A Differentiation Account of Recognition Memory: - Specialized Research AI at MIT

A Differentiation Account of Recognition Memory:
Evidence from fMRI

Amy H. Criss1*, Mark E. Wheeler2*, and James L. McClelland3

Abstract

■ Differentiation models of recognition memory predict a
strength-based mirror effect in the distributions of subjective
memory strength. Subjective memory strength should increase
for targets and simultaneously decrease for foils following a
strongly encoded list compared with a weakly encoded list. An
alternative explanation for the strength-based mirror effect is that
participants adopt a stricter criterion following a strong list than
a weak list. Behavioral experiments support the differentiation
account. The purpose of this study was to identify the neural

bases for these differences. Encoding strength was manipulated
(strong, weak) in a rapid event-related fMRI paradigm. To inves-
tigate the effect of retrieval context on foils, foils were presented
in test blocks containing strong or weak targets. Imaging analy-
ses identified regions in which activity increased faster for foils
tested after a strong list than a weak list. The results are inter-
preted in support of a differentiation account of memory and
are suggestive that the angular gyrus plays a role in evaluating evi-
dence related to the memory decision, even for new items. ■

INTRODUCTION

Episodic memory is the ability to mentally time travel to a
past experience. One method for testing episodic memory
is a recognition task where participants are asked to en-
dorse targets that were studied and reject foils that were
not. Memory has been extensively studied with imaging
and computational modeling techniques. However,
research connecting the two fields is slim (cf, Norman &
OʼReilly, 2003). Our goal is to initiate a framework for
combining behavioral analysis, imaging, and modeling to
understand the role of strength in recognition.

Models of memory have successfully accounted for
many details of performance (see Malmberg, 2008). One
exception is the role of list strength. When strength is
manipulated between lists, the result is a strength-based
mirror effect (SBME; Stretch & Wixted, 1998). Hit rates
(HR) are higher and false alarm rates (FAR) are lower for
a strongly encoded list compared with a weakly encoded
list and typically the HR differences are larger than the
FAR differences. Higher HRs for a strongly encoded list
are predicted by all models of memory. A challenge to
many models, however, is posed by the finding that the
FAR differs between the strong and weak lists. Foils are
drawn from the same set of items and are randomly placed
into a test list following weak or strong encoding. There is
no objective difference between foils tested after a weakly
versus a strongly encoded list other than the encoding
conditions of the target items. The criterion shift and dif-

1Syracuse University, 2University of Pittsburgh, 3Stanford University
*A. H. C. and M. E. W. contributed equally to this article.

ferentiation assumptions have been offered as competing
accounts for the SBME.

The Criterion Shift Assumption

One assumption is meta-cognitive; participants become
aware that accuracy for a strong list is high, during encod-
ing or the initial test trials, and consequently adopt a
strict criterion. The reduction in FAR for a strong relative
to a weak study list is accounted for by a change in the
criterion. This assumption is prominent in the class of
models that assumes the subjective memory strength of
unrelated foils is not affected by encoding strength, as
shown in Figure 1 (top). Dual process models assume
two different sources on which to base the memory deci-
sion (e.g., recollecting the specific details of the event or
an overall feeling of familiarity absent the details) whereas
single process models assume just one basis for memory
decisions. The criterion shift hypothesis has been used
in both types of models, with the change in criterion af-
fecting familiarity-based decisions (Cary & Reder, 2003;
Stretch & Wixted, 1998). Critically, these models assume
that the memorial evidence that a foil is from the study
list (e.g., subjective memory strength) does not differ for
strong-list and weak-list foils.

Differentiation Models

In differentiation models, foils following a weakly and a
strongly encoded list differ in their distributions of sub-
jective memory strength. Such models need not assume
a change in the criterion to account for the SBME (see

Journal of Cognitive Neuroscience 25:3, pp. 421–435

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Whether strengthening items on a list results in a change
in the memory distribution for foils and targets or a change
in criterion placement is an important theoretical ques-
tion. Discriminating between a differentiation account
and criterion shift account of the SBME with only HRs
and FARs is impossible because the signal detection
parameters are saturated (four data points and four critical
parameters). Evidence for differentiation models is accu-
mulating with studies using more informative dependent
measures. For example, direct ratings of subjective mem-
ory strength support a priori predictions of differentiation
models. That is, participant-generated distributions of
memory strength following a strong and a weak list differ
as predicted by differentiation models. Critically, these
participant-generated distributions of memory strength
do not change with differences in target probability, a
classic response bias manipulation (Criss, 2009).

Analysis of RT distributions within the diffusion model
framework (e.g., Ratcliff, 1978) support differentiation
models (cf, Starns, White, & Ratcliff, 2010). The diffusion
model describes how information is accumulated to reach
a decision. The better the quality of evidence provided by
the stimulus or the decision-maker, the faster the rate of
evidence accumulation, called the drift rate. In recognition
memory, drift rate maps onto subjective memory strength
(Ratcliff, 1978), thus differences in drift rate were pre-
dicted for strong-list foils and weak-list foils. Empirical dis-
tributions of RT were best fit by differences in the rate of
evidence accumulation (e.g., drift rate) for strong and weak
targets and strong-list and weak-list foils, with larger mag-
nitudes of drift rates for targets and foils from the strong
list (Criss, 2010). The magnitude of the strength differences
and corresponding changes in drift rate need not be and
was not identical for targets and foils. In contrast, manipu-
lating criterion by changing the percentage of targets at test
resulted in a different pattern of RT distributions that was
best accounted for by changes in the starting point of the
evidence accumulation process, not drift rate (Criss, 2010).
Despite a considerable body of data, described above,
supporting predictions of differentiation models, it may be
possible to account for the same findings within a criterion
shift account. For this reason, we explored the possibility that
evidence from functional imaging studies could provide ad-
ditional evidence about the two accounts. The critical ques-
tion in the debate between criterion shift and differentiation
accounts of list strength is whether a change in response
bias or memory strength is necessary for the changes in
the FAR. We attempt to answer that question by comparing
strong versus weak conditions within brain regions where
activation is related to memory strength or response bias.

Neural Correlates of Memory Strength and
Response Bias

The neural correlates of episodic memory have been well
documented (for reviews, see Wagner, Shannon, Kahn, &
Buckner, 2005; Buckner & Wheeler, 2001). A collection

Figure 1. Distributions of memory strength illustrating a criterion
shift account of the SBME (top). The vertical line represents the
criterion placement. Distributions generated from a differentiation
model showing that the memory strength of targets and foils differs
for strong and weak lists (bottom).

