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The Electrophysiological Signature of Remember–Know Is
Confounded with Memory Strength and Cannot Be
Interpreted as Evidence for Dual-process Theory
of Recognition
Noam Brezis*, Zohar Z. Bronfman*, Galit Yovel, and Yonatan Goshen-Gottstein
Abstract
■The quantity and nature of the processes underlying recogni-
tion memory remains an open question. A majority of behavioral,
neuropsychological, and brain studies have suggested that recog-
nition memory is supported by two dissociable processes: recol-
lection and familiarity. It has been conversely argued, however,
that recollection and familiarity map onto a single continuum of
mnemonic strength and hence that recognition memory is medi-
ated by a single process. Previous electrophysiological studies
found marked dissociations between recollection and familiarity,
which have been widely held as corroborating the dual-process
account. However, it remains unknown whether a strength inter-
pretation can likewise apply for these findings. Here we describe
an ERP study, using a modified remember–know (RK) procedure,
which allowed us to control for mnemonic strength. We find that
ERPs of high and low mnemonic strength mimicked the electro-
physiological distinction between R and K responses, in a lateral
posterior component (LPC), 500–1000 msec poststimulus onset.
Critically, when contrasting strength with RK experience, by com-
paring weak R to strong K responses, the electrophysiological
signal mapped onto strength, not onto subjective RK experience.
Invoking the LPC as support for dual-process accounts may,
therefore, be amiss. ■
INTRODUCTION
Recognition memory reflects one’s ability to distinguish
between a previously encountered event and a new event.
Tests of recognition have been the focus of extensive
research, spanning several decades (Mandler, 1980;
Atkinson & Juola, 1974; Shepard, 1967; for a recent re-
view, see Malmberg, 2008). Perhaps the most fundamen-
tal question that has directed this research addresses the
quantity and nature of the mnemonic processes under-
lying recognition performance. Behavioral, neuropsycho-
logical, and brain studies have provided converging support
in favor of a family of dual-process models, according to
which two distinct processes can potentially give rise to a
recognition judgment: recollection and familiarity (for
different variants of such models, see Onyper, Zhang, &
Howard, 2010; Wixted & Mickes, 2010; Rotello, Macmillan,
& Reeder, 2004; Yonelinas, 1999). Recollection entails the
episodic retrieval of contextual details about the previously
encountered stimulus, whereas familiarity refers to the feel-
ing of acquaintance with the stimulus, which can occur even
when recognition is devoid of any contextual information
(discussed and reviewed in Eichenbaum, Yonelinas, &
Ranganath, 2007; Yonelinas, 2002; Brown & Aggleton,
2001; Yonelinas, Kroll, Dobbins, Lazzara, & Knight, 1998;
Jacoby, 1991; Jacoby & Dallas, 1981). In this article, we will
question the extent to which neurophysiological evidence
that has been widely cited in favor of dual-process models
(Eichenbaum et al., 2007; Rugg & Curran, 2007; Vilberg &
Rugg, 2007; Diana, Reder, Arndt, & Park, 2006) can in fact
be used to support this family of models.
A prominent task that is used to contrast recollection
and familiarity is that of “remember–know”(RK; Tulving,
1985). In this task, participants study a list of items (e.g.,
words) and are then presented with a list comprising
both studied (targets) and unstudied (lures) items, which
they are asked to classify as either “old”(previously stud-
ied) or “new.”Following each “old”classification, par-
ticipants are asked to make a judgment regarding their
subjective experience as to whether they “remember”
(R) studying the item (indicating that recognition accom-
panied by an experience of episodic recollection with
contextual details from encoding; Rosenstreich & Goshen-
Gottstein, 2015) or whether they “know”(K) the item
had been presented (indicating a mere feeling of famil-
iarity; sometimes, participants are also given a third choice,
that of guess (G), when the judgment of the item as “old”
couldbeattributedtoneitherrecollectionnorfamil-
iarity, so as to reduce possible response bias; Gardiner,
Ramponi, & Richardson-Klavehn, 1998). The common
interpretation of RKG judgments is that estimates of
Tel-Aviv University
*Co-first authors.
© Massachusetts Institute of Technology Journal of Cognitive Neuroscience X:Y, pp. 1–15
doi:10.1162/jocn_a_01053
recollection can be based on R responses, whereas those of
familiarity on K responses (e.g., Yonelinas & Jacoby, 1995;
but see Moran & Goshen-Gottstein, 2015; Wais, Mickes, &
Wixted, 2008). Dissociations between estimates of recol-
lection and familiarity are thus interpreted as reflecting
the operation of qualitative distinct mnemonic signals,
conceived of as recollection and familiarity.
To illustrate, some manipulations (e.g., dividing atten-
tion) have been shown to affect estimates of recollection
performance to a greater extent than familiarity, whereas
others (e.g., shifts in response criterion) show the reverse
pattern (for a comprehensive review, see Yonelinas, 2002;
for a critique, see Moran & Goshen-Gottstein, 2015, sec-
tion 4.1.1). Within the neuroscientific literature, examples
of such dissociations include fMRI studies, demonstrating
that the hippocampus is more active in recollective as
compared with familiarity-based decisions, whereas para-
hippocampal activity shows the opposite pattern (e.g.,
Vilberg & Rugg, 2007; Yonelinas, Otten, Shaw, & Rugg,
2005; Henson, Rugg, Shallice, Josephs, & Dolan, 1999;
reviewed in Diana, Yonelinas, & Ranganath, 2007); lesion
studies, showing that hippocampal damage is associated
with impaired recollection and that the surrounding tem-
poral lobe supports familiarity (e.g., Yonelinas et al., 2002;
but see Manns, Hopkins, Reed, Kitchener, & Squire, 2003);
and—the focus of the current article—electrophysiological
studies, demonstrating dissociative patterns of ERPs for
recollection and familiarity (e.g., Woodruff, Hayama, &
Rugg, 2006; reviewed in Rugg & Curran, 2007).
An alternative to dual-process accounts, however, sug-
gests that only a single process underlies recognition
judgments, represented by a unitary variable—mnemonic
strength (Dunn, 2004; Donaldson, 1996). Specifically, the
mnemonic signal is viewed as part of a continuous dis-
tribution of mnemonic strength, with recognition deci-
sions mediated by a signal detection process
1
(Swets &
Green, 1963). More precisely, it has been proposed that
a low criterion and a high criterion are set on the strength
distribution. On trials in which the signal exceeds the
high criterion (i.e., trials with “strong memory”), an R re-
sponse is made, with a K response given on trials in which
the signal lies between the two criteria (i.e., “weak memory”
trials; see Donaldson, 1996). Finally, items are judged
as “new,”when their signal falls below the low criterion.
Thus, the more parsimonious single-process account sug-
gests that the distinction between R and K reflects differ-
ent degrees of memory strength rather than qualitatively
distinct memory processes.
The single-process account predicts that R responses
will be both more accurate and more confident than K
responses, because the former are associated with a
stronger signal. Indeed, in the vast majority of the behav-
ioral studies that have measured the accuracy and confi-
dence of R and K responses—presumably reflecting
mnemonic strength—R responses were found to be
“stronger”than K (e.g., Rotello & Zeng, 2008; Peter,
Mickes, & Wixted, 2007; Dunn, 2004; Wixted & Stretch,
2004). It thus turns out that many of the behavioral dis-
sociations that were originally interpreted as support for
the recollection–familiarity distinction were easily—and
more parsimoniously—interpreted by the single-process
model (Wixted & Stretch, 2004).
