the purposes of parametric statistical
Subjects demonstrated excellent accuracy during the FAM blocks
blocks improved gradually over six runs (mean ? SEM; run 1,
36 ? 3%; run 6, 61 ? 5%) (Fig. 2), representing a significantly
BoettigerandD’Esposito•RuleLearningintheFrontalCortex J.Neurosci.,March9,2005 • 25(10):2723–2732 • 2725
chance for all subjects (group mean ? SEM; 35 ? 3%) (Fig. 2).
Two effective strategies are possible in the NR condition: (1)
subjects could attempt to learn the correct response associated
with each of the 20 stimuli, which represents a load well beyond
normal working memory capacity; and (2) subjects could iden-
sets and consistently press one of these buttons for all stimuli,
yielding chance performance (50%). By combining these two
approaches, subjects could potentially perform above chance.
Our results indicate that subjects were either using strategy 1 or
Debriefing of subjects after scanning and examination of behav-
ioral responses confirmed this inference.
In these analyses, we tested specific ROIs for activations corre-
lated with learning or executing rules. Anatomical ROIs were de-
human primate electrophysiology literature (see Materials and
contiguous activation clusters in a contrast of the Nov rule versus
Novel rule learning versus familiar rule execution
We found NOV selective (NOV?FAM) activations consistently
across subjects in four regions where the magnitude of BOLD
responses to the NOV blocks were significantly larger relative to
ples are shown in Fig. 3A. To test whether these differences re-
flected selectivity for learning per se and not effects of differing
degrees of uncertainty, stimulus novelty, or error feedback, we
fROIs between the NOV and NR conditions. We consistently
found greater activation for the NOV blocks (Fig. 3B), supporting
learning associative S-R rules. The significance levels were p ?
2 ? 10?4, p ? 0.03, p ? 8 ? 10?5, and p ? 0.01 for the MFG,
SMA, PCG, and striatum fROIs, respectively.
Although NOV?FAM activations identified by this novel rule
versus familiar rule contrast are putative sites of rule acquisition,
FAM?NOV activations are predicted to be involved in recalling,
maintaining, or implementing previously learned rules. We ob-
served such activations, consistently across subjects, in two left
frontal regions: the left FP and the dorsal premotor area (dPM)
level of certainty, stimulus familiarity, or positive feedback were
driving this effect, we again compared the response magnitudes
within these fROIs from the NOV and NR conditions. We consis-
tently observed relatively greater BOLD signal in the NOV condi-
tion in the FP fROIs ( p ? 0.004) (Fig. 3D), supporting a contri-
bution to rule representation or implementation in this brain
area. However, the differences in the SFG between the NOV and
NR conditions showed only a trend toward a significant differ-
ence ( p ? 0.16) (Fig. 3D).
Nov?Fam temporal decay
as the rule is acquired, activity in these areas would decrease.
Moreover, if the activity were strictly learning related, we would
text of NOV blocks. Supporting this hypothesis, activations in the
MFG and the SMA fROIs show significant decreases from the
early to late runs only for the NOV condition (Fig. 4, top panels).
However, activity in the PCG and striatum, although clearly se-
lective for the learning condition, did not fit this profile, indicat-
ing that these regions likely play a qualitatively different role in
the rule-learning process.
A second prediction regarding sites engaged in rule learning is
that the BOLD responses in such areas would be inversely corre-
lated with accuracy for the NOV (i.e., learning) condition but not
for the two nonlearning control conditions. In other words, we
MFG Right 40,42,26
and late). The data represent all sessions (n ? 14). Plots show the mean ? SEM for each
condition.E, FAM;?, NOV;,NR.
