Dissertation Report - final

RUNNING HEAD: SPECIFIC INTERFERENCE WITHIN THE RECONSOLIDATION WINDOW

Are declarative memories sensitive to specific post-retrieval interference?

620036721, University of Exeter

2 SPECIFIC INTERFERENCE WITHIN THE RECONSOLIDATION WINDOW

Acknowledgements

With thanks to my supervisor, Dr. Nicolas Dumay, for his encouragement and support during the

completion of this project.


Abstract

According to the reconsolidation hypothesis, the retrieval of a consolidated memory may

briefly return it to a plastic state, making it vulnerable to retroactive interference. However,

Dumay and Tremblett (in prep.) found no reduction in memory strength for declarative

memories that were reactivated before 12 hours of unspecific interference from daily

encoding. Since the reconsolidation hypothesis cannot accommodate for such findings, we

sought to examine whether retrieved memories are altered by specific interference (i.e.,

novel, yet similar learning). We reasoned that finding reconsolidation effects a day after

reactivated memories have been exposed to specific interference would challenge the

assumption that consolidation and reconsolidation operate under similar boundary conditions.

Participants learned two lists of novel words (e.g., frenzylk) on day one. On day two, they

reactivated one list, then learned corruptors (e.g., frenzyl) for all words memorized on the

previous day. We tested reconsolidation-related effects on day three, expecting weaker

memories for the non-overlapping phoneme (e.g., ‘k’ in frenzylk) in reactivated compared to

non-reactivated words. Notwithstanding our predictions, cued recall data showed an overall

memory boost for reactivated compared to non-reactivated words, and no difference in the

percentage of omissions on the last letter. Similarly, phoneme monitoring data revealed

slightly stronger memories for reactivated compared to non-reactivated words. Since past

research provides no clear guide regarding experimental parameters that unequivocally

induce reconsolidation effects, our results allow several interpretations. We discuss our

findings in relation to available research on boundary conditions that constrain

reconsolidation.


Newly formed memory traces are vulnerable to interference from subsequent encoding

(Müller & Pilzecker, 1900). Yet, during a period of reduced interference, such as sleep, they

undergo a stabilisation process (Wixted, 2004). This process is referred to as consolidation, and

it has traditionally been thought to keep memories unchanged (McGaugh, 1966, 2000). However,

in the 1960s, Misanin, Miller, and Lewis (1968) posited that reactivation renders memories labile

again, contradicting the canonic consolidation view. Consistent with this notion, a seminal study

by Nader, Schafe, and LeDoux (2000) showed that a protein synthesis inhibitor can block a

consolidated memory upon reactivation. There is now relatively wide consensus about the

reconsolidation hypothesis, which states that reactivation renders memories in a state of

temporary plasticity that is followed by another period of stabilisation (Dudai & Eisenberg,

2004). Despite sharing many features with consolidation, the reconsolidation process is not

considered to be merely another cycle of consolidation (Dudai, 2012; Lee, Everitt, & Thomas,

2004; Tronson & Taylor, 2007). While consolidation strengthens a newly formed memory to

make it available for later retrieval, reconsolidation is thought to facilitate memory updating

(e.g., Lee, 2010; Merlo, Milton, & Everitt, 2015; Tronel & Alberini, 2007; Alberini, 2011; Hardt,

Einarsson, & Nader, 2010).

Neuroscientific research has provided accumulating evidence in favour of the

reconsolidation hypothesis. Amnesia-inducing drugs have been shown to block consolidated

memories upon retrieval in various species (Alberini & LeDoux, 2013). Using fear conditioning

paradigms, several studies confirmed that reconsolidation also occurs in humans. Soeter and

Kindt (2010) used propranolol to manipulate the reconsolidation of learned fear responses. A day

after learning a fear association, the experimental group received propranolol before memory

reactivation, while a control group received propranolol without reactivating the target memory.


A session of extinction on day three showed reduced fear expression in the experimental group,

but not in the control group. Importantly, similar results were obtained one month after the

original learning took place, suggesting that the reconsolidation manipulation blocked the return

of fear. The possibility to reduce the strength of fear memories has generated substantial interest

in reconsolidation. This interest partly arouse from its potential applications to reduce the

physiological reactions in disorders caused by maladaptive fear memories (Agren, 2014).

Notably, reconsolidation-based treatments have already been reported to induce attenuation of

trauma symptoms in PTSD patients (Brunet et al., 2008; Brunet et al., 2011; Poundja, Sanche,

Tremblay, & Brunet, 2012).

