Fast Learning of Simple Perceptual Discriminations
Reduces Brain Activation in Working Memory
and in High-level Auditory Regions
Luba Daikhin and Merav Ahissar
Abstract
■ Introducing simple stimulus regularities facilitates learning
of both simple and complex tasks. This facilitation may reflect
an implicit change in the strategies used to solve the task when
successful predictions regarding incoming stimuli can be
formed. We studied the modifications in brain activity associated with fast perceptual learning based on regularity detection.
We administered a two-tone frequency discrimination task and
measured brain activation (fMRI) under two conditions: with
and without a repeated reference tone. Although participants
could not explicitly tell the difference between these two conditions, the introduced regularity affected both performance
and the pattern of brain activation. The “No-Reference” condi-
INTRODUCTION
The dynamics of perceptual learning, particularly its initial stages, are not well understood. Previous studies have
focused on the specificity of learning to trained stimuli,
which was shown to be consistent with the specificity
of the sensory areas (Spang, Grimsen, Herzog, & Fahle,
2010; Van Wassenhove & Nagarajan, 2007; Amitay, Hawkey,
& Moore, 2005; Seitz & Watanabe, 2005; Demany &
Semal, 2002; Ahissar & Hochstein, 1993, 1996; Levi &
Polat, 1996; Karni & Sagi, 1991). However, such specificity mainly characterizes later stages of learning, when
some expertise had been obtained ( Jeter, Dosher, Liu, &
Lu, 2010; Ahissar & Hochstein, 1997; Karni & Sagi,
1993). Ahissar and Hochstein (Ahissar, Nahum, Nelken,
& Hochstein, 2009; Ahissar & Hochstein, 1997, 2004)
suggested that when finer resolution is required, perceptual learning may progress backwards along the perceptual hierarchy from crude generalizing representations
to more local ones. This theory, termed the Reverse
Hierarchy Theory, posits that perceptual learning is not
limited to a specific brain site and progresses from highto lower-level areas with practice. Nevertheless, it does
not address the brain mechanisms underlying the very
early stages of learning, when the task and its broad
stimulus characteristics need to be sorted out. This initial
stage is typically short and difficult to track and hence has
The Hebrew University of Jerusalem
© 2015 Massachusetts Institute of Technology
tion induced a larger activation in frontoparietal areas known to
be part of the working memory network. However, only the
condition with a reference showed fast learning, which was
accompanied by a reduction of activity in two regions: the left
intraparietal area, involved in stimulus retention, and the posterior superior-temporal area, involved in representing auditory
regularities. We propose that this joint reduction reflects a reduction in the need for online storage of the compared tones.
We further suggest that this change reflects an implicit strategic
shift “backwards” from reliance mainly on working memory networks in the “No-Reference” condition to increased reliance on
detected regularities stored in high-level auditory networks. ■
rarely been studied, although it is probably crucial to
subsequent learning dynamics (e.g., Ortiz & Wright,
2009; Hawkey, Amitay, & Moore, 2004; Karni, Jezzard,
Adams, Turner, & Ungerleider, 1995).
One of the key features of the training procedure, particularly at the early training stages, is the consistency of
stimuli across consecutive trials. Consistent training with
similar stimuli leads to fast, condition-specific (Cohen,
Daikhin, & Ahissar, 2013) learning (e.g., Otto, Herzog,
Fahle, & Zhaoping, 2006), whereas training with a broad
range of stimuli, whose sequence is not predictable, leads
to slow learning (Parkosadze, Otto, Malaniya, Kezeli, &
Herzog, 2008) if any (e.g., Kuai, Zhang, Klein, Levi, &
Yu, 2005; Adini, Wilkonsky, Haspel, Tsodyks, & Sagi,
2004; Yu, Klein, & Levi, 2004). A very clear example of
this dissociation was recently reported in the auditory
modality for training on frequency (pitch) discrimination
between sequentially presented tones. Whereas discrimination between tones whose frequency was randomly chosen from a broad frequency range improved
slowly (within hundreds of trials), substantial and fast
improvement was achieved when the first tone in a pair
had a fixed frequency (Nahum, Daikhin, Lubin, Cohen, &
Ahissar, 2010). This rapid improvement was attributed to
the ability to form effective predictions for the incoming
stimuli when training with stimuli that obeyed a simple
regularity (Ahissar et al., 2009; Ahissar & Hochstein,
2004). Here we inquired whether the impact of introducing simple regularities that facilitate learning, perhaps
Journal of Cognitive Neuroscience 27:7, pp. 1308–1321
doi:10.1162/jocn_a_00786
by facilitating the “reverse hierarchy” process, is accompanied by a detectable concurrent change in the pattern
of brain activation.
Although serial discrimination is considered a simple
perceptual task, it requires two types of management
processes, both of which involve frontoparietal networks.
