Dissociable Effects of Top-Down and Bottom-Up Attention during Episodic Encoding

Abstract

It is well established that the formation of memories for life's experiences-episodic memory-is influenced by how we attend to those experiences, yet the neural mechanisms by which attention shapes episodic encoding are still unclear. We investigated how top-down and bottom-up attention contribute to memory encoding of visual objects in humans by manipulating both types of attention during fMRI of episodic memory formation. We show that dorsal parietal cortex-specifically, intraparietal sulcus (IPS)-was engaged during top-down attention and was also recruited during the successful formation of episodic memories. By contrast, bottom-up attention engaged ventral parietal cortex-specifically, temporoparietal junction (TPJ)-and was also more active during encoding failure. Functional connectivity analyses revealed further dissociations in how top-down and bottom-up attention influenced encoding: while both IPS and TPJ influenced activity in perceptual cortices thought to represent the information being encoded (fusiform/lateral occipital cortex), they each exerted opposite effects on memory encoding. Specifically, during a preparatory period preceding stimulus presentation, a stronger drive from IPS was associated with a higher likelihood that the subsequently attended stimulus would be encoded. By contrast, during stimulus processing, stronger connectivity with TPJ was associated with a lower likelihood the stimulus would be successfully encoded. These findings suggest that during encoding of visual objects into episodic memory, top-down and bottom-up attention can have opposite influences on perceptual areas that subserve visual object representation, suggesting that one manner in which attention modulates memory is by altering the perceptual processing of to-be-encoded stimuli.

Full text

Behavioral/Systems/Cognitive
Dissociable Effects of Top-Down and Bottom-Up Attention
during Episodic Encoding
Melina R. Uncapher,
1
J. Benjamin Hutchinson,
1
and Anthony D. Wagner
1,2
1
Department of Psychology and
2
Neurosciences Program, Stanford University, Stanford, California 94305-2130
It is well established that the formation of memories for life’s experiences— episodic memory—is influenced by how we attend to those
experiences, yet the neural mechanisms by which attention shapes episodic encoding are still unclear. We investigated how top-down and
bottom-up attention contribute to memory encoding of visual objects in humans by manipulating both types of attention during fMRI of
episodic memory formation. We show that dorsal parietal cortex—specifically, intraparietal sulcus (IPS)—was engaged during top-
down attention and was also recruited during the successful formation of episodic memories. By contrast, bottom-up attention engaged
ventral parietal cortex—specifically, temporoparietal junction (TPJ)—and was also more active during encoding failure. Functional
connectivity analyses revealed further dissociations in how top-down and bottom-up attention influenced encoding: while both IPS and
TPJ influenced activity in perceptual cortices thought to represent the information being encoded (fusiform/lateral occipital cortex), they
each exerted opposite effects on memory encoding. Specifically, during a preparatory period preceding stimulus presentation, a stronger
drive from IPS was associated with a higher likelihood that the subsequently attended stimulus would be encoded. By contrast, during
stimulus processing, stronger connectivity with TPJ was associated with a lower likelihood the stimulus would be successfully encoded.
These findings suggest that during encoding of visual objects into episodic memory, top-down and bottom-up attention can have
opposite influences on perceptual areas that subserve visual object representation, suggesting that one manner in which attention
modulates memory is by altering the perceptual processing of to-be-encoded stimuli.
Introduction
The creation of new memories for life’s experiences is intimately
linked to how we attend (Craik and Lockhart, 1972). During an
event, we may willfully direct attention to information relevant to
ongoing goals (“top-down” attention) or our attention may be
captured by salient information (“bottom-up” attention) (Kast-
ner and Ungerleider, 2000; Corbetta and Shulman, 2002). How
such attentive acts shape event encoding is unclear.
The neurobiology of top-down and bottom-up attention is
posited to consist of separable yet interacting frontoparietal net-
works (Corbetta and Shulman, 2002), and recent work has begun
to consider how the parietal components of these networks are
engaged during event encoding (Uncapher and Wagner, 2009).
During attention paradigms, functional imaging data indicate
that dorsal posterior parietal cortex (dPPC) activity tracks de-
mands on top-down attention, and ventral posterior parietal cor-
tex (vPPC) activity tracks recruitment of bottom-up attention
(for review, see Corbetta et al., 2008). During event encoding, the
fMRI signal in frontal and parietal regions frequently predicts
whether the event will be subsequently remembered or forgotten
(“subsequent memory effects”) (for review, see Paller and Wag-
ner, 2002; Rugg et al., 2002; Blumenfeld and Ranganath, 2006;
Uncapher and Wagner, 2009). A recent across-study analysis of
the encoding literature raised the possibility that PPC subsequent
memory effects anatomically overlap with PPC correlates of at-
tention (Uncapher and Wagner, 2009). Moreover, the majority
of PPC foci demonstrating enhanced activity for events later re-
membered versus forgotten (“positive” subsequent memory ef-
fects) localized to dPPC, whereas foci demonstrating enhanced
activity for events later forgotten versus remembered (“negative”
subsequent memory effects) localized exclusively to vPPC.
These data motivated the “dual-attention encoding hypothe-
sis,” which posits that top-down and bottom-up attention may
differentially foster encoding success and failure, respectively
(Uncapher and Wagner, 2009). This hypothesis builds on evi-
dence that top-down attention enhances cortical representations
of attended information (Corbetta et al., 1990; Noudoost et al.,
2010) (for review, see Beck and Kastner, 2009) and thus may
increase the probability that attended information ultimately
projects to the medial temporal lobe (MTL) for encoding (e.g.,
Moscovitch and Umilta, 1990; Uncapher and Rugg, 2009). From
this perspective, positive subsequent memory effects emerge
when dPPC-mediated top-down attention is directed toward in-
formation that will be relevant to later remembering. By contrast,
engagement of vPPC-mediated bottom-up attention may lead to
later memory failure when attention is captured by information
not relevant or helpful to later remembering (Otten and Rugg,
2001a; Wagner and Davachi, 2001; Cabeza et al., 2008; Uncapher
and Wagner, 2009).
Received Jan. 10, 2011; revised July 5, 2011; accepted July 13, 2011.
Author contributions: M.R.U. and A.D.W. designed research; M.R.U. and J.B.H. performed research; M.R.U. con-
tributed unpublished reagents/analytic tools; M.R.U. analyzed data; M.R.U. and A.D.W. wrote the paper.
This research was supported by National Institute of Mental Health Grants 5R01-MH080309 and
F32-MH084475.
Correspondence should be addressed to Melina R. Uncapher, Stanford Memory Laboratory, Jordan Hall, Building
420, Stanford, CA 94305-2130. E-mail: melina.u@stanford.edu.
DOI:10.1523/JNEUROSCI.0152-11.2011
Copyright © 2011 the authors 0270-6474/11/3112613-16$15.00/0
The Journal of Neuroscience, August 31, 2011 •31(35):12613–12628 • 12613

Because consideration of PPC attention mechanisms on event
encoding has largely come from across-study comparisons, it re-
mains unclear whether parietal fluctuations that predict the mne-
monic fate of events reflect the influence of top-down and
bottom-up attention on episodic encoding. Here we imple-
mented a paradigm that provides independent assays of attention
and encoding, within subjects, to determine (1) whether dPPC
and vPPC show attention-sensitive activity that is also predictive
of subsequent memory success or failure, and (2) whether the
manner in which these attention-sensitive regions dynamically
interact with the rest of the brain predicts the mnemonic fate of
an event.
Materials and Methods
Participants. Data are reported from 18 right-handed, native English-
speaking volunteers (9 female; age range: 18 –27 years; mean age: 20.4
years; SD: 2.9 years), each of whom gave written informed consent in
accordance with procedures approved by the Institutional Review Board
of Stanford University. All participants were recruited from the Stanford
University community, reported no history of neurological trauma or
disease, and were remunerated at a rate of $20/h. Data from two addi-
tional participants were acquired but excluded from analyses, due to
their failure to exhibit a behavioral reorienting effect (see Behavioral task,
below).
Stimulus materials. Stimuli consisted of 660 black-and-white line
drawings of common objects and 80 artificial “greeble” objects (Gauthier
and Tarr, 1997). Common objects were drawn from the International
Picture Naming Project (Szekely et al., 2004) and from a set previously
used by Uncapher and Rugg (2009). Greebles and common objects were
transformed into black-and-white line drawings in Photoshop CS, ver-
sion 8.0 (Adobe Systems). For the purpose of counterbalancing, com-
mon objects were divided into 33 sets of 20 objects each, and greebles into
5 sets of 16 objects each. Object sets were rotated across study and test lists
across subjects, and greebles were rotated across study lists. For each
subject, 10 study lists were created from these sets, each containing 40
objects and 16 greebles. In each study list, 46 stimuli were presented in the
cued location (“valid” trials, see Behavioral task, below; 32 objects and 14
greebles), and the remaining 10 stimuli were presented in the noncued
location (“invalid” trials; 8 objects and 2 greebles), yielding an 18% prob-
ability that a stimulus would be invalidly cued.
Of the 400 common objects encountered during the study phase, 350
were carried forward into the test phase (to minimize test list length).
Thus, in each of 10 sessions, 35 common objects served as the critical
study items (27 valid and 8 invalid). In the later memory test, 180 com-
mon objects served as foils. A separate set of 10 common objects and 3
greebles was used to create a practice study list.
Behavioral task. Within the present task context, “top-down atten-
tion” refers to the goal-directed allocation and maintenance of visu-
ospatial attention evoked by a preparatory cue (described below), and
“bottom-up attention” refers to a shift of visuospatial attention evoked
by an object occurring outside the focus of attention. We are agnostic as
to whether bottom-up shifts of attention occur by means of automatic,
involuntary, or “exogenous” processes versus goal-directed, voluntary,
or “endogenous” processes (for review, see Corbetta et al., 2008; Posner
and Cohen, 1984; Downar et al., 2001; de Fockert et al., 2004; Kincade et
al., 2005; Serences et al., 2005; Indovina and Macaluso, 2007; Burrows
and Moore, 2009).
The experiment consisted of 10 scanned incidental study sessions fol-
lowed by one nonscanned memory test. Each study session lasted ⬃6.5
min, and the memory test lasted ⬃40 min. The interval between the end
of the study phase and the start of test phase was ⬃10–15 min. Partici-
pants received instructions and practiced the study task before entering
the scan suite; study task performance was analyzed during this training
session to ensure that participants understood and complied with the
task instructions (see below). Moreover, eye movements were visually
monitored by two investigators, and feedback was given to the partici-
pant to ensure that participants could perform the study task while main-
taining fixation; participants were trained to ceiling level, continuing
until no visually detectable saccades occurred at any point during the
training session. We acknowledge that this training does not ensure that
eye movements did not occur during scanning. However, given recent
studies of overt versus covert attentional shifts that demonstrate that the
same frontoparietal regions are engaged when eye movements occur and
when they do not (Ikkai and Curtis, 2008), we believe the possible pres-
ence of eye movements in the present study does not alter the interpre-
tation of the findings. We also note that eye movements were not
recorded during scanning in many prior studies of Posner attention cue-
ing, including the study to which we compare our attention-related ef-
fects (Corbetta et al., 2000).
During each study session, participants viewed a black screen contain-
ing two white boxes (subtending 5° horizontal and vertical angles) ap-
pearing to the right and left of a white central fixation crosshair (Fig. 1).
The nearest edges of the boxes subtended a 1° horizontal angle from
central fixation. The beginning of each trial was indicated by the appear-
ance of a green arrow cue, which replaced the central fixation for 1 s. The
arrow pointed to the left or right, cuing subjects to covertly shift their
attention (without moving their eyes) to the corresponding box. After a
variable cue-to-stimulus interval (CSI)—1, 3, or 5 s—an object appeared
for 500 ms in one of the two boxes. On 82% of trials, the object appeared
in the cued box (valid trials), while on the remaining trials it appeared in
the noncued box (invalid trials). Upon appearance of the object, partic-
ipants were to indicate whether the stimulus represented a real object or
belonged to the artificial object class of greebles (index and middle finger
key press, respectively); the response hand was counterbalanced across
subjects. Speed and accuracy were given equal emphasis in the task in-
structions. A variable intertrial interval (ITI) of 1.5, 3.5, or 5.5 s separated
the offset of the object stimulus from the onset of the following cue. The
variable CSIs and ITIs were pseudorandomly distributed across trials
such that the regressors in the general linear model (GLM) (see fMRI data
analysis) that estimated the neural responses to cues and objects were
minimally correlated (⬍0.13), thus allowing activity elicited by each
phase in the trial (cue and object) to be independently assessed. Stimuli
were presented in pseudorandom order, with no more than three trials of
one item type (objects/greebles) occurring consecutively. Stimuli were
projected onto a mirror mounted on the MRI headcoil.
Immediately following the final study session, participants were trans-
ferred from the scanner to a neighboring testing suite and received in-
structions for the surprise memory test. The test list comprised 350
studied (old) and 180 unstudied (new) common objects, centrally pre-
sented, individually, in pseudorandom order. For each test object, par-
ticipants were to indicate whether or not they had encountered the item
in any of the study sessions, and to indicate their level of confidence. One
of four responses (sure old, unsure old, unsure new, sure new) was made
with the index or middle fingers of each hand. Old and new responses
were made using separate hands, with high and low confidence indicated
by middle and index fingers, respectively. The mapping of hands to old or
new responses was counterbalanced across participants.
Each test trial began when the white fixation crosshair changed to red
for 500 ms (Fig. 1), after which the test object was presented and re-
mained onscreen until a response was made or until 4 s elapsed (if no
response was made). If the object was judged to be new, the test advanced
to the next trial. If the object was judged to be old, it remained onscreen
for up to 4 more seconds, during which time participants attempted to
recollect the location at which the object had appeared during the study
(cued with the appearance of a “Left?”, “Right?” prompt) (Fig. 1). Par-
ticipants made a left or right index finger button press to indicate mem-
ory for the object appearing on the left or right side of the screen,
respectively, and made their best guess when uncertain. A 1.5 s ITI (dis-
playing a white fixation crosshair) separated test trials. To mitigate fa-
tigue, participants were given a self-paced break after each third of the
test.
fMRI data acquisition. Whole-brain imaging was performed ona3T
Signa MR scanner (GE Medical Systems). Anatomical images were col-
lected using a high-resolution T1-weighted spoiled gradient recalled
(SPGR) pulse sequence (130 slices; 1.5 mm thick; 256 ⫻256 matrix; 0.86
mm
2
in-plane resolution). Functional images were obtained from 30
4-mm-thick axial slices, aligned to the anterior commissure–posterior
12614 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding

commissure plane and positioned to give full coverage of the cerebrum
and most of the cerebellum, using a T2*-weighted, two-dimensional
gradient-echo spiral-in/out pulse sequence (TR ⫽2s;TE⫽30 ms; flip
angle ⫽75°; 64 ⫻64 matrix; 3.44 mm
2
in-plane resolution). Data were
acquired in 10 sessions, comprised of 190 volumes each. Volumes within
sessions were acquired continuously in a descending sequential order.
The first four volumes were discarded to allow tissue magnetization to
achieve a steady state.
fMRI data analysis. Data preprocessing and statistical analyses were
performed with Statistical Parametric Mapping (SPM5, Wellcome
Department of Cognitive Neurology, London, UK: http://www.fil.
ion.ucl.ac.uk/spm/software/spm5/), implemented in MATLAB 7.7
(Mathworks). For each participant, all volumes were realigned spatially
to the first volume, and then to the across-run mean volume. All volumes
were corrected for differences in acquisition times between slices (tem-
porally realigned to the acquisition of the middle slice). The anatomical
volume was coregistered to the mean functional volume, and then a
unified segmentation procedure (Ashburner and Friston, 2005) was ap-
plied to segment the anatomical volume into gray matter, white matter,
and CSF. The segmented images were deformed to probabilistic maps of
each tissue type in Montreal Neurological Institute (MNI) space, and the
resulting deformation parameters were applied to the functional images
for normalization. Functional images were resampled into 3 mm
3
voxels
using nonlinear basis functions (Ashburner and Friston, 1999). Func-
tional images were concatenated across sessions. Normalized functional
images were smoothed with an isotropic 8 mm full-width at half-
maximum (FWHM) Gaussian kernel.
Univariate analyses. Statistical analyses were performed in two stages
of a mixed-effects model. In the first stage, neural activity was modeled by
a delta function (impulse event) at the onset of each cue and each stim-
ulus. These functions were convolved with a canonical hemodynamic
response function (HRF) and its temporal and dispersion derivatives
(Friston et al., 1998) to yield regressors in a GLM that modeled the BOLD
response to each event type. The two derivatives modeled variance in
latency and duration, respectively. Analyses of the parameter estimates
pertaining to the dispersion derivative of cue-related effects are reported
below (parameter estimates pertaining to the derivatives of other items
contributed no theoretically meaningful information beyond that con-
tributed by the canonical HRF, and thus are not reported).
The time series were high-pass filtered to 1/128 Hz to remove low-
frequency noise and scaled to a grand mean of 100 across both voxels and
scans. As described below, parameter estimates for events of interest were
estimated using one of two GLMs. Nonsphericity of the error covariance
was accommodated by an AR(1) model, in which the temporal autocor-
relation was estimated by pooling over suprathreshold voxels (Friston et
al., 2002). The parameters for each covariate and the hyperparameters
governing the error covariance were estimated using restricted maxi-
mum likelihood. Effects of interest were tested using linear contrasts of
the parameter estimates. These contrasts were carried forward to a sec-
ond stage in which subjects were treated as a random effect. Unless oth-
Figure1. Experimentaldesign.Left,During the study phase, subjects were scanned while incidentallyencodingaseriesof visually presented objects and greebles in a variantofthePosnercueing
paradigm. Items were preceded by an arrow cue, pointing to a box on the left or right side of the screen. Subjects were to covertly shift attention to the cued box, in anticipation of an upcoming item.
Items appeared after a variable duration (see Materials and Methods, Behavioral task) in either the cued location (validly cued trial) or the uncued location (invalidly cued trial). Subjects made a
buttonpresstoindicatetheyhad identified a real object or a greeble, regardless of whethertheitemappearedinthe validly or invalidly cued location. Right, Following the study phase, subjectswere
administered a surprise memory test for the real objects. Subjects made one of four memory responses indicating whether they recognized the item from the previous study phase (old) or whether
the object was unstudied (new), as well as their level of confidence in their decision (sure or unsure). If an old response was given, subjects were cued to make a source judgment for the location in
which the item was studied (left or right).
Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding J. Neurosci., August 31, 2011 •31(35):12613–12628 • 12615

erwise specified, whole-brain analyses were used when no strong regional
a priori hypothesis was possible; in these cases, only effects surviving an
uncorrected threshold of p⬍0.001 and including four or more contig-
uous voxels were interpreted. When we held an a priori hypothesis about
the localization of a predicted effect, we corrected for multiple compar-
isons by using a small-volume correction employing an FWE rate based
on the theory of random Gaussian fields (Worsley et al., 1996). The peak
voxels of clusters exhibiting reliable effects are reported in MNI
coordinates.
Regions of overlap between the outcomes of two contrasts were iden-
tified by inclusively (or “conjunctively”) masking the relevant SPMs.
When two contrasts are independent, the statistical significance of the
resulting SPM can be computed using Fisher’s method of estimating the
conjoint significance of independent tests (Fisher, 1950; Lazar et al.,
2002). For each inclusive masking procedure, the constituent contrasts
were determined to be independent with tests of orthogonality for linear
contrasts. This test concludes that two linear contrasts are statistically
independent if the sum of the products of the coefficients is equal to zero
(i.e., for contrasts A and B: if a
1
b
1
⫹a
2
b
2
⫹a
3
b
3
⫹a
4
b
4
⫽0, then
contrasts are orthogonal). The goal of inclusive masking in the present
study was to search within regions that exhibited one pattern of activity
to identify whether any voxels also showed a second pattern. As such, we
maintained the original threshold of the contrast identifying the first
pattern (p⬍0.001), using a threshold of p⬍0.05 for the masking
contrast, to give a conjoint significance of p⬍0.0005. Exclusive (or
“disjunctive”) masking was used to identify voxels where effects were not
shared between two contrasts. The SPM constituting the exclusive mask
was thresholded at p⬍0.10, whereas the contrast to be masked was
thresholded at p⬍0.001. Note that the more liberal the threshold of an
exclusive mask, the more conservative is the masking procedure.
To identify the relationship between attention effects and encoding
effects, we orthogonalized the factors in our design: (1) top-down atten-
tion effects were identified by interrogating cue-related activity for all
items, collapsing over subsequent memory status, and bottom-up atten-
tion effects were identified by comparing validly cued versus invalidly
cued items; and (2) positive and negative subsequent memory effects
were identified by comparing subsequently remembered versus forgot-
ten items. To accommodate these analyses, two GLMs were estimated.
The first was optimized to model stimulus-related effects (i.e., subse-
quent memory effects and bottom-up attention effects). Study trials were
segregated according to subsequent memory [high confidence hits
(HCHs), low confidence hits, and misses (Ms)] and cue validity (valid,
invalid), resulting in six object-related regressors. Two additional regres-
sors modeled greebles (validly and invalidly cued). Objects for which
memory was not later tested were modeled as events of no interest, as
were objects for which a response was omitted at test. Nine additional
regressors modeled the cue-related activity associated with each of the
aforementioned stimulus types. Finally, six regressors modeled move-
ment-related variance (three rigid-body translations and three rotations
determined from the realignment stage), and session-specific constant
terms modeled the mean over scans in each session.
The second GLM implemented a parametric modulation analysis de-
signed to detect top-down attention effects. Previous Posner cueing stud-
ies (Corbetta et al., 2000) identified top-down attention effects by
isolating activity elicited by cues to shift covert attention. To separately
estimate neural responses associated with cues versus stimuli, prior stud-
ies used “catch trials” in which no stimulus appeared postcue (Corbetta
et al., 2000). Here we opted to decorrelate cue- and stimulus-related
activity by parametrically varying the CSI (1, 3, or 5 s). Furthermore,
because cue-related activity could reflect not only top-down attention
effects but also low-level visual responses to the cue itself, we used a
parametric modulation analysis to distinguish activity transiently re-
sponding to the cue from that responding across the duration the vari-
able CSIs. In other words, by isolating BOLD responses that were elicited
by the cue to shift attention and that were also sustained throughout the
variable interval over which attention was to be maintained, we could
rule out the possibility that any cue-related effects were simply a reflec-
tion of low-level visual responses. To accomplish this, we included an
orthogonal variable in the GLM to isolate top-down effects, identifying
cue-related activity that increased with increasing CSI duration. It should
be noted that because the HRF effectively integrates the total activity over
a period of a few seconds, parametric modulators identify activity that is
modulated in duration or magnitude (or both). As we discuss below, the
obtained data suggest that the parametric modulator captured variance
in duration of the cue-related HRF. In sum, for this second GLM, one
regressor modeled all cue-related activity, and a second regressor para-
metrically modulated the first according to the CSI following each cue.
Thus, the second regressor for each subject in this GLM was of equal
length to the first regressor and comprised a vector of values where each
value represented the duration of the CSI for the corresponding cue. This
regressor identified voxels in which cue-related activity varied as a linear
function of the duration over which attention was to be maintained. To
accommodate the remainder of the known variance, a third regressor
modeled all stimulus-related activity (not segregated according to subse-
quent memory or validity), and movement and session effects were mod-
eled as described above.
An alternative method of interrogating sustained BOLD responses
across the CSI is to analyze effects associated with the dispersion deriva-
tive of the cue-related HRF. In the present design, cue-related attention
effects (but not cue-related visual effects) might show a more sustained
response, which would be captured by significant loading on the disper-
sion derivative. Accordingly, below we report the cue-related dispersion
derivative outcome, which confirmed the parametric modulator out-
come. However, we note that a dispersion derivative analysis is less op-
timal than a parametric modulation analysis for our design for two
reasons. First, a dispersion derivative analysis can only accommodate
variance in the duration of the HRF up to ⬃1 s, and here the CSI varied
from 1, 3, or 5 s. Second, the differing durations of the CSI are subopti-
mally modeled by a dispersion derivative, which is fit across all trial types
(i.e., 1, 3, and 5 s CSIs). This trial-by-trial variability is better modeled by
a parametric modulation analysis (Henson, 2007).
Response profiles of regions of interest (ROIs) identified in mapwise
analyses were investigated using the deconvolution algorithm imple-
mented in MarsBar (marsbar.sourceforge.net). This algorithm decon-
volves the BOLD signal in the ROI using a finite impulse response
function, which assumes no shape for the hemodynamic response. From
these analyses, the clusterwise integrated percentage signal change was
extracted for further characterization of the functional response (see
Results for details).
Connectivity analyses. The second main goal of the present study was to
determine whether the functional dynamics of the putative dorsal and
ventral attention networks changed when an event memory was effec-
tively formed relative to when one was not. An understanding of how
these attention networks dynamically interact with other neural struc-
tures during the encoding of event information may inform how episodic
memories are created. We therefore sought to determine whether neural
components of the dorsal and ventral attention networks showed con-
nectivity profiles that predicted later memory success or failure.
Multivariate connectivity analyses were conducted by submitting seed
regions—ROIs identified in the univariate analyses as exhibiting top-
down or bottom-up attention effects (see next paragraph)—to psycho-
physiological interaction (PPI) analyses to determine whether they
showed memory-related functional connectivity with other regions in
the brain (Friston et al., 1997). In this manner, we investigated whether
the connectivity between attention-related regions and other brain re-
gions differed as a function of encoding success or failure.
Seed regions were components of PPC that exhibited relevant atten-
tion effects: a left dPPC [medial intraparietal sulcus (mIPS), extending
into superior parietal lobule (SPL)] region that displayed top-down at-
tention effects, and bilateral vPPC (TPJ) regions that displayed bottom-up
attention effects (for details, see Results, Neural correlates of top-down
and bottom-up attention subsection). Using standard PPI analysis tech-
niques, seed clusters were individually defined for each subject on the
basis of the random-effects group analyses. For each subject, the data for
each seed region was the principal eigenvariate of all significant (p⬍
0.05) voxels withina4mmsphere, centered on the local peak maximum
that fell within 2⫻FWHM of the smoothing kernel (i.e., 16 mm) and was
within the anatomical region of interest, identified from each subject’s
12616 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding

normalized structural scan. These subject-specific timeseries represented
the physiological component of the PPI. All but one of the 18 subjects
met all criteria for every seed region; PPI analyses were performed on
these 17 subjects. The timeseries were adjusted for variance associated
with effects of no interest, and then a deconvolution with the hemody-
namic responses was performed. The resultant vector was weighted by a
contrast vector representing the relevant psychological factor (in this
case, a subsequent memory contrast: HCH vs M), and then reconvolved
with the hemodynamic responses. The outcome of this process formed
the “psychophysiological interaction,” or PPI regressor. This regressor
models the between-condition difference in regression slopes between
each voxel in the brain and the seed region. This PPI regressor was en-
tered into a GLM, along with regressors modeling the main effects of the
psychological and physiological factors (i.e., the condition contrast vec-
tor and the timeseries, respectively). In line with the univariate GLMs, we
additionally modeled movement-related effects as well as session-specific
constant terms. Because PPI analyses are inherently less powered than
their univariate counterparts (as only the unshared variance among the
three regressors is attributed to the interaction term, or the PPI regres-
sor), we adopted a whole-brain uncorrected threshold of p⬍0.005, four
voxel extent. Voxels that surpassed threshold in these PPI analyses can be
interpreted as showing a significant difference in connectivity with the
attention-related seed region as a function of later memory outcome.
Results
Behavioral performance
Study task
Participants performed at ceiling on the object/greeble discrimi-
nation task for items that were cued validly and invalidly (0.98,
SE ⫽0.0002/0.99, SE ⫽0.0002, respectively). All but two of 20
participants showed evidence of attentional reorienting, as in-
dexed by longer response times (RTs) to items appearing in the
invalidly cued relative to the validly cued location; as noted
above, data from the two subjects failing to show this behavioral
reorienting effect were omitted from all analyses. Data from the
18 participants submitted to analysis revealed that the reorienting
effect (all items: F
(1,17)
⫽21.00, p⬍0.001) (Table 1) was signif-
icant for both common objects (t
(17)
⫽5.97, p⬍0.001) and
greebles (t
(17)
⫽2.91, p⬍0.01). The CSI between the orienting
cue and the onset of the object did not impact the reorienting
effect, as evidenced by the absence of an interaction between CSI
and validity (F⬍1).
Memory task
Overall memory performance. Recognition memory was estimated by
calculating the difference in the probabilities of an old response to an
old vs a new item [discrimination measure, Pr ⫽(pOld 兩Old) ⫺
(pOld 兩New)]. Recognition memory performance was superior
when participants were highly confident in their old/new decision
(Pr
hi conf
⫽0.30, SE ⫽0.03) relative to when a low-confidence re-
sponse was given (Pr
low conf
⫽0.07, SE ⫽0.01) (Pr
hi conf
vs Pr
low conf
,
t
(17)
⫽5.75, p⬍0.001). Analyses of d⬘, excluding three participants
who had zero high-confidence false alarms, resulted in the same
pattern as the Pr analyses: high-confidence d⬘⫽1.25, SE ⫽0.11 vs
low-confidence d⬘⫽0.29, SE ⫽0.08.
For studied items that were confidently endorsed as old, par-
ticipants were well above chance (0.5) when indicating the loca-
tion in which the object was studied (“source memory” accuracy:
mean ⫽0.71, SE ⫽0.03; t
(17)
⫽6.54, p⬍0.001). This was not the
case, however, when low-confidence judgments were given
(mean ⫽0.52, SE ⫽0.02; t
(17)
⬍1). Thus, because both recogni-
tion and source memory performance were poor for low-
confidence responses, the fMRI subsequent memory analyses
focused on comparison of HCHs versus Ms.
Valid versus invalid memory performance. The need to reorient
attention to invalidly cued objects at study negatively impacted
subsequent memory for those objects (Table 2). Specifically, in-
validly cued objects were later confidently recognized less often
than were validly cued objects (t
(17)
⫽5.18, p⬍0.0005). How-
ever, when invalidly cued objects were subsequently recognized
with high confidence, source memory performance was equiva-
lent to that for validly cued objects (t
(17)
⬍1). This finding sug-
gests that the following neuroimaging comparisons of valid and
invalid subsequent memory effects (HCH vs M) were not biased
in favor of conditions associated with superior source memory.
Such a bias would be introduced, however, if subsequent memory
analyses were expanded to include all recognized items (rather
than restricted to HCHs), as source memory was better for all hits
in the valid versus invalid conditions (t
(17)
⫽1.9, p⬍0.04). This
finding of biased source memory for all hits, but unbiased source
memory for high-confidence hits, reinforces the restriction of
subsequent memory analyses to high-confidence hits. Finally,
study task RTs conditionalized as a function of later memory
performance revealed that study RTs did not differ as a function
of subsequent memory (Table 1) (valid HCH vs M: t
(17)
⬍1;
invalid HCH vs M: t
(17)
⬍1). This finding rules out the possibility
that the neural subsequent memory effects were simply a conse-
quence of the amount of time spent initially processing study
items (i.e., differential “duty cycles”).
Neuroimaging findings
The present experiment was designed to directly assess the degree
to which top-down and bottom-up attention mechanisms con-
Table 1. Response times to critical study items (objects that were later tested for memory) as a function of study CSI and subsequent memory
According to study CSI According to later memory
1 s CSI 3 s CSI 5 s CSI Across CSIs HCH LCH M
Validly cued items(ms) 734 (43) 714 (41) 708 (42) 719 (42) 715 (40) 713 (44) 721 (42)
Invalidly cued items(ms) 881 (49) 851 (47) 842 (40) 859 (46) 873 (55) 830 (52) 864 (45)
Values are given as the means of the median response times (SEs) to critical study items. LCH, Low-confidence hits.
Table 2. Recognition and source memory performance with source memory conditionalized on recognition memory
Recognition memory (proportion of old or new items)
Source memory (proportion correct)High confidence Low confidence
Hits False alarms Hits False alarms High confidence Low confidence
Validly cued items 0.37 (0.04) 0.04 (0.01) 0.19 (0.02) 0.13 (0.02) 0.72 (0.04) 0.52 (0.03)
Invalidly cued items 0.26 (0.04) 0.21 (0.02) 0.69 (0.04) 0.50 (0.03)
Values are given as mean (SE).
Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding J. Neurosci., August 31, 2011 •31(35):12613–12628 • 12617

tribute to the formation of event memo-
ries. Our analysis strategy first considered
the factors of attention and encoding sep-
arately, and then examined the relation-
ship between them by (1) investigating
regional overlap between attention and
encoding effects, and (2) investigating
connectivity effects.
Neural correlates of top-down and
bottom-up attention
We first examined whether our attention
paradigm gave rise to patterns of activity
similar to those observed in prior Posner
cueing studies (which primarily used de-
tection tasks with repeated simple shapes,
whereas our paradigm included a dis-
crimination task and trial-unique mean-
ingful objects). Based on prior studies,
top-down attention effects were predicted
(1) to be elicited by the arrow cues to shift
attention and (2) to vary in duration ac-
cording to the interval over which atten-
tion was maintained (the variable CSI)
(see Experimental procedures). Support-
ing these predictions, a parametric modu-
lation analysis revealed that cue-related
activity (cue ⬎fixation) varied positively
according to CSI in the frontal eye fields
(FEFs), the left mIPS, extending into SPL
(Fig. 2; Table 3). These findings are con-
sistent with an extensive literature sug-
gesting that these frontoparietal regions form a dorsal attention
network (Corbetta et al., 1993; Nobre et al., 1997; Kastner et al.,
1999; Hopfinger et al., 2000; Sylvester et al., 2007; for review, see
Corbetta et al., 2008).
Confirming the outcome of the parametric modulation anal-
ysis, analysis of nonmodulated cue effects (i.e., the canonical cue-
related HRF) revealed the same set of regions (bilateral FEF and
left mIPS/SPL). Notably, this contrast also identified an addi-
tional set of regions in visual cortex (bilateral visual cortex, cen-
tered on [12, ⫺99, 6] and [⫺18, ⫺93, 6]; z⫽4.17), consistent
with the idea that the nonmodulated effects reflect a combination
of low-level visual and high-level attention-related processes. Fi-
nally, to determine which nonmodulated effects exhibited a
sustained response, we inclusively masked the nonmodulated
effects (p⬍0.001) with the dispersion derivative contrast (p⬍
0.05). The outcome of this procedure revealed 34 voxels in
mIPS/SPL (centered on [⫺24, ⫺57, 54]; z⫽3.66) that showed
a more sustained response than the canonical cue-related
HRF, further confirming the findings from the parametric
modulation analysis.
To identify neural correlates of bottom-up attention, objects
appearing in unexpected locations were contrasted with those
appearing in expected locations (i.e., invalidly ⬎validly cued
objects). Multiple regions were more active when the object was
invalidly cued, including bilateral TPJ (Fig. 2; Table 3). This pat-
tern is consistent with the proposal that TPJ is a key component
of a “ventral attention network” that mediates stimulus-driven
reorienting of attention (Corbetta et al., 2008). Additional re-
gions revealed in this contrast included FEF and IPS (Fig. 2; Table
3), a finding consistent with the hypothesis that stimulus-driven
reorienting of attention triggers recruitment of the dorsal atten-
tion network (Corbetta et al., 2002, 2008; Giessing et al., 2006;
Shulman et al., 2009). That is, effects revealed by this contrast
may reflect the consequences of a stimulus-driven salience calcu-
lation (mediated in part by TPJ), which in turn serves to drive
shifts in the locus of visuospatial attention (partially mediated by
an FEF-IPS/SPL network) (Burrows and Moore, 2009; Shulman
et al., 2009).
It is notable that these “validity effects” appear to overlap with
the CSI-varying cue-related effects in medial— but not lateral—
IPS (Fig. 2, yellow). This apparent dissociation would extend
recent evidence suggesting that lateral and medial IPS function-
ally differ (Nelson et al., 2010), with mIPS differentially tracking
demands on top-down attention (Hutchinson et al., 2009; Sest-
ieri et al., 2010; Uncapher et al., 2010). Here, mIPS was engaged
during the components of the Posner task that are thought to
recruit top-down attention (most directly evidenced by the para-
metrically modulated cue-related contrast, and indirectly evi-
denced by the validity contrast, for reasons described in the
previous paragraph). Interestingly, this mIPS/SPL region appears
to anatomically overlap with that revealed in a recent meta-
analysis of the top-down attention literature (compare Fig. 2,
present mIPS/SPL top-down effects, yellow ⫹green, Uncapher et
al., 2010, their Fig. 1, top-down attention Activation Likelihood
Estimation map).
Importantly, the apparent selectivity of the present top-down
effects to mIPS/SPL, not including lateral IPS, was confirmed by
a disjunction analysis (i.e., identification of regions that exhibit
significant effects in one contrast but not in the other). To iden-
tify regions that exhibited validity effects but not CSI-varying
cue-related activity, we masked the validity contrast (p⬍0.001)
Figure 2. Top-down and bottom-up attention effects. Top, Regions that exhibit cue-related activity that was modulated
according to the duration over which attention was to be maintained (green; top-down attention effects); regions that exhibit
greater activity in response to objects that appeared in an unexpected versus expected location (red; bottom-up attention effects).
Regionaloverlapofthetwoeffects appears in yellow. Effects are thresholded at p⬍0.001 on astandardizedbrain(PALS-B12atlas
using Caret5: http://brainvis.wustl.edu/wiki/index.php/Caret:About). Bottom, The observed hemodynamic responses are plotted
for relevant clusters (data pooled across activated voxels falling within a 6-mm-radius sphere centered on the peak voxel in the
cluster; note that top-down attention effects were elicited by cues, and bottom-up attention effects by objects). L IPS/SPL, Left
IPS/SPL; L TPJ, left TPJ; R TPJ, right TPJ.
12618 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding

with the parametrically modulated cue-related contrast (thresh-
olded at a lenient level, p⬍0.10, to create a stringent mask).
Importantly, left lateral IPS survived this disjunction analysis,
indicating that it exhibited validity effects but no evidence of
cue-related activity. We also performed the reverse disjunction
analysis, masking the cue-related contrast (p⬍0.001) with the
validity contrast (thresholded at a lenient level, p⬍0.10). This
procedure did not reveal a disjunction in left mIPS/SPL (nor in
FEF), suggesting that medial (and not lateral) parietal subregions
are engaged during both the cue-related and validity contrasts
(again, this finding is compatible with the view that top-down
attention mechanisms can be triggered by stimulus-driven sa-
lience that drives the reorienting of attention). Together, these
findings support the hypothesis that lateral and medial IPS func-
tionally differ, with mIPS/SPL differentially tracking demands on
top-down attention.
Neural correlates of episodic encoding
Prior studies identifying the neural correlates of encoding have
contrasted study items that were subsequently remembered ver-
sus forgotten (Brewer et al., 1998; Wagner et al., 1998; Henson et
al., 1999) (for review, see Wagner et al., 1999; Paller and Wagner,
2002; Spaniol et al., 2009). Accordingly, we first analyzed
stimulus-related activity on trials most analogous to prior subse-
quent memory experiments, namely, trials on which the object
appeared in an expected location (validly cued trials), and was
later remembered with high confidence (HCH) versus later for-
gotten (M). Consistent with the literature on MTL- and PPC-
encoding effects (for respective review, see Davachi, 2006;
Uncapher and Wagner, 2009), this contrast revealed positive sub-
sequent memory effects (HCH ⬎M) in multiple regions, includ-
ing bilateral hippocampus and right posterior IPS (Fig. 3; Table
4). Also evident were bilateral clusters that encompassed fusi-
Table 3. Parietal and frontal regions exhibiting top-down and bottom-up attention effects, subsets of which demonstrate overlap with subsequent memory effects
⬃BA
MNI coordinates
Peak Z(no. voxels) SMEsxyz
Top-down attention Overlap with Positive SMEs
R FEF 6/4 39 ⫺3 63 4.04 (172) 39 ⫺363
R FEF 4/6 27 ⫺9 57 3.35
R FEF 4 30 ⫺3 66 3.45 30 ⫺366
R precentral gyrus 4 39 ⫺6 51 4.03 45 0 57
R SEF/CEF 6 48 0 48 4.02 48 0 48
L FEF 6/4 ⫺33 ⫺6 60 5.17 (480) ⫺30 ⫺345
L FEF 4 ⫺15 ⫺9 63 3.37
L precentral gyrus 4 ⫺45 ⫺6 45 4.87 ⫺45 ⫺645
L precentral gyrus 4 ⫺36 ⫺18 51 3.71
L SEF/CEF 6 ⫺6 6 54 4.28 ⫺3357
L mIPS/SPL 7 ⫺30 ⫺48 57 3.89 (125)
L mIPS/SPL 7 ⫺27 ⫺60 57 3.75 ⫺27 ⫺57 51
L mIPS 7 ⫺30 ⫺42 48 3.74
L mIPS 7 ⫺36 ⫺30 39 3.20
Bottom-up attention Overlap with Negative SMEs
L IFS 46 ⫺30 30 24 3.57 (37)
L IFS 46 ⫺36 24 36 3.47
L IFG 46 ⫺42 30 30 3.48
R IFG 44 54 21 27 3.28 (4)
R anterior insula 16 48 21 ⫺3 3.97 (82)
R anterior insula 16 42 27 ⫺6 3.92
L anterior insula 16 ⫺36 18 ⫺3 3.57 (13)
L SEF 6/8 ⫺9 9 54 3.29 (4)
R FEF 6 45 3 48 4.79 (306)
R FEF 6 30 3 54 4.23
R SEF 6 12 9 60 4.37
R MFG 44/6 36 9 33 3.55
L FEF 6 ⫺24 0 54 4.70 (96)
R TPJ 40 63 ⫺36 36 4.93 (597) 57 ⫺39 36
R TPJ 40 54 ⫺39 30 4.72 51 ⫺39 33
R TPJ 40 63 ⫺42 24 4.72 60 ⫺45 30
R TPJ 40 60 ⫺51 15 4.45 60 ⫺54 24
R lateral IPS 7 45 ⫺45 51 3.68 48 ⫺42 51
R mIPS 7 36 ⫺51 42 4.25
R precuneus/posterior cingulate 7/31 18 ⫺54 42 3.55
L lateral IPS 7 ⫺30 ⫺45 39 4.98 (590)
L lateral IPS 7 ⫺27 ⫺69 36 4.49
L TPJ 40 ⫺48 ⫺51 39 4.23 ⫺48 ⫺51 39
L TPJ 40 ⫺57 ⫺57 24 3.63 ⫺57 ⫺57 24
L precuneus 7 ⫺12 ⫺57 51 4.36 ⫺9⫺60 45
R precuneus/posterior cingulate 7/31 15 ⫺66 54 3.84 (82)
R precuneus/posterior cingulate 7/31 9 ⫺63 42 3.62 9 ⫺63 42
R precuneus/posterior cingulate 7/31 12 ⫺54 54 3.37
Given the large spatial extent of the clusters, peaks and subpeaks listed; coordinates with associated cluster size denote cluster peak, and subpeaks are contiguous local maxima ⬎8 mm apart. Clusters that also exhibit subsequent memory
effects (i.e., survived the conjunction analysis between attention and memory contrasts) are denoted in the last columns (peak coordinates identified from the conjunction analysis listed). Zvalues refer to the peak or subpeak of the listed
cluster. L, Left; R, right; SEF, supplementary eye field; CEF, cingulate eye field; IFS, inferior frontal sulcus; IFG, inferior frontal gyrus; MFG, medial frontal gyrus; SME, subsequent memory effect.
Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding J. Neurosci., August 31, 2011 •31(35):12613–12628 • 12619

form and lateral occipital (LO) cortex (Fig. 3); these ventral
temporal-occipital foci appear to include the LO complex, which
is implicated in visual object representation (Malach et al., 1995;
Grill-Spector et al., 2001; Grill-Spector and Malach, 2004). In
support of this interpretation, an analysis of common objects vs
greebles identified a large swath of activity overlapping these fusi-
form and LO subsequent memory effects (available from the
corresponding author upon request). Additionally, a positive
subsequent memory effect was evident in left ventrolateral pre-
frontal cortex (VLPFC), a region consistently associated with ep-
isodic encoding success for verbalizable and/or meaningful
stimuli (Kirchhoff et al., 2000; Baker et al., 2001; Davachi et al.,
2001; Otten and Rugg, 2001b; Clark and Wagner, 2003). [Note
that subsequent memory effects were computed by comparing all
HCHs (i.e., collapsed across source memory accuracy) to misses;
to identify effects of source memory, we compared HCHs with
and without source memory in the subset of participants (n⫽15)
with five or more trials in each condition. No region demon-
strated greater encoding activity for HCHs with source memory
at standard statistical thresholds (p⬍0.001), whereas activation
in right TPJ was greater during HCHs without source memory.
Because these source memory analyses were underpowered,
however, interpretative caution is warranted.]
Positive subsequent memory effects are often accompanied by
negative subsequent memory effects, or regions that show an
enhanced response to items later forgotten relative to items later
remembered. Consistent with prior reports (Otten and Rugg,
2001a; Wagner and Davachi, 2001; Daselaar et al., 2004; Gon-
salves et al., 2004; Reynolds et al., 2004; Turk-Browne et al., 2006;
Chua et al., 2007; Otten, 2007; Park and Rugg, 2008), negative
subsequent memory effects (M ⬎HCH) were observed in right
vPPC, including TPJ, and in medial aspects of parietal and pre-
frontal cortex (Fig. 3; Table 4). These negative subsequent mem-
ory effects, as well as the preceding positive subsequent memory
effects, were associated with object-related activity (analogous
analyses of cue-related activity did not reveal significant corre-
lates of subsequent memory at standard statistical thresholds)
(Otten et al., 2006).
Overlap of attention and encoding effects
Stimulus-related signals that vary with subsequent memory may
reflect a variety of processes. We therefore adopted a functional
localizer logic, using the top-down attention contrast to con-
strain the process space being investigated. To do so, we looked
within the regions engaged during top-down attention (opera-
tionalized as parametrically modulated cue-related activity) to
assess whether stimulus period activity in these regions tracked
subsequent memory. In other words, to determine whether the
neural correlates of top-down attention overlapped with those of
successful episodic encoding, we used a conjunction analysis be-
tween the regions identified by the parametrically modulated
cue-related contrast and those exhibiting positive object-related
subsequent memory effects. Specifically, the cue-related contrast
(thresholded at p⬍0.001) was inclusively masked with the pos-
itive subsequent memory contrast for validly cued objects
(thresholded at p⬍0.05). This conjunction analysis (at a con-
joint threshold of p⬍0.0005) revealed that the bilateral FEF and
left mIPS/SPL regions that exhibited a top-down cueing effect
were again engaged when objects appeared (postcue), and this
engagement was predictive of subsequent memory (Fig. 4, green;
Figure3. Subsequentmemoryeffects.Left,Greater activity in response to validly cued objects that were laterconfidentlyrememberedrelativetoforgotten (positive subsequent memory effects;
warm colors) was observed in bilateral hippocampus, left VLPFC, right posterior IPS, and bilateral LO/fusiform. By contrast, right TPJ exhibited greater activity in response to validly cued objects that
were later forgotten relative to confidently remembered (negative subsequent memory effects; cool colors). Hippocampal effects are displayed on mean across-subject anatomical images; all other
effects are surface rendered on a standardized brain. Display thresholds are p⬍0.001. Right, Observed hemodynamic responses to objects are plotted for relevant clusters (data pooled across all
voxels in the cluster). L Hip, Left hippocampus; R Hip, right hippocampus; R pIPS, right posterior IPS; R TPJ, right TPJ; R LO/Fus, right lateral occipital and fusiform cortex; L VLPFC, left ventrolateral
prefrontal cortex; L LO/Fus, left lateral occipital and fusiform cortex.
12620 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding

Table 3, denoted in final column). [Note that additional analyses
comparing HCHs with and without source memory failed to
reveal a difference in encoding activation in left mIPS/SPL and
FEF. Again, due to concerns about power, future studies are
needed to determine whether left mIPS/SPL activation during
encoding is specifically predictive of memory for item–location
associations and/or is related to item memory.]
Negative subsequent memory effects in TPJ have been posited
to reflect the disruptive consequences of the bottom-up capture
of attention during encoding, perhaps marking the diversion of
attention by irrelevant event information (Otten and Rugg,
2001a; Wagner and Davachi, 2001; Uncapher and Wagner,
2009). Here, we explicitly tested this account by examining the
relationship between the parietal structures engaged during the
bottom-up capture of attention (i.e., greater activation to inval-
idly cued relative to validly cued objects) and those demonstrat-
ing negative subsequent memory effects for validly cued objects.
Importantly, inclusive masking of the contrast identifying
bottom-up attention effects (thresholded at p⬍0.001) with that
revealing negative subsequent memory effects (thresholded at
p⬍0.05) revealed overlap in bilateral TPJ (Fig. 4, blue; Table 3,
denoted in final column). This pattern suggests that, at least for
validly cued objects, greater TPJ activation may reflect the cap-
ture of bottom-up attention by irrelevant event features. Quali-
tative inspection of the observed hemodynamic responses in
bilateral TPJ (Fig. 4) further indicated that TPJ responded max-
imally to items that appeared outside the current focus of atten-
tion (invalidly cued objects) and were later forgotten, and the
least to items that appeared where expected (validly cued objects)
and were later remembered. This observation of a negative sub-
sequent memory effect in TPJ on invalidly cued trials—wherein
bottom-up attention was presumably captured by the to-be-
encoded object—was unexpected, as the capture of bottom-up
attention by such objects was predicted a priori to foster their
encoding. Accordingly, we next explored the subsequent mem-
ory effects on invalid trials in greater detail.
Encoding of objects outside the focus of attention
Prior subsequent memory studies have investigated encoding
correlates for stimuli appearing in expected locations. In most
cases, the spatial contingency was limited to one location (central
fixation); in others, this contingency extended to multiple loca-
tions (Cansino et al., 2002; Sommer et al., 2005a,b; Uncapher et
al., 2006; Uncapher and Rugg, 2009). To our knowledge, no study
has examined whether encoding mechanisms are similar or dif-
ferent for stimuli presented outside versus inside the current fo-
cus of top-down attention. The present design—in which spatial
expectations were probabilistically confirmed (validly cued ob-
jects) or violated (invalidly cued objects)—provided leverage on
this question, though we note that this analysis is inherently un-
derpowered due to the relatively lower frequency of invalidly
cued objects (with an average of 62 trials contributing to subse-
quent memory analyses of invalid objects vs 214 for valid ob-
jects). (Note that the limited number of invalid trials precluded
subsequent source memory analyses from being conducted.)
To complement the preceding subsequent memory analysis
for validly cued objects, we first identified positive and negative
subsequent memory effects for invalidly cued objects [i.e.,
(HCH ⬎M)
invalid
and (M ⬎HCH)
invalid
]. A positive subsequent
memory effect for invalidly cued objects was observed in right
fusiform (Fig. 5A; Table 4); the absence of other positive corre-
Table 4. Brain regions associated with encoding success (positive subsequent
memory effects) or failure (negative subsequent memory effects) for items
presented in expected locations (valid trials) or unexpected locations (invalid
trials)
⬃BA
MNI coordinates Peak Z
(no. voxels)xyz
Valid trials
Positive subsequent memory effects
L IFG 45 ⫺39 24 15 3.29 (4)
L IFG/inferior frontal junction 44/6 ⫺42 0 27 4.14 (93)
R posterior/medial SFG 6/4 6 ⫺18 57 3.43 (4)
L putamen — ⫺33 0 ⫺9 3.69 (8)
R putamen — 27 ⫺6⫺12 3.57 (28)
L posterior hippocampus — ⫺24 ⫺33 ⫺6 3.60 (19)
R posterior hippocampus — 33 ⫺39 ⫺6 3.71 (14)
R fusiform 37 48 ⫺54 ⫺6 4.08 (55)
L fusiform 37 ⫺48 ⫺57 ⫺6 3.90 (97)
R posterior IPS 7/19 33 ⫺72 33 4.08 (18)
Negative subsequent memory effects
R medial superior rostral gyrus 10 15 60 15 4.23 (40)
R anterior/medial SFG 9 15 42 42 3.38 (15)
R TPJ 40/39 51 ⫺48 39 3.99 (98)
BL posterior cingulate 31 ⫺6⫺69 36 3.69 (36)
Invalid trials
Positive subsequent memory effects
R fusiform 37 39 ⫺45 ⫺15 3.40 (6)
Negative subsequent memory effects
R DLPFC 9 45 12 45 3.60 (6)
R IPS/TPJ 7/40 36 ⫺39 42 3.56 (52)
R SPL 5 24 ⫺48 63 3.31 (4)
R precuneus 7 12 ⫺63 51 4.01 (7)
R parietal-occipital sulcus 18/17 6 ⫺78 39 3.26 (6)
Z values refer to the peak of the cluster (subpeaks available from the corresponding author upon request). L, Left; R,
right; BL, bilateral; SFG, superior frontal gyrus; DLPFC, dorsolateral prefrontal cortex.
Figure 4. Overlap of attention and encoding effects. Top, Regions exhibiting both top-down
attention and positive subsequent memory effects for validly cued objects are displayed in
green, and those showing both bottom-up attention and negative subsequent memory
effects are displayed in blue. Data displayed at conjoint thresholds of p⬍0.0005. Bottom,
Observed hemodynamic responses are plotted for PPC clusters, according to subsequent
memory, and validity condition at study (data pooled across all voxels in each cluster). L
mIPS/SPL, Left mIPS/SPL; L TPJ, left TPJ; R TPJ, right TPJ; SME, subsequent memory effect;
HCH valid, validly cued HCHs; M valid, validly cued Ms; HCH invalid, invalidly cued HCHs; M
invalid, invalidly cued Ms.
Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding J. Neurosci., August 31, 2011 •31(35):12613–12628 • 12621

lates of encoding at the standard statistical threshold (p⬍0.001)
is likely due to low power. A follow-up statistically independent
analysis of the percentage signal change for this cluster further
revealed a modest, but significant, positive subsequent memory
effect for validly cued objects (t
(17)
⫽2.34; p⬍0.02). Strikingly,
the negative subsequent memory analysis revealed unexpected
effects in dPPC, including in right IPS and SPL (Fig. 5B; Table 4)
(similar effects were observed in left IPS/SPL when the statistical
threshold was relaxed to p⬍0.005). This was surprising given the
finding from a recent meta-analysis that all prior negative subse-
quent memory effects in parietal cortex have been reported in
vPPC, with none in dPPC (Uncapher and Wagner, 2009). Even in
the few studies that have directly manipulated goal-directed at-
tention in a subsequent memory paradigm (Kensinger et al.,
2003; Uncapher and Rugg, 2005, 2008, 2009), there was no dPPC
activity reported to be associated with subsequent forgetting.
We next sought to identify regions where subsequent memory
effects differed according to whether objects appeared in ex-
pected, relative to unexpected, locations. Voxel-level interaction
analyses [i.e., (HCH ⬎M)
invalid
⬎(HCH ⬎M)
valid
, and vice
versa] revealed that, while no regions showed positive encoding-
related activity that was greater for invalid than valid trials (even
when the threshold of the interaction was dropped to p⬍0.01),
several regions—including the right IPS and SPL regions previ-
ously identified— exhibited the opposite pattern (valid ⬎invalid
subsequent memory effects) (Fig. 5B; Table 5). Qualitative in-
spection of the signal in both regions (Fig. 5C) revealed a striking
pattern of findings in SPL: a positive subsequent memory pattern
for valid trials, reversing to a negative subsequent memory pat-
tern for invalid trials. This reversal of positive to negative effects
for items appearing inside versus outside the focus of attention
suggests that SPL may serve to bias the processing of informa-
tion appearing in the cued location, at the expense of information
appearing in the noncued location. In so doing, SPL-mediated
spatial attention processes may facilitate and hinder encoding,
respectively. Thus, this reversal of positive to negative subsequent
memory effects perhaps explains why dPPC-negative subsequent
memory effects have not been reported in prior studies, as none
have systematically manipulated whether objects appeared inside
versus outside the current focus of attention.
To evaluate the hypothesis that the reversal from positive to
negative subsequent memory effects in SPL reflects the engage-
ment of top-down attention, we inclusively masked the interac-
tion contrast with the cue-related parametric analysis described
above (p⬍0.001 and p⬍0.05, respectively). This procedure
confirmed that a subset of the SPL cluster (6 of 26 voxels) showed
both effects. Similarly, when we performed the reverse masking
procedure (inclusively masking the cue-related parametric anal-
ysis with the validity ⫻subsequent memory interaction analysis),
a large proportion of the cluster (81 of the 125 voxels) exhibited
both effects. Thus, the deployment of top-down attention ap-
pears to interact with stimulus location to either promote mem-
ory (if the stimulus appears in the cued location) or hinder
memory (if it appears in the noncued location).
One interpretation of the foregoing findings is that the more
top-down attention is deployed to the expected location (as in-
dexed by top-down attention effects in mIPS/SPL), the greater
the reflexive reorienting response when the item appears in the
unexpected location (as indexed by bottom-up attention effects
in TPJ); under such circumstances, the redeployment of atten-
tion may be ineffective in promoting stimulus encoding (as in-
dexed by negative subsequent memory effects in TPJ and SPL
during invalid trials). Consistent with the former hypothesis,
prior data indicate that expectations about where stimuli will
appear modulate the magnitude of TPJ reorienting responses.
For instance, Vossel et al. (2006) reported TPJ modulation ac-
cording to two different levels of cue validity (cues were either 90
or 60% predictive of the location of the upcoming target), with a
robust TPJ validity effect (invalid ⬎valid activity) being observed
in the high-expectancy condition, but little to no effect being
observed in the low-expectancy condition.
Figure 5. Subsequent memory effects for items outside the current focus of attention (in-
valid trials). A, A positive subsequent memory effect for invalidly cued items was observed in
right fusiform. B, Negative subsequent memory effects for invalidly cued items (blue) were
observedinventral parietal regions, but also—strikingly—in dorsalparietalregions(see text).
Regions that demonstrated a greater subsequent memory effect for objects appearing inside
versus outside the focus of attention (valid vs invalid) are displayed in red. A subset of regions
demonstrating this interaction also showed a negative subsequent memory effect for invalid
items: right IPS and SPL (purple). C, To illustrate the nature of the interaction in right IPS and
SPL, subsequent memory effects for valid and invalid conditions are plotted for each region
(clusterwise mean parameter estimates and SEs shown for clusters identified from the interac-
tion contrast). All display thresholds are p⬍0.001. R Fus, Right fusiform cortex; R IPS, right IPS;
R SPL, right SPL; L SPL, left SPL; SME, subsequent memory effect.
Table 5. Brain regions where subsequent memory effects differ according to
whether items appear in expected versus unexpected locations
⬃BA
MNI coordinates Peak Z
(no. voxels)xyz
Valid ⬎invalid subsequent memory effects
L anterior SFG 9 ⫺18 45 27 3.24 (5)
R putamen — 27 ⫺6⫺6 3.65 (17)
R central sulcus 3/4 51 ⫺12 39 3.40 (12)
L central sulcus 3/4 ⫺48 ⫺12 42 3.46 (26)
R SPL 7 30 ⫺39 63 4.25 (26)
R IPS 7 42 ⫺45 45 3.41 (8)
R precuneus 7 12 ⫺60 51 4.03 (16)
L precuneus 7 ⫺12 ⫺66 51 3.23 (4)
Invalid ⬎valid subsequent memory effects
12622 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding

To test the hypothesis that the more top-down attention is
deployed to the expected location, the greater the activity in TPJ
when a stimulus appears in the unexpected (vs expected) loca-
tion, we examined whether a relationship existed between mIPS/
SPL top-down attention effects and TPJ validity effects. To do so,
we regressed the extracted percentage signal change for each ef-
fect in each subject, and found a significantly positive correlation
across subjects (right TPJ: r⫽0.52, p⬍0.02; left TPJ: r⫽0.45,
p⬍0.03). In other words, those subjects that showed the greatest
CSI-varying cue-related activity in mIPS/SPL also showed the
greatest difference in TPJ activity when items appeared in the
unexpected, versus expected, location. To identify whether this
correlation between regions also had a direct consequence for
memory formation (as described previously), we next performed
a multilinear regression analysis on the miss rate of items studied
in the unexpected location. This analysis revealed a trend toward
higher correlations between mIPS/SPL and TPJ effects, and
higher miss rates for items in the unexpected location (F⫽3.262,
p⬍0.07), suggesting that those subjects exhibiting the greatest
reorienting response in TPJ to items in the unexpected location
(presumably due to allocating more top-down attention to the
expected location, as indexed by mIPS/SPL activity) also had
more difficulty effectively encoding items in unexpected loca-
tions into memory (as indexed by higher miss rates for these
items).
Functional connectivity of attention-sensitive regions
during encoding
The foregoing univariate analyses revealed that regions exhibit-
ing top-down and bottom-up attention effects were also differ-
entially associated with encoding success and failure. To
understand the dynamic nature of this relationship between at-
tention and memory formation, we next sought to characterize
how attention-related parietal regions interact with the rest of the
brain during the formation of event memories. To this end, we
implemented PPI analyses to identify neural structures whose
connectivity with attention-related parietal regions changed in a
memory-related fashion. It should be noted that all PPI analyses
use independent factors to identify and interrogate the data; in
other words, we used the factor of attention to identify our seed
ROIs, and then searched for regions whose connectivity with
these seeds differed according to subsequent memory.
Top-down attention seeds. First, we identified which regions
throughout the brain that the mIPS/SPL region exhibiting top-
down attention effects in our univariate analyses interacted with
during the preparatory (cue) period. Specifically, the analysis
identified regions where connectivity with mIPS/SPL differed
during trials on which the object was subsequently remembered
versus forgotten (“subsequent memory connectivity effects”).
Several regions exhibited such effects (i.e., stronger connectivity
with mIPS/SPL during preparatory periods wherein the subse-
quently presented object was later remembered relative to forgot-
ten) (Fig. 6A; Table 6), including the left LO/fusiform region that
showed a positive subsequent memory effect when processing
validly cued objects (Fig. 3). Thus, stronger preparatory coupling
between mIPS/SPL regions that mediate top-down attention and
LO/fusiform regions that represent visual object form appears to
facilitate encoding of subsequently encountered objects. Consis-
tent with this interpretation, an across-subject regression re-
vealed that subjects who showed stronger subsequent memory
connectivity between mIPS/SPL and LO/fusiform (during the
cue period) tended to demonstrate superior recognition memory
at test (i.e., exhibited higher Pr for high-confidence judgments)
relative to subjects who showed weaker connectivity effects
(r
(15)
⫽0.414, p⬍0.05) (Fig. 6A).
In addition to connectivity increases during the preparatory
period that tracked later memory outcome, mIPS/SPL also exhib-
ited connectivity decreases with other regions (i.e., showed
weaker connectivity during trials where the object was later re-
membered vs forgotten). These regions included a large cluster in
right angular gyrus (AnG) (Fig. 6A; Table 6). AnG is regarded as
Figure 6. Parietal attention seeds show connectivity that differs according to encoding success or failure. A, Regions whose connectivity with left mIPS/SPL (green sphere, peak mIPS/SPL
coordinatesofthetop-down attention contrast are illustrated in Fig. 2) wasstrongerduringpreparatory periods of trials resulting in objects beingrememberedversusforgotten(positive subsequent
connectivity effects; warm colors); regions showing the opposite effect, or stronger connectivity with left mIPS/SPL during preparatory periods leading to forgotten versus remembered objects
(negative subsequent connectivity effects) are displayed in cool colors [note that the small cluster in dorsal AnG (coordinates 39, ⫺57, 33) is in fact spatially contiguous with the large ventral AnG
clusterthathasbeensurface rendered dorsally (for details, see Table 6)]. Scatterplotillustratesthatindividual differences in the strength of the positive subsequentconnectivityeffectin LO/fusiform
duringencodingcorrelateswith across-subject differences in later memory performance,suchthatthestronger the mIPS/SPL–LO/fusiform connectivity, the superior thelatermemoryperformance.
B, Regions whose connectivity with left or right TPJ (Fig. 2, red spheres; peak TPJ coordinates from the bottom-up attention contrast illustrated) was stronger during the presentation of objects that
were later remembered versus forgotten, and vice versa (positive and negative subsequent connectivity effects). The coloring scheme is the same as that used in A. L mIPS/SPL, Left mIPS/SPL; L TPJ,
left TPJ; R TPJ, right TPJ; L LO/Fus; left LO/fusiform; R LO/Fus, right LO/fusiform; R AnG, right AnG.
Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding J. Neurosci., August 31, 2011 •31(35):12613–12628 • 12623