Further evidence for reconsolidation comes from the observation that new encoding

following reactivation can impair the original memory (Walker, Brakefield, Hobson, &

Stickgold, 2003; Boccia, Blake, Acosta, & Baratti, 2005). Walker and colleagues (2003) were

among the first to study reconsolidation in humans using a behavioural interference paradigm.

On day one, subjects were trained on a finger-tapping sequence. The following day, the

reactivation group rehearsed the sequence and immediately learned a second sequence, while the

non-reactivation (i.e., control) group learned the second sequence without rehearsing the initial

sequence. A test on the third day revealed impaired recall of the initial sequence in the

reactivation group, but intact knowledge in the non-reactivation group. These findings suggest

that interference from learning similar information upon memory reactivation can lead to

impaired recall. Similar results were obtained in a series of studies looking at the reconsolidation

of procedural memories (e.g., Censor, Dimyan, & Cohen, 2010, 2014).

Interference paradigms offer a means to study memory reconsolidation non-invasively.

They are particularly useful for targeting declarative and procedural memories, since no safe


pharmacological blocker is currently known to interfere with their reconsolidation in humans

(Schiller & Phelps, 2011). These paradigms have been traditionally used in consolidation

research (Müller & Pilzecker, 1900; Robertson, 2012), where unspecific interference has been

documented to interrupt the consolidation process in a time-dependent manner (Wixted, 2004).

The current view in the consolidation literature is that newly encoded memories and to-be-

consolidated memories compete for plasticity-related mechanisms, which prevents initial

memories from being consolidated (Mednick et al., 2011). Surprisingly, and in contrast with

consolidation research, a study in our laboratories demonstrated that unspecific interference does

not affect declarative memories rendered plastic by reactivation (Dumay & Tremblett, in prep.).

The day following encoding of nonsense words, half of the subjects reactivated one list in the

morning, subjecting it to 12 hours of unspecific interference from daily mental activity. The rest

of participants reactivated the same list before sleep, exposing the memory trace to minimal

interference. On day three, explicit and implicit memory measures revealed that the expected

reduction in memory strength for reactivated memories did not take place. This suggests that

reactivated memories are robust to unspecific interference. Two concerns arise.

One concern is that reconsolidation might not occur in declarative memories. However,

there are several reports of reconsolidation in declarative memories using behavioural

interference paradigms (e.g., Chan & LaPaglia, 2013; Jacques & Schacter, 2013; Forcato et al.,

2007; Hupbach, Hardt, Gomez, & Nadel, 2008). Indeed, some studies are susceptible to

alternative explanations due to the nature of the paradigms employed. Using a typical three-day

reconsolidation paradigm, Forcato and colleagues (2007) had subjects reactivate overnight-

consolidated lists through a pair associate (e.g., OEN-SRO) coupled with context cues (e.g., light

colour). On the third day, they found that learning a second list up to six hours after reactivation


lead to less retrieval-induced forgetting (i.e., a phenomenon observed only when a retrieved

memory is intact). However, Deliens and colleagues (2013) found that learning a pair-associate

that is similar to an already-learnt pair-associate activated the latter and interfered with its

encoding. It may be that results from the Forcato and associates (2007) study might have arisen

from interference at encoding, rather than reconsolidation. Furthermore, an alternative model

(Sederberg, Gershman, Polyn, & Norman, 2011) was proposed to explain reports of

reconsolidation in episodic memories by Hupbach and colleagues (2008, see Agren, 2014).

Despite the existence of some questionable reconsolidation demonstrations, a recent study has

provided evidence for reconsolidation in declarative memories using electroconvulsive therapy,

which produces seizure activity (Kroes et al., 2014). These researchers found that

electroconvulsive therapy applied after memory reactivation blocked the reconsolidation of

emotional declarative emotional memories in reactivated, but not in non-reactivated memories in

patients with depression. This study provides support for the notion that emotional declarative

memories can, in fact, be altered during the reconsolidation window.