First, as in any new task (or situation), its basic structure
in terms of neural representations should be set (Miller
& Cohen 2001). Many studies suggest that this tasksetting is implemented by high-level networks, which
include extensive frontal and parietal regions. These
networks are largely general-purpose and form the
“task-set” for various tasks (and were hence termed “the
multiple-demand” network; Duncan, 2010; Duncan &
Owen, 2000). Second, task performance requires the
retention of the relevant value of the first stimulus in each
trial during the interstimulus interval and a comparison of
this value with that of the second stimulus. This retainand-compare process is a working memory operation
(e.g., Romo, Brody, Hernández, & Lemus, 1999). Such
operations were also shown to activate frontoparietal
regions, which were thus termed the working memory
network (Fedorenko, Behr, & Kanwisher, 2011; Koelsch
et al., 2009; Baldo & Dronkers, 2006; Rainer, Asaad, &
Miller, 1998). The exact role of this network in the retainand-compare operation is still being debated. Previous
studies have suggested that these working memory areas
both manage and store the task-relevant stimuli. However,
very recent studies (reviewed in Sreenivasan, Curtis, &
D’Esposito, 2014) posit that the stimuli are stored in posterior sensory areas, and the role of the working memory
network primarily involves task-related management.
Additional related term is “attentional resources,” whose
recruitment when a task is generally more demanding also
activate partially overalapping posterior-parietal regions
(Magen, Emmanouil, McMains, Kastner, & Treisman, 2009).
The behavioral observation that a simple regularity in
perceptual discriminations leads to fast perceptual learning, which is specific to the trained regularity (Cohen
et al., 2013; Nahum, Daikhin, et al., 2010), implies that
the load on management processes decreases. This decrease is expected because utilizing the regularity, that
is, the repeated reference, leads to increased reliance
on the internal representation (of the reference), which
partially replaces the need to actively retain the first
stimulus in each trial. We therefore hypothesized that a
condition with no regularity would place a heavier load
on management processes and hence would induce
a higher activation in the frontoparietal network. We
further hypothesized that frontoparietal activity would
quickly decrease when effective practice with the regularity containing condition led to the formation of a reliable
prediction of the expected stimuli, and discrimination
would increasingly rely on this stored regularity. Moreover, we reasoned that we may be able to track the formation of this auditory prediction in a high-level auditory
area. This area is expected to show high activity at the
initial stages of learning the regularity and then decrease
its activity with repetitions of this regularity (Karni et al.,
1995, 1998), as long as the reference containing condition is not interrupted.
To test these hypotheses, we measured both behavior
and the BOLD response when participants performed a
simple perceptual two-tone frequency discrimination
task. On the basis of the observations of Nahum, Daikhin,
et al. (2010), participants in the current study were
administered the following two conditions. In one, the
same tone was consistently presented in the first interval
of each trial. This regularity is known to be detected
quickly and yields fast and substantial improvement
(Cohen et al., 2013; Nahum, Daikhin, et al., 2010). In
the second condition, the same task and similar stimuli
(though drawn from a broader frequency range) were
used, but there was no cross-trial tone repetition. In this
condition, participants’ improvement has been reported
to be very slow and does not reach the same level of
performance even after many practice sessions.
We presented blocks of these two conditions in an interleaved manner (3 blocks of one condition followed by
3 blocks of the other condition). Because the stimuli were
similar and the task was the same, participants were unaware
of the switch in conditions. We asked which brain areas
were sensitive to the difference between the two conditions,
and activity in which brain areas was modified as a function
of the rapid improvement we anticipated in the condition
involving a simple, easily detected stimulus regularity.
METHODS
Participants
Nineteen participants (age = 29 ± 5 years; 10 women)
took part in the study. Each of them performed frequency discrimination and another task (not reported
here) in the magnet (except one, who was administered
only the frequency discrimination task) and had an additional anatomical scan at the end of the session. Before
entering the scanner, participants practiced a short version
of the behavioral protocol that they performed during
scanning. Participants signed an informed consent form
and were paid for their participation.
Experimental Procedure
In the frequency discrimination task participants were
presented with pairs of tones and were asked to decide
(and respond with a right/left button press) which tone
was higher. The tones were 50 msec long, the ISI was
∼600 msec, and the trial duration (onset to onset) was
2 sec. We measured frequency discrimination under two
conditions in a single fMRI run: (1) No-Reference condition (No-Ref; see schematic illustration in Figure 1A) with
no cross-trial consistency. In this condition, the first tone
was chosen from a frequency range of 800–1200 Hz and
Daikhin and Ahissar
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Figure 1. Frequency discrimination—Experimental design. (A) A schematic illustration of five trials of the No-Ref condition (left), which
contained no cross-trial stimulus repetitions, and of the Ref-1st condition (right), in which a 1000-Hz reference tone appeared first on every trial.
(B) Frequency differences between the two tones in each trial on the 180 trials of each condition (No-Ref, blue; Ref-1st, green). Vertical dotted lines
illustrate the division of the sequences into blocks of 12 trials as presented in the scanner. The condition was switched after a triad of blocks.
(C) A schematic illustration of blocks composing the experimental procedure inside the fMRI scanner (No-Ref, blue; Ref-1st, green). Gray denotes
periods of rest. Three blocks of one condition were followed by three blocks of the other condition.
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Volume 27, Number 7
the second tone was chosen according to the frequency
differences shown in Figure 1B (see description below).
(2) Reference-1st condition (Ref-1st; see schematic illustration in Figure 1A), which employed the same procedure, but the first tone in each pair was always 1000 Hz.
In both conditions, we administered sequences of tone
pairs, with an initially large (20%) frequency difference,
which got gradually smaller. The specific characteristics
of these sequences (shown in Figure 1B) were based
on the average frequency differences (of naive performers in their first assessment) obtained in an adaptive
version of these conditions, which converged to 80%
correct (see Nahum, Daikhin, et al., 2010). The purpose
of using the sequences that both converged to the same
level of performance (80% correct) was intended to control for difficulty differences between the conditions.