Figure 1, bottom; Criss & McClelland, 2006; McClelland &
Chappell, 1998; Shiffrin & Steyvers, 1997). Differentiation
models assume that better encoding of target items re-
sults in more accurate memory traces. The more accurate
a given memory trace, the less likely it is that it will match
a foil decreasing the FAR. In other words, the more that
is known about an item, the less confusable that item is
with other items. This assumption causes the distribution
of subjective memory strength to increase for targets and
simultaneously decrease for foils tested after a strong
list compared with a weak list (see Criss, 2006, 2009,
2010). Like all memory models, differentiation models
do include a criterion that can be used strategically. For
example, the criterion is changed in response to the pro-
portion of targets on the test list (e.g., Criss, 2009). In
this sense the differentiation hypothesis is more robust
whereas the criterion placement hypothesis can be re-
futed either by no evidence of a criterion shift or by evi-
dence for differentiation. For the present purposes, the
critical point made by differentiation models is that the
criterion does not play a causal role in the SBME.

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of retrieval success areas (RSA) consistently show differ-
ential activation when hits are contrasted with correct re-
jections (CRs) in recognition memory tasks. RSA include
inferior and superior parietal cortex, prefrontal cortex, pre-
cuneus, and cingulate gyrus (Simons et al., 2008; Wheeler &
Buckner, 2004; Konishi, Wheeler, Donaldson, & Buckner,
2000; McDermott, Jones, Petersen, Lageman, & Roediger,
2000; Henson, Rugg, Shallice, Josephs, & Dolan, 1999).

Very few studies of RSA have explicitly differentiated
between the contribution of memory strength and re-
sponse bias to memory performance and the resulting
neural activation in these areas. Many studies have shown
that some RSA track factors related to memory strength.
For example, regions in or near the left intraparietal sul-
cus are modulated by the subjective memory decision
(e.g., differential activity to false alarms vs. misses; Kahn,
Davachi, & Wagner, 2004; Wheeler & Buckner, 2003) and
left lateral parietal regions near the angular gyrus (AG) are
modulated by whether the response is recollection or fa-
miliarity based (Vilberg & Rugg, 2008; Wheeler & Buckner,
2004; Henson et al., 1999). Using subjective ratings of
strength, other studies have found a set of regions in which
activity correlates positively with memory strength, in-
cluding left lateral parietal and inferior frontal cortex, left
thalamus, and bilateral medial parietal cortex (Montaldi,
Spencer, Roberts, & Mayes, 2006; Yonelinas, Otten, Shaw,
& Rugg, 2005). Relatively few studies have evaluated the
role of RSA in response bias manipulations. Herron, Henson,
and Rugg (2004) manipulated the percentage of test items
that were targets and found that, as the ratio of old to new
items decreased, the difference in Hit and CR activation
increased in left superior parietal, left inferior frontal, and
bilateral anterior frontal regions.

OʼConnor, Han, and Dobbins (2010) found correlations
between signal detection measures of memory strength
0) and response bias (c) with the contrast of Hit–CR
(d
across many frontal and parietal regions including several
RSA. Like OʼConnor et al., we differentiate between those
RSA that are modulated by response bias (RSAc) and
those RSA modulated by accuracy (RSAd0).

Predictions

The goal of this study was to evaluate whether a crite-
rion shift or differentiation causes list strength effects for
FARs. Responses to targets as a function of list strength are
less informative because both accounts predict an increase
in HRs with list strength. The theories differ in their pre-
dictions for the memory strength of foils: Differentiation
models predict that the memory strength of foils decreases
as strength of targets increase and criterion shift models do
not. We therefore focus our analyses on differences in ac-
tivations produced by foils at different levels of list strength.
We first identify retrieval success regions and determine
0, and therefore
whether those areas are correlated with d
considered candidate memory strength areas or are corre-
lated with c, and therefore considered candidate response

bias areas. Such areas are designated RSAd0 and RSAc, respec-
tively. We then compare strong-list foils and weak-list foils
within the RSAc and RSAd0 to evaluate whether criterion shift
or differentiation accounts best describe the processes un-
derlying the SBME. If a criterion shift underlies list strength
effects, then we should see greater activity for strong list foil
trials than weak-list foil trials in RSAc (reflecting a list-wide
shift in the decision process).

If differentiation underlies list strength effects, we should
see differences between strong-list and weak-list foils in
regions sensitive to memory strength. However, there are
two alternative ways of thinking of how these differences will
be manifest. One possibility is that RSA represent the
strength of memory activation. In this case, illustrated in
Figure 1, strong-list foils should produce even less activa-
tion than weak-list foils in these areas. Another possibility
and the one that is our primary focus here is that these
areas should be thought of as associated with accumulation
of evidence toward either of the two possible responses.
Strong-list foils lead to faster accumulation of evidence
toward the “new’ response than weak-list foils. Thus, if
RSA contain accumulators of evidence, we would expect to
see a faster change of activation in these areas to strong-list
foils than to weak-list foils (consistent with the Criss, 2010,
application of the diffusion model to the SBME), especially
in the most confident responses that represent the extreme
edges of the memory strength distributions.

The neural correlates of information accumulation have
been well documented in nonhuman primates and include
FEFs, lateral intraparietal area, superior colliculus, and
dorsolateral pFC (Gold & Shadlen, 2007; Schall, 2001).
Relatively less research has evaluated the neural substrates
associated with evidence accumulation in humans. Ho,
Brown, and Serences (2009) identified a subregion of the
right insula whose activity is consistent with a modality-
independent evidence accumulator. In another case, Ploran
et al. (2007) controlled the amount of information pro-
vided by the stimulus as a means of manipulating informa-
tion accumulation. Information accumulation regions were
defined as those that became active immediately following
onset of the stimulus (e.g., early in the time course) and
whose subsequent rate of increase in activity was related
to the time taken to identify the stimulus. The rise in activ-
ity was steep for easy stimuli (identified early) and gradually
decreased with stimulus difficulty. We are not aware of any
studies that have evaluated the neural correlates of infor-
mation accumulation in memory. Our focus builds on the
rationale of Ploran et al. (2007), leading us to compare
the rate of information accumulation in strong-list versus
weak-list foils; however, we also consider alternatives.

METHODS

Participants

Twenty-nine right-handed native English-speaking volun-
teers with normal or corrected-to-normal vision participated

Criss, Wheeler, and McClelland

423

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in the study. Data from six participants were discarded:
three for chance level performance, two for insufficient
numbers of observations in some conditions, and one for
excessive head motion. Of the remaining 23 participants,
15 were women. Participants ranged in age from 21 to
29 (M = 21.9). Participants gave informed consent as re-
quired by the institutional review board of the University
of Pittsburgh and were paid $25/hr for participation.

Stimuli

The word pool consisted of 700 words between 4 and
14 letters in length with a log frequency range between
8 and 14 (M = 10.483) among approximately 131 million
words in the corpus (Balota et al., 2007).