Rather than concluding that declarative memories do not undergo reconsolidation based

on negative findings, another question that arises from the work of Dumay and Tremblett (in

prep.) would be whether a memory that is destabilised by reactivation is sensitive to particular

types of retroactive interference. This is the focus of the current investigation. Most studies that

observed reconsolidation effects using interference paradigms employed an interfering agent that

overlapped to a certain degree with the reactivated memory. In the Walker and associates (2003)

study, for instance, the new motor-tapping sequence (e.g., 2-3-1-4-2) was performed using the

same fingers as the original sequence (e.g., 4-1-3-2-4). In another study that reported

reconsolidation effects, participants were trained participants on a list of nonsense syllables that


had the same length as the corrupting learning (e.g., ITE-OBN vs. OEN-SRO), both being in

rioplantese Spanish (Forcato, Argibay, Pedreira, & Maldonado, 2009). Furthermore, research

using conditioning paradigms on rats has used post-reactivation learning shares features with the

original ‘conditioned stimulus – unconditioned response’ contingency (e.g., Boccia et al., 2005;

Boccia, Blake, Krawczyk, & Baratti, 2010).

A number of researchers advocate for the need to have a certain degree of similarity

between target memory and corrupting information to detect reconsolidation (e.g., Besnard,

Caboche, & Laroche, 2012; Hupbach, 2011). Besnard and colleagues (2012) proposed a model

to explain how similarity between the previously memorised experiences and potential

interference at memory recall may engage competing consolidation and reconsolidation

mechanisms. He argued that a high level of similarity results in updating the previous memory,

while a low level of similarity reduces the likelihood of engaging in reconsolidation by triggering

cellular mechanisms responsible for consolidation. According to this model, if consolidation is

initiated, then the assumed interfering information is stored in an independent memory trace,

separate from the original memory. This theory could explain findings that learning new

information upon retrieval engages consolidation in some instances (Tronel, Milekic, & Alberini,

2005) and reconsolidation in others (Lee, 2010).

The notion that reconsolidation has ‘boundary conditions’ has been proposed to name

parameters that constrain the observation of the reconsolidation process. Due to these constraints,

reconsolidation research has obtained a mixed bag of results. For example, old memories were

found not to be sensitive to reconsolidation manipulations (Suzuki et al., 2004; Milekic &

Alberini, 2002), neither were strong memories (e.g., Lee, Ciano, Thomas, & Everitt, 2005;

Debiec, LeDoux, & Nader, 2002), memories that do not encounter a mismatch between original


memory and expected outcome at reactivation (e.g., Sevenster, Beckers, & Kindt, 2013; Morris

et al., 2006; Pedreira, Pérez-Cuesta, & Maldonado, 2004), and memories reactivated indirectly

(Debiec, Doyère, Nader, & LeDoux, 2006). However, there are opposing findings for some

reports of boundary conditions. Older and stronger memories reconsolidate under different sets

of experimental parameters, such as larger doses of amnesic agents or longer reactivation

sessions (Debiec & LeDoux, 2004; Bustos, Maldonado, & Molina, 2009). The literature on

boundary conditions that inhibit reconsolidation has inconsistencies and lacks comprehensive

accounts. It thus needing further attention.

Of special relevance for the current investigation is a recent study which sought to

compare the effects of specific and unspecific post-retrieval interference (Chan & LaPaglia,

2013; Experiments 4 & 5). Subjects learned about a false terrorist attack from a video. The

following day, half of the subjects reactivated the memory trace, while the rest performed a

distractor task. After five minutes, they found impaired memory in the misinformation condition,

but not in the distractor condition. The effect was not observed when the same parameters were

used to examine non-specific interference (i.e., misinformation items did not target the

reactivated memory, being related to a story about drug trafficking). The authors asserted that the

interference agent needs to be specific to the original memory to observe reconsolidation.

Although Chan and LaPaglia’s (2013) interpretation is of interest, their results do not

necessarily demonstrate that the original memory has been modified through reconsolidation.

Testing memory immediately after the relearning phase is at odds with the reconsolidation

hypothesis, which states that the results of the reactivation-corruption manipulation should be

visible only after reconsolidation has been completed (Nader et al., 2000). In numerous studies,

reconsolidation appears to take at least two to four hours (Stickgold & Walker, 2007). There is


evidence that behaviours remained unchanged immediately after exposure to interference

(Walker et al., 2003), and also two (Suzuki et al., 2004) or four hours (Duvarci & Nader, 2004;

Debiec & LeDoux, 2004) after blocking reconsolidation. Thus, memory performance measured

five minutes before the end of interference phase might not reflect the effects of a completed

reconsolidation process in the Chan and LaPaglia (2013) study. Rather, it might be that new

learning was established as a separate memory trace and competed for reactivation with the

original memory (cf. Deliens et al., 2013).