Note that, in an adaptive protocol (similar accuracy of
performance), the Ref-1st condition converges to much
lower thresholds (Figure 1B). Each condition consisted
of 180 trials, presented in 15 blocks of 12 trials each
(24 sec per block separated by 9 sec of rest). Each condition was presented in three consecutive blocks and
was then switched to the other condition (order counterbalanced across participants). Thus, a single run contained
five triads of blocks of each condition (see Figure 1C).
Participants were typically unaware of the condition
switch. Participants were asked to keep their eyes closed
throughout the entire measurement. RT and accuracy of
performance were collected while participants performed
the task inside the fMRI scanner. These were analyzed
using repeated-measures ANOVAs with Condition (NoRef, Ref-1st) and Block (First, Third) as within-participant
factors.
fMRI Scanning Procedure
Scanning was performed in a 3T scanner (Magnetom
TimTrio System 3.0 T (Tim (102 × 32) TQ) Erlangen,
Germany). For each participant, functional (T2*-weighted)
and high-resolution anatomical reference data sets (T1weighted) were acquired. Functional measurements were
obtained with a single EPI sequence with an echo time of
30 msec and a repetition time of 3000 msec. Acquisition of
the slices was arranged uniformly within the repetition
time interval. The matrix acquired was 80 × 80 with a
field of view of 240 cm, resulting in an in-plane resolution
of 3 × 3 mm. The slice thickness was 3 mm. Anatomical
scans were measured with a 3-D gradient-echo with a 1 ×
1 × 1 mm resolution.
fMRI Data Analysis
Anatomical and functional data were analyzed using the
Brain Voyager QX Software package (The Netherlands).
Functional data were corrected for motion using a trilinear estimation and interpolation. To correct for the
temporal offset between the slices acquired in one scan,
a cubic spline interpolation was applied. A temporal high
pass with three cycles/points and linear trend removal
were used for baseline correction of the signal. The
functional images collected were coregistered with the
anatomical images. Anatomical images were then transformed into the Talairach space.
The statistical evaluation was based on a least-squares
estimation using the general linear model (GLM). The
design matrix was generated using a hemodynamic response function. The time course of the BOLD signal
obtained during the task was initially modeled using
two predictors (one predictor per condition) as illustrated by the pattern of coloring in Figure 1C. To inspect
within-condition changes, that is, the difference between
the first and third blocks within a condition triad, the
time course of the BOLD signal was remodeled using
a separate predictor for each block within the triad
(i.e., 3 predictors per condition × 2 conditions).
Multisubject random effects GLM and repeated-measures
ANOVAs of beta values with Condition (No-Ref, Ref-1st)
and Block (First, Third) as within-participant factors
were applied to the data. The data were z-transformed
before entering the random effects analyses. The results
were corrected for multiple comparisons using a clustersize limitation. Applying a cluster-level statistical threshold estimator, a minimal cluster size was determined at the
chosen significance level (see figures) for each volume map.
We first identified cortical areas that were positively and
significantly activated by the frequency discrimination
task: (all conditions) > rest, random effects GLM contrast.
The obtained map served as a mask for testing our
hypotheses (see Figure 2A). Using voxel-wise repeatedmeasures ANOVA of beta values, we examined which
brain areas were sensitive to the differences between
the conditions. To test sensitivity to the task conditions
while controlling for the behavioral difference, an ANCOVA
on the beta values obtained from each of the conditionsensitive regions (separately for each region) was run
with condition as within-participant factor and behavioral gain (ACC(Ref-1st) − ACC(No-Ref )) as a covariate.
To assess within-condition learning-related changes,
we remodeled the data using a different predictor for
each of the three blocks within a triad, obtaining six
predictors—three for each condition. We then compared
the beta values obtained in the first and third blocks by
applying voxel-wise repeated-measures ANOVA on the
areas within the mask (see Figure 2A), separately for
each condition. We examined which areas consistently
changed their activity from the first to the third block of
within-condition block triads. To assess learning-related
modifications during the entire session, we remodeled
the data using a different predictor for each block, obtaining 30 predictors—15 for each condition. We then compared the beta values obtained in the third block of the
first triad to those obtained in the third block of the last
triad by applying voxel-wise repeated-measures ANOVA
on the areas within the mask (Figure 2A).
Daikhin and Ahissar
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Figure 2. Functional anatomy
of the two-tone frequency
discrimination task. (A) A
whole-brain activation map of
the frequency discrimination,
all-conditions > rest contrast,
was obtained by applying
random effects GLM (corrected
by cluster size at a p < .05
threshold). Significant t values
for positive fit between the
BOLD signal and the modeled
conditions are presented using
an orange–yellow color scale.
N = 19. Lateral (top) and
medial (middle) views of the
representative inflated cortex
are shown. The bottom
row shows two transverse
anatomical slices that
demonstrate the involvement
of the left BG (right) and
cerebellum (left). (B) Wholebrain activation map of the
auditory-stimuli > rest contrast
obtained from a subgroup of
participants (random effects
GLM; n = 5; corrected by
cluster size at a p < .01
threshold) during the auditory
localizer task. Significant t values
for positive fit between the
BOLD signal and the modeled
condition are presented
using an orange–yellow
color scale.