Behavioral Paradigm

Studies focused on the cause of the SBME typically manip-
ulate strength via repetition, and we follow that tradition
here (though deep vs. shallow encoding may also produce
an SBME). Critically, the mechanisms underlying recog-
nition models operate in the same way whether informa-
tion is strengthened via encoding task or repetition (e.g.,
Shiffrin, Ratcliff, & Clark, 1990).

At study, participants received a weak and a strong
block, order counterbalanced. Each study list consisted of
100 unique words. For the weak lists, study words were
presented a single time for 1.5 sec with a 500-msec ISI.
For the strong lists, study words were presented for five
such presentations and the entire set of 100 words was
randomly ordered anew and presented before any word
repeated (equating study test lag for the most recent
presentation of weak and strong targets). The test list
began immediately after the study list with 100 targets
and 100 foils each presented for 750 msec followed by
a 2250 msec ISI. Participants responded to the question
“was this word on the list you just studied?” on a 4-point
scale (sure yes, maybe yes, maybe no, sure no). The test
trials were intermixed with 100 fixation trials, also 3 sec
in duration. Order of the test trials was generated using
the Buracas and Boynton (2002) method.

Image Acquisition

Images were acquired on a 3-T Siemens (Malvern, PA)
MAGNETOM Allegra system at the University of Pittsburghʼs
Brain Imaging Research Center. Before functional imaging,
a T1-weighted high-resolution magnetization prepared
rapid gradient-echo image (192 parasagittal slices, 1 mm3
voxels, repetition time = 1540 msec, echo time = 3.04 msec,
flip angle = 8°, inversion time = 800 msec) was acquired.
Functional images were collected during task perfor-
mance using a T2*-weighted echo-planar pulse sequence
sensitive to BOLD contrast (Kwong et al., 1992; Ogawa
et al., 1992; repetition time = 1500 msec, echo time =
25 msec, flip angle = 79°, in-plane resolution = 3.2 ×

3.2 mm, slice thickness = 3.5 mm, 35 slices, interleaved
acquisition). The first five image acquisitions per run
were discarded to allow net magnetization to reach a
steady state.

Procedure

The entire experiment was conducted inside the scanner;
however, functional images were only collected during
the test blocks. Participants were fully informed about
the experimental design before entering the scanner. A
brief practice block preceded the experimental blocks.
Responses were collected with a glove on each hand. In-
dex fingers corresponded to “sure” and middle fingers
corresponded to “maybe.” The hand used to respond
yes or no was counterbalanced across subjects. Partici-
pants were asked to respond as quickly as possible with-
out sacrificing accuracy. The experiment was conducted
using E-prime (Psychology Tools, Inc., Pittsburgh, PA)
and stimuli were projected from the rear of the scanner
to a mirror positioned above the participantsʼ eyes. Anal-
ysis of ROI-based data was conducted using JMP software
( Version 8, SAS Institute, Inc., Cary, NC).

Functional MRI Data Analysis

Functional data were corrected for slice timing differences
using sinc interpolation, realigning all slices to the first
slice. Head motion was corrected within and across runs
using a rigid body algorithm with three translational and
three rotational parameters (Snyder, 1996). Whole-brain
adjustment normalized the modal voxel value for all par-
ticipants to a value of 1000 to facilitate comparison be-
tween data sets (Ojemann et al., 1997).

After preprocessing, functional data from each partici-
pant were analyzed on a voxel-by-voxel basis using a gen-
eral linear model (GLM) approach (Ollinger, Shulman, &
Corbetta, 2001; Miezin, Maccotta, Ollinger, Petersen, &
Buckner, 2000; Friston, Jezzard, & Turner, 1994). The
BOLD data in each voxel were modeled as the sum of
coded effects at each time point, produced by modeled
events and by unexplained variance. Event regressors
were coded into each model at trial onset according to
list strength (strong, weak), item type (target, foil), and
accuracy (correct, incorrect). Events were modeled
over 12 time points beginning at trial onset, producing
a time course of BOLD activity for each event spanning
18 sec. Linear trend and constant regressors were in-
cluded for each run. A series of delta functions described
event-related effects as estimates of the percent signal
change from the baseline term. This approach makes
no assumptions about the shape of the BOLD response.
Image processing and analyses were conducted using soft-
ware developed at Washington University (Ollinger et al.,
2001).

To identify RSA, an ANOVA was conducted using GLM
parameter estimates from each participant, with Subject

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treated as a random factor and Time (each whole-brain
volume) as a repeated measure. A voxelwise mixed effects
repeated-measures ANOVA, with fixed factors of Strength
(strong, weak), Item Type (target, foil) and Time (12 time
points), was computed on correct trials, collapsing over
confidence response. This analysis produced a set of
main effect and interaction images, one for each term
in the ANOVA model. The interaction image of Item
Type (correct target vs. correct foil) × Time was used to
define retrieval success ROIs. This image displays the
degree to which activity on correct target trials differs from
activity on correct foil trials across the 12 modeled time
points.