Current study

To our knowledge, reports of reconsolidation published to date employed specific

interference. Based on this observation and on the model proposed by Besnard and associates

(2012), we assess whether declarative memories are sensitive to specific interference upon

retrieval. Finding reconsolidation effects when interference is specific, rather that unspecific

(other factors held constant) would demonstrate that having similarity between target learning

and corrupting information is a boundary condition for reconsolidation. We therefore adopt the

same learning and reactivation procedures from Dumay & Tremblett (in prep.). We employed a

three-day paradigm that aims to alter previously consolidated lexical memories following their

reactivation (Fig. 1). The use of a three-day paradigm, instead of a two-day paradigm as in Chan

and LaPaglia (2013) avoids potential problems with estimating the end of the reconsolidation

phase.


On day one, subjects formed memories of novel words (e.g., frenzylk). Half of these

memories were reactivated on day two, before learning corruptors (i.e., novel , but similar

words) for all memories encoded the previous day (e.g., frenzyl). On day three, memory for

reactivated and non-reactivated words was assessed using three tasks. In the phoneme

monitoring task, subjects detected phonemes in spoken words. The task measures how existing

phonological knowledge about the word stimulus contributes to phoneme recognition. Stronger

memories facilitate speed and accuracy (cf. Frauenfelder, Segui, & Dijkstra, 1990). We predict

that detection of the non-overlapping phoneme (“k” in frenzilk) would be slower and more liable

to errors in the case of reactivated compared to non-reactivated words. We then tested explicit

recall with a cued recall task. Recalling word endings is expected to be poorer for reactivated

compared to non-reactivated words (Fig. 2). Moreover, we posit that there will be more errors on

the last phoneme for reactivated compared to non-reactivated words.


A pause detection (i.e., lexical interference) task was lastly employed. In this task,

participants detected short silences in target words. This was a necessary measure as it shows

whether the information presented in the assumed reconsolidation window replaces the original

memory, or it has been stored in a separate memory trace. The rationale is that upon hearing a

word, the same input elicits parallel processing of multiple phonologically-similar words until

one passes the uniqueness point (McClelland & Elman, 1986). If one has memory for both

frenzylk and frenzyl, the two memories will compete for activation when presented with the

spoken word frenzylk. The speed at which the pause is detected depends on the degree of the

lexical activity existent at the point where the pause is inserted (Mattys & Clark, 2002). Hence, if

one has separate memories for target words and corruptors (rather than one updated memory),

the activation of the phonological neighbour would produce interference, thus slowing down or

even impeding pause detection. Accordingly, our expectation is that there will be slower reaction

times and more errors for pauses embedded into non-reactivated compared to reactivated words

(Fig. 3).


If as predicted, the data would constitute the first unambiguous evidence that specific

interference facilitates reconsolidation. As a more general implication, these predicted results

would add to the accumulating body of evidence that reconsolidation is not merely another cycle

of consolidation (e.g., Dudai, 2012).

Method

Subjects

Thirty subjects aged 19-25 (M=21.37; SD=1.92) were tested (15 females, 15 males).

They were native UK-English speakers (i.e., had UK-English as their first learnt and most skilled

language) and had no known hearing, language, sleep, or memory deficits. They consented to

keep their usual sleeping and drinking patterns throughout the experiment, and not to exert

mental effort between sessions. Subjects were students at the University of Exeter, recruited


through convenience sampling. Sweets or course credits were given for their participation. This

study was approved by University of Exeter’s Ethics Committee (Appendix A).

Design

A within-subjects design was used to compare memory strength for reactivated versus

non-reactivated words. Forty-eight words were equally split into three separate lists (A, B, and

C). Each participant learned two of these lists (e.g., A and B) on day one. On day two, they

reactivated one list (e.g., A or B). Lastly, on day three, we tested memory for both lists and for

baseline words. The baseline was set to ensure that performance changed depending on learning

and memory strength, and not due to task proficiency or to other extraneous factors. All three

lists (i.e., A, B, and C) had each possible role (i.e., reactivated, non-reactivated, and baseline) in

turn among six groups of participants.

Baseline and target words were further divided into two lists for the pause detection task

(i.e., pause-present and pause-absent words). In the sound detection task each word appeared

twice, first time requiring a ‘yes’ response and the second time requiring a ‘no’ response.