To compare the areas that we found with those reported in
literature, we calculated the distance in anatomical (1 mm3)
voxels between the peak voxels of the regions reported in
the literature and the areas found in our study.
Comparison to the Primary Auditory Cortex
Because we used simple auditory stimuli and a basic
auditory discrimination task, we were interested in the
impact of the experimental conditions on the primary
auditory cortex, which shows automatic responses to
auditory stimuli. To specifically compare our results to
the dynamics of the signal there, we ran an auditory localizer on a subgroup of participants (n = 5). During the
localizer period, participants were presented with auditory stimuli with rich and varying spectral content but
with no clear semantic association. These included white
noise, broad-band noise, pink noise, pitch shifts, intensity
modulations, and sound effects (fading in, fading out,
tremolo, stretching [paulstretch], amplitude modulation
[wahwah], inversion). The stimulus duration was 1 sec.
The stimuli were consecutively presented in seven blocks
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of 18 stimuli each. The blocks were separated by 15 sec
of rest. Participants were requested to listen to the stimuli with their eyes closed. They were not asked to perform any task. Figure 2B shows the obtained auditory
area resulting from the contrast: stimuli > rest (GLM random effects). The center of mass of the obtained area is similar to the areas identified as primary auditory cortex in the
literature (x + 1, y + 1, z + 2 from the peak voxel reported
in Lockwood et al., 1999; x − 5, y + 1, z + 1 from the peak
voxel reported in Binder et al., 2000). This area was used as a
control ROI to compare beta values and average time
courses with the regions that were obtained in each of our
experimental questions. Importantly, ROI-based repeatedmeasures ANOVAs of beta values in the auditory area yielded
no significant effects for the frequency discrimination task.
RESULTS
The Pattern of Activation Induced by Two-Tone
Frequency Discrimination
Figure 2A shows the map of brain areas positively activated
by the conditions of the frequency discrimination task. The
Volume 27, Number 7
map shows involvement of auditory areas in the superior
temporal gyri and sulci of both hemispheres. It also shows
the somatosensory and motor areas in the precentral and
postcentral gyri of the left hemisphere together with premotor areas associated with participants’ motor responses
(with their right hand) and planning. Additionally, it shows
activation of the inferior prefrontal regions and parietal
areas, evident mainly in the left hemisphere. Extensive
involvement of the cerebellum and BG is also shown.
Unexpectedly, we also found activation of the visual areas,
as is visible in the medial view of both hemispheres,
although participants’ eyes were closed throughout the
assessments. Subsequent analyses were based on this map.
Figure 2B shows the whole-brain activation map obtained from the subgroup of five participants who were
presented with an auditory localizer stimuli in the scanner.
This localizer was composed of a sequence of auditory
stimuli with rich and varying spectral content. The marked
area was significantly more activated during the auditory
stimulus presentation compared to rest, when there was
no auditory stimulation ( p < .01, corrected by cluster size).
This area served as a control ROI for comparing beta values
and average time courses with the regions obtained from
analyzing frequency discrimination activations.
Sensitivity to Task Condition—With and
Without Stimulus Regularity
To assess which areas were differentially activated by the
two conditions (Condition effect), we applied a voxel-wise
repeated-measures ANOVA on the beta values obtained
from the contrast: all-conditions > rest, shown in Figure 2A.
The comparison between the two task conditions revealed several areas that were differentially activated by
the two conditions. These were mainly high-level areas
in the left hemisphere, as shown in Figure 3A: lateral prefrontal (L-supPrefrontal; −46, −1, 33; L-infPrefrontal;
−52, 0, 23), premotor (L-Premotor; −26, −16, 53), posterior intraparietal (L-intraParietal; −32, −60, 47; L-intraParietal-2; −40, −47, 44), superior parietal (L-supParietal;
−19, −74, 46). As shown in Figure 3A, several small areas
in the right hemisphere also showed differential sensitivity
to the task conditions: middle temporal (R-midTemporal;
48, −30, 1), medial premotor (R-medial-Premotor; 4, 4, 48),
and medial occipital (R-medial-Occipital; 6, 70, −21).
This condition-sensitive increase of activity in the left
prefrontal and parietal areas was in line with our prediction, as these regions are known to be part of the
working memory network involved in managing the
retention of sounds (Prefrontal: x, y + 6, z + 3 from
the peak voxel reported in Zatorre, Perry, Beckett,
Westbury, & Evans, 1998; x, y + 4, z − 7; x, y − 2, z +
7 from the peak voxels reported in Gaab, Gaser, Zaehle,
Jancke, & Schlaug, 2003; x, y + 2, z + 7; x, y − 4, z +
6 from the peak voxels reported in Koelsch et al., 2009;
parietal areas include the peak voxels reported in these studies). The premotor region has been reported to be involved
in processing linguistic information and in auditory–motor
interface (Friederici, Kotz, Scott, & Obleser, 2010; Obleser
& Kotz, 2010; Friederici, Makuuchi, & Bahlmann, 2009;
Obleser, Wise, Alex Dresner, & Scott, 2007; Hickok &
Poeppel, 2000, 2004; Davis & Johnsrude, 2003). The right
middle temporal region was previously associated with
auditory processing and working memory for pitch
(Johnsrude, Penhune, & Zatorre, 2000; Zatorre & Samson,
1991). The involvement of the additional areas in the right
hemisphere was not predicted by our working memory
hypothesis.