During group analyses, BOLD data were resampled into
2-mm isotropic voxels and transformed into stereotaxic atlas
space by aligning an individual participantʼs T1-weighted
image to a Talairach atlas-transformed T1-weighted tem-
plate using a series of affine transformations (Fox, Snyder,
Barch, Gusnard, & Raichle, 2005; Michelon, Snyder,
Buckner, McAvoy, & Zacks, 2003; Lancaster et al., 1995;
Talairach & Tournoux, 1988). The ANOVA produced an
F-to-z-transformed statistical image, smoothed using
a 6-mm FWHM Gaussian kernel, for each term in the
ANOVA. These uncorrected images were then corrected
for multiple comparisons and sphericity. Criteria for multi-
ple comparison corrections were based on Monte Carlo
simulations (McAvoy, Ollinger, & Buckner, 2001), with a
cluster-size Type I error rate of p < .05 at a 70 voxel extent. Although all reported data are from voxels meeting correc- tion criteria, both corrected and uncorrected images were retained for use in the ROI definition procedure (described in the next section). Some activations and ROIs are dis- played on cortical surface representations using Caret soft- ware (Van Essen et al., 2001) using the population-average, landmark, and surface-based atlas (Van Essen, 2005). Region Definition Procedures To define ROIs, uncorrected smoothed group z statistical images were resmoothed using a 4-mm hard sphere kernel to reduce the number of peaks in the volume. An algorithm searched for the location of signal change peaks exceeding p < .001 significance, and ROI volumes were grown up to a maximum 10 mm radius of contiguous voxels around the peak coordinates, including only voxels passing threshold. Peaks separated by <10 mm were con- solidated by averaging their xyz coordinates. Voxels failing to pass the sphericity and multiple comparisons correc- tions described in the previous section were then excluded from the ROIs. Using this approach, only corrected voxels were retained in the ROIs. Correlational Analyses of fMRI and Behavioral Measures The relationship between memory performance and re- trieval success activity was assessed by determining the 0 and c statistics cor- degree to which a participantʼs d related with an estimate of the retrieval success effect (RSE), the difference in peak activity on Hit and CR trials (OʼConnor et al., 2010). To compute peak activity in each ROI, the magnitude of the BOLD response on correct target and foil trials was averaged over Time Points 4, 5, and 6 (4.5, 6.0, and 7.5 sec from trial onset). This range of time points was selected because it includes the peak time points in most regions. We note that retrieval suc- cess has been characterized both by an interaction of Item Type and Time (in Functional MRI Data Analysis section) and by differences in peak activity between correct target and foil trials (this section). The two ap- proaches should have high agreement because most of the time the interaction of item type with time was significant due to differences at peak. This was verified by evaluating Hit–CR differences at the peak of the time course (defined as Time Points 4, 5, and 6) in each ROI (listed in Table 3). We conducted a 2 × 2 ANOVA with factors of Item Type (target, foil) and Strength (strong, weak). The main effect of Item Type was sig- nificant ( p < .05) in all ROIs except ROI 8, left thalamus, F(1, 22) = 2.89, p = .09, no corrections for multiple comparisons. The ROI analysis was followed by an exploratory voxel- wise analysis in which correct target and foil time series were convolved with a gamma function. This approach was chosen because it provided increased power, helpful in voxelwise analyses, relative to the time-course-based analysis used in the ROI analysis. The scale parameter 0 and c in two separate anal- (β) was regressed against d yses. Images were smoothed using a 4-mm FWHM Gaussian smoothing kernel. The resulting statistical maps were corrected for multiple comparisons as described in the previous section, with a cluster size threshold of 100 voxels and Type I error rate of p < .05 (McAvoy et al., 2001). RESULTS Behavioral Results As shown in Table 1, we find an SBME as expected. One- tailed paired t tests showed that HRs were higher for strong than weak targets, t(22) = 8.74, p < .001. FARs were numerically smaller for strong-list than weak-list foils, but the magnitude failed to reach significance, t(22) = 1.200, p = .122. The power to detect significant changes in be- havior is relatively low given the small number of partici- pants. We collected pilot data in the behavioral paradigm in which 18 volunteers participated in the exact behavioral paradigm used in the scanner including fixation trials. The combined data (excluding 1 pilot participant whose per- formance was at chance) show an SBME: Strong HRs are higher than weak HRs, t(39) = 9.39, p < .001, and strong-list FARs are lower than weak-list FARs, t(39) = 2.47, p = .009. For comparison with neural activation Criss, Wheeler, and McClelland 425 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 5 / 3 2 5 4 / 2 3 1 / 1 4 9 2 4 1 4 / 9 1 4 7 9 7 o 8 c 6 n 5 _ 7 a / _ j 0 o 0 c 2 n 9 2 _ a p _ d 0 0 b 2 y 9 g 2 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j / t f . u s e r o n 1 7 M a y 2 0 2 1 Table 1. The Probability of Calling a Test Item Old as a Function of Type of Test Item and List Strength fMRI Subjects Strong Weak Pilot and fMRI Participants Strong Weak Targets Foils .775 (.026) .350 (.036) .609 (.031) .390 (.031) Targets Foils .795 (.019) .310 (.032) .603 (.026) .377 (.027) times and for archival purposes, median RT is included in Table 2. Imaging Results Retrieval Success The retrieval success analysis revealed reliably different Hit versus CR activity in or near bilateral middle frontal gyrus (MFG) and the left posterior cingulate gyrus, AG, thalamus (thal), and precuneus (Table 3 and Figure 2). These locations are consistent with commonly reported RSA (Simons et al., 2008; Vilberg & Rugg, 2008; Wagner et al., 2005; Buckner & Wheeler, 2001). To investigate the nature of the RSEs, ROIs were defined from the corrected retrieval success statistical map (see Methods) and activity on strong-list and weak-list target and foil trials, averaging over levels of confidence, was evaluated in each ROI. The time course of the BOLD response from four regions are displayed in Figure 3. Consistent with prior reports (Nelson et al., 2010; Wheeler & Buckner, 2004), activity in the AG ROI (Figure 3D) displayed a decrease relative to the GLM baseline term, with a greater mag- nitude of decrease on foil than target trials (Figure 3D). Whereas activity in retrieval success ROIs is often greater on target than on foil trials, activity in four of the retrieval success ROIs showed the reverse pattern. These regions were located in or near bilateral MFG (Table 3, Nos. 3, 5, and 6) and left precuneus (Table 3, No. 7). This pattern of activation may reflect facilitation in processing related to prior exposure of the targets (e.g., Grill-Spector, Henson, & Martin, 2006). Identifying RSAc and RSAd0 To identify memory strength and response bias compo- nents, the peak activity on Hit minus peak activity on CR trials was computed for each participant. For each par- ticipant, this value was correlated with two measures of Table 2. Median RT in Milliseconds for Each Condition and Each Response Type “Old” “New” Targets Foils Strong 527 903.5 Weak 705 828.5 Strong Weak 975 797.5 837 796 behavioral performance, bias (c) to identify RSAc and sen- 0) to identify RSAd0. Strong and weak trials were sitivity (d analyzed separately. R values for each ROI for strong and weak conditions are listed in Table 3. This analysis identified correlations of the RSE with c in both left MFG ROIs in the strong condition only (R = .47, p = .02 and R = .42, p = .04). RSE did not correlate with 0 for the strong condition in either of these regions (R = d −.32, p = .13 and R = −.35, p = .11). However, a correla- 0 was found in the left AG ROI in the strong tion with d condition (R = .53, p = .009). The RSE in AG did not, however, correlate with c (R = .26, p = .23). The only 0 was the left other retrieval success ROI correlating with d caudate nucleus (R = .46, p = .03), but only in the weak condition. Correlations for the strong condition in the AG ROI are plotted in Figure 4A. On the basis of these analyses, the left AG is a RSAd0 and candidate memory strength re- gion, and the left and right MFG are RSAc and candidate response bias regions. Analysis of Foils in RSAd0 If differentiation underlies list strength effects, we should see faster onset of information accumulation for strong-list than weak-list foil trials in RSAd0. As described earlier, in an information accumulation framework (e.g., diffusion model), stimuli with high-quality evidence accumulate activity toward a decision boundary quicker than stimuli with lower quality evidence. According to differentiation models, strong-list foils have higher-quality evidence (more extreme memory evidence) than weak-list foils, and there- fore, strength should affect the rate of increase in activity from trial onset. CRs (not FARs) provide critical evidence of the difference in rate of accumulation because they end at the correct decision boundary, whereas FARs ter- minate at the wrong decision boundary (and may not fol- low the expected pattern based on the magnitude of the drift rates). To evaluate this prediction, we used the Ploran et al. (2007) method, focusing analysis on activity early in the time series, just after trial onset (averaged across Time Points 2 and 3). Ploran et al. (2007) found early differences in the time to reach peak activity (rather than the magnitude of peak activation) for stimuli that were easier to identify in a perceptual task (e.g., provided better evidence). Following their logic, we looked for differences in early activation for strong-list foils that are easier to reject (e.g., provided better evidence). A differentiation account would be supported by strength-based differences in the magnitude 426 Journal of Cognitive Neuroscience Volume 25, Number 3 D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 5 / 3 2 5 4 / 2 3 1 / 1 4 9 2 4 1 4 / 9 1 4 7 9 7 o 8 c 6 n 5 _ 7 a / _ j 0 o 0 c 2 n 9 2 _ a p _ d 0 0 b 2 y 9 g 2 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j t / f . u s e r o n 1 7 M a y 2 0 2 1 Table 3. ROI Atlas Coordinates and Approximate Anatomic Locations of Retrieval Success Regions and Correlations with d 0 and c No. Hem Anatomic Location x 1 2 3 4 5 6 7 8 9 10 11 L L R L L L L L L L L Post. Cingulate G. Cuneus Mid. Frontal G. Thalamus Mid. Frontal G. Mid. Frontal G. Precuneus Thalamus Post. Cingulate G. Angular G. Caudate −01 −04 +22 −10 −24 −17 −13 −03 −03 −42 −10 y −39 −79 −07 −05 −03 +04 −68 −17 −47 −70 +08 z +29 +34 +55 +14 +55 +57 +52 +11 +17 +37 +08 ∼BA #Vox c-str c-wk 0 d -str 0 -wk d 31 19 6 NA 6 6 7 NA 30 39 NA 306 289 191 141 171 104 74 93 106 74 80 .18 .03 .27 −.05 .47* .42* .37 .15 .22 .26 −.27 .17 .02 −.03 .09 .34 −.19 .37 −.02 .05 .24 .08 .32 .36 −.39 −.13 −.32 −.35 .28 .15 .37 .53** −.21 .21 .23 −.24 −.09 −.15 .12 −.22 −.28 .10 .35 .46* Hem = hemisphere; L = left; R = right; G = gyrus; Inf = inferior; Mid = middle; Post = posterior; BA = Brodmannʼs area; NA = not applicable; #vox = number of voxels in ROI; all locations and Brodmannʼs areas are approximate. The rightmost four columns are Pearson r values for the para- meter (c or d 0) and RSE for the strong (str) and weak (wk) conditions. *p < .05. **p ≤ .01. of activity at Time Points 2 and 3 (strong-list foil CRs > weak-
list foil CRs, indicating a faster approach to peak activation
for strong-list foils) in the left AG, the candidate memory
strength region. From a diffusion model perspective, the peak
magnitudes need not differ; however the rate of accumula-
tion should be faster for strong-list foils. However, t tests

