Brain and Cognition 90 (2014) 8–18

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Brain and Cognition journal homepage: www.elsevier.com/locate/b&c

Trial-to-trial dynamics of selective long-term-memory retrieval with continuously changing retrieval targets Jasmin M. Kizilirmak a,⇑, Frank Rösler b, Patrick H. Khader c a

Clinic for Neurology, Otto-von-Guericke University of Magdeburg, Germany Department of Biological and Neuropsychology, University of Hamburg, Germany c Department of Psychology, Ludwig Maximilian University of Munich, Germany b

a r t i c l e

i n f o

Article history: Accepted 30 April 2014

Keywords: Long-term memory Selective retrieval Cognitive control ERPs SCPs Slow waves

a b s t r a c t How do we control the successive retrieval of behaviorally relevant information from long-term memory (LTM) without being distracted by other potential retrieval targets associated to the same retrieval cues? Here, we approach this question by investigating the nature of trial-by-trial dynamics of selective LTM retrieval, i.e., in how far retrieval in one trial has detrimental or facilitatory effects on selective retrieval in the following trial. Participants first learned associations between retrieval cues and targets, with one cue always being linked to three targets, forming small associative networks. In successive trials, participants had to access either the same or a different target belonging to either the same or a different cue. We found that retrieval times were faster for targets that had already been relevant in the previous trial, with this facilitatory effect being substantially weaker when the associative network changed in which the targets were embedded. Moreover, staying within the same network still had a facilitatory effect even if the target changed, which became evident in a relatively higher memory performance in comparison to a network change. Furthermore, event-related brain potentials (ERPs) showed topographically and temporally dissociable correlates of these effects, suggesting that they result from combined influences of distinct processes that aid memory retrieval when relevant and irrelevant targets change their status from trial to trial. Taken together, the present study provides insight into the different processing stages of memory retrieval when fast switches between retrieval targets are required. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Different items in memory are often associated with the same retrieval cue. For example, when writing this introduction, the contents of many journal papers have to be retrieved in a specific order, but all of these long-term memory (LTM) representations are related to one and the same super-ordinate cue, e.g., ‘‘the control of memory retrieval’’. How do we manage to retrieve just one currently relevant association without being distracted by other associations triggered by the same retrieval cue? Theories about the organization of LTM assume that information is represented in networks in which representations are connected according to their associative strength and in which retrieval of one item causes a spreading of activation across associated items (Anderson, 1976, 1983). Depending on the task, this spreading of activation may lead to a facilitation of retrieval (Rubin &

⇑ Corresponding author. Address: Clinic for Neurology, Otto-von-Guericke University of Magdeburg, Leipziger Str. 44 (Zenit I), 39120 Magdeburg, Germany. E-mail address: [email protected] (J.M. Kizilirmak). http://dx.doi.org/10.1016/j.bandc.2014.04.013 0278-2626/Ó 2014 Elsevier Inc. All rights reserved.

Olson, 1980), but can also result in retrieval interference (e.g., Bäuml, 2008; Bäuml, Pastötter, & Hanslmayr, 2010; Ciranni & Shimamura, 1999). Specifically, when selective retrieval of only one target associated with a cue is required, there is evidence for activation spread leading to interference due to the activation of other items associated with the same cue that compete for retrieval (Johnson & Anderson, 2004; Levy & Anderson, 2002). In free recall tasks, in which all items associated with a cue have to be retrieved anyway, the activation spread has been reported to be beneficial as it shortens the RT for consecutive retrieval (Lorch, 1982; Neely, 1976; Posner & Snyder, 1975). However, in other retrieval tasks in which only one retrieval target is relevant cognitive-control processes are assumed to regulate interfering activation patterns by selectively enhancing relevant and/or by inhibiting associated but irrelevant information. As a consequence, relevant associations are strengthened, and thus better retrieved on later occasions, while irrelevant associations are weakened and less well accessed later on (Anderson, 2003; Anderson & Spellman, 1995; Bäuml & Samenieh, 2010; Goodmon & Anderson, 2011; Johansson, Aslan, Bäuml, Gäbel, & Mecklinger, 2007; Wimber, Rutschmann, Greenlee, & Bäuml, 2008). The