Figure 3B shows the beta values (averaged across participants and blocks) obtained from each of the conditionsensitive regions. Beta values from the auditory cortex
are also presented for comparison. The plot shows that
the condition effect stems from higher beta values in
the No-Ref condition. In contrast, the auditory cortex
shows no difference between the beta values of the two
conditions, F(1, 18) = 0, p = .99, in spite of being highly
activated by the task. Figure 3C shows the time courses
of the BOLD signal, indicating that the No-Ref condition
induced a larger BOLD signal. Again, this conditionspecific increase in activity was not found in the auditory
cortex.
Although we aimed for attaining equal levels of difficulty
(and hence of general attentional resources) in the two
conditions and therefore used larger frequency differences
in the No-Ref condition (based on Nahum, Daikhin, et al.,
2010; see Methods), this condition was still slightly more
difficult. Specifically, participants were less accurate (95 ±
1% vs. 89 ± 2% correct for Ref-1st vs. No-Ref; repeatedmeasures ANOVA, main effect of Condition: F(1, 18) =
19.33, p < .001) and somewhat slower (479 ± 20 msec vs.
530 ± 22 msec for Ref-1st vs. No-Ref, repeated-measures
ANOVA, main effect of Condition: F(1, 18) = 19.2, p <
.001), although they were not asked to be quick (but
there was a fixed time interval of 1.4 sec between trials).
The difference in activity patterns between these conditions may thus be attributed to this small, yet significant,
difference in the required attentional resources rather than
a difference in the ability to form effective predictions.
To control for this alternative account, we compared
the beta values of the two conditions obtained for each
condition-sensitive region (Figure 3A) by regressing out
the behavioral difference. We ran an ANCOVA on the
beta values obtained from each of the condition-sensitive
regions (separately for each region) with Condition as
the within-participant factor and Behavioral gain (ACC
(Ref-1st) − ACC(No-Ref )) as the covariate. In the parietal
areas, the difference between the conditions remained
significant even when the behavioral difference was
controlled for (L-intraparietal: F(1, 17) = 5.3, p = .03;
L-intraparietal-2: F(1, 17) = 4.3, p = .05; L-sup-parietal:
F(1, 17) = 11.2, p = .004). Similarly, the left premotor
area and the right temporal region retained the significant difference between conditions, F(1, 17) = 7, p =
.02, and F(1, 17) = 5, p = .04, respectively. However,
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Volume 27, Number 7
the difference between the conditions decreased and
became only marginally significant in the prefrontal areas
(L-sup-Prefrontal: F(1, 17) = 3.5, p = .08; L-inf-Prefrontal:
F(1, 17) = 3.2, p = .09). This reduction is in line with
previous reports of prefrontal sensitivity to task difficulty
(Fuster, 2001; Grady et al., 1996). Areas in the right hemisphere were also sensitive to this control. The right medialoccipital area became only marginally condition-sensitive
(R-medial-Occipital: F(1, 17) = 4.2, p = .06), and the right
medial-premotor area did not remain condition-sensitive
(R-medial-Premotor: F(1, 17) = 1.7, p = .21).
Taken together, the pattern of increased activity under
the No-Ref condition indicates that this condition activates working memory networks to a greater extent than
the Ref-1st condition, although the task was the same and
participants were unaware of the difference between the
conditions. The differences in brain activity, particularly
those related to the posterior parietal region, cannot be
attributed to a general difference in the overall attentional efforts required by these two conditions.
Fast Learning in the Regularity
Containing Condition
The behavioral advantage of the Ref-1st over the No-Ref
condition was reflected in the different dynamics of performance in the two conditions. Figure 4A shows the
average (cross-participant) accuracy in each block of each
condition. As expected, in the Ref-1st condition (left
plot) performance improved quickly. Accuracy increased
between consecutive short blocks (12 trials each) of this
condition (93 ± 1.7% in the first blocks of the block triad,
vs. 97.6 ± 1% in the last blocks, t = −3.43, p = .003, in a
paired, two-tailed t test). However, this improvement was
specific to this condition, that is, to the specific pattern of
stimuli, and was degraded whenever No-Ref blocks were
introduced.
By contrast, performance in the No-Ref blocks (Figure 4A,
right plot) did not show significant improvement after
mild amounts of practice (88 ± 2 vs. 89.5 ± 2, t =
−1.18, p = .25, in a paired, two-tailed t test), in line with
previous findings of very slow improvement in this condition (Nahum, Daikhin, et al., 2010).
To assess within-triad changes in brain activity, we
remodeled the data using a different predictor for each of
the three blocks within a triad, obtaining six predictors—
three for each condition. We then compared the beta
values obtained under the first and third blocks apply-
ing voxel-wise repeated-measures ANOVAs on the areas
within the mask (see Figure 2A), separately for each
condition. Figure 4B shows two regions that showed
sensitivity to block in the Ref-1st condition. The NoRef condition is not shown because the comparison failed
to reach significance for any of the activated areas (at the
chosen significance level, p < .01, cluster-size corrected),
in line with the lack of significant behavioral improvement
between consecutive blocks of this condition.