2726 • J.Neurosci.,March9,2005 • 25(10):2723–2732BoettigerandD’Esposito•RuleLearningintheFrontalCortex
we found such correlations in the right MFG, the SMA, and the
left PCG (Fig. 5A). A weaker correlation between NOV block ac-
variability across subjects in the strength of these correlations,
which led us to investigate whether a subject’s BOLD-NOV accu-
rules. Indeed, there was a significant correlation between BOLD-
NOV accuracy Z-scores and overall accuracy in the NOV condi-
tions, an index of a subject’s facility at learning the new rules, in
the SMA, MFG, and PCG fROIs (Fig. 5B) (R2? 0.47, p ? 0.007;
R2? 0.59, p ? 0.002; and R2? 0.34, p ? 0.03, respectively).
high and low learners in their BOLD-NOV accuracy Z-scores in
the MFG, SMA, and PCG (unpaired t tests; p ? 0.008, p ? 0.02,
and p ? 0.03, respectively) (Fig. 5C). The striatum fROI did not
only distinguished between good and poor learners in the NOV
learning condition, not in the FAM and NR control conditions
(Fig. 5D). These robust, condition-specific correlations between
with the hypothesis that these areas go “off-line” as successful
learning is achieved, and not simply because of time-dependent
effects unrelated to learning.
block-by-block basis and found strong
blockwise correlations between many of
the fROIs. For example, as shown for two
subjects in Figure 6A (left panels), the ac-
tivation sites in the right MFG and SMA
were strongly covarying during perfor-
mance of this task. All four of the
NOV?FAM fROIs demonstrated significant
positive correlations (maximum p ?
predictive value regarding subject perfor-
mance, we used a median split to divide
the subjects into high and low performers,
based on total accuracy scores. We found
that the correlations between two fROI
pairs significantly distinguished between
the high- and low-performing groups:
SMA/MFG and SMA/striatum (Fig. 6B).
Strong correlations between the SMA and
accurate performance of this task. The
toward distinguishing these two groups,
again with stronger correlations favoring
more accurate performance, but it did not
quite reach statistical significance ( p ?
0.11). Additional evidence that successful
performance of this task depends strongly
on coordination between the MFG and
SMA is found in the regression of SMA/
MFG correlation coefficients against total
accuracy scores (Fig. 6C). Here, we found
that the variations in the level of coactiva-
tion of these two fROIs across subjects could account for 63% of
the variance in subject accuracy scores. Although not conclusive,
these results suggest that the SMA is functionally engaged with
either directly or via indirect connections or common inputs.
Map-wise random-effects analyses
To uncover activations within areas with signal/noise character-
we used a map-wise exploratory analysis of modulation of the
BOLD response on the basis of S-R rule novelty. This approach
in the individual subject ROI-based analyses detailed above and
ysis uncovered additional consistent NOV?FAM activations in the
left MFG, the right PCG, the left anterior insula, the right IFG
(opercular portion), and the right IFS (Fig. 7, warm colors).
evidence that the vlPFC plays an important role in the formation
of S-R associations. Meanwhile, using known S-R rules was con-
firmed to increase activity in the left SFG and FP relative to new
rule learning. The map-wise analysis also uncovered two consis-
a more ventral site in the left insula, and the bilateral SFG. The
(C)-selective activity of representative single subjects overlaid onto a standardized anatomical image N?F, NOV?FAM; F?N,
ROI analysis. ROI delimited statistical parametric t maps contrasting NOV versus FAM activity. A, C, NOV (A)- and FAM
BoettigerandD’Esposito•RuleLearningintheFrontalCortexJ.Neurosci.,March9,2005 • 25(10):2723–2732 • 2727
premotor cortex (BA8B) and a portion of the dlPFC (BA9),
which is more cytoarchitectonically similar to BA8 than the rest
of the dlPFC (BA46 and BA9/46) (Petrides and Pandya, 1999).
Assignment of this activation to the prefrontal or premotor cor-
tex is thus ambiguous, however, based on (1) the consistent acti-
vations we observed in the dPM (BA6/8) of individual subjects,
(2) the anatomical connectivity of BA8B, but not BA9, with infe-
rior parietal areas, and (3) the previous implication of BA8B in
visuospatial function, we tentatively interpret these peaks as ros-
tralmost dPM. See Table 1 for peak coordinates and t values.
Our study investigated the neural correlates of learning abstract
S-R rules and applying such previously learned rules. Impor-
learning. These results help to reconcile discrepancies between
data from neurophysiological studies (Hoshi et al., 1998; White
studies (Stuss et al., 2000), which clearly implicate the dlPFC in
rule learning and utilization, and neuroimaging studies, which
have not uncovered the robust dlPFC rule or rule-learning activ-
ity (Deiber et al., 1997; Toni and Passingham, 1999; Toni et al.,
2001; Bunge et al., 2003; Eliassen et al., 2003). Our task design,
which required higher-order organization of many lower-order
S-R rules and which incorporated sustained periods of higher-
order rule or set maintenance, may have enabled this finding.