Two findings, however, seem immune to a single-
process interpretation. In a study by (Ingram, Mickes, &
Wixted, 2012), participants were asked to make three
types of metarecognition judgments. Specifically, they
were asked not only to make confidence and R–K judg-
ments but also to make source memory judgments regard-
ing the color in which the word had been presented at
study. Because the RK task is not process pure, memory
for details from the study episode (i.e., source memory)
was found not only for R but also for K. Critically, better
source memory was associated with R judgments than with
K judgments, and this was true even when K judgments
were more accurate—that is stronger—than R judgments.
The data were also submitted to state-trace analysis. State-
trace analysis is a powerful method for determining the
minimal number of latent variables that need be postu-
lated as mediating two dependent variables (see Newell
& Dunn, 2008, for a review of state-trace analysis). The
analysis revealed that at least two processes—presumably
recollection and familiarity—need be postulated to account
for the different metacognitive recognition judgments
(for similar results, see Wixted & Mickes, 2010).
A second study (Kafkas & Montaldi, 2012) used both
fMRI and eye tracking. This study found differences in
pupil dilation, fixation patterns, and hippocampal activity
when comparing familiarity- and recollection-based judg-
ments, all of which were made with high confidence and
with similar levels of accuracy. Thus, strength alone could
not account for the data, hence requiring a dual-process
mechanism. These important findings, however, await
replication.
As just described, in behavioral studies, the existence
of evidence for dual-process models remains a point of
contention. In contrast, in cognitive neuroscience the
idea of two processes mediating recognition (and in par-
ticular R–K judgments) is held as self-evident. This is
probably due to the highly replicable neuroimaging and
electrophysiological dissociations that have been reported
for R–K judgments. It is important to examine, therefore,
whether these dissociation can stand up to the same scien-
tific scrutiny afforded to behavioral studies and, in particu-
lar, to withstand the control for memory strength.
Two neuroimaging studies have recently attempted to
address the role of strength in distinguishing between
single- and dual-process accounts. In the first study,
Cohn, Moscovitch, Lahat, and McAndrews (2009) asked
participants to study cue–target words pairs. Subsequently,
participants made two successive old–new judgments
regarding the target word. For the first judgment, the tar-
get appeared by itself. For the second, the target appeared
alongside either its cue word or alongside a new, un-
studied lure word. The idea was to create a setup whereby
2 Journal of Cognitive Neuroscience Volume X, Number Y
the same words might be judged K in the first test but, by
virtue of being presented with contextual information in
the second test, be judged R in the second. Brain activity
was compared for items judged K during the first rec-
ognition test (target alone), to activity in the subsequent
recognition test (target + cue), depending on whether
the target was still judged K as compared with targets
for which judgments were changed to R. Results revealed
increased hippocampal activity when words that were first
identified as K were later judged as R as compared with
when these words retained their K status. These results
were interpreted as supporting a qualitative recollection–
familiarity distinction that does not result from differences
in strength.
Still, these results do not provide unequivocal support
for the notion that R and K are associated with qualita-
tively different types of processes. The increased activity
for targets that were subsequently judged as R may have
reflected the fact that these words were now accompa-
nied by a retrieval cue, thereby increasing the quantity
of mnemonic information (i.e., strength) available for re-
trieval. Indeed, in a follow-up analysis to the Cohn et al.
(2009) data, Smith, Wixted, and Squire (2011) showed
that the subsequent R judgments were considerably
more accurate (i.e., strong) than the words that were
subsequently judged K, even when only considering
the K judgments made with high confidence. Thus, the
more parsimonious mnemonic strength interpretation
can accommodate the Cohn et al. (2009) results in a
straightforward manner.
The second fMRI study that controlled for strength
used a modified R–K procedure in which mean accuracy
and confidence (and hence strength) of R and K deci-
sions could be equated (Smith et al., 2011). To achieve
this, the authors had the participants respond during
retrieval, whether the presented word was studied
(“old”) or not (“new”) concurrently with their confidence
in this response (using a 1–20 scale). When the partici-
pants gave an “old”response (indicating recognition),
they were further asked to make an RKG judgment.
The authors were thus able to equate the average con-
fidence and accuracy of the R and K responses by dilut-
ing the strength of the highest-confidence R responses
(i.e., the “strongest”remember responses) with lower-
confidence R responses, until reaching a point in which
the mean confidence and accuracy of the strong R and
strong K categories were not significantly different.
Results replicated the standard finding of increased
hippocampal activity for R as compared with K responses.
Critically, however, hippocampal activation levels asso-
ciated with strong K were just as high as those associated
with strong R. In line with a strength interpretation of
RK, this finding suggests that hippocampal activity is
associated with a strong, as opposed to a weak, response
rather than with a response that reflects a recollection
signal, as opposed to a qualitatively different familiarly
signal.
Our brief review of the two relevant fMRI studies re-
vealed no unequivocal neuronal signature for a qualita-
tive difference between the processes reflected by R
and K responses (but see Discussion). In this article,
we wished to explore the possibility that, although no
spatial neuronal signature has yet been found to be de-
cidedly associated with R–K responses, different tem-
poral patterns of activity may be associated with R and
K responses. To evaluate this possibility, we conducted
an ERP study. We asked whether even while controlling
for strength, as did Smith et al. (2011), an ERP signal that
distinguishes between R and K responses would be
found.
A considerable number of ERP studies have previously
identified distinct electrophysiological signatures elicited
by R and K judgments ( Yu & Rugg, 2010; Woodruff et al.,
2006; Düzel, Yonelinas, Mangun, Heinze, & Tulving,
1997). In these studies, R responses were found to elicit
a positive waveform diverging from K responses around
500–1000 msec poststimulus, mostly over the parietal
electrodes and in weaker form, over frontal electrodes
(reviewed in Rugg & Curran, 2007). Although this com-
ponent has received numerous names (the most com-
mon is the late positive component [LPC]), we refer to
it here as the RK effect. These results are in accordance
with the suggestions (Curran, 2000; Rugg et al., 1998)
that familiarity is associated with a relatively early (300–
500 msec) frontal ERP component (but see Paller, Voss,
& Boehm, 2007; Yovel & Paller, 2004, for a different
interpretation, according to which this effect reflects con-
ceptual priming) and that recollection is associated with a
later positive component over the parietal region (around
500–1000 msec).
To the best of our knowledge, however, in all electro-
physiological studies in which accuracy was reported, R
and K judgments were confounded with this proxy of
strength. That is, accuracy was systematically higher for
R responses than for K responses (e.g., see Table 1 in
Düzel et al., 1997; Table 1 in Woodruff et al., 2006; and
Table 1 in Yu & Rugg, 2010).
Interestingly, a recent study, which compared the ERPs
of recollection and familiarity by contrasting items with
or without source memory, rather than R–K judgments,
reported dissociable ERP signals even after controlling for
strength (Woroch & Gonsalves, 2010). However, and as
we elaborate in the Discussion, the method that was
used for controlling strength focused on hit trials that
constitute only a part of the mnemonic strength distribu-
tion and is therefore insufficient for equating the strength
of the compared response categories (see Discussion).
Taken together, to date there is no evidence that the
electrophysiological dissociation found for R and K judg-
ments does not reflect differences in strength associated
with these responses rather than a qualitative difference
(cf. Freeman, Dennis, & Dunn, 2010).