The two regions that showed a main effect of Block
were located in the left hemisphere: in the intraparietal
area (L-intralParietal; −38, −45, 42) and in posterior
superior temporal area (L-supTemporal; −49, −47, 13).
The intraparietal area is associated with the storing of
information (Koelsch et al., 2009; Baldo & Dronkers,
2006), although it is probably not the site of storage itself
(Sreenivasan et al., 2014; Magen et al., 2009). The posterior superior temporal region is associated with analysis
of temporal auditory structures at different levels of
complexity (Obleser & Kotz, 2010; Friederici et al., 2009;
Davis & Johnsrude, 2003; Binder et al., 2000). Figure 4C
and D shows a reduction in activity in these areas between the first and third blocks. Beta values and time
courses of activity for the auditory cortex are also presented. Here, in spite of high beta values and high
BOLD signals, there was no significant effect of Block
(ROI repeated-measures ANOVA, Block effect: F(1, 18) =
1.1, p = .31), suggesting that this area is not part of the
fast “learning network” whose activity is modified across
consecutive blocks of Ref-1st.
The results described above only show the effects of
within-triad learning. To assess the possible effects of
learning during the entire session (across block triads),
we compared the activity and the behavior in the third
block of the first triad with that in the third block of
the last triad. We remodeled the data using a different
predictor for each block (see Methods) and applied a
voxel-wise repeated-measures ANOVA to the masked
voxels (Figure 2A) with Block (First third, Last third) and
Condition (No-Ref, Ref-1st) as within-participant factors.
There was no significant difference in the measured brain
activity (no areas showed differential activity at the p < .05
threshold, corrected by cluster size). Behavior did not
improve during the session, and there was even a small
tendency for some accumulated fatigue (Ref-1st, 99.6%
vs. 95%: t = 1.46, p = .16; No-Ref, 97% vs. 90%: t = 2.1,
p = .05). There was no evidence of cross-triad learning in
any of the two conditions.
Figure 3. Sensitivity to task condition. (A) Voxel-wise repeated-measures ANOVA of the beta values within (all conditions) > rest contrast
map—main effect of Condition (corrected by cluster size at a p < .05 threshold). (B) Beta values obtained from each of the condition-sensitive
regions (averaged across participants and blocks) for the No-Ref (blue) and Ref-1st (green) conditions. Beta values from the primary auditory ROI are
also presented for comparison. Error bars indicate cross-participant standard error. (C) Time courses of the BOLD signal obtained from each of
these regions (averaged across participants and blocks) for the No-Ref (blue) and Ref-1st (green) conditions. Time courses of the BOLD signal from
the primary auditory ROI are also presented for comparison. Talairach coordinates indicate the center of mass for each region. Error bars denote
cross-participant standard error. Pink backgrounds denote the duration of a block.
Daikhin and Ahissar
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Figure 4. Sensitivity to block. (A) Average performance accuracy for each of the blocks in the Ref-1st (left) and No-Ref (right) conditions. First blocks
of each triad are marked by dashed bars, second blocks are marked by empty bars, and third blocks are marked by filled bars. Participants only
showed fast, condition-specific improvement in the Ref-1st condition. (B) Voxel-wise repeated-measures ANOVA of the beta values within (all
conditions) > rest contrast map; a main effect of Block (corrected by cluster size at a p < .01 threshold). Beta values are shown only for the
Ref-1st condition because, consistent with the lack of behavioral improvement, no region showed a significant block effect in the first versus third
block comparison under the No-Ref condition. (C) Beta values obtained from each of the block-sensitive regions in the Ref-1st condition for the
first (dashed bars) and third (filled bars) blocks. Beta values from the auditory ROI are also presented for comparison. Error bars indicate crossparticipant standard error. (D) Time courses of the BOLD signal obtained from each of the block-sensitive regions for the first (dashed) and the
third (solid) blocks. Error bars indicate cross-participant standard error. Time courses of the BOLD signal from the auditory ROI are also presented
for comparison. Talairach coordinates indicate the center of mass for each region. Pink backgrounds denote the duration of a block.
1316
Journal of Cognitive Neuroscience
Volume 27, Number 7
DISCUSSION
We studied the dynamics of brain activation during the
performance of a two-tone frequency discrimination task
in two conditions: with (Ref-1st) and without (No-Ref ),
an easily detected regularity in the stimulation pattern
of consecutive trials. We conducted three ANOVAs that
tested (1) which areas were activated differentially under
these two similar behavioral conditions and (2) which
areas modified their activity across consecutive blocks
of the same condition (two separate ANOVAs for the
two conditions, respectively). In addition, we tested
potential impact of a behavioral difference between the
two conditions on the activity in the condition-sensitive
regions (ANCOVA results) as well as possible cross-triad
learning. The findings showed that participants were
typically unaware of the existence of the two different
conditions. This is not surprising given the common behavioral task and trial structure and the similar range of stimuli (in the No-Ref condition 800–1200 Hz; in the Ref-1st
condition vs. 950–1050 Hz, except for the broader few
first trials).