No effect was expected on negative responses in phoneme monitoring, since they engage

checking mechanisms that conflict with the memory facilitation effect (cf. Frauenfelder &

Peeters, 1990). In terms of pause detection, we recorded responses on pause-absent trials

although they were not required in Mattys and Clark’s (2002) original pause detection task. The

rationale is that inhibition or failure to detect pause absence result from the allocation of general

resources to lexical competition (cf. Gaskell & Dumay, 2003). An advantage is that processing

can proceed uninterrupted, unlike in the case of pause-present trials where the pause may slightly


disrupt processing when resources are not taken up completely by competition (Gaskell &

Dumay, 2003).

Materials

Stimuli were adapted from Dumay and Gaskell (2012). There were 48 target novel

words, all derived from existing bi-syllabic English words by adding a two-consonant suffix to a

stem (e.g., “frenzy” + “lk”). Corruptors differed from target words by lacking the final consonant

(e.g., “frenzy” + “l”). They were recorded separately, being compatible with target words.

Additionally, compatible recordings of the stem words were used for cued recall and pause

detection.

All stimuli were presented over Beyer Dynamic DT 770 Pro/80 headphones. Subjects

used a Trust 1200 gamepad to start and respond to the phoneme monitoring task and to the pause

detection task. For cued recall, responses were recorded by a Sennheiser pc151 microphone. A

soundproof computer booth was allocated per subject. For practical reasons, the booth was

chosen randomly for each session. However, all computer booths were identical and excluded

subjects’ belongings.

Procedure

Procedure is schematically represented in Fig. 1. Stimuli were presented through DMDX,

which also collected latencies and voice recordings.

Day 1 (after 6 pm). After signing a consent form that detailed experimental procedure

and inclusion criteria (Appendix B), subjects performed a sound detection task (Fig. 4). A prompt

instructed them to decide quickly and accurately if spoken words presented over the headphones

contained the sound presented on the screen. It also required subjects to learn the words at their

best because they will be asked to perform a memory test. A 1000-ms break followed each


stimulus presentation. Six target sounds (/L/, /K/, /M/, /S/, /T/, /N/) were used for six set

repetitions (i.e., each set was repeated 36 times as in Dumay & Gaskell, 2012). Break lengths

between blocks lasted until participants decided to press a key. The task encouraged attention to

each trial and took approximately 40 minutes.

Day 2 (after 6 pm). Subjects retrieved half of the words by responding to the same sound

detection task. Each set was reactivated six times, once with each target sound. The retrieval

session took approximately 10 minutes. The same sound detection task ran subjects through

corruptors for each of the 32 target words for 36 times with the same instructions.

Day 3 (before 12 pm). Subjects performed a series of memory tasks. In the phoneme

monitoring task (Fig. 5), a phoneme appeared quickly on the screen (200 ms). Subjects were

required to respond promptly and accurately if the spoken word that followed contained this

phoneme or not. The phoneme appeared at a random position.


A cued recall task followed. Upon hearing stems (e.g., frenzy), subjects articulated what

they recalled from the respective word learnt on day one.

Finally, a pause detection task was performed. The task started with 167 practice trials.

Quick and accurate ‘yes’ or ‘no’ decisions were required depending whether the spoken word

contained a short silence. A 200-ms artificial pause was inserted after the stem (e.g., frenzy_lk).

Subjects had 3 s to respond from stimulus onset, and a 1-s break before the next trial. Response

latencies were measured starting from pause onset.

After completing the experiment, subjects were asked to report unusual sleeping times or

alcohol consumption over the duration of the experiment. Oral debriefing followed.

Results

Responses were missing for three subjects in phoneme monitoring, three in cued recall,

and one in pause detection. All analyses on proportional data (e.g., error rates) were conducted

after performing arcsine transformations to achieve normality.


Phoneme monitoring

Performance on phoneme monitoring was analysed through a repeated measures

ANOVA. We examined how response latencies and error rates vary as a function of word

condition (baseline, non-reactivated, or reactivated). Responses given earlier than 200 ms or

2000 ms after stimulus presentation were not recorded. The analysis included only trials that

contained the target phoneme.

Response latencies. Analysis of response latencies included only correct trials. The word

learning manipulation was successful (F2,56=16.340, p<.001), as revealed by the significantly

longer response latencies to baseline words compared to non-reactivated words and reactivated

words (see Fig. 6a for mean response latencies). The difference was significant both in the case

of non-reactivated words (F1,28=17.173, p<.001) and reactivated words (F1,28=27.890, p<.001).