a key parietal component in the default-mode network, which is
thought to be involved in internally oriented or self-related cog-
nition (for review, see Buckner et al., 2008, Bressler and Menon,
2010). Thus, one interpretation of this greater mIPS/SPL–AnG
connectivity during trials where objects are later forgotten versus
remembered is that directing attention internally before the ex-
ternal presentation of a stimulus may be detrimental to the en-
coding of that information.
Bottom-up attention seeds. We next investigated whether the
bilateral TPJ regions exhibiting bottom-up attention effects (dur-
ing the stimulus-processing period) showed connectivity that
differed according to later memory. We therefore looked for sub-
sequent memory connectivity effects using the right and left TPJ
regions as seeds. We found no regions that showed positive sub-
sequent memory connectivity effects with the right TPJ seed and
only a few that did so with the left TPJ seed (Table 6). By contrast,
the TPJ seeds showed robust negative subsequent memory con-
nectivity effects with a broad set of regions (Table 6). Strikingly,
the LO/fusiform region that showed positive connectivity effects
with the mIPS/SPL seed showed the opposite pattern of connec-
tivity with TPJ (Fig. 6B). The TPJ seeds also showed negative
subsequent memory connectivity effects with bilateral regions
encompassing parahippocampal and fusiform cortices (Fig. 6B);
parahippocampal cortex has been associated with the spatial en-
coding of objects (for review, see Eichenbaum and Lipton, 2008).
Thus, successful object encoding appears to be associated with
reduced coupling between TPJ regions (implicated in reflexive
orienting) and LO/fusiform regions (implicated in visual object
representation), as well as with parahippocampal regions (impli-
cated in the encoding of objects in space).
Eye movements
Eye movements were not monitored in the scanner, raising the
possibility that the observed subsequent memory effects could, in
theory, reflect differential BOLD responses associated with eye
movement patterns (e.g., objects fixated might be associated with
more effective encoding and thus a higher likelihood of being
subsequently remembered). While the absence of eye-tracking
data precludes a definitive assessment of this possibility, we be-
lieve it unlikely for a number of reasons. First, it is unlikely that
Table 6. Regions where connectivity with attention-related seed regions differs according to later memory success or failure
⬃BA
MNI coordinates
Peak Z(no. voxels)xyz
Top-down attention seed
Left mIPS/SPL
Greater connectivity during encoding success
R medial thalamus — 6 0 ⫺6 2.84 (7)
R superior temporal sulcus 22 57 ⫺39 6 3.57 (17)
L fusiform 37 ⫺36 ⫺51 ⫺9 2.89 (5)
L LO cortex 37 ⫺51 ⫺63 ⫺6 3.06 (6)
L LO cortex 37 ⫺45 ⫺78 ⫺3 3.20 (33)
BL medial occipital cortex 17 0 ⫺96 9 2.87 (4)
Greater connectivity during encoding failure
R midbrain — 3 ⫺36 ⫺27 2.65 (4)
R AnG 39 42 ⫺57 27 3.51 (84)
Bottom-up attention seeds
Right TPJ
Greater connectivity during encoding success — — — —
Greater connectivity during encoding failure
BL medial SFG 9 0 51 39 3.12 (17)
L IFG 45/44 ⫺51 24 15 2.83 (5)
BL anterior cingulate 24 6 9 27 2.98 (13)
BL medial thalamus — 0 3 ⫺9 3.00 (7)
R parahippocampal gyrus 36 24 ⫺33 ⫺21 2.99 (8)
L parahippocampal gyrus/fusiform 36/37 ⫺24 ⫺36 ⫺21 3.16 (21)
R retrosplenial 29 21 ⫺51 9 3.07 (26)
L retrosplenial 29 ⫺15 ⫺51 12 3.33 (47)
BL retrosplenial 29 0 ⫺63 12 2.81 (6)
R LO cortex 19 45 ⫺63 ⫺9 3.11 (15)
Left TPJ
Greater connectivity during encoding success
L putamen — ⫺24 9 12 3.01 (4)
R precentral gyrus 4 60 ⫺3 42 2.94 (30)
L retrosplenial 29 ⫺9⫺39 21 2.78 (4)
R posterior cingulate 31 6 ⫺63 36 2.90 (9)
Greater connectivity during encoding failure
L DLPFC 46 ⫺39 33 9 2.84 (6)
BL medial thalamus — ⫺3⫺3⫺9 2.79 (5)
R fusiform 37 27 ⫺39 ⫺24 3.21 (22)
L fusiform 37 ⫺30 ⫺42 ⫺21 3.06 (19)
L parahippocampal gyrus 36 ⫺27 ⫺48 ⫺6 2.90 (8)
R parahippocampal gyrus 36 36 ⫺51 ⫺9 3.41 (15)
L LO cortex 19 ⫺39 ⫺60 ⫺6 2.73 (5)
L LO cortex 19 ⫺39 ⫺75 ⫺3 2.92 (6)
L parietal-occipital sulcus 18/17 ⫺12 ⫺81 24 3.06 (4)
DLPFC, Dorsolateral prefrontal cortex.
12624 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding

extensive eye movements occurred during scanning, because par-
ticipants were able to maintain accurate fixation during the be-
havioral training session, and an extensive literature suggests that
participants tend not to saccade when trained to covertly orient
(Posner and Cohen, 1980). Second, if eye movements did occur,
to the extent that differences in fixating the to-be-remembered
object account for differences in subsequent memory outcome,
one might predict that RTs during the object discrimination task
would be faster for objects in fixation relative to those peripher-
ally viewed. However, RTs were comparable during the encoding
of later remembered and later forgotten stimuli (see Behavioral
performance) (Table 1). Third, subsequent memory effects have
been consistently observed in IPS across studies in which all stim-
uli fall at the center of the visual field (for review, see Uncapher
and Wagner, 2009). Under such conditions, subjects learn that
the center of the visual field is task/goal-relevant, and it is to their
advantage to fixate this region to effectively perceive and make
decisions about presented stimuli. Fourth, even under conditions
where stimuli are presented rapidly in the center of the visual
field, making eye movements suboptimal and therefore unlikely,
subsequent memory effects are observed in IPS. For instance,
Otten and Rugg (2001b) identified subsequent memory effects in
IPS/SPL for items presented for 300 ms, with no RT differences
between items that were later remembered versus forgotten.
Finally, we note that studies that have systematically mapped
the effects of eye movements or overt shifts of attention have
consistently identified bilateral parietal activity, corresponding to
saccades to contralateral visual hemifields (for review, see Silver
and Kastner, 2009). Our critical analysis (overlap of top-down
attention effects and subsequent memory effects) identified uni-
lateral activity in left IPS (Fig. 4). This lateralization of effects was
not due to thresholding, as the effects remained left lateralized
when the threshold of the attention effects was reduced from p⬍
0.001 to a liberal value of 0.01. Given that the items were pre-
sented with equal probability to the two sides of the screen, it
seems unlikely that fluctuations in the magnitude of the unilat-
eral IPS effects reflect eye movements to both sides of the screen.
We would note that this pattern of left-lateralized IPS top-down
attention effects has also been found in prior Posner studies,
where eye movements were monitored during scanning and
found to be negligible (Doricchi et al., 2010).
Summary of fMRI findings
The univariate data demonstrated a relationship between neural
correlates of attention and episodic encoding of objects: top-
down attention effects and encoding success effects overlapped in
dPPC (mIPS/SPL), and bottom-up attention effects and encod-
ing failure effects overlapped in vPPC (TPJ). The deployment of
top-down attention interacted with stimulus location to either
promote memory (if the stimulus appeared in the cued location)
or hinder memory (if it appeared in the noncued location). The
multivariate connectivity findings suggest that episodic encoding
of objects is influenced by parietal interactions with regions rep-
resenting visual object information (LO/fusiform), with a posi-
tive influence from top-down attention-related regions (mIPS/
SPL) and a negative influence from bottom-up attention-related
regions (TPJ).
Discussion
Based on prior observations of encoding success effects localized
predominantly to dPPC and encoding failure effects localized
exclusively to vPPC, we previously speculated that top-down and
bottom-up attention may have distinct influences on event en-
coding (Uncapher and Wagner, 2009). The present study directly
tested this dual-attention encoding hypothesis by manipulating
top-down and bottom-up attention during an incidental encod-
ing paradigm. Three main findings advance understanding of
how attention regulates memory formation. First, for objects ap-
pearing in expected locations, dPPC regions engaged during the
controlled allocation of visuospatial attention were positively
correlated with episodic memory formation, whereas vPPC re-
gions engaged during stimulus-driven attentional capture were
negatively correlated with memory formation. Second, we pro-
vide novel evidence that the deployment of top-down attention is
not always beneficial for encoding, as the dorsal attention net-
work was also associated with encoding failure when objects ap-
peared outside the focus of attention. Finally, connectivity
analyses revealed that, during the formation of memories for
objects, top-down and bottom-up attention appear to have op-
posite influences on perceptual cortical areas that subserve visual
object representation, suggesting that one manner in which at-
tention modulates memory is by altering the perceptual process-
ing of to-be-encoded stimuli.
Top-down attention and episodic encoding
We propose that the observed overlap between neural correlates
of top-down attention and encoding success in dPPC is indicative
of the extent to which the deployment of top-down attention
supports memory encoding. In other words, the overlap of effects
may reflect engagement of top-down attention mechanisms dur-
ing the study task, which in turn increases the probability that
attended information will progress through the cortical hierarchy
to converge on MTL mechanisms for encoding into memory.
Top-down attention can enhance the firing rate of neurons
representing goal-relevant elements of an experience (Desimone
and Duncan, 1995; McAdams and Maunsell, 1999; Treue and
Martinez-Trujillo, 1999; Boynton, 2005) (for review, see Treue,
2001), and attention is thought to influence between-region
communication by altering oscillatory coupling (Saalmann et al.,
2007; Gregoriou et al., 2009a), preparing regions to receive input
at the most excitable phase of their oscillatory activity (Gregoriou
et al., 2009b). The hippocampus, by virtue of its apical position in
the neural processing hierarchy (Felleman and Van Essen, 1991),
is proposed to be the recipient of cortically processed event fea-
tures (for review, see Eichenbaum et al., 2007), and cortical–
hippocampal coupling is associated with successful episodic
encoding (Fell et al., 2001). By facilitating the cortical represen-
tations of goal-relevant stimuli, top-down attention—partially
subserved by an FEF–mIPS/SPL network— could foster the
propagation of higher-fidelity representations to the hippocam-
pus, resulting in a higher probability of stimulus encoding into
memory (“biased input hypothesis”) (Uncapher and Rugg,
2009).
The present data lend support for this perspective. First, acti-
vation in mIPS/SPL exhibited a top-down attention effect during
the preparatory period and predicted later memory success dur-
ing the object processing period. Second, activity in this mIPS/
SPL region predicted response profiles in object-sensitive regions
of ventral temporo-occipital cortex, namely LO and fusiform.
Importantly, this functional coupling was stronger during prepa-
ratory periods of trials for which the subsequently presented ob-
ject was later remembered versus later forgotten, and the strength
of this mIPS/SPL–LO/fusiform coupling difference correlated
with across-subject differences in later memory performance. Fi-
nally, activity in bilateral fusiform, LO, and hippocampus was
greater during the viewing of objects that would be later remem-
Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding J. Neurosci., August 31, 2011 •31(35):12613–12628 • 12625