Following Smith et al. (2011), who demonstrated a
confound of the classic hippocampal recollective signal
Brezis et al. 3
with strength, we too used a modified R–K procedure,
allowing us to explore possible confounds with strength
of the classic ERP RK signal. In our experiment, after
studying a list of words, participants made recognition
judgments (old/new) concurrently with judgments of
confidence (for each response type, 1 = low to 4 = high;
seeFigure1).Whenan“old”response was made, par-
ticipants were further asked to make an RKG judgment.
Importantly, each participant attended two test sessions:
the first, immediately following the study phase, and
the second, following approximately 1 week. We antici-
pated that this design would increase the variability in
accuracy and confidence (i.e., strength) associated with
R and K judgments. We reasoned that such variability
would enable us to conduct electrophysiological com-
parisons between R and K responses that could be equated
with regard to their strength (estimated by both accuracy
and confidence). We could so explore whether once equat-
ing for strength, the R–K ERP effect at the 500–100 msec
poststimulus window would still emerge.
METHODS
Participants
Overall, 25 participants (age range = 18–28 years) took
part in Session I of the experiment. After two participants
dropped out, the remaining 23 participants took part in
both sessions. All participants were undergraduate stu-
dents, were naive to the purpose of the experiment,
and had normal or corrected-to-normal vision. Partici-
pants were awarded either course credit for their partic-
ipation or a financial compensation (250 NIS; equivalent
to about $60). The procedures of the experiment were
approved by the ethics committee of Tel-Aviv University.
Stimulus Materials and Procedure
Participants were asked to memorize five lists of 80 words
each (for a total of 400 words) for a future recognition
test. The words were three to four letters long. Each list
had similar frequency, measured by the average number
of Google search engine results for each of the words
(M= 1,810 K; SD = 154 K). During study phase, each
trial began with a 400-msec fixation cross, after which a
single white word (font size 30) appeared on gray back-
ground at the center of the screen for 2.5 sec. To ensure
that participants pay attention to the words, they were
asked to classify each word as either pleasant or unpleasant
while the word was present on screen. Participants were
told that this classification was purely subjective and that
there is no correct response. After completing the study
block of 80 words, participants received a 5-min break
and proceeded to the test block.
The test list contained the original 80 words (targets)
randomly intermixed with 16 additional new words
(lures). Thus, we had a new/old ratio of 1:5, a ratio that
allowed us to obtain a large number of trials of interest
(cf. Smith et al., 2011). During test, each trial began with
a 300-msec fixation cross, followed by the presentation of
one word for 2 sec, after which a response prompt ap-
peared (depicted in Figure 1), indicating the participants
to report, using the computer keyboard, their recogni-
tion judgment (“old”/“new”) concurrently with their con-
fidence level (for each response type, 1–4, where 1 =
least confident responses and 4 = most confident re-
sponses; see Figure 1). If the participants made an “old”
judgment, they were subsequently prompted to make an
RKG judgment, as described by Gardiner and Parkin
(1990). Note that participants were instructed to respond
R only if their recognition judgment was accompanied by
a subjective reexperiencing of context details from the
study episode, otherwise the participants were asked to
respond K. During practice, participants were asked to
justify their R decisions to the experimenter. No feedback
for the participants’responses was given in the experi-
mental trials. The four possible hand response mappings
(old/new and remember/know) were counterbalanced
between participants. Following approximately 7 days,
participants returned for a second experimental session
in which they were presented with a list of 560 words,
comprising all the previously studied words (400), the
original lures (80), and additional new lures (80)—all ran-
domly intermixed. The original lures were discarded
from analysis of Session II. Participants were asked to
judge each word as either “old”(studied in the first ex-
perimental session) or “new”to rate confidence and to
make RKG judgments in a manner identical to that of
Session I.
At the beginning of the study phase, participants under-
went a short practice session, identical to the experi-
mental design, with a list comprising only 16 study
words and 20 test words (different words than in the
Figure 1. The modified RKG procedure used in the experiment.
Participants studied lists of words, after which they were tested for
recognition memory, by conveying whether the test word was old
(studied) or new concurrently with four-level confidence rating. If an
“old”response was made, participants were further asked to report if
recognition was accompanied by a subjective reexperiencing of the
word during the study episode (“remember”), or if recognition was
devoid of such a feeling (“know”), or to report that they guessed
that the item was old.
4 Journal of Cognitive Neuroscience Volume X, Number Y
experimental sessions). All stimuli were generated using
Matlab and were presented on a 19-in. monitor, viewed
at a distance of 41 cm. The screen resolution was set to
1024 × 768 pixels, and the monitor had a refresh rate
of 60 Hz.
ERP Recording and Data Analysis
EEG was continuously reordered during the study and
test phases of both sessions from 64 Ag/AgCl active elec-
trodes embedded in a flexible cap (BioSemi Active Two
system, Amsterdam, The Netherlands; www.biosemi.com)
at a sampling rate of 256 Hz. Electrode arrangement was
based on the International 10/20 system (American Electro-
encephalographic Society). Two electrodes were placed on
the left and right mastoids to be used as reference channels
for offline data analysis. Vertical and horizontal eye move-
ments were monitored by recording EOG from electrode
pairs placed above and below the left eye and one on each
outer canthus.
Data Analysis
EEG data were digitally high-pass filtered (−3dBat0.1Hz,
zero-phase) and low-pass filtered at 30 Hz, and were
epoched (1000-msec duration; 80-msec prestimulus base-
line) offline. Before epoching, the EEG data were corrected
using ICA transformation. Next, trials in which EEG signal
during the epoch of interest was above 70 mV or below
−70 mV were manually discarded. To be included in the
analysis of a condition, a minimum of 10 trials was required
for every participant. For presentation purposes only, the
averaged ERPs were digitally smoothed using a 4-point
averaging window (Nitschke, Miller, & Cook, 1998). For
the analysis of the ERP data, see Results section.
RESULTS
Behavioral Performance
The distribution of participants’responses in both sessions
is described in Figure 2. As can be seen in Session I, the
majority of items that were judged “old”were classified by
the participants as R with the highest confidence level 4
(i.e., R4). In Session II, participants responded with greater
variability. In particular, of the 311 Session 1 R4 responses,
only a third (=104) endured in Session 2. The former R4
responses turned into R3, as well as all levels of K (K1, K2,
K3, K4). Thus, as expected, in comparison with Session 1,
Session 2 responses were less clustered and more diverse.
Participants’recognition accuracy, calculated as [hit rate/
(hit rate + false alarm rate)]
2
(i.e., percent correct), was
higher in Session I as compared with Session II (Session
I = .85; Session II = .64; t(22) = 11.4; p<.0001)and
was above-chance (.5) in both (Session I: t(24) = 18.7;
p< .0001; Session II: t(22) = 7.2; p< .0001).
3
Importantly,
as is typically found, mean R responses were more accurate
and confident than mean K responses in both Session I
(Accuracy: R = .95; K = .58; t(24) = 8.6; p< .0001;
Confidence: R = 3.96; K = 3.09; t(24) = 11.33; p<
.0001) and Session II (Accuracy: R = .82; K = .59; t(22) =
8.08; p< .0001; Confidence: R = 3.76; K = 2.74; t(22) =
18.34; p< .0001). In both sessions, both R and K
responses were significantly above chance ( p< .05 for
all comparisons).
The finding of higher accuracy for R as compared with
K responses is consistent with previous studies (e.g., Yu
& Rugg, 2010; Woodruff et al., 2006; Düzel et al., 1997).