Figure 2. Corrected retrieval success map, projected onto inflated
cortical surfaces. Lateral view of the left and right hemispheres are in
the first and second row, respectively. Top row right side shows a dorsal
view of both hemispheres. Bottom row right side shows a medial view
of the left hemisphere. The reliability of activation is indicated by the
scale bar, in z score units. L = left; Cun = cuneus; PCC = posterior
cingulate cortex; Pre = precuneus; Thal = thalamus.

(one-tailed) revealed no strong > weak differences in the
left AG or any other retrieval success ROI (all p ≥ .23).

Because the most robust memory strength effects are
observed in the most confident responses (e.g., see Criss,
2009, 2010), we performed the same analyses using only
“sure” trials. Including confidence in the analysis required
us to eliminate five participants who lacked a sufficient
number of trials in each condition. This analysis revealed
a significant early strength effect (strong > weak) on “sure”
responses to foils in the left AG ROI, t(17) = 2.01, p = .03,
shown in the left half of Figure 4B (“early”). None of the
other retrieval success ROIs approached significance. Peak
activity in AG did not differ between conditions, as shown
in Figure 4B (“peak”). It should be noted that the “peak”
activations in this case are actually well below the baseline.
This is true both for Hits and CR (see Figure 3D). This is
consistent with our account under the assumption that
there is an overall drop in activation in this region during
memory task performance; the evidence accumulation
process may occur as a positive-going activation on top
of this overall drop in activation, or it may occur as a de-
crease in activity. Although we have stressed the possibil-
ity that left AG might profitably be viewed as an evidence
accumulator region, with activation increasing as evidence
is accumulated, one may also consider the possibility that
AG activation might correspond to the total memory activa-
tion produced by the stimulus. In this way of thinking, we
would expect the BOLD signal strength to be greatest for
strong targets, then to decrease for weak targets and
weak-list foils, with the lowest signal strength for strong-list
foils (as seen in Figure 1, bottom panel). The expected dif-
ference between weak-list and strong-list foils is visually

Criss, Wheeler, and McClelland

427

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Figure 3. Time courses of the
BOLD response for a subset of
the RSA. Percent signal change
from baseline is plotted over
12 time points from stimulus
onset for Post Cingulate Gyrus
(A), Mid Frontal Gyrus (B), Left
Precuneus (C), and Angular
Gyrus (D).

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suggestive in left AG (see Figure 3D). However, that differ-
ence in the peak the BOLD response is not significant.

Analysis of RSAc

If a criterion shift underlies list strength effects, then we
should see different activity for strong-list compared with
weak-list foil trials in RSAc (left and right MFG). Data from
each retrieval success ROI were entered into separate
mixed effects repeated-measures ANOVA models, with
Item Type (correct target, correct foil), Strength (strong,

weak), and Time (12 time points) as factors (described
in Retrieval Success section). A list strength effect was
evaluated using the Strength × Time and Item Type ×
Strength × Time interaction terms. No interactions of
these types were observed in the retrieval success ROIs.
Thus RSAc failed to respond to list strength.

In Analysis of Foils in RSAd0 section, we report that five
participants were excluded from analyses of “sure” re-
sponses to foils because they lacked a sufficient number
of such responses. To ensure that these participants did
not obscure the analysis just reported, we reran the analysis

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Journal of Cognitive Neuroscience

Volume 25, Number 3

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0; left) and
Figure 4. The two panels in Panel A show the correlation between activity in left AG and measures of performance. Discrimination (d
bias (c; middle) are plotted as a function of RSE (Hit–CR percent signal change) for each individual participant. The RSE is correlated with d0 but
not c. Panel B shows the magnitude of activation early (averaged over Time Points 2 and 3) and at peak (averaged over Time Points 4, 5, and 6) in AG.
Differences between strong-list and weak-list foils early in the time course indicate differences in the rate of evidence accumulation, consistent with
differentiation models.

excluding those participants. No interactions were observed.
Furthermore, we conducted the ANOVAs separately for
“sure” and “maybe” responses and found no interactions.
Despite multiple attempts, we found no evidence support-
ing the hypothesis that RSAc respond to list strength.