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retrieval-practice paradigm (Anderson, Bjork, & Bjork, 1994) allows to study such effects. It has shown that memory of non-practiced associations can be impaired just by practicing other related associations after initial encoding (Anderson et al., 1994; Bäuml, 2008; Ciranni & Shimamura, 1999; Levy & Anderson, 2002). This ‘‘retrieval-induced forgetting’’ (RIF) suggests that existing but notretrieved associations are weakened, possibly actively inhibited, if other related associations are repeatedly activated. On the other hand, it has been shown that, depending on the status of the associations, selective retrieval can also lead to enhanced accessibility of non-practiced associations with a cue through spreading activation, e.g., when previously to-be-forgotten associations need to be remembered (Bäuml & Samenieh, 2010; Dobler & Bäuml, 2012) or when the information is highly integrated and testing is delayed about 24 h (Chan, 2009). To conclude, depending on the retrieval task facilitatory or detrimental effects can be observed. The present study builds upon these findings. However, rather than studying such effects in distinct practice and retrieval episodes, as has been done in previous studies, we investigated which processes, detrimental or facilitatory, or both, govern retrieval on a trial-by-trial basis, i.e., when irrelevant associations (potential retrieval competitors) in one trial might become relevant in the immediately following trial. 1.1. Paradigm To investigate dynamic changes of retrieval processes, we manipulated trial-to-trial changes and repetitions of cue–target associations. Participants established small associative networks in LTM, each consisting of an animal and three characteristics that were learned as pictures/symbols (i.e., body weight, sociability, and distance of habitat; see Fig. 1A). Participants were informed that a memory test would follow. During the retrieval task (Fig. 1B), the animals’ names served as cues, indicating the relevant associative network. An immediately following stimulus indicated

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one of the three associated characteristics that was the retrieval target of the present trial. In each retrieval trial (Fig. 1B), only one target had to be retrieved, the other two had to be ignored. While all trials involved the same task, i.e., to retrieve one of three possible cue–target associations, they differed in their retrieval history, yielding the four different experimental conditions that are depicted in Fig. 2, i.e., the cue and the target could remain the same in two consecutive trials i  1 and i (condition #1), the cue could be repeated while the target changed (condition #2), the cue could change while the target remained the same (condition #3), or both cue and target changed (condition #4). Condition #4 served as a baseline condition for which no immediate trial-to-trial effect of selective retrieval – be it facilitatory or inhibitory – was to be expected. The conditions were motivated by studies in other fields that successfully investigated continuously changing demands on interference control by manipulating sequences of trials, e.g., in task-switching and backward-inhibition experiments (e.g., Dreher & Berman, 2002; Mayr & Keele, 2000), or selective-attention studies (e.g., Neill, 1997; Stadler & Hogan, 1996; for a review see Tipper, 2001). These studies proved the existence of highly dynamic trialto-trial control processes that reduce interference (from competing task sets or visually presented stimuli) by facilitating the repeated processing of the target information or impairing the processing of previously irrelevant competitors for the next trial(s). By transferring this concept of trial-to-trial interference control to LTM retrieval, we wanted to find out whether similar dynamic control processes are involved when the interference arises on the level of LTM representations. 1.2. Hypotheses Compared to all other conditions, it can be expected that condition #1 in which everything is repeated would be the easiest condition, leading to the shortest RTs and fewest errors, because it is a

Fig. 1. Trial events of the task. (A) Exemplary trial of the learning phase. Subjects had to memorize three visually presented characteristic values for each of 25 animals. (B) Exemplary trial of the retrieval task. Subjects had to retrieve one value associated with an animal and compare the remembered value with the one marked on an analog scale (colored bars). On each end of a scale, the smallest (left) and biggest (right) values of the characteristic were presented as icons. The minimum presentation duration of the display was 3000 ms.