Critically, it poses a strong constraint on the interpreta-
tion of electrophysiological dissociations between R and
K judgments, in that these dissociations may stem from
difference in strength rather than from a qualitative differ-
ence in the underlying neural mechanism. This highlights
the fact that, when contrasting the electrophysiological
Figure 2. Mean number of responses across trials and across participants for old–new recognition judgments, depicted separately for Session I (left)
and Session II (right). For items judged “old,”responses are broken down into the 12 categories obtained by crossing confidence (1–4) with
subjective judgment (RKG). Session II took place approximately 1 week following Session I.
Brezis et al. 5
signature of R and K judgments or for that matter any
comparison between R and K, it is imperative to control
for differences in strength, as will be conducted in the
analyses to follow.
ERPs
We first focus on the effects obtained from RK judg-
ments, which are the focus of the present investigation.
At the end of this section, we return to analyze the old/
new effect.
At a descriptive level and consistent with previous
studies, in Session I, we found an RK component (differ-
ences in the ERPs elicited by items judged R as compared
with K, around 500–1000 msec poststimulus onset) at
frontal and parietal regions (Figure 3; hereafter, the RK
effect). The mean amplitude of the ERP associated with
R responses was higher than that of K responses.
For this and all subsequent ERP analyses in this arti-
cle, we followed the procedure described by Vilberg,
Moosavi, and Rugg (2006) and others (Roberts, Tsivilis,
& Mayes, 2013; Woodruff et al., 2006). Specifically, ROIs
were created by averaging mean amplitudes at clusters of
three electrode sites to create left and right frontal re-
gions (F1, F3, F5 for left; F2, F4, F6 for right) and pari-
etal (P1, P3, P5 for left; P2, P4, P6 for right). Unless
stated otherwise, the dependent variable is the mean
amplitude between 500 and 1000 msec poststimulus onset
(the latency window was chosen based on previous stud-
ies; see Yu & Rugg, 2010; Woodruff et al., 2006; Düzel
et al., 1997). ANOVAs were Geisser–Greenhouse corrected
for nonsphericity.
We submitted the data to a 4 (ROI: left frontal, right
frontal, left parietal, right parietal) × 2 (subjective judg-
ment: R, K) repeated-measures ANOVA. We found a
significant main effect for subjective judgment (F(1, 21) =
12.96; MSE = 7.25; p= .002) and significant ROI × Subjective
Judgment interaction (F(2.07, 43.38) = 4.96; MSE =.89,
p= .01). Post hoc comparisons revealed that the ROIs
showed a significant effect of subjective RK judgment with
the strongest effect in parietal regions (left parietal; t(23) =
3.43; p= .002; right parietal; t(23) = 4.56; p= .0001),
a weaker but significant effect in the right frontal region
(t(21) = 2.69; p= .014), and no significant effect in the
left frontal region (t(21) = 1.58; p=.13).
In Session II, wherein participants were tested a sec-
ond time, a week later, for recognition of words studied
in Session I, similar subjective RK judgment effects were
found. In Session II too, the RK component was found
(repeated-measures ANOVA of ROI × Subjective Judg-
ment revealed a judgment main effect; F(1, 21) = 8.88;
MSE = 6.7; p= .007; and no ROI × Judgment interaction;
F(1.77, 37.27) = .23; MSE =.87;p= .76). Post hoc compar-
isons revealed that the effect of subjective RK judgment
was significant in each of the ROIs (right frontal; t(21) =
2.86; p= .009; left parietal; t(21) = 2.63; p= .016; right
parietal; t(21) = 2.64; p= .015; left frontal region; t(21) =
2.65; p= .015; Figure 4).
Inspection of the data revealed no interaction between
the ERPs of subjective RK judgment, ROI, and the two
sessions. Indeed, upon submitting these factors into a
three-way repeated-measures ANOVA, with 2 (subjective
judgment) × 4 (ROI) × 2 (session), no evidence was
found for a three-way interaction between the factors
Figure 3. ERPs for “remember”and “know”responses in Session I,
shown for four ROIs (starting from top left and clockwise: left frontal,
right frontal, right parietal, and left parietal). Remember responses
elicited significantly higher amplitude than know responses in the time
window between 500 and 1000 msec poststimulus onset in three of
these four ROIs.
Figure 4. ERPs for “remember”and “know”responses in Session II,
in which participants were retested for their recognition memory
7 ± 3 days following Session I. Data are shown for four ROIs (starting
from top left and clockwise: left frontal, right frontal, right parietal,
and left parietal). In Session II, as in Session I, in these ROIs,
R responses were associated with significantly higher amplitude than
K responses, between 500 and 1000 msec poststimulus onset.
6 Journal of Cognitive Neuroscience Volume X, Number Y
(F(1.62, 29.08) = 1.05; MSE = 1.28; p=.35)norfora
two-way interaction between Response × Session (F(1,
18) = .32; MSE =5.52;p=.58).
Therefore, to obtain more trials per response category
in the critical analyses reported below, we collapsed the
data from the two sessions. Importantly, as can be seen in
Figure 5, we found that the ERP RK effect in the collapsed
data was similar to the effects observed in each of the
sessions separately. An ANOVA of ROI × RK revealed
an ERP RK effect, F(1, 24) = 26.58; MSE =5.34;p<
.0001, with post hoc comparisons revealed the effect to
be significant in each of the designate ROIs (left frontal;
t(24) = 2.36; p= .027; right frontal; t(24) = 4.1; p<
.0005; left parietal; t(24) = 4.99; p< .0001; right parietal;
t(24) = 5.98; p< .0001).
Finally, we returned to examine the classic old/new
effect (e.g., Yu & Rugg, 2010; Woodruff et al., 2006) to
see if it emerged in our data. In the time window pre-
viously reported, between 0.2 and 0.5 sec poststimulus
onset, we observed a difference in the mean amplitude
of new items (means = 1.11, 0.93, −0.58, −0.68), as
compared with R and K items (see Figure A1, Appendix).
Note, however, that our old/new effect did not reach
statistical significance. This was probably a function of the
fact that because we sought to obtain many responses
for R and K at various confidence levels, we resorted to
using a much lower proportion of “new”(20%) as com-
pared with “old”(80%) responses. As a result of the few
“new”responses, our study enjoyed low power in com-
parison with ERP studies reporting significant old/new
effects.
In summary, we were successful in replicating previous
electrophysiological studies (Roberts et al., 2013; Vilberg
et al., 2006; Woodruff et al., 2006; Düzel et al., 1997) and
found the RK effect in each session separately, as well as
collapsed across the two sessions, in parietal regions and
to a lesser extent, albeit still significant, in frontal regions.
As described above (see Behavioral Performance sec-
tion), R responses in our study were made with greater
confidence and accuracy than K responses in both ses-
sions. Hence, the reported RK effect, which is typically
attributed to subjective RK judgment, may be mediated
by the difference in strength rather than in qualitatively
distinct neural processes.
To assess the effects of this confound on the inter-
pretation of the ERP effect, we next analyzed the ERPs
of high- as compared with low-confidence responses.
4
To obtain a large number of trials per condition, high-
confidence responses were defined as trials in which
confidence was rated maximal (4), and low confidence
comprised the rest of responses (ratings 1–3; cf. Smith
et al., 2011, for a similar procedure). Indeed high (mean
amplitude = 1.48; SE = .48) versus low (mean amplitude =
.19; SE =.49) confidence comparison yielded an effect,
which is almost indistinguishable from the RK effect,
and was found to be significant when submitted to a 4
(ROI) × 2 (confidence) repeated-measures ANOVA (F(1,
24) = 16.75; MSE =4.98;p= .001), for the main effect of
confidence.