bered versus forgotten. Collectively, these findings are consistent
with a role of top-down attention in promoting encoding by (1)
preparing object representation processes in LO/fusiform for in-
coming information, with a stronger drive promoting better en-
coding, and (2) fostering effective propagation of these putatively
higher-fidelity visual representations to the hippocampus. Fu-
ture studies using intracranial electrocorticography (Jacobs and
Kahana, 2010), with independent electrodes in LO/fusiform and
in hippocampus, will provide a means to test the role of
attention-mediated oscillatory coupling in visual object encoding
into episodic memory.
Bottom-up attention and episodic encoding
The dual-attention encoding hypothesis posits that memory fail-
ure can result when attention is captured by event information
that is not the target of a subsequent retrieval attempt. That is, if
attention is shifted away from information that will be the target
of subsequent retrieval, encoding will be hindered for this infor-
mation. Here, the overlap between bottom-up attention and neg-
ative subsequent memory effects for validly cued objects in
bilateral TPJ lends support for the proposal that attention capture
can hinder event encoding. Factors that engage the ventral atten-
tion network are debated, but at least include an unexpected or
salience dimension (Posner and Cohen, 1984; Jonides and Yantis,
1988). One possibility is that attention was captured by nonex-
perimental variables (e.g., unexpected change in scanner noise,
urge to move, or internally oriented cognition) or stimulus-
related variables (e.g., oddly shaped or shaded object), consistent
with data indicating that TPJ is engaged by not only spatial but
also nonspatial (feature-based) information (Linden et al., 1999;
Marois et al., 2000; Braver et al., 2001; Kiehl et al., 2001; Serences
et al., 2005). Thus, while future studies are needed to gain lever-
age on the nature of the attention-capturing information leading
to subsequent forgetting of target objects, the present data pro-
vide evidence that a reorienting mechanism in TPJ is directly
associated with later forgetting.
Intuitively, it is easy to understand that attentional capture of
extrastimulus information would draw processing resources
from the target stimulus, leading to poorer memory for the stim-
ulus. What is less clear is what happens when the attention-
capturing information is stimulus related. Recent studies reveal
an equivocal pattern: while items that are distinctive in some way
(and therefore attention capturing) often enjoy a mnemonic ad-
vantage (von Restorff, 1933), this is not always the case. Strange et
al. (2000) reported an interaction of the “von Restorff effect” and
depth of processing. Perceptually distinctive items were better
remembered only if studied under “shallow” encoding condi-
tions (where superficial aspects of the items were emphasized)
(Fabiani et al., 1990), whereas emotionally distinctive items were
always remembered better. Importantly, semantically distinctive
items were remembered worse when attention was paid to that
dimension during study. Summerfield and Mangels (2006)
additionally showed that items appearing at unpredictable
times (distinctive in the temporal dimension) were more
poorly remembered than predictable items.
Here we examined the consequences on memory when items
appeared in an unexpected location. Like the semantic and tem-
poral dimensions, items that appeared in an unexpected location
suffered a mnemonic disadvantage. As in previous studies, this
may be due to an interaction between our encoding task and the
attention-capturing dimension. If attention to the unexpected
spatial location occurred at the expense of some other memory-
promoting dimension, such as semantic elaboration, memory for
the objects would suffer. Another explanatory factor may be the
degree to which attention-capturing information is used as a re-
trieval cue. To the degree that spatial information is a poor re-
trieval cue, attention being captured by the spatial dimension at
study may hinder later memory performance.
The present findings also advance understanding of how
memories are formed for objects appearing outside the current
focus of attention (i.e., invalidly cued objects). Our data revealed
that, rather than recruiting a new set of mechanisms, the mecha-
nisms associated with encoding objects outside attentional fo-
cus—in fusiform cortex—are a subset of those engaged when
objects are within attentional focus. Strikingly, presenting objects
outside attentional focus also revealed a novel negative subse-
quent memory effect in the dorsal attention network (SPL). To
date, activation in this top-down network has been exclusively
associated with subsequent memory success, rather than failure
(Uncapher and Wagner, 2009). This negative effect reversed to a
positive effect when objects appeared in the expected location,
suggesting that top-down biasing of attention interacts with ex-
pectation to either promote or hinder memory formation. Thus,
previous findings that the top-down attention network exclu-
sively promotes memory encoding may be due to a lack of expec-
tation violations in prior studies. Collectively, the present
findings highlight the role of perceptual cortices in visual object
encoding, and the modulatory influence that attention has on
activity in these regions.
Summary
The present data provide strong evidence that top-down and
bottom-up attention mechanisms in parietal cortex influence ep-
isodic encoding, with mIPS/SPL-mediated top-down attention
generally serving to promote memory formation, and TPJ-
mediated bottom-up attention serving to hinder memory forma-
tion (at least within the present experimental context). Here we
offer a mechanistic explanation for how to reconcile effects of
attention that may appear inconsistent across the literature (Un-
capher and Wagner, 2009); namely, that the presence of positive
or negative subsequent memory effects in dorsal or ventral PPC
can be predicted based on whether one exerts experimental con-
trol over the focus of attention. These findings of direct overlap
between attention and episodic encoding appear to stand in con-
trast to the pattern observed during episodic retrieval, where the
overlap between parietal attention and memory effects is a topic
of current debate (Cabeza et al., 2008; Ciaramelli et al., 2008;
Hutchinson et al., 2009; Uncapher et al., 2010). As such, the
present data highlight the importance of attention during event
processing for later remembering (Craik et al., 1996).
References
Ashburner J, Friston KJ (1999) Nonlinear spatial normalization using basis
functions. Hum Brain Mapp 7:254 –266.
Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage
26:839–851.
Baker JT, Sanders AL, Maccotta L, Buckner RL (2001) Neural correlates of
verbal memory encoding during semantic and structural processing tasks.
Neuroreport 12:1251–1256.
Beck DM, Kastner S (2009) Top-down and bottom-up mechanisms in bi-
asing competition in the human brain. Vis Res 49:1154–1165.
Blumenfeld RS, Ranganath C (2006) Dorsolateral prefrontal cortex pro-
motes long-term memory formation through its role in working memory
organization. J Neurosci 26:916–925.
Boynton GM (2005) Attention and visual perception. Curr Opin Neurobiol
15:465–469.
Braver TS, Barch DM, Gray JR, Molfese DL, Snyder A (2001) Anterior cin-
12626 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding

gulate cortex and response conflict: effects of frequency, inhibition and
errors. Cereb Cortex 11:825–836.
Bressler SL, Menon V (2010) Large-scale brain networks in cognition:
emerging methods and principles. Trends Cogn Sci 14:277–290.
Brewer JB, Zhao Z, Desmond JE, Glover GH, Gabrieli JD (1998) Making
memories: brain activity that predicts how well visual experience will be
remembered. Science 281:1185–1187.
Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default
network: anatomy, function, and relevance to disease. Ann N Y Acad Sci
1124:1–38.
Burrows BE, Moore T (2009) Influence and limitations of popout in the
selection of salient visual stimuli by area V4 neurons. J Neurosci
29:15169–15177.
Cabeza R, Ciaramelli E, Olson IR, Moscovitch M (2008) The parietal cortex
and episodic memory: an attentional account. Nat Rev Neurosci
9:613–625.
Cansino S, Maquet P, Dolan RJ, Rugg MD (2002) Brain activity underlying
encoding and retrieval of source memory. Cereb Cortex 12:1048–1056.
Chua EF, Schacter DL, Rand-Giovannetti E, Sperling RA (2007) Evidence
for a specific role of the anterior hippocampal region in successful asso-
ciative encoding. Hippocampus 17:1071–1080.
Ciaramelli E, Grady CL, Moscovitch M (2008) Top-down and bottom-up
attention to memory: a hypothesis (AtoM) on the role of the posterior
parietal cortex in memory retrieval. Neuropsychologia 46:1828–1851.
Clark D, Wagner AD (2003) Assembling and encoding word representa-
tions: fMRI subsequent memory effects implicate a role for phonological
control. Neuropsychologia 41:304–317.
Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-
driven attention in the brain. Nat Rev Neurosci 3:201–215.
Corbetta M, Miezin FM, Dobmeyer S, Shulman GL, Petersen SE (1990) At-
tentional modulation of neural processing of shape, color, and velocity in
humans. Science 248:1556–1559.
Corbetta M, Miezin FM, Shulman GL, Petersen SE (1993) A PET study of
visuospatial attention. J Neurosci 13:1202–1226.
Corbetta M, Kincade JM, Ollinger JM, McAvoy MP, Shulman GL (2000)
Voluntary orienting is dissociated from target detection in human poste-
rior parietal cortex. Nat Neurosci 3:292–297.
Corbetta M, Kincade JM, Shulman GL (2002) Neural systems for visual
orienting and their relationships to spatial working memory. J Cogn Neu-
rosci 14:508–523.
Corbetta M, Patel G, Shulman GL (2008) The reorienting system of the
human brain: from environment to theory of mind. Neuron 58:306 –324.
Craik FIM, Lockhart RS (1972) Levels of processing: a framework for mem-
ory research. J Verbal Learn Verbal Behav 11:671–684.
Craik FIM, Govoni R, Naveh-Benjamin M, Anderson ND (1996) The effects
of divided attention on encoding and retrieval processes in human mem-
ory. J Exp Psychol Gen 125:159–180.
Daselaar SM, Prince SE, Cabeza R (2004) When less means more: deactiva-
tions during encoding that predict subsequent memory. Neuroimage
23:921–927.
Davachi L (2006) Item, context and relational episodic encoding in humans.
Curr Opin Neurobiol 16:693–700.
Davachi L, Maril A, Wagner AD (2001) When keeping in mind supports
later bringing to mind: neural markers of phonological rehearsal predict
subsequent remembering. J Cogn Neurosci 13:1059–1070.
de Fockert J, Rees G, Frith C, Lavie N (2004) Neural correlates of attentional
capture in visual search. J Cogn Neurosci 16:751–759.
Desimone R, Duncan J (1995) Neural mechanisms of selective visual atten-
tion. Annu Rev Neurosci 18:193–222.
Doricchi F, Macci E, Silvetti M, Macaluso E (2010) Neural correlates of the
spatial and expectancy components of endogenous and stimulus-driven
orienting of attention in the Posner task. Cereb Cortex 20:1574–1585.
Downar J, Crawley AP, Mikulis DJ, Davis KD (2001) The effect of task rel-
evance on the cortical response to changes in visual and auditory stimuli:
an event-related fMRI study. Neuroimage 14:1256–1267.
Eichenbaum H, Lipton PA (2008) Towards a functional organization of the
medial temporal lobe memory system: role of the parahippocampal and
medial entorhinal cortical areas. Hippocampus 18:1314–1324.
Eichenbaum H, Yonelinas AP, Ranganath C (2007) The medial temporal
lobe and recognition memory. Annu Rev Neurosci 30:123–152.
Fabiani M, Karis D, Donchin E (1990) Effects of mnemonic strategy manip-
ulation in a Von Restorff paradigm. Electroencephalogr Clin Neuro-
physiol 75:22–35.
Fell J, Klaver P, Lehnertz K, Grunwald T, Schaller C, Elger CE, Ferna´ndez G
(2001) Human memory formation is accompanied by rhinal-
hippocampal coupling and decoupling. Nat Neurosci 4:1259–1264.
Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in
the primate cerebral cortex. Cereb Cortex 1:1–47.
Fisher R (1950) Statistical methods for research workers. London: Oliver
and Boyd.
Friston KJ, Buechel C, Fink GR, Morris J, Rolls E, Dolan RJ (1997) Psycho-
physiological and modulatory interactions in neuroimaging. Neuroimage
6:218–229.
Friston KJ, Fletcher P, Josephs O, Holmes A, Rugg MD, Turner R (1998)
Event-related fMRI: characterizing differential responses. Neuroimage
7:30–40.
Friston KJ, Glaser DE, Henson RN, Kiebel S, Phillips C, Ashburner J (2002)
Classical and Bayesian inference in neuroimaging: applications. Neuro-
image 16:484–512.
Gauthier I, Tarr MJ (1997) Becoming a “Greeble” expert: exploring mech-
anisms for face recognition. Vis Res 37:1673–1682.
Giessing C, Thiel CM, Ro¨sler F, Fink GR (2006) The modulatory effects of
nicotine on parietal cortex activity in a cued target detection task depend
on cue reliability. Neuroscience 137:853–864.
Gonsalves B, Reber PJ, Gitelman DR, Parrish TB, Mesulam MM, Paller KA
(2004) Neural evidence that vivid imagining can lead to false remember-
ing. Psychol Sci 15:655–660.
Gregoriou GG, Gotts SJ, Zhou H, Desimone R (2009a) High-frequency,
long-range coupling between prefrontal and visual cortex during atten-
tion. Science 324:1207–1210.
Gregoriou GG, Gotts SJ, Zhou H, Desimone R (2009b) Long-range neural
coupling through synchronization with attention. Prog Brain Res
176:35–45.
Grill-Spector K, Malach R (2004) The human visual cortex. Annu Rev Neu-
rosci 27:649–677.
Grill-Spector K, Kourtzi Z, Kanwisher N (2001) The lateral occipital com-
plex and its role in object recognition. Vis Res 41:1409–1422.
Henson R (2007) . Efficient experimental design for fMRI. In: Statistical
parametric mapping: the analysis of brain images (Friston K, Ashburner J,
Kiebel S, Nichols T, Penny W, eds), pp 193–210. Elsevier: London.
Henson RN, Rugg MD, Shallice T, Josephs O, Dolan RJ (1999) Recollection
and familiarity in recognition memory: an event-related functional mag-
netic resonance imaging study. J Neurosci 19:3962–3972.
Hopfinger JB, Buonocore MH, Mangun GR (2000) The neural mechanisms
of top-down attentional control. Nat Neurosci 3:284–291.
Hutchinson JB, Uncapher MR, Wagner AD (2009) Posterior parietal cortex
and episodic retrieval: convergent and divergent effects of attention and
memory. Learn Mem 16:343–356.
Ikkai A, Curtis CE (2008) Cortical activity time locked to the shift and main-
tenance of spatial attention. Cereb Cortex 18:1384–1394.
Indovina I, Macaluso E (2007) Dissociation of stimulus relevance and sa-
liency factors during shifts of visuospatial attention. Cereb Cortex
17:1701–1711.
Jacobs J, Kahana MJ (2010) Direct brain recordings fuel advances in cogni-
tive electrophysiology. Trends Cogn Sci 14:162–171.
Jonides J, Yantis S (1988) Uniqueness of abrupt visual onset in capturing
attention. Percept Psychophys 43:346–354.
Kastner S, Ungerleider LG (2000) Mechanisms of visual attention in the
human cortex. Annu Rev Neurosci 23:315–341.
Kastner S, Pinsk MA, De Weerd P, Desimone R, Ungerleider LG (1999)
Increased activity in human visual cortex during directed attention in the
absence of visual stimulation. Neuron 22:751–761.
Kensinger EA, Clarke RJ, Corkin S (2003) What neural correlates underlie
successful encoding and retrieval? A functional magnetic resonance im-
aging study using a divided attention paradigm. J Neurosci 23:2407–2415.
Kiehl KA, Laurens KR, Duty TL, Forster BB, Liddle PF (2001) Neural
sources involved in auditory target detection and novelty processing: an
event-related fMRI study. Psychophysiology 38:133–142.
Kincade JM, Abrams RA, Astafiev SV, Shulman GL, Corbetta M (2005) An
event-related functional magnetic resonance imaging study of voluntary
and stimulus-driven orienting of attention. J Neurosci 25:4593–4604.
Kirchhoff BA, Wagner AD, Maril A, Stern CE (2000) Prefrontal-temporal
Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding J. Neurosci., August 31, 2011 •31(35):12613–12628 • 12627