We hypothesized that the condition effect would
reveal different levels of activation within the working
memory network, because the No-Ref condition, which
contained no regularities, placed a heavier load on working memory processes. This is because online management of comparison and retention of stimuli was more
demanding in the No-Ref condition (Cohen et al., 2013;
Nahum, Daikhin, et al., 2010). As hypothesized, the
condition-sensitive regions were mainly located in the
left frontoparietal and premotor areas, which have been
associated with the working memory network for sound
(Koelsch et al., 2009; Gaab et al., 2003; Zatorre et al.,
1998; premotor area–auditory–motor interface: Hickok
& Poeppel, 2000, 2004). Furthermore, increased reliance
on successful stimulus-specific predictions in the Ref-1st
condition perhaps also reduced the activity related to
task-setting, because performance becomes more automatic (Miller & Cohen, 2001). The lack of cross-triad
learning in either of the two conditions suggests that this
reduction cannot be explained as manifesting a general
decrease in difficulty and hence in the need to allocate
general attentional resources.
In contrast to the Ref-1st condition in which fast improvement was observed across consecutive short blocks,
no such improvement was found in the No-Ref condition.
This was expected from previous studies using this discrimination task (Nahum, Daikhin, et al., 2010) and other
discrimination tasks when many stimuli were used with
no repeated pattern. In these studies, even with a more
limited range of stimuli, when several repeated references
were used in a randomly chosen sequence (“roving conditions”; Clarke, Grzeczkowski, Mast, Gauthier, & Herzog,
2014; Herzog, Aberg, Frémaux, Gerstner, & Sprekeler,
2012; Parkosadze et al., 2008), improvement was either
absent or small and very slow. Our No-Ref condition is an
extreme case of roving, in which stimuli were randomly
chosen from a flat distribution. Interestingly, this variability was sufficient to block the fast learning of the
simple Ref-1st condition to the extent that performance
did not improve between the first and last triad of this
condition.
However, as expected, there was fast within-triad
improvement in the Ref-1st condition. This improvement
was specific to the simple predictable stimulation pattern
of this condition and was interrupted (performance was
degraded) by intervening No-Ref blocks. This interference
was expected, because No-Ref blocks violate the expected
pattern of stimulation that underlies the fast improvement. The cross-block (first to third) behavioral improvement was accompanied by modifications in two specific
regions, namely, the left intraparietal area and the left
posterior superior temporal area.
We interpret these results in the framework of the
Reverse Hierarchy Theory, which suggests that successful
detection of task-informative lower-level representations
enables a gradual reliance on lower-level populations
(e.g., Ahissar et al., 2009). In other words, we propose
that the temporal region retains the detected auditory
regularity whereas the intraparietal region controls this
retention. Auditory regularity was successfully detected
only in the Ref-1st condition. In this condition, the population that best decodes the average frequency is a reliable predictor in each trial, because this is the frequency
of the first tone of every pair. In the No-Ref condition, the
frequency within a trial could not be reliably predicted.
Thus, when this simple regularity was detected, the
“managing effort” required from the intraparietal region
may have decreased. This claim of a division of labor is
based on recent imaging studies that suggest that the
high-fidelity representations of stimuli in working memory
are kept in perceptual areas, whereas intraparietal regions
only “manage” retention efforts (reviewed in Sreenivasan
et al., 2014). Indeed, a study that aimed to assess this
question directly concluded that intraparietal regions are
not the site of storage itself but of the attentional resources required for keeping online storage (Magen
et al., 2009).
An alternative account to the pattern of reduction of
activation relates to a general reduction in task difficulty,
which perhaps was greater in the Ref-1st condition,
which was learned faster. This interpretation is unlikely.
Comparing the two conditions while controlling for the
behavioral difference (ANCOVA results) did not eliminate
the condition effect in the posterior parietal region. It
did, however, reduce the significance of the frontal
region in the condition effect, suggesting that for this
region, we cannot rule out a contribution of the small
difference in the overall difficulty of the two conditions.
Note, however, that participants were completely unaware of the switch between conditions or a change in
the effort they were required to allocate at different
stages of the session. This reported introspection is in
Daikhin and Ahissar
1317
line with the lack of a general improvement or a general
change in activity during the session.
The Benefit of the Regularity—Integration
of Interpretations
The cognitive literature attributes a unique role to the
detection of regularities in sounds. For example, the
MMN ERP component (Näätänen, 1992) is considered
an automatic response of the auditory cortex to a violation of regularities (e.g., Näätänen, Paavilainen, Rinne, &
Alho, 2007; Picton, Alain, Otten, Ritter, & Achim, 2000).
Our own interpretation of the fast improvement in Ref1st in fact stresses its easily detected regularity (within
fewer than 10 trials), as described in a series of previous
studies (Cohen et al., 2013; Oganian & Ahissar, 2012;
Nahum, Daikhin, et al., 2010).
For example, Nahum, Daikhin, et al. (2010) interpreted
this improvement as stemming from a shift from an initial
working memory-based comparison of the stimuli presented in the two intervals of the trial to a comparison
with an internal representation of the constant reference.
This interpretation was also based on monkey studies
(reviewed in Romo & de Lafuente, 2012) that found that,
in the No-Ref condition, well-trained monkeys compare
stimuli and activate working memory areas (premotor,
prefrontal, parietal), thus producing “delayed activity.”