In the following analyses, we addressed the following
concern. In the same manner that our earlier analysis of
subjective RK judgments was confounded with confi-
dence, the current analysis of confidence was confounded
with subjective RK judgment. The question thus remained
open of whether the ERP RK effect reflects differences in
subjective judgments—reflecting an ERP signature of
recollection—or alternatively, differences between con-
fidence levels—reflecting different magnitudes of mne-
monic strength.
In the first analysis to address this question, we com-
pared recognition judgments that were made with high
as compared with low confidence and controlled for
subjective RK judgments. To this end, we sampled a
subset of ERPs that were associated with high- and low-
confidence judgments, with the constraint that each level
of confidence comprised the same quantity (on average,
30 trials per category) of R and K responses. The sam-
pling procedure was carried for each participant sepa-
rately,witheachsamplingiteration being averaged
across the participants generating a single line in the Fig-
ure 6. Because R and K trials were randomly sampled
from the confidence categories, we repeated this proce-
dure 200 times, such that, for each iteration, the sam-
pling was performed with replacement from the entire
range. Importantly, the high- and low-confidence samples
differed not only in confidence but in accuracy as well—
both measures reflecting memory strength—with 86%
accuracy for the high-confidence sample, as compared
Figure 5. ERPs for “remember”and “know”responses collapsed across
both sessions. Data are shown for four ROIs (starting from top left
and clockwise: left frontal, right frontal, right parietal, and left parietal).
In these ROIs, remember responses were associated with significantly
higher amplitude than know responses, between 500 and 1000 msec
poststimulus onset.
Brezis et al. 7
with 65% for the low-confidence sample (t(13) = 7.38,
p< .0001). We thus obtained a sampling distribution of
ERPs for responses for which confidence and accuracy
did not diverge, while controlling for possible differ-
ences in subjective RK judgments. Thus, to the extent
that different ERPs are found between the responses re-
flecting differences in mnemonic strength, the different
ERPsignalscouldbeattributedtostrengthbutnotto
subjective RK judgments.
The resultant sampling distribution is represented in
Figure 6 (see Figure A2 in the Appendix, in which means
and 95% confidence intervals are shown, rather than the
entire sampling distribution). Examination of the figure
revealed that, as in the case of the R–K comparison, ERPs
of high confidence were associated with higher ampli-
tude around 500–1000 msec, mainly in the parietal regions
(mean amplitudes [high/ low]: left frontal = [1.63/1.09];
right frontal = [1.26/1.06]; left parietal = [1.18/0.18]; right
parietal = [0.37/−0.3]).
To test the significance of these results, we computed—
based on the values obtained in our sampling procedure—
the probability (frequency) that the mean amplitude
within the relevant time interval (0.5–1 sec) of the high-
confidence condition will be higher than that of the
low-confidence condition. This probability was found to
be higher than .95 for the parietal regions and higher than
.85 for the frontal regions. Thus, even though high- and
low-confidence trials were entirely equated with respect
to the proportion of R–K response, an effect entirely con-
sistent with the classic electrophysiological RK compo-
nents still emerged. This lends credence to the idea that
confidence, rather than subjective RK judgment, may drive
the classic RK effect.
Next, we conducted the complementary sampling distri-
bution analysis, in which R and K responses were sampled
with equal amounts of low- and high-confidence ratings.
Unfortunately, despite equating R and K responses for
confidence, mean accuracy levels of R responses, 85%,
remained significantly higher than those of K responses,
65% (t(13) = 5.78; p< .0001). Thus, confidence and accu-
racy, two measures that reflect memory strength, diverged
in this analysis. As a result, although we successfully elimi-
nated the confound between RK and one correlate of
strength (confidence), we were unsuccessful in eliminating
the confound between RK and another—perhaps more
reliable—correlate of strength (accuracy; see Discussion
for further treatment of these ideas). Because of our failed
attempt to deconfound the ERP RK effect from accuracy,
no ERP analysis was undertaken.
Finally, and most importantly, we placed RK responses
in opposition to confidence.
5
To this end, we compared
responses that were judged R, yet made with low confi-
dence, with those judged K, made with high confidence.
Specifically, we compared low-confidence (rated 1–3) R
responses to high-confidence (rated 4) K responses (46
and 53 trials on average, per low-R and high-K responses,
respectively). Note that our two measures of mnemonic
Figure 6. Sampling distribution of ERPs elicited by high- and
low-confidence judgments, with identical quantities of “remember”
and “know”responses in each category. Data are pooled across the
two sessions and shown for four ROIs (starting from top left and
clockwise: left frontal, right frontal, right parietal, and left parietal).
High-confidence responses were associated with higher amplitude
in these ROIs, between 500 and 1000 msec poststimulus onset.
The sampling distribution was achieved by sampling a subset of
ERPs that were associated with high and low confidence, with the
same quantity of R and K responses per confidence category
(30 trials on average). This subsampling procedure was iterated
200 times, with samples drawn with replacement per iteration.
Figure 7. ERPs for weak “remember”responses (confidence 1–3)
and strong “know”responses (confidence 4) in the time window of
500–1000 msec. Data are pooled across the two sessions and shown
for four ROIs (starting from top left and clockwise: left frontal, right
frontal, right parietal, and left parietal). No evidence was found that the
ERP signal for the weak remember responses differed from that of
strong know responses. Note that strong and weak responses were
designated as such based on significant differences between their
mean confidence ratings. Mean accuracy did not differ significantly.
8 Journal of Cognitive Neuroscience Volume X, Number Y
strength—confidence and accuracy—did not diverge,
with R responses made with low confidence yielding
levels of accuracy no different from the high-confidence
K responses (R = .83; K = .81; t(12) = .3; p= .77).
Importantly, if a subjective RK ERP effect would still
emerge, this would provide striking evidence that the
LPC was not mediated by strength. If, however, the effect
is mediated by strength, then the RK component would
now be eliminated or would even show a reversal (with
K, not R, judgments yielding higher amplitudes because
of the their elevated strength). Thus, this analysis is
critical, in that it can differentiate between two radically
different alternatives.
Figure 7 depicts the ERPs for the weak R responses and
strong K responses. Examination of the figure revealed
that the ERPs of R and K were highly similar. We sub-
mitted the data to a 4 (ROI: left frontal, right frontal, left
parietal, right parietal) × 2 (subjective judgment: R, K)
repeated-measures ANOVA and found no R/K effect (F(1,
11) = .049; p= .83) and no RK effect within each ROI
(p=.7,forallttests).
Critically, a closer examination of the Figure 7 revealed
that, at the descriptive level, the amplitude of K re-
sponses was higher than that of R responses. Indeed, the
mean amplitudes for strong K responses (overall mean
across ROIs = 1.34; SE = 1.12) were higher in three of
four ROIs in the critical time window of 500–1000 msec
than for weak R responses (overall mean = 1.1; SE = 1.12).
The higher amplitudes for strong K are not surprising, con-
sidering that the ERP response may reflect strength rather
than subjective RK judgments. We therefore performed an
exploratory analysis wherein we asked to what extent were
the higher amplitudes for strong K responses more prev-
alent than higher amplitudes for weak R responses.