Our single subject analytical approach may have also allowed us
to detect activations that were robust across subjects, but small
and spatially variable, and less easily detected in map-wise group
ity when subjects were learning a rule, relative to applying an
tal regions: left vPM cortex, dorsomedial PM cortex (SMA/pre-
SMA), and right dlPFC. We propose distinct, although likely in-
nitude and behavioral performance. Activity in these areas was
modulated as a function of performance accuracy during learn-
ing blocks: as performance improved, activity decreased in these
regions (Fig. 5). Moreover, the strength of these correlations in
the dlPFC, SMA, and vPM predicted a subject’s ability to learn
novel sets; the best learners showed the strongest correlations
(Fig. 5B). Dividing the subjects into “good” versus “poor” learn-
ers and comparing activity-accuracy correlation strengths sug-
gests that engagement of these three regions during the initial
problem-solving phase of rule learning and subsequent disen-
gagement during the expertise phase represents successful learn-
ing of higher-order S-R rules.
An indication of interaction between these areas rather than
just coincident activation comes from correlation analyses (Fig.
6). Within subjects, activity in the right MFG and right anterior
striatum were highly correlated with activity in the SMA/pre-
SMA. Also, the strength of these correlations was highly predic-
tive of subject performance; the most accurate subjects showed
SMA/pre-SMA acting in concert is particularly compelling, be-
cause the strength of this correlation accounts for nearly two-
thirds of the variability in subject accuracy. Although the time
based on these data, recent tract-tracing studies in humans indi-
cate that the SMA/pre-SMA is indeed anatomically connected to
What roles do these areas play, and what is a plausible func-
tional circuit? The SMA has been implicated in internally guided
response selection (Cunnington et al., 2002), maintaining con-
textually appropriate possible response sets (Rushworth et al.,
learning (Hikosaka et al., 1999; Sakai et al., 1999). Moreover, the
learning, bridging perceptual learning and motor learning cir-
cuitry (Nakahara et al., 2001). In the learning condition of our
task, subjects must initially guess at responses, but after accumu-
combination of implicit learning-driven, stimulus-cued re-
sponding and explicit hypothesis testing. The anterior striatum,
implicated in reward-based learning and reported to demon-
strate selective responses predicting immediate rewards (Tanaka
et al., 2004), may monitor feedback during S-R rule learning.
ing conditional visuomotor learning indicate that these areas
form a functional loop for acquiring and representing S-R rules
(Hadj-Bouziane et al., 2003; Brasted and Wise, 2004). Interest-
ingly, both groups have shown an increase in striatal firing as
2728 • J.Neurosci.,March9,2005 • 25(10):2723–2732BoettigerandD’Esposito•RuleLearningintheFrontalCortex
tom right). Although those studies investigated the lateral dPM
and primarily the putamen, our results suggests that the medial
dPM and caudate may be functioning in a similar manner via a
During S-R rule learning, the dlPFC may recall previous S-R
tion to determine an organizing rule to
hold in mind. This is consistent with
dlPFC-lesioned patients having difficulty
maintaining set in the Wisconsin Card
damage impairing flexible rule learning
(Milner, 1963; Luria, 1969; Perrett, 1973;
1997; Murray et al., 2000). Support is also
lent to this idea by monkey neurophysiol-
ogy data demonstrating rule-selective ac-
tivity in the dlPFC (Hoshi et al., 1998;
White and Wise, 1999; Asaad et al., 2000;
Wallis et al., 2001; Wallis and Miller,
ing in humans weakly implicated the
during the scan session (Toni et al., 2001).
However, there was a confound of stimu-
stimuli in the learning and control condi-
tions were not matched for perceptual
complexity. Unlike previous rule-learning
nize many individual S-R associations ac-
cording to categorical or set-based rules.
load, or the degree of manipulation (e.g.,
ory, both of which are expected to highly
engage the dlPFC (Duncan and Owen,
2000; Levy and Goldman-Rakic, 2000;
Postle et al., 2000; Rowe et al., 2000). Ei-
individual associations from the vlPFC,
eas, which are not directly connected.