Comparison with OʼConnor et al. (2010)
OʼConnor et al. (2010) identified a region of left parietal
lobe that correlated significantly with bias (c), but not
0). Together with a voxel-based analysis, their
sensitivity (d
findings suggest that the function of the parietal RSA are
related more to overcoming response bias than success
at retrieval or memory accuracy. To compare our findings
with those of OʼConnor and colleagues (2010), a 10-mm
diameter sphere was created around their peak AG co-
ordinate (Talairach x, y, z = −42, −50, 41), after using the
nonlinear transformation method developed by Matthew
Brett (imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach)
to convert their Montreal Neurological Institute (MNI) co-
ordinates to Talairach atlas space. The resulting ROI, dis-
played in Figure 5 along with our retrieval success ROIs, is
located anterior to our AG RSAc. Time courses were ex-
tracted for Hits and CRs, and correlation analyses were per-
formed. Consistent with the findings of OʼConnor et al., we
found that the RSE in their ROI correlated positively and
reliably with c, but only in the strong condition (R = 0.42,
p = .04; weak: R = 0.30, p = .17), as shown in Figure 5.
0 for strong (R = .18,
There were no correlations with d
p = 42) or weak (R = .07, p = .76) conditions.

voxel-by-voxel analyses, with Subject as a random factor.
The z-transformed and multiple-comparison corrected
images are shown in Figure 6A, overlaid onto an inflated
left hemisphere cortical surface. Regions in which activity
0 were found in bilateral MFG
significantly correlated with d
near BA 6 and BA 10, left postcentral gyrus, left AG (−36,
−70, 39), and bilateral cerebellum (Table 4).

Regions in which activity correlated positively with c
included bilateral precentral and postcentral gyrus, bilateral
MFG, bilateral cerebellum, right inferior parietal lobe, and
right fusiform gyrus (Figure 6A; Table 5). Many of the re-
gions in this map were located in or near structures typi-
cally involved in motor planning or execution, including
premotor cortex, primary somatosensory/motor cortex,
cerebellum, and BG. All regions in the left parietal lobe
were located anterior or superior to the lateral parietal
0. There were very few voxels
regions that correlated with d
in which retrieval success activity correlated with both
0 and c (Figure 6B, overlap would appear in violet).
d
None of these voxels formed an isolated region, but instead
0
existed at the borders of regions correlating either with d
or c. This observation indicates a sharp distinction between
regions involved in signaling successful retrieval and those
involved in attentional or strategic demands.

The AG region identified in the exploratory voxelwise
correlation analysis was located near the AG retrieval success
ROI reported in Table 3. The spatial relationship between
these two regions was explored by overlaying them onto
an inflated cortical surface. As shown in Figure 6B, there
was a high degree of overlap between the ROIs.

Exploratory Voxelwise Analysis of the Relationship
0
between the RSE and d

and c

DISCUSSION

Differentiation in Episodic Memory

To explore the distribution of voxels sensitive to d
RSE was correlated with d

0 and c,
0 and c in separate mixed-effects

The goal of this article was to use fMRI to evaluate whether
differentiation or criterion shifts underlie the SBME in

Criss, Wheeler, and McClelland

429

0 (left) and c

Figure 5. The ROI derived
from OʼConnor and colleagues
(2010) is shown in green,
overlaid with the retrieval
success ROIs (red) onto an
inflated cortical surface. The
bottom row plot d
(right) as a function of RSE
(HR–CR percent signal change)
from the OʼConnor ROI. Each
data point represents values
from a single subject. c but not
0 is correlated with RSE in this
d
ROI, replicating the OʼConnor
et al. finding.

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behavioral data. It is of course possible that both differen-
tiation and a criterion shift contribute to the magnitude of
the SBME. However, several imaging analyses converged
to indicate accumulator-like behavior in or near the AG,
supporting a differentiation account. AG showed a Hit–
CR difference that grew in magnitude across subjects as
0 increased. This region also displayed early differential
d
strength-based effects in the foil conditions, with activity
increasing more during the early time points for strong-
list foils than weak-list foils on high confidence trials (Fig-
ure 4B, “early”). A voxelwise analysis revealed a region
0 and that overlapped
in left AG that correlated with d
with the left AG retrieval success ROI (Figure 6B). Finally,
AG activity showed no relation to response bias. One inter-
pretation of this set of converging findings is that the left
AG serves as one source of information accumulation rel-
evant for a memory decision. However, a conclusive state-
ment about the role of the AG in memory awaits further
evidence.

A variety of criterion shift models can mimic predictions
from differentiation models (e.g., see Stretch & Wixted,
1998). However, this reasoning fails when faced with the
full set of relevant data. Investigations of differentiation
with purely behavioral data included an important com-

parison condition that is not included here, namely, a con-
dition manipulating the percentage of test trials that are
targets. This manipulation is widely regarded as a response
bias manipulation. If both paradigms (% target manipula-
tion and SBME) are driven by changes in criterion location,
then a similar pattern of behavior should emerge. In a
sophisticated analysis of RT distributions (Criss, 2010)
and of participant-generated memory strength distribu-
tions (Criss, 2009), qualitatively different patterns of
results, requiring different model parameters, emerged.
Two different processes appear to underlie behavioral per-
formance in the SBME and % target paradigms. Our data
cannot evaluate the neural correlates of manipulating
target probability. Fortunately, there are two reports mea-
suring fMRI in a % target paradigm discussed in the
Response Bias in Anterior and Superior Parietal Lobe sec-
tion. As a preview, both experiments are consistent with
the conclusion we have reached based on the data re-
ported here: Regions sensitive to response bias are not
the same as regions sensitive to memory accuracy.

Characterizing neural activation as reflecting memory
strength and response bias and especially describing the
role of the left AG as reflecting differentiation is a slightly
different nomenclature than is typical in the literature.

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Journal of Cognitive Neuroscience

Volume 25, Number 3

of parietal regions and evaluated RSEs from six studies
across regions. Their analyses also categorized the left AG
(−45, −67, 36 in MNI-to-Talairach coordinates) with other
default mode regions, including the right AG, posterior
cingulate, medial and superior frontal, and anterior tem-
poral cortex. Vincent and colleagues used hippocampal
ROIs as seed points and found correlated voxels in bilateral
AG (Kahn, Andrews-Hanna, Vincent, Snyder, & Buckner,
2008; Vincent et al., 2006).