To this end, we examined the mean amplitude of strong
K and weak R, in the four ROIs and across the 12 partici-
pants whom fulfilled the ERP inclusion criterion, of having
at least 10 trials per condition (see Methods section). We
found that in 33 of 48 data points (ROIs × Participants),
the mean amplitude of strong K was higher than that of
weak R. Using binomial test, which computes the probabil-
ity of obtaining 33 or more higher amplitudes out of 48 by
chance, we found that the mean amplitude associated with
strong K responses was significantly higher than that asso-
ciated with weak R responses, p= .007. Moreover, as can be
seen in Figure 8, we found the same pattern associated with
weak R and strong K (i.e., indistinguishable ERPs), sepa-
rately in Session I (26 and 32 trials on average per R and K
responses, respectively) and Session II (43 and 41 trials
6
).
Our findings suggest that the LPC, typically labeled the
RK effect, may in fact not correspond to the subjective ex-
perience of RK. Instead, the LPC may reflect the strength of
responses. If so, the neural activity associated with R as
compared with K may reflect a difference that is quanti-
tative not qualitative. This is a highly important conclusion,
when considering that this electrophysical signal has so
often been cited in support of dual-process theories. In
the Discussion, we discuss the critical question of why
the magnitude of the RK effect in our final analysis (weak
Rs vs. strong Ks) was small (compare to Figures 3 and 4), if
in fact this effect reflects strength rather than subjective
RK experience.
DISCUSSION
Considerable support in favor of the dual-process ac-
count of recognition memory has been documented from
Figure 8. ERPs for weak “remember”responses (confidence 1–3) and strong “know”responses (confidence 4), separately for Sessions I and II.
Data are shown for four ROIs (starting from top left and clockwise: left frontal, right frontal, right parietal, and left parietal). In parallel to the findings
for the pooled data, here too, for each session separately, weak remember responses do not differ from strong know responses.
Brezis et al. 9
behavioral and brain studies that employed the RK para-
digm. These studies (reviewed in Diana et al., 2007; Rugg
& Curran, 2007) have shown behavioral and brain disso-
ciations between R responses, on which estimates of recol-
lection can be based, and K responses, on which estimates
of familiarity can be based. Indeed, because of the highly
replicable neuroimaging and electrophysiological dis-
sociations that have been reported for R–K judgments,
the idea of two processes mediating recognition (and in
particular RK judgments) is held as self-evident in the
cognitive neuroscience literature.
Still, from the dawn of the RK distinction, it has been
argued that the empirical data are equally consistent and
more parsimonious with a single-process account of rec-
ognition memory. The single-process account sug-
gests that R and K reflect different levels of mnemonic
strength, which, based on signal detection theory (Swets
& Green, 1963), can be approximated by confidence and
accuracy.
Here, we conducted an ERP study, aimed at contrast-
ing R and K responses while controlling for their respec-
tive strength. We first replicated the classic finding (Roberts
et al., 2013; Vilberg et al., 2006; Woodruff et al., 2006; Düzel
et al., 1997) and found that the ERPs associated with R
have higher ERP amplitudes than ERPs of K responses,
in the time window between 500 and 1000 msec post-
stimulus onset, an effect known as the LPC and the ERP
RK effect. This result was found in the immediate recog-
nition test (Session I; Figure 3), in the delayed test, follow-
ing a week (Session II; Figure 4), and also when ERPs were
analyzed for the collapsed data (both sessions; Figure 5).
Critically, in our study, as in previous studies (e.g., Wixted
& Stretch, 2004), R responses were considerably more
accurate and confident than K responses, suggesting that
the R and K responses were confounded with strength.
The source of the ERP RK effect thus remains elusive.
Consistent with the idea that strength may mediate the
effect, we observed that when contrasting high- and low-
confidence ratings, reflecting strong and weak memories,
the magnitude of the electrophysiological signal was
higher for high- as compared with low-confidence rating.
Critically, this effect was seen in the same time window as
the classic RK effect. This result was not surprising, in that
R responses are correlated with confidence judgments.
Still, it illustrates the problematic nature of the traditional
comparison between R and K responses, when memory
strength is not controlled for.
To isolate the effect of strength, we repeatedly sam-
pled an equal amount of strong (high confidence) and
weak (low confidence) R and K responses. Comparing
the ERPs of strong and weak memories, we found that
high-confidence responses were still associated with
more positive-going waveforms than those elicited by
low-confidence responses, with confidence modulating a
phasic parietal positivity (Figure 6). This observation is
consistent with previous studies, which associated mne-
monic confidence to the parietal lobe. For example,
patients with bilateral parietal lesion exhibit reduced con-
fidence in their source recollection abilities (Simons, Peers,
Mazuz, Berryhill, & Olson, 2009). Moreover, healthy indi-
viduals show increasing BOLD activations in the lateral
parietal cortex with increasing judgments of confidence
of recognition judgments (reviewed and discussed in
Cabeza, Ciaramelli, & Moscovitch, 2012).
Of primary concern to our endeavor, however, was the
fact that the ERP effect of high and low confidence ex-
hibited a striking resemblance to the RK ERP effect. This
suggests that strength, rather than qualitatively different
neuronal signals, may mediate the classic ERP effect.
Demonstrations of an ERP component (the LPC) for high-
as compared with low-confidence RK responses is con-
sistent with studies of source memory confidence (Woroch
& Gonsalves, 2010) as well as other reports ( Woodruff
et al., 2006; Curran, 2004). For example, Woodruff et al.
(2006) reported that response confidence (operation-
alized by contrasting confident responses, old and new,
with unconfident ones) elicited a positive-going ERP that
was topographically dissociable from the effect obtained
when contrasting R judgments from highly confident
familiar-only judgments. Because old and new items were
combined in the analysis, it could be argued that confi-
dence may reflect a construct other than strength, with
this construct—confidence—modulating the ERP effect.
Importantly, our demonstration is the first to show that
this confidence component emerges even when a strict
control was directly imposed on subjective RK responses
by equating the number of R and K responses in each
confidence level. Moreover, our effect did not involve a
blend of old and new items, thereby making inference
oftheunderlyingprocessintheexperimentmore
straightforward. It thus highlights the possibility that this
componentmay,infact,notbeasignatureofsubjective
RK experience, but rather, as we propose, a signature of
confidence.
Higher Amplitudes for K than for R When
Placing RK in Opposition to Strength
and Their Interpretation
In our most decisive analysis, we placed R and K responses
in opposition to strength by comparing low-confidence R
responses and high-confidence K responses. Importantly,
in the opposition analysis, the two types of stimuli did not
differ in their accuracy level. Although an ANOVA did not
reveal a significant difference between the mean ampli-
tudes of strong K and weak R, the binomial test revealed
significantly higher amplitudes for K than for R, with the
effect being small in magnitude.
We argue that the higher amplitudes for K reflected
the higher confidence with which these responses had
been made. Thus, strength turned out to have the higher
prognostic value when put into opposition with RK,
regarding the ERP signal in the 500–1000 msec time win-
dow poststimulus onset (i.e., the LPC).
10 Journal of Cognitive Neuroscience Volume X, Number Y
To reiterate, while being statistically reliable, the effect
in the opposition analysis was exploratory. It thus war-
rants further validation in future studies. More intriguingly,
the effect was numerically very small. Thus, a discrep-
ancy exists in the magnitude of the effect observed for
high- and low-confidence responses when controlling
for RK (see Figures 3–5) and the magnitude of the effect
we found in the opposition analysis, which was much
smaller.