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Fig. 2. Experimental conditions. Retrieval-trial sequences that represent the conditions: distinct changes and repetitions of association types (targets) and networks (cues).

typical positive priming situation in which the same cue–target association has to be accessed a second time. Note that with the present paradigm such a facilitation can be interpreted as repetition priming on the level of memory representations instead of a perceived stimulus or motor response, as we especially controlled for stimulus and response priming (for details, see Section 2.3). Whereas the prediction for the full repetition condition is relatively straightforward, the predictions for the other three conditions strongly depend on the principal process that is assumed to regulate the trial-to-trial dynamics of selective LTM retrieval, i.e., inhibition or facilitation: On the one hand, the retrieval-induced-forgetting effect (Anderson, 2003) suggests that retrieval competition between associations with the same cue triggers inhibitory control. Accordingly, the consecutive retrieval of different targets associated with the same cue (condition #2) should be more effortful and should lead to poorer performance than the consecutive retrieval of completely unrelated targets (condition #4). However, because in our study all associations related to one cue are learned in one learning session to the same extent, the opposite result is also conceivable, i.e., that, due to substantial over-learning, each network will be highly integrated, and thus retrieval competition might be relatively weak while simultaneously evoked spreading activation might lead to a facilitation of the subsequent retrieval of another target associated the same cue (Anderson, 1983; Chan, 2009; Reder & Anderson, 1980). Then, performance should be better when shifting from one target to another associated with the same cue (condition #2) than shifting between unrelated targets (condition #4). Finally, as participants learned distinct cue–target networks, conditions with a cue change should not differ, regardless of whether the target is repeated or not (condition #3 = condition #4). In both cases a new network has to be accessed. However, conceptual priming cannot be excluded completely in the cue-change target-repetition condition (#3) because the target category, i.e., weight, distance, or sociability, is repeated. This could lead to some processing facilitation. To conclude, the obtained pattern of results can help to determine the kind of processes that are related to trialby-trial control of selective LTM retrieval. 1.3. Event-related potentials related to potentially involved cognitive processes Alongside behavioral data, we recorded event-related brain potentials (ERPs) to study the temporal dynamics and possible qualitative and quantitative differences of the involved retrieval

processes that are likely to be reflected in topographical and amplitude differences (Otten & Rugg, 2005; Picton et al., 2000). In a previous study, we found evidence for ERP differences depending on the retrieval demands of successive trials (Kizilirmak, Rösler, & Khader, 2012), i.e., when the number of to-be-retrieved associations changed from one trial to the next. The present study builds upon this finding, but rather than focusing on retrieval load, we here want to delineate the processes involved in successively retrieving associations from LTM belonging to either the same or to different associative networks. In contrast to our previous study, here retrieval load was held constant. Moreover, we now included the repetition of cue and target (condition #1) in order to enable a full factorial design. Based on our previous study and on general considerations about the temporal evolution of ERP differences related to memory retrieval (e.g., Rugg & Curran, 2007), we expected ERP effects to show up in three different time windows after the onset of the retrieval cue, i.e., 250–500 ms, 500–1000 ms, and 1000–3000 ms. There are two ERP components during the first two time windows that are classically related to memory processes. The first component is usually found around 350–400 ms with a topographical maximum at mid-frontal electrodes. It has been observed for familiar old, i.e., recognized, in contrast to unfamiliar new items, while it does not vary with the degree of recollection of the learning event (Rugg & Curran, 2007; Vilberg, Moosavi, & Rugg, 2006). Although there is an ongoing debate whether it reflects unconscious conceptual priming or rather conscious recognition (Bridger, Bader, Kriukova, Unger, & Mecklinger, 2012; Mecklinger, Frings, & Rosburg, 2012; Paller, Lucas, & Voss, 2012), this effect is generally assumed to reflect relatively automatic retrieval processes. Our paradigm does not allow dissociating familiarity from conceptual priming effects, however, if an effect in this time window will be found, we can be relatively confident that it reflects automatic processes. The mid-frontal old/new effect has never been investigated on a trial-by-trial basis, but it can be expected to be especially pronounced for the condition in which both cue and target are repeated (cf. Fig. 2: condition #1 > condition #2), because in target-change trials the target is relatively ‘‘new’’ compared to target-repetition trials. Therefore, this ERP effect can be seen as an indicator of processes that facilitate performance in trial-by-trial selective retrieval. If such an effect would also occur for the condition in which the target is repeated although the cue had changed, it would indicate that not only specific cue–target associations are primed/recognized, but that also the target