However, latencies to reactivated words did not differ significantly from those to non-reactivated

words (F1,28=1.515, p=.227). Surprisingly, it appears that reactivation slightly boosted memory

strength (see Fig. 6a).


Error rates. There was a significant main effect of reactivation on yes responses

(F2,56=7.854, p<.001). In line with latencies, error rates were higher on baseline than on learned

words (Fig. 6b). There were significantly more errors on baseline compared to non-reactivated

(F1,28=10.264, p=.003) or with reactivated words (F1,28=13.412, p=.001). Notwithstanding our

predictions, there was no significant difference between non-reactivated and reactivated words

(F1,28=1.289, p=.266). Akin to latencies, there was a trend in the opposite direction, with more

errors on average for reactivated compared to non-reactivated words.

Cued recall

Responses on cued recall indicated better knowledge of reactivated words, which is

contrary to our predictions (cf. Fig. 7a). A repeated measures ANOVA conducted on the

proportion of accurate responses demonstrated that the difference was significant (F1,27=7.004,

p=.013). The difference was still significant after performing an arcsine transformation to

achieve normality (F1,27=7.785, p=.009).


After normalizing data, an ANOVA was carried out to examine the proportion of errors

on the last consonant (i.e., the consonant that distinguishes a target word from its corruptor).

Although we predicted that cued recall responses will show more omissions of the last consonant

in the reactivation condition, we found no significant difference between the percentage errors on

the last consonant between reactivated and non-reactivated words (F1,27=1.323, p=.260). Still,

there was a trend in the expected direction (see Fig 7b).


Pause detection

A repeated measures ANOVA was used to analyse performance on pause-present and

pause-absent trials. We assessed how lexical interference varies as a function of word condition

(i.e., reactivated, non-reactivated, baseline).

Response latencies. Only correct trials were included in the analysis. We recorded

responses between 200 ms and 2000 ms after stimulus presentation. There was a significant main

effect of word category on pause-present trials (Fig 8a; F2,58=3.512, p=.036). At odds with

predictions, subjects detected pauses in the reactivated words significantly more slowly than

those in the non-reactivated words (Fig. 8a, F1,29=4.395, p=.045). Yet, only performance on non-

reactivated words significantly differed from performance on baseline words (F1,29=6.640,

p=.015), with significantly faster responses on non-reactivated words. There was no significant

difference between reactivated words and baseline words (F1,29=.046, p<.832). Although there

was no significant main effect observed in the pause-absent trials, the trend was in line with

pause-present responses (Fig. 8a; F2,58=.492, p=.614).


Error rates. There was a significant effect of word category (F2,58=8.484, p<.001).

Analogous to latency scores, there were significantly more errors on reactivated words compared

to non-reactivated words (Fig 8b; F1,29=16.842; p<.001). In contrast with latencies, percentage

errors for non-reactivated words did not differ from those for baseline words (F1,29=.290,

p=.594). Only reactivated words differed from baseline words (F1,29=15.140, p<.001). Again,

there was no significant main effect of word category on pause-absent trials (F2,58=2.090,

p=.132), although responses aligned on average with performance on pause-resent trials.

Discussion

Knowledge of boundary conditions is crucial for theorizing reconsolidation, yet a

comprehensive account of these is missing. This study aimed to assess whether specific

interference can alter memories following reactivation. Interference specificity has been a

common denominator of studies that have obtained evidence for reconsolidation to date (e.g.,

Walker et al., 2003; Boccia et al., 2005; Boccia et al., 2010). To demonstrate unequivocally that


specificity of interference is a boundary condition for reconsolidation, one should be able to find

evidence of reconsolidation when using specific, but not unspecific interference – other factors

being held constant. A previous study has shown that unspecific interference does not influence

the reconsolidation process (Dumay & Tremblett, in prep.). Thus, we adapted the learning and

reactivation procedure used by Dumay and Tremblett (in prep.) in attempting to make a case that

specific interference is needed to observe reconsolidation. On the third experimental day, we

compared memories for reactivated words (which were expected to be in a plastic state prior to

potential interference) with memory for non-reactivated words (which were expected to be

robust to interference).

We expected that learning corruptors after memory reactivation would update memory

for reactivated words. Notwithstanding this prediction, we found that errors on the non-

overlapping consonant in cued-recall did not significantly differ between reactivation and non-

reactivation word conditions. We also anticipated that cued recall and phoneme monitoring

performance would reveal stronger memories for non-reactivated compared to reactivated words.