One possible explanation for this discrepancy is that
two different psychological processes underlie confi-
dence and subjective RK experience and that the LPC is
mediated by both. Because the two processes worked in
opposition, the effect shrunk in magnitude. We know of
no data to refute this interpretation. Still, it is noteworthy
than when strength and confidence were placed in oppo-
sition, the result reflected the effect of confidence and
not that of RK. More importantly, this interpretation
seems unlikely, in that if strength and RK are different
psychological processes—entailing different neurological
computations—it seems unlikely that the neural systems
involved for the different computations would yield the
identical electrophysiological signal. Thus, and most im-
portantly from our perspective, the question of whether
the amplitude of high-confidence K responses is indeed
higher- than low-confidence R responses is not at the
heart of our argument. Rather, our critical observation
is that R does not correlate with higher amplitude than
K, when strength is controlled for.
We propose an alternate interpretation to the small
effect we found in the opposition analysis. We interpret
the LPC to be a signature of strength alone, not RK. If so,
there are no neuronal generators that produce an electro-
physiological RK signal. We propose that when asked to
classify memories as either containing (R) or not contain-
ing (K) contextual detail, participants interpret this task
to mean (relatively) strong and (relatively) weak memories,
respectively. Thus, high-confidence K responses may be
understood by participants to describe a subclass of the
relatively weak memories (K = weak) nested within the
class of strong, high-confidence memories. Likewise, low-
confidence R responses describe a subclass of the rela-
tively strong memories (R = strong) nested within the
class of weak (low confidence) memories.
Imagine if you will, a hypothetical continuum of strength,
onto which participants classify their responses, ranging
from low (confidence) K response—representing the
weakest memories—followed by low R responses, then
by high K responses, and ending with high R responses.
If the LPC indeed reflects differences in memory strength
between two conditions, then the magnitude of this effect
for levels that are close together on the continuum (low
R and high K) should indeed be smaller than that which
is found for two levels that are further apart (high R and
low K). This accounts both for the emergence of an effect
corresponding to the strength (reflected in confidence) of
the trace and for the small magnitude of this effect.
Other ERP Evidence for an RK Dissociation
When Controlling for Strength?
As we have shown, our results call into question the inter-
pretation of the ERP RK effect (the LPC) as supporting a
dual-process account of recognition memory. Rather, a
unitary strength interpretation can account for the ERP
effect.
Interestingly, a recent study, which compared the ERPs
of recollection and familiarity, by contrasting items with
or without source memory, had attempted to control for
confounded strength as well (Woroch & Gonsalves, 2010).
In this study, participants made old/new judgments along-
side confidence rating and, subsequently, for items judged
“old,”made source memory judgments accompanied by
ratings of the source confidence. The authors compared
ERP signal for high confidence in source memory with
those of low confidence in source memory. Critically, to
control for strength, the authors analyzed only source
judgments that were made for items that first had cor-
rectly been judged “old”(i.e., item hits). Likewise, the
authors compared the ERPs of high and low old/new
confidence—only for items that were subsequently fol-
lowed by source miss, again arguing that high and low
confidence could not be attributed to differences in source.
Woroch and Gonsalves (2010) found that item and source
memory were associated with dissociable ERP effects,
presumably showing an effect similar to the RK effect
while controlling for strength.
Note however that the comparisons made in this study
did not properly control for strength as a potential con-
founded variable. This is because isolating only hit or
miss trials does not translate to isolating a nonvariable
magnitude strength. To control for strength, the entire
distribution from which the items are sampled must be
considered,
7
preferably using a measure of accuracy, as
we have done in our analyses. Thus, it could be the case
that the distribution of the items that preceded high-
confidence source judgments was stronger (i.e., more
accurate) than that which preceded the low-confidence
source judgments. Without measures of the accuracy of
these preceding trials, this (likely) possibility cannot be
ruled out.
fMRI Evidence for an RK Dissociation When
Controlling for Strength?
In contrast to the RK ERP signal, other neuroscientific evi-
dence may favor of the familiarity–recollection distinction.
For example, Rugg and colleagues have conducted a num-
ber of fMRI studies showing that familiarity–recollection
hippocampal effects cannot be accounted for with refer-
ence to memory strength (summarized in Rugg et al.,
2012). Also, Song, Jeneson, and Squire (2011) found that
activity in the hippocampus is related to the encoding
of familiarity-based item memory, independent of sub-
sequent recollection-based success. Yet further support
Brezis et al. 11
for the dual-system account was provided by Staresina,
Fell, Dunn, Axmacher, and Henson (2013), who combined
intracranial EEG recordings and state-trace analyses and
concluded that the signals in hippocampus and the peri-
rhinal cortex stem from dissociable systems. The results
reported by Ingram et al. (2012) likewise lend putative
support for a dual-system account. Future studies must
nonetheless validate these various observations to
further establish the familiarity–recollection distinction.
To summarize, the ERP signal of subjective RK expe-
rience has often been cited as providing converging
evidence for dual-process models of recognition and,
more specifically, for the idea that two processes medi-
ate RK responses. Our results question this evidence
and suggest that this ERP component reflects strength
rather than two types of subjective experience. A very
similar idea, using fMRI, was presented by Smith et al.
(2011), who demonstrated that hippocampal activity
was predicated not on recollection but on strong mem-
ory traces.
It is noteworthy that, in the ERP study reported by
Woodruff et al. (2006), a double dissociation or, more
formally, two single dissociations were observed (for a
discussion of the difference between single and double
dissociations, see Dunn & Kirsner, 1988). The first dis-
sociation was the RK parietal effect. The second was an
early frontal effect, which showed a monotonic relation-
ship with familiarity strength and no sensitivity to recol-
lection. Although our results do not challenge the early
frontal effect, we argue that the later RK effect is con-
founded with strength. If so, the available data can sup-
port only a single dissociation between R and K. As is
widely accepted, a single dissociation is insufficient to
deduce two independent systems or processes (e.g.,
Dunn & Kirsner, 1988).
How Do Our ERP Results Affect Different Variants
of Dual-process Theories?
Does our demonstration that the RK signal fails to sup-
port dual-process models apply to all variants of dual-
process models? The failure certainly applies to the
original dual-process signal detection model (Yonelinas,
1994), which described familiarity as a continuous process
whereas recollection, as an all-or-none high-threshold
process. This model assumed recollection and familiarity
to be distinct processes, and the emergence of an ERP
component was—erroneously, we argue—brought as sup-
port for this idea.
Other recent variants of dual-process models could
have also relied on the existence of an ERP component
for subjective RK experience as evidence for the exis-
tence of the two processes underlying R and K judg-
ments. The continuous dual-process model (Wixted &
Mickes, 2010; cf. the VRDP model of recognition, Onyper
et al., 2010) interprets the RK task as mediated by two
continuous signal detection processes, one for recollec-
tion and one for familiarity. When the recollection signal
is above the criterion of the recollection distribution, an
R judgment is made; otherwise, a K judgment is made. As
described in the Introduction, Ingram et al. (2012) found
a dissociation between strength and source memory,
with higher levels of source memory found for weak
R judgments than for strong K judgments. However,
their finding of a dependent variable—source memory—
corresponding to subjective experience but not to
strength was—alas—not mirrored in our dependent vari-
able, which was the amplitude of the ERP component.
Future research should address this apparent discrepancy
by including source judgments in the ERP paradigm re-
ported here.
Yet another model is the contextual information ac-
count (Rugg & Vilberg, 2013; Rugg et al., 2012). This
model suggests that hippocampal activity during retrieval
reflects the amount of contextual information that is as-
sociated with the test item, rather than the subjective
sense of recollection or the strength of an undifferen-
tiated memory signal.