circuitry for episodic encoding and subsequent memory. J Neurosci
20:6173–6180.
Lazar NA, Luna B, Sweeney JA, Eddy WF (2002) Combining brains: a survey
of methods for statistical pooling of information. Neuroimage
16:538–550.
Linden DE, Prvulovic D, Formisano E, Vo¨llinger M, Zanella FE, Goebel R,
Dierks T (1999) The functional neuroanatomy of target detection: an
fMRI study of visual and auditory oddball tasks. Cereb Cortex 9:815– 823.
Malach R, Reppas JB, Benson RR, Kwong KK, Jiang H, Kennedy WA, Ledden
PJ, Brady TJ, Rosen BR, Tootell RB (1995) Object-related activity re-
vealed by functional magnetic resonance imaging in human occipital cor-
tex. Proc Natl Acad Sci U S A 92:8135– 8139.
Marois R, Leung HC, Gore JC (2000) A stimulus-driven approach to object
identity and location processing in the human brain. Neuron 25:717–728.
McAdams CJ, Maunsell JH (1999) Effects of attention on orientation-
tuning functions of single neurons in macaque cortical area V4. J Neuro-
sci 19:431–441.
Moscovitch M, Umilta C (1990) Modularity and neuropsychology: mod-
ules and central processes in attention and memory. In: Modular deficits
in Alzheimer’s disease (Schwartz MF, ed), pp 1–59. Cambridge, MA:
MIT/Bradford.
Nelson SM, Cohen AL, Power JD, Wig GS, Miezin FM, Wheeler ME, Vela-
nova K, Donaldson DI, Phillips JS, Schlaggar BL, Petersen SE (2010) A
parcellation scheme for human left lateral parietal cortex. Neuron
67:156–170.
Nobre AC, Sebestyen GN, Gitelman DR, Mesulam MM, Frackowiak RS, Frith
CD (1997) Functional localization of the system for visuospatial atten-
tion using positron emission tomography. Brain 120:515–533.
Noudoost B, Chang MH, Steinmetz NA, Moore T (2010) Top-down con-
trol of visual attention. Curr Opin Neurobiol 20:183–190.
Otten LJ (2007) Fragments of a larger whole: retrieval cues constrain ob-
served neural correlates of memory encoding. Cereb Cortex 17:2030–
2038.
Otten LJ, Rugg MD (2001a) When more means less: neural activity related
to unsuccessful memory encoding. Curr Biol 11:1528–1530.
Otten LJ, Rugg MD (2001b) Task-dependency of the neural correlates of
episodic encoding as measured by fMRI. Cereb Cortex 11:1150–1160.
Otten LJ, Quayle AH, Akram S, Ditewig TA, Rugg MD (2006) Brain activity
before an event predicts later recollection. Nat Neurosci 9:489–491.
Paller KA, Wagner AD (2002) Observing the transformation of experience
into memory. Trends Cogn Sci 6:93–102.
Park H, Rugg MD (2008) The relationship between study processing and
the effects of cue congruency at retrieval: fMRI support for transfer ap-
propriate processing. Cereb Cortex 18:868–875.
Posner MI, Cohen Y (1980) Attention and the control of movements. In:
Tutorials in motor behavior (Stelmach GE, Requin J, eds), pp 243–258.
New York: North-Holland.
Posner MI, Cohen Y (1984) Components of visual orienting. In: Attention
and performance X (Bouma H, Bowhuis D, eds), pp 531–556. Hillsdale,
NJ: Erlbaum.
Reynolds JR, Donaldson DI, Wagner AD, Braver TS (2004) Item- and task-
level processes in the left inferior prefrontal cortex: positive and negative
correlates of encoding. Neuroimage 21:1472–1483.
Rugg MD, Otten LJ, Henson RN (2002) The neural basis of episodic mem-
ory: evidence from functional neuroimaging. Philos Trans R Soc Lond B
Biol Sci 357:1097–1110.
Saalmann YB, Pigarev IN, Vidyasagar TR (2007) Neural mechanisms of vi-
sual attention: how top-down feedback highlights relevant locations. Sci-
ence 316:1612–1615.
Serences JT, Shomstein S, Leber AB, Golay X, Egeth HE, Yantis S (2005)
Coordination of voluntary and stimulus-driven attentional control in
human cortex. Psychol Sci 16:114–122.
Sestieri C, Shulman GL, Corbetta M (2010) Attention to memory and the
environment: functional specialization and dynamic competition in hu-
man posterior parietal cortex. J Neurosci 30:8445–8456.
Shulman GL, Astafiev SV, Franke D, Pope DL, Snyder AZ, McAvoy MP,
Corbetta M (2009) Interaction of stimulus-driven reorienting and ex-
pectation in ventral and dorsal frontoparietal and basal ganglia-cortical
networks. J Neurosci 29:4392–4407.
Silver MA, Kastner S (2009) Topographic maps in human frontal and pari-
etal cortex. Trends Cogn Sci 13:488–495.
Sommer T, Rose M, Gla¨scher J, Wolbers T, Bu¨ chel C (2005a) Dissociable
contributions within the medial temporal lobe to encoding of object-
location associations. Learn Mem 12:343–351.
Sommer T, Rose M, Weiller C, Bu¨chel C (2005b) Contributions of occipital
parietal and parahippocampal cortex to encoding of object-location as-
sociations. Neuropsychologia 43:732–743.
Spaniol J, Davidson PS, Kim AS, Han H, Moscovitch M, Grady CL (2009)
Event-related fMRI studies of episodic encoding and retrieval: meta-
analyses using activation likelihood estimation. Neuropsychologia 47:
1765–1779.
Strange BA, Henson RN, Friston KJ, Dolan RJ (2000) Brain mechanisms for
detecting perceptual semantic and emotional deviance. Neuroimage
12:425–433.
Summerfield C, Mangels JA (2006) Dissociable neural mechanisms for en-
coding predictable and unpredictable events. J Cogn Neurosci 18:1120–
1132.
Sylvester CM, Shulman GL, Jack AI, Corbetta M (2007) Asymmetry of an-
ticipatory activity in visual cortex predicts the locus of attention and
perception. J Neurosci 27:14424–14433.
Szekely A, Jacobsen T, D’Amico S, Devescovi A, Andonova E, Herron D, Lu
CC, Pechmann T, Pleh C, Wicha N, Federmeier K, Gerdjikova I, Gutierrez
G, Hung D, Hsu J, Iyer G, Kohnert K, Mehotcheva T, Orozco-Figueroa A,
Tzeng A, Tzeng O, Arevalo A, Vargha A, Butler AC, Buffington R, Bates E
(2004) A new on-line resource for psycholinguistic studies. J Mem Lang
51:247–250.
Treue S (2001) Neural correlates of attention in primate visual cortex.
Trends Neurosci 24:295–300.
Treue S, Martínez Trujillo JC (1999) Feature-based attention influences
motion processing gain in macaque visual cortex. Nature 399:575–579.
Turk-Browne NB, Yi DJ, Chun MM (2006) Linking implicit and explicit
memory: common encoding factors and shared representations. Neuron
49:917–927.
Uncapher MR, Rugg MD (2005) Effects of divided attention on fMRI cor-
relates of memory encoding. J Cogn Neurosci 17:1923–1935.
Uncapher MR, Rugg MD (2008) Fractionation of the component processes
underlying successful episodic encoding: a combined fMRI and divided-
attention study. J Cogn Neurosci 20:240–254.
Uncapher MR, Rugg MD (2009) Selecting for memory? The influence of
selective attention on the mnemonic binding of contextual information.
J Neurosci 29:8270–8279.
Uncapher MR, Wagner AD (2009) Posterior parietal cortex and episodic
encoding: insights from fMRI subsequent memory effects and dual-
attention theory. Neurobiol Learn Mem 91:139 –154.
Uncapher MR, Otten LJ, Rugg MD (2006) Episodic encoding is more than
the sum of its parts: an fMRI investigation of multifeatural contextual
encoding. Neuron 52:547–556.
Uncapher MR, Hutchinson JB, Wagner AD (2010) A roadmap to brain
mapping: toward a functional map of human parietal cortex. Neuron
67:5–8.
von Restorff H (1933) U
¨ber die wirkung von bereichsbildungen im spuren-
feld. Psychol Res 18:299–342.
Vossel S, Thiel CM, Fink GR (2006) Cue validity modulates the neural cor-
relates of covert endogenous orienting of attention in parietal and frontal
cortex. Neuroimage 32:1257–1264.
Wagner AD, Davachi L (2001) Cognitive neuroscience: forgetting of things
past. Curr Biol 11:R964–R967.
Wagner AD, Schacter DL, Rotte M, Koutstaal W, Maril A, Dale AM, Rosen
BR, Buckner RL (1998) Building memories: remembering and forget-
ting of verbal experiences as predicted by brain activity. Science
281:1188–1191.
Wagner AD, Koutstaal W, Schacter DL (1999) When encoding yields re-
membering: insights from event-related neuroimaging. Philos Trans R
Soc Lond B Biol Sci 354:1307–1324.
Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC (1996) A
unified statistical approach for determining significant signals in images
of cerebral activation. Hum Brain Mapp 4:58–73.
12628 •J. Neurosci., August 31, 2011 •31(35):12613–12628 Uncapher et al. •Top-Down and Bottom-Up Attention and Encoding