In sharp contrast to our expectations, cued recall responses indicated better memory strength for

reactivated than non-reactivated words. Phoneme monitoring revealed a similar pattern of

performance in the form of a non-significant trend: There were longer latencies and more errors

on non-reactivated words compared to reactivated words.

Our last prediction was that non-reactivated words and their respective corruptors would

be stored in separate memory traces, while reactivated words would blend with corruptors in one

updated memory. Thus, we anticipated that reactivated words would receive less interference in

a lexical competition task (i.e., the pause detection task) compared to non-reactivated words.

However, we found that reactivated words received more interference than non-reactivated


words (expressed as longer latencies and more errors in pause detection). This suggests that,

contrary to our prediction, reactivated words and corruptors were stored in separate memories.

In sum, it appears that reactivated memories were updated in strength instead of memory

content. We propose that this finding does not necessarily rebut the reconsolidation hypothesis.

To reconcile the discrepancy between our results and results obtained in previous reconsolidation

reports (Walker et al., 2003), we must question the ability of our experimental parameters to

initiate reconsolidation. At the moment, there is no reconsolidation protocol found to reliably

destabilize reactivated memories (Merlo et al., 2015). Drawing on available findings on

boundary conditions of reconsolidation, we derive at least four interpretations of our data.

First, we speculate that the reactivation protocol could have prevented reconsolidation

from proceeding. A number of studies have demonstrated that successful engagement in

reconsolidation processes depends on having a mismatch between what is expected to occur

upon reactivation and what actually occurs (Pedreira et al., 2004; Forcato et al., 2009; Sevenster

et al., 2013; Jacques & Schacter, 2013). This boundary condition has been regarded as prediction

error. The underlying principle is that encountering prediction error signals that the initial

learning is no longer adequate and new learning is required instead (Sevenster et al., 2013). In

the present study, the reactivation session consisted of the same experience that took place

during the learning phase, which means that there was no prediction error during reactivation.

This may have caused the memory to strengthen itself because information of the same quality

was acquired during the plastic period of the reactivated memory. In support of this argument, a

recent study found that lack of prediction error at reactivation resulted in memory strengthening

(Jaques & Schacter, 2013). In this study, subjects went on a museum tour carrying a camera that

was taking photos automatically. On the following day, they viewed photos that were


presumably taken by their camera. Half of the photos matched the encoding experience, while

the other half were modified versions of the original photos, being altered in angle, height, or

timing. A recognition test on the third day revealed that memories were stronger when

reactivation matched, rather than mismatched with encoding. Importantly, they found that the

mismatching retrieval session was also associated with increased false recognition of photos

illustrating stops that participants did not experience during the museum tour. The latter indicates

that having prediction error when retrieving an episodic memory can result in memory updating.

Based on these findings, it is tempting to speculate that the lack of prediction error might have

brought to the strengthening effect observed in reactivated words.

A second interpretation derives from limitations in how the prediction error hypothesis is

currently formulated. Most studies that used prediction error at reactivation proceeded on the

assumption that prediction error should occur throughout the reactivation session in order to

detect reconsolidation. However, Walker and associates (2003) provided evidence for

reconsolidation despite reactivating memories through a short reiteration of the learning phase.

Nonetheless, it can be argued that prediction error was still generated in this study. The study

used a target sequence (4-1-3-2-4) that overlapped with the corrupting sequence (2-3-1-4-2) in

content and sequence length, but differed in aspects such as finger repetition. On a similar note,

our study has attempted to induce prediction error by replacing the presentation of the

consolidated word frenzylk during sound detection with the novel word frenzyl. The

inconsistency between our findings and those by Walker and colleagues (2003) might arise from

using different degrees of similarity between target memories and corrupting learning. According

to the prediction error hypothesis, a large degree of similarity may prevent a mismatch from


occurring simply because only a small amount of information would be added to the original

memory.

As such, it is conceivable that our procedure might have failed to induce prediction error

by using corruptors that highly overlapped with target information. Future studies that aim to

investigate the role of interference specificity may benefit from using corruptors that overlap to

various degrees with initial learning. If further research does not validate the prediction that less

similarity allows reconsolidation to proceed, it may be that other characteristics of reactivation

deter the successful engagement in reconsolidation. Ideally, future research should find a method

of measuring the optimum degree of similarity without depending on the occurrence of

reconsolidation itself (which is constrained by other boundary conditions).