8,9
Indeed, Yu, Johnson, and Rugg
(2012) reported that R judgments that were accompa-
nied by inaccurate or low-confident source judgments
were accompanied by hippocampal activity that was
similar to that elicited by K judgments. On the basis of
this account, it could be argued that weak Rs and strong
Ks share a similar magnitude of contextual information.
Still, this would yield a prediction of ERPs of equal mag-
nitude for the two response groups, which may in fact be
reflected in our findings. In fact, this interpretation could
also apply to the CDP model ( Wixted & Mickes, 2010).
Nonetheless, we note that our exploratory analysis re-
vealed an effect of significantly larger amplitudes for
strong K responses as compared with weak R. Thus, only
future research, using confirmatory analysis to replicate
this significant effect, can help determine whether these
alternative dual-process models can accommodate our
findings.
Finally, as we have seen in our data, as compared
with K responses, R responses are made with higher
confidence and accuracy. This result is typical and is
also reflected in the stochastic dependence found be-
tween the retrieval of different context attributes (as a
marker of binding) for R, but not for K items (Meiser,
Sattler, and Weißer, 2008). From this perspective, one
could argue that differences in strength are not a con-
found (as we have argued in this article) but rather an
emerging property of the type of information that is
brought to be bear to the decision in R versus K re-
sponses (see Diana & Ranganath, 2011, for further
discussion).
However, according to our understanding, if strength
is an emergent property of the two kinds of informa-
tion, then we should resort to the most parsimonious
account of the data. That is, if data conceptualized as
representing two qualitatively different processes can be
reduced to a strength account, then why conceptualized
12 Journal of Cognitive Neuroscience Volume X, Number Y
the data in the first place to represent two processes?
After all, a more parsimonious single process account
ofstrengthcandoaperfectly good job in accounting
for the data (cf. Didi-Barnea, Pereman, & Goshen-
Gottstein, in press). This is precisely our point in this
article. To account for the RK ERP effect, we need only
evoke a strength account. Hence ERP data cannot and
should not be cited as evidence in favor of dual-process
models.
To conclude, this study cast doubts on the interpreta-
tion of a pivotal neuroscientific finding, which has been
taken to support a qualitative distinction between recol-
lection and familiarity (e.g., Yu & Rugg, 2010; Vilberg
et al., 2006; Woodruff et al., 2006; Henson et al., 1999;
Düzel et al., 1997). Our results suggest that the previ-
ously observed electrophysiological differences between
R and K (or more generally, recollection and familiarly)
may have emerged because of the systematic confound-
ing of these response categories with different levels of
confidence and accuracy. Indeed, we propose that these
electrophysiological differences reflect strength of the
memory trace rather than subjective RK experience. We
argue, therefore, that in much the same way that that
hippocampal activation levels may perhaps be asso-
ciated with strength rather than with recollection (Smith
et al., 2011; but see Kafkas & Montaldi, 2012), the RK ERP
effect seems to be associated with strength, rather than
with recollection. It thus seems that the majority of pre-
vious electrophysiological and fMRI studies cannot be
cited as evidence for dual-process models. At bottom,
any future study that aims to characterize qualitative pro-
cess(es) underlying recognition memory, using any mea-
sure of performance—behavioral or neuroscientific—must
control for strength while contrasting recollection and
familiarity.
Acknowledgments
This research was supported by the Israel Science Foundation
(Grant No. 1922/15).
Reprint requests should be sent to Yonatan Goshen-Gottstein
or Noam Brezis, School of Psychology, Tel-Aviv University,
Tel-Aviv, Israel, 11111, or via e-mail: goshengott@gmaill.com,
noambrezis@mail.tau.ac.il.
Notes
1. More precisely, decisions have been argued to be mediated
by an unequal variance signal detection process (Dunn, 2004;
Wixted & Stretch, 2004) The idea of unequal variance stands
in contrast to different variants of dual-process models, which
posit that familiarity conforms to an equal variance signal detec-
tion process (but see Moran & Goshen-Gottstein, 2015). Note
that in this article we do not focus on the mechanisms under-
lying recognition judgments. Rather we focus on the more
fundamental question regarding the number of signals upon
which such mechanisms operate—one, according to the
single-process model and two, according to dual-process models.
2. In our design, this measure is equivalent to proportion
correct because it is only computed when participants gave
an “old”response. Indeed, the measure we report was within
categories of an “old”response, when comparing different re-
sponse groups (e.g., accuracy in Session I vs. Session II; accu-
racy for Rs vs. accuracy for Ks). Note that different measures are
used for accuracy (sensitivity) of the classification of “old”versus
“new”responses (e.g., d
0
; hitR –FAR; SPPV). These measures are
indeed the measures of choice to estimate overall sensitivity but
should not be used to estimate accuracy within a particular re-
sponse group (see Rotello, Heit, & Dubé, 2015; Rotello, Masson,
& Verde, 2008, for a discussion of considerations underlying
the choice of particular measures of sensitivity).
3. Following Smith et al. (2011), “guess”responses were
excluded from this and all subsequent analyses.
4. An electrophysiological comparison of high and low confi-
dence has been reported (Curran, 2004; Finnigan, Humphreys,
Dennis, & Geffen, 2002; reviewed in Rugg & Curran, 2007;
Woodruff et al., 2006, their Figure 7). However, the high–low
Figure A1. ERPs of R, K, and new responses, for the collapsed data
across the two sessions between 0 and 0.5 sec poststimulus onset.
Figure A2. Sampling distribution of ERPs elicited by high- and
low-confidence judgments, with identical quantities of “remember”and
“know”responses in each category. The figure depicts the same analysis
as depicted in Figure 6, yet with means and 95% confidence intervals
(shown by shaded bands), rather than the entire sampling distribution.
APPENDIX
Brezis et al. 13
confidence comparison was never made in conjunction with
that of RK. Thus, it has not been possible to contrast these
two comparisons. In particular, the effect of RK on confidence
(and vice versa) has never been controlled for. We describe
such controls in subsequent analyses.
5. An attempt to compare R and K at the same levels of con-
fidence (R4 vs. K4 and R1-3 vs. K1-3), resulted in, alas, signifi-
cantly higher accuracy for R than for K (R4 =.92;K4 = .75; t(24) =
3.73; p= .001; R123 =.87; K123 = .55; t(24) = 7; p< .0001).
Thus, this comparison too was unsuccessful in controlling for
strength, with no ERP analysis perused.
6. The combined amount of trials from the two sessions is
greater than the amount of trials in the pooled data. This is
because the pooled analysis allowed us to obtain a larger
amount of trials per category and hence included more par-
ticipants, some of which were excluded when analyzing each
session separately (see Methods for exclusion criteria).
7. To illustrate, consider 100 low-confidence trials and 100
high-confidence trials, with 60 hits in the former and 80 hits
in the latter. Clearly, the two distributions are associated with
different strength, yet when analyzing only hits, this difference
is not accounted for.
8. Note, however, that as the authors themselves acknowl-
edge, recognition accuracy systematically correlates with the
amount of information recollected.
9. Interestingly, according to the contextual account, parietal
activity is considered to reflect metacognitive aspects of recol-
lection, such as confidence judgments. This suggestion, how-
ever, is inconsistent with our result, whereby weak (low
confidence) R and strong (high confidence) K yielded similar
ERPs over the parietal regions.
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Brezis et al. 15
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