The AG may temporarily maintain information retrieved
from memory as indicated by several studies showing a
relationship between the magnitude of signal change and
the degree to which retrieval involves recollection. For
example, studies using source memory or remember/
know (RK) paradigms have been associated with less of a
decrease in activity in lateral parietal ROIs near the AG
when retrieval involves recollection than when it involves
familiarity (Wheeler & Buckner, 2004; Dobbins, Foley,
Schacter, & Wagner, 2002; Henson et al., 1999). Two stud-
ies using a graded memory strength measure (Montaldi
et al., 2006; Yonelinas et al., 2005) found nearby parietal
regions ( Yonelinas: −33, −56, 36; Montaldi: −39, −68,
39; MNI-to-Talairach coordinates) in which there was less
of a decrease in activity as subjective memory strength
increased. Vilberg and Rugg (2007) had subjects study pairs
of picture stimuli and used a variant of the RK paradigm
with two levels of remember response based on amount
of recollected information (i.e., with [R2] or without [R1]
the studied associate). They found a parietal region near
the AG (−39, −77, 40 in MNI-to-Talairach coordinates) that
was selective for remember responses and increased ac-
tivity with amount recollected (R2 > R1). The authors
posited that the region operates as an “episodic buffer”

Table 4. List of ROIs with Positive Correlations between
0
RSE and d

# Hem Anatomic Location

∼BA #Vox

L Med. Frontal G.

−07 −24

R Med. Frontal G.

07 10

Postcentral G.

Cerebellum

−29 −28

−06 −41

47 −63 −32 NA

L Mid. Frontal G.

−38

13 10

L Mid. Temporal G.

−54 −18 −20 21

196

152

169

141

222

109

106

Angular G./Precuneus −36 −70

39 39/19

102

Cerebellum

05 −25 −34 NA

−15 −42 −48 NA

−37 −44 −49 NA

108

133

126

Hem = hemisphere; R = right; L = left; Med = medial; Mid = middle;
G = gyrus; BA = Brodmannʼs area; #vox = number of voxels in ROI; all
locations and Brodmannʼs areas are approximate.

Criss, Wheeler, and McClelland

431

Figure 6. Significant and corrected voxelwise correlations with c (red)
and d0 (blue) are overlaid onto an inflated cortical surface of the left
hemisphere (top). The overlap between activations in or near the AG
0 voxelwise correlation (blue), and
from the retrieval success (red), d
analyses is shown in the bottom. Areas of overlap are in violet.

Therefore, the following discussion is aimed at placing our
results in the context of prior findings and considering the
possibility of reinterpreting prior results.

Memory Retrieval and Left AG

The left parietal cortex, including AG, is frequently re-
ported in fMRI studies of retrieval success (Simons et al.,
2008). Activity in some parts of AG tends to decrease be-
low baseline during performance of goal-directed tasks
suggesting it is part of the “default mode” network. Using
functional connectivity density mapping, Tomasi and
Volkow (2011) found that bilateral AG belonged to a de-
fault mode network that included the parahippocampal
gyrus and medial parietal and frontal cortex.

Analyses of spontaneous low-frequency fluctuations
in BOLD fMRI data demonstrate a link between AG and
medial-temporal and parietal structures. Nelson and col-
leagues (2010) used resting state functional connectiv-
ity analyses and graph theoretical tools to define a grid

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Table 5. List of ROIs with Positive Correlations between
RSE and C

No. Hem Anatomic Location

∼BA #Vox

Mid. Frontal G.

Inf. Parietal Lobe

Postcentral G.

Mid. Frontal G.

Postcentral G.

Precentral G.

Cerebellum

−34 −00

48 −62

−47 −26

−52 −22

−31 −21

−40 −09

−17 −47 −37 NA

Postcentral G.

−21 −39

Cerebellum

−03 −42 −35 NA

Inf. Temporal G.

40 −05 −40

Cerebellum

33 −55 −34 NA

−33 −41 −42 NA

26 −41 −32 NA

Mid. Frontal G.

20 −02

Red Nucleus

−08 −22 −04 NA

Precentral G.

−35 −05

Pulvinar

Precuneus

Cerebellum

−13 −27

06 NA

−12 −62

32 −38 −43 NA

Mid. Temporal G. −47

01 −30

Fusiform G.

26 −91 −13

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162

159

101

135

116

118

141

204

129

113

107

114

146

115

101

106

155

119

108

157

158

Hem = hemisphere; L = left; R = right; Inf = inferior; G = gyrus;
Mid = middle; BA = Brodmannʼs area; #vox = number of voxels in
ROI; all locations and Brodmannʼs areas are approximate.

for on-line maintenance of retrieved information. Similarly,
Guerin and Miller (2011) found a more ventral left parietal/
middle temporal region (−44, −64, 22 in MNI-to-Talairach
coordinates) that decreased activity less as more informa-
tion was retrieved. Although unilateral lesions to the lateral
parietal do not affect source recollection of word labels and
faces (Simons et al., 2008), there is evidence that bilateral
lesions are associated with reduced perceptual detail in
recollection (Berryhill, Phuong, Picasso, Cabeza, & Olson,
2007). This inconsistency between lesion and fMRI studies
deserves further attention. Collectively these studies have
been used to conclude that AG is associated with recol-
lection, and the association with the amount of content
precluded a role in memory decisions. However, under
an accumulator hypothesis, the amount of content may
very well play an important role: The better the evidence
for the memory decision, the faster the information ac-

cumulates toward a decision and the faster AG activity
changes. In other words, the accumulator hypothesis
provides an alternative explanation attributing the role of
AG in recollection tasks to high evidence situations.

It has also been proposed that parietal regions play
a role in attention to memory. Proposals include the
Attention-to-Memory (AtoM; Cabeza, Ciaramelli, Olson, &
Moscovitch, 2008; Ciaramelli, Grady, & Moscovitch, 2008)
and the Dual-Attentional Processes (Cabeza, 2008) hy-
potheses. In these accounts, ventral parietal regions
mediate reflexive attentional capture of relevant retrieved
information whereas dorsal parietal regions are involved
in top–down control. In the AtoM framework, the AG is
included in the reflexive ventral system and should be
more active during target and foil decisions made with
high than low confidence because the former items are
more salient (Cabeza, 2008). To test whether our findings
in AG were consistent with AtoM, we conducted a 2 ×
2 ANOVA on the peak estimates from “sure” and “maybe”
targets and foils (collapsed over list strength) on the
18 participants with sufficient data. This analysis identified
a main effect of Confidence (sure > maybe; F(1, 17) =
13.87, p < .001) and, as expected due to the region definition procedure, a main effect of Item Type (target >
foil) with no interaction ( p = .31). Thus, consistent with
AtoM predictions, activity was greater on “sure” than
“maybe” trials for both targets and foils. However, this
is also consistent with accumulator activity as described
next.