Thirdly, reactivation length may have influenced our results. Several associative memory

studies have found that repeated cue presentations upon memory reactivation lead to extinction

instead of the expected reconsolidation effects (Inda, Muravieva, & Alberini, 2011; Lee, Milton,

& Everitt, 2006). A shorter reactivation session, in contrast, resulted in reconsolidation.

Extinction represents a decrease in a conditioned response (Quirk & Mueller, 2008). Unlike

reconsolidation, extinction is underpinned by a mechanism that enables the formation of a

separate memory which, in turn, inhibits the previously learnt association. It has recently been

demonstrated that these two processes are mutually exclusive (Merlo et al., 2014). This study

showed that repeated cue presentations lead to the termination of the reconsolidation process.

Building on these findings, if the reactivation session was too long in our study, then the

corruptors appeared outside the reconsolidation window. Following this line of thought,

corruptors were actually learnt when memories were no longer plastic. As a result, they did not

produce the expected shift in the initial memory.


The increase in memory strength concurrent with reactivation might have then resulted

from rehearsing the initial memory during the reconsolidation window. Several studies have

obtained strengthening effects using different reactivation durations and parameters of memory

strength and age (Inda et al., 2011; Rodriguez-Ortiz, Balderas, Garcia-DeLaTorre, & Bermudez-

Rattoni, 2012; Lee, 2008; Karpicke & Roediger, 2008; Roediger & Butler, 2011, Coccoz,

Maldonado, & Delorenzi, 2011). It still remains an open question how the reactivation length

varies as a function of memory strength and memory age. At this stage, we cannot draw

definitive conclusions about the adequacy of our reactivation length.

Lastly, we should consider the possibility that the reactivation session has been too short

to induce memory destabilisation. This interpretation is supported by findings by Bustos and

colleagues (2009). In this study, one-minute reactivation of a strong associative fear memory did

not induce reconsolidation, while a three-to-five-minutes reactivation session successfully led to

reconsolidation. This suggests that insufficient reactivation leaves reactivated memories in an

insensitive state. It is tempting to argue that the consolidation of corruptors as separate memory

traces arouse from the fact that memories were not destabilised from the very beginning.

However, our results show a memory boost concurrent with reactivation. This position would

predict that memory strengthening occurs irrespective of the fact that memories are in a plastic or

in an insensitive state. If memory strengthening cannot occur outside the reconsolidation

window, then the memory has been successfully destabilised in this instance. While such cases

have yet not been reported, available evidence only shows a correlation between reconsolidation

and memory strengthening, and not a causal effect (Alberini, 2011).


Conclusion and Future Directions

In conclusion, we show that the manipulation of reconsolidation through exposure to

similar learning after reactivation can boost the initial memory. Although we expected that

specific interference would affect memory reconsolidation, our results did not support this

prediction. This study highlights a number of gaps in the literature that reduce the feasibility of

disrupting reconsolidation. Past research has obtained mixed results using different reactivation

paradigms. Determining the characteristics of reactivation that induce reconsolidation would be a

fruitful avenue for research. In the absence of a framework that could guide the choice of

reactivation duration for declarative memory reconsolidation, it is impossible to make firm

assertions about the ability of the retrieval session to induce reconsolidation based on associative

memory research per se. Also, we cannot fully disambiguate between an explanation that

pertains to lack of prediction error at reactivation and one that pertains to reactivation length.

While research has been clear about the necessity to induce prediction error, how to induce

prediction error remains an open question. Outside the issues with the retrieval session, it

remains to be established whether a high degree of similarity between corruptors and target

memory can conflict with prediction error. It is difficult to draw conclusions about the right

amount of similarity based on reconsolidation itself. If anything, determining boundary

conditions for reconsolidation would be a fruitful avenue for research.

The present study is the first to investigate the need to use specific interference to observe

reconsolidation using a three-day procedure. The use of a three-day procedure, instead of a two-

day procedure as in Chan and LaPaglia (2013) avoids potential difficulties with estimating the

timeframe of the reconsolidation window. An understanding of the limits and operating

conditions of reconsolidation is crucial for understanding mechanisms responsible for plasticity


in human memories. In addition to its theoretical value, the possibility to alter memories could

also have therapeutic value. Retrieval-induced plasticity could be manipulated to alter

consolidated pathogenic memories for PTSD (Brunet et al., 2011). Since situations that offer

opportunities for memory updating can alternatively strengthen the original memory and lead to

new learning, it is essential to be able to select optimal reactivation and interference specificity

parameters in order to attain the expected memory outcome.


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