Previous neuroimaging studies of rule
learning have consistently implicated the
vlPFC (Deiber et al., 1997; Toni and Pass-
ingham, 1999; Toni et al., 2001; Eliassen
et al., 2003). Moreover, vlPFC lesions
severely impair S-R learning (Murray
Gaffan, 1998; Bussey et al., 2002), and
monkey neurophysiological data show
association-selective neurons in the vlPFC
(Asaad et al., 1998; White and Wise, 1999;
Wallis et al., 2001). We did not find con-
sistent vlPFC activations in the individual
is the type of analysis used in previous studies. Activation sites in
the vlPFC may represent individual S-R outcome contingencies.
Maintaining such representations in working memory may be
individual contingencies is required for effective learning to take
place. Such function is likely the domain of the dlPFC and is
against average accuracy for the NOV condition, an index of learning in this task. C, Data as in B median split according to S-R
BoettigerandD’Esposito•RuleLearningintheFrontalCortexJ.Neurosci.,March9,2005 • 25(10):2723–2732 • 2729
probably a process that more strongly distinguishes between our
NOV and FAM rule conditions. Moreover, our task bears a resem-
Spiering, 2004). The type of category learning most studied with
neuroimaging is based on the dot pattern prototype distortion
in a number of key ways. First, these studies have generally re-
quired category member/nonmember judgments (“A, not-A”),
and have identified neural changes associated with the outcome
1998; Aizenstein et al., 2000). Vogels et al. (2002) used a design
more similar to ours, requiring “A, B, or neither” judgments of
asymptoted, again focusing on outcome. A study by Seger et al.
(2000) used a different prototype distortion task and bears the
closest resemblance to our task. In that study, subjects learned to
distinguish two visual categories but again included prescan
training and did not include a condition in which learned cate-
gory judgments were made. However, our present findings sup-
as playing a key role in visual category learning.
We propose that in our task, the SMA/pre-SMA represents
possible S-R associations and interacts with the dlPFC, where
the cued S-R pairs in the SMA. Two fMRI studies support this
idea. The first detected dlPFC activity reflecting context-
selecting premotor representations associating stimuli and re-
sponses and exerting top–down control over the premotor cor-
tex to bias the selection of a motor response (Koechlin et al.,
2003). As successful learning proceeds, S-R associations are es-
atum and lateral premotor cortex, where they can be subse-
quently accessed and used without extensive evaluation. If true,
learning proceeds, which is consistent with our data.
Familiar rule blocks preferentially recruited distinctly different
areas, which included the FP and SFG. The finding of FAM?NOV
in the SMA and MFG ROIs was highly predictive of a subject’s overall task performance
activityin NOVruleblocksand FAMruleblocks,overlaidonastandardT1-weightedanatomical
2730 • J.Neurosci.,March9,2005 • 25(10):2723–2732BoettigerandD’Esposito•RuleLearningintheFrontalCortex
activity in the SFG, a premotor region, fits nicely with recent
monkey neurophysiology data indicting that dPM robustly rep-
resents well learned rules (Wallis and Miller, 2003). Map-wise
analyses further implicated sites in the rostral anterior cingulate
has been implicated in the processing of internal mental states
the FAM rule condition, leaving subjects with the “mental space”
the outcome of actions [ACC (Van Veen et al., 2001; Hadland et
could speculate that subjects were evaluating their performance
in the task during the less demanding (FAM) blocks. This is con-
sistent with the subjects’ postscan reports of being highly con-
scious of their performance, perhaps heightened by monetary
performance bonuses. However, a more recent hypothesis re-
garding fronto-polar function suggested that it may be best un-
derstood as a place that coordinates multiple subgoals in the
context of an overall goal (Ramnani and Owen, 2004) with the
medial portion putatively engaged for partially anticipated, ver-
al., 2003). Our results may better fit this hypothesis, given that
selecting the appropriate response for each trial. An explanation
for less activity in the NOV condition could be that the associative
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