AG Demonstrates Accumulator-like Activity
during Episodic Memory

As noted above, the AG region displayed differential
strength-based effects early in the time course for foils,
suggesting that the momentary signal in this region may
be related to the impending memory decision. This find-
ing is similar to the patterns of accumulating activity in a
perceptual identification task where participants identify
an item that is masked and slowly revealed (Ploran,
Tremel, Nelson, & Wheeler, 2011; Wheeler, Petersen,
Nelson, Ploran, & Velanova, 2008; Ploran et al., 2007). In
those studies, the onset of activity in left parietal cortex
ROIs occurred early in the trial and increased at a rate
that correlated with the time of identification: Activity
increased faster when identification occurred earlier in
the trial. The observed patterns of data may reflect an
integration-to-bound mechanism (Gold & Shadlen, 2007;
Hanes & Schall, 1996) because the information processed
was relevant to the decision. That parietal damage does
not cause major disruption to memory suggests that the
AG is not the sole source of evidence. We note that Ploran
et al. (2007) also reported “accumulator-like” regions that
decreased rather than increased activity relative to base-
line (Figure 3B from that manuscript). Thus, the absolute
direction of activity (negative in the AG region reported

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here) may be less important than the pattern of accumula-
tion. We also note that their peak voxels (Ploran et al.,
2007: −26 −68 38; Ploran et al., 2011: −24, −57, 45
and −24, −71, 34) were ∼15 mm closer to the midline
of the cerebral hemispheres than the peak voxel in our
AG (AG: −42, −70, 37).

In our data, left AG is activated more for correct target
than foil trials and more for “sure” than “maybe” trials. This
U-shaped function is consistent with findings from sev-
eral other studies (i.e., Daselaar, Fleck, & Cabeza, 2006;
Yonelinas et al., 2005). This is the expected pattern of data
obtained when considering SBME data with the diffusion
model framework and is consistent with some models of
evidence accumulation in macaque LIP (e.g., Mazurek,
Roitman, Ditterich, & Shadlen, 2003): Items receive a
high-confidence response because they provide high-
quality evidence, which has a faster rate of accumulation.
As an alternative to the notion that left AG should be
viewed as an evidence accumulator region, we also con-
sidered the possibility that left AG activation might cor-
respond to the total memory strength produced by the
stimulus. This is consistent with a number of features
of the data, including that this is a RSAd0. This possibility
is also consistent with the finding of Rissman, Greely,
and Wagner (2010) that activity in left AG (among other
areas) is predictive of subjective memory for items. Un-
der this view, we would expect less activation in AG from
strong-list foil CRs than from weak-list foil CRs (as illus-
trated in Figure 1, bottom panel), and such a trend is
visually apparent after onset of the memory probe (Time
Points 5 and 6, Figure 3D). However, this finding was not
statistically reliable at our estimated peak time of Time
Points 4, 5 and 6, and more confident CRs were asso-
ciated with greater, not less activation that less confident
CRs, a finding more consistent with the evidence accu-
mulator hypothesis. The picture presented by the full
pattern is tantalizing in suggesting possible roles for both
evidence accumulation and total memory activation in
the same brain area. We look forward to further investi-
gations that may help clarify the possibly complex role of
left AG in recognition memory.

Response Bias in Anterior and Superior
Parietal Lobe

Our response bias analyses found parietal regions that
were located anterior and superior to the AG regions track-
ing retrieval success. These included a region (Figure 5)
taken from a recent study (OʼConnor et al., 2010) reporting
0) in parietal cortex
a significant correlation with c (but not d
and regions defined in a voxelwise analysis correlating c
with RSE on a subject-by-subject basis (Figure 6A). Some-
what strikingly, the correlation between c and RSE in our
estimate of the OʼConnor ROI matched very well with the
correlation in their ROI, indicating that the finding is reli-
able across independent studies. Plotting peak coordinates
from those studies revealed a section of left lateral parietal

cortex near the AG and supramarginal gyrus that is fre-
quently associated with memory operations (Hit > CR)
but rarely reported in studies of attention. Findings from
the current study are thus consistent with the claim by
OʼConnor et al. (2010) that anterior and superior regions
of the left parietal cortex are related to strategic attention,
but inconsistent in that lateral regions were found to be
associated with operations pertaining to the memory de-
cision. OʼConnor et al. reported no ROIs where the RSE
0 may be
correlated with d
related to differences in task or region selection (e.g., the
0 and c correlations were conducted on an unpublished
d
data set, not the experiment manipulating cue validity
reported in their article).

0. The discrepant findings for d

Our findings are consistent with data reported by
Aminoff et al. (2011) and Herron et al. (2004), who
manipulated response bias by varying the percent of tar-
gets in the test block. In both of these studies and in our
data, there is considerable overlap with RSA and regions
modulated by response bias. As suggested by OʼConnor
et al., the term “retrieval success” is perhaps a misnomer
as these regions reflect the contribution of both memory
retrieval and response bias. Another common finding
across these studies is a lack of response bias-dependent
activation for left AG. Herron et al. report an RSE but no
interaction with response bias for a region near left AG
(MNI coordinates −33, −72, 30) and Aminoff et al. do
not report a contribution of AG in a regression analysis
accounting for their data. Finally, all three studies report
many regions of parietal cortex that are modulated by
response bias.

Summary

We found evidence suggesting a differentiation account
of the SBME, specifically the AG may be accumulating
or maintaining accumulated evidence. Furthermore, we
found regions whose retrieval success activity correlates
0 and with response bias with few such areas that
with d
overlap. However, theories of parietal function in memory
are diverse (OʼConnor et al., 2010; Cabeza, 2008; Ciaramelli
et al., 2008; Vilberg & Rugg, 2008; Wagner et al., 2005;
Wheeler & Buckner, 2003), and a definitive statement of
the role of the AG in memory awaits further research.

Acknowledgments
Modeling and other data analysis was supported by the National
Science Foundation (0951612 to A. H. C.). This research was
also supported by the National Institute of Mental Health
(R01-MH086492 to M. E. W. and P50-MH64445 to J. L. M.).
We thank Sarah Woo for assistance with data collection and
processing.

Reprint requests should be sent to Amy H. Criss, Department of
Psychology, Syracuse University, 477 Huntington Hall, Syracuse,
NY 13244, or via e-mail: acriss@syr.edu.

Criss, Wheeler, and McClelland

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1 A Differentiation Account of Recognition Memory: image

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