However, when trained on the Ref-1st condition, monkeys do not compare stimuli online and do not produce
delayed activity in higher level areas (Romo & Salinas,
2003; Brody, Hernández, Zainos, Lemus, & Romo, 2002;
Romo et al., 1999; Hernández, Salinas, García, & Romo,
1997). Rather, they compare the second stimulus to the
previously trained fixed reference stimulus maintained in
their long-term memory, although no neural signature
was found for the storage of this trained reference
stimulus.
However, we also found that participants keep track
and are heavily affected by the statistics of the experiment even when it contains no regularities, as in the case
of No-Ref. Raviv, Ahissar, and Loewenstein (2012) suggested a simple model (inspired by Bayesian rules),
accounting for these effects. The model proposes that
rather than comparing the two stimuli within a trial, listeners compare the second tone to a combined representation of the frequency of the first tone (which is
noisy because of the working memory noise added
during the retention interval) and the prior. The prior
in this case is simply the average frequency of the first
tone on previous trials. According to Raviv et al.’s model,
the same mechanism could have been automatically
implemented in both the No-Ref and Ref-1st conditions.
Indeed, the same simple model also accounts for participants’ behavior when a reference is introduced (Raviv,
Lieder, Loewenstein, & Ahissar, 2014), suggesting that,
in spite of its substantial behavioral advantage, Ref-1st
may not be a qualitatively different condition.
1318
Journal of Cognitive Neuroscience
However, these two perspectives can be reconciled.
Raviv et al.’s model does not take into account the reliability of the prior, which differs considerably between
the two conditions: in the No-Ref condition, its reliability
is low, whereas under the Ref-1st condition, its reliability
is high. The weight assigned to the prior should depend
on its reliability. Studies have shown that people are
sensitive to the reliability with which recent data indicate the current state of the data (Nassar et al., 2012).
Moreover, this reliability is also reflected in pupil diameter,
implying that it is tightly linked with activity in attentional
systems. This finding is consistent with the idea that the
activity in the intraparietal regions is sensitive to the reliability of the prior. It decreases when the estimated reliability is increased, because the required attentional resources
can consequently be reduced. Thus, the decrease in intraparietal activity may reflect the gradual switch of reliance
from the externally presented stimulus to the temporally
stored prior, as its estimated reliability is increased.
This interpretation suggests that learning the reliable
prior should be reflected in a decrease of activity in the
posterior superior temporal region. A decrease of activity
is a marker of the initial, fast, but condition-specific stage
of learning (e.g., Karni et al., 1995, 1998). The mechanism underlying this “habituation-like” pattern is not well
understood and may reflect a match between the successfully detected prior and the incoming first tone,
which would lead to a stimulus-specific suppression,
whereas the failure of such a match (mismatch) in the
No-Ref condition would not yield suppression.
The notion that this area is involved in auditory regularity detection and, perhaps, in the storage of detected
regularities is consistent with previous studies that associated this area with the analysis of temporal structures at
different levels of complexity (peak voxel is located at:
x + 2, y + 3, z + 4 from the peak voxel reported in Davis
& Johnsrude, 2003; x, y + 5, z from the peak voxel
reported in Friederici et al., 2009; x, y + 4, z + 5 from
the peak voxel reported in Obleser & Kotz, 2010).
By extension, with additional, cross-day learning, the
response to this prior should gradually increase, as was
reported by Karni et al., following several weeks of practice on a simple finger sequencing task (Karni et al., 1995,
1998). It may evolve into an area of expertise that gradually stores more elaborate priors.
No Indication of Regularity Learning in the
Auditory Cortex
As expected, our frequency discrimination paradigm activated the primary auditory cortex. However, its response
did not differ between conditions or blocks. Importantly,
although the Ref-1st condition contained a smaller range
of tones than the No-Ref condition, which could have
induced greater adaptation in areas with narrow frequency tuning curves, no such reduction was found. This
observation is congruent with observations both at the
Volume 27, Number 7
level of single neurons (Kajikawa, de La Mothe, Blumell,
& Hackett, 2005; Recanzone, Guard, & Phan, 2000; Ehret
& Schreiner, 1997; Howard et al., 1996) and with ERP
(Daikhin & Ahissar, 2012) reports of broad adaptation
tuning. Nevertheless, we cannot exclude the possibility
that a different experimental design specifically aimed
at measuring the working memory-based retention and
comparison processes would have revealed working
memory-related activity in the auditory cortex (e.g.
Brechmann et al., 2007; Zatorre & Samson, 1991).
In conclusion, we found that the degree of regularity
affects the pattern of brain activity even in simple discrimination tasks. The frontoparietal network involved
in working memory is activated to a greater extent when
no regularity is introduced. When a simple regularity is
introduced, an effective prior is formed, leading to reduced activity in a region that controls retention (left
intraparietal) and in a region that stores this effective
prior (posterior superior-temporal region). We posit that
this orchestrated modification in brain activity reflects a
quick and implicit shift “backwards” when a reliable prior
is detected. In other words, task performance relies more
on posterior networks that store effective priors than on
laborious online computations.
Acknowledgments
This work was supported by ISF grant 616/11 and the HUJI and
EPFL Brain Collaboration. In addition, we thank Avi Mendelson
and Tanya Orlov for their constructive comments and help with
data analysis.
Reprint requests should be sent to Merav Ahissar, Department
of Psychology and the Edmond and Lily Safra Center for Brain
Sciences, Hebrew University of Jerusalem, Israel 91905, or via
e-mail: msmerava@gmail.com.
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