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dimension itself can profit from a repetition, suggesting conceptual priming. The second component is usually observed with a peak around 500 and 800 ms and a topographical maximum over parietal electrodes (Rugg & Curran, 2007; Wilding, 2000). It has been associated with intentional recollection, i.e., not only recognizing an item as being old, but also being able to recall other information associated with the item. The effect has also been shown to be sensitive to the amount of information recollected (Vilberg et al., 2006) and the recency of its last encounter (Grove & Wilding, 2009). Since recollection can only be facilitated for cue and target repetition, we hypothesize that this component should only show an effect for target repetition vs. target change in cue repetition (condition #1 > condition #2), but not in cue-change trials (condition #3 = condition #4). That is, even though target repetition might lead to familiarity effects even for cue change, since only the target category is repeated but not the target representation itself, there should be no benefit in recollection. Lastly, the third time window is at the latency of slow cortical potentials (SCPs). In our previous study, we had focused the analysis on these ERPs and found that their amplitude was systematically affected by the necessity to adjust retrieval-control processes from trial to trial (see Kizilirmak et al., 2012 for details). Both the topography and amplitude of negative-going SCPs generally show a close correspondence with BOLD effects (e.g., He & Raichle, 2009; Jost, Khader, Burke, Bien, & Rösler, 2011; see Khader, Schicke, Röder, & Rösler, 2008, for a review), which is why their latency does not necessarily correspond to the temporal occurrence of the underlying cognitive process. Negative SCP shifts most likely reflect a sustained field potential arising from the depolarization of the apical dendrites of neocortical pyramidal cells (e.g., Speckmann & Elger, 2005). Therefore, negative SCPs can give a rough estimate about cortical structures which are involved in specific processing episodes. One effect in our previous study had a frontal topography and suggested that control mechanisms during selective retrieval affect targets associated with the same cue more than targets that are fully unrelated. We interpreted this effect as reflecting control processes that might facilitate an attentional shift from one target to another associated with the same cue, in line with right lateral prefrontal cortex being associated with the control of response interference and resolution (Aron, 2007; Aron, Robbins, & Poldrack, 2004) and with an attention shift facilitated by inhibiting a previously attended object, location, or dimension (Hampshire, Chamberlain, Monti, Duncan, & Owen, 2010). Another SCP effect had a bilateral parietal topography and emerged when retrieval load decreased from three to one. We interpreted this effect in line with the finding that parietal regions (i.e., intraparietal sulcus, superior parietal lobe) are involved in the modulation of attention, and probably also in the modulation of internal attention towards memory representations (Cabeza, Ciaramelli, Olson, & Moscovitch, 2008; Chun, Golomb, & TurkBrowne, 2011; Ciaramelli, Grady, Levine, Ween, & Moscovitch, 2010; De Brigard, 2012). Based on these previous observations and on related effects reported in the literature, we arrived at specific predictions with respect to frontal and parietal SCPs: First, we expect a right-frontal ERP effect for shifts from one target to another related to the same cue (compared to repeatedly accessing the same target), because here the previously accessed target might have to be inhibited and/or the focus on the relevant needs to be enhanced due to interference within the cue–target network (contrast of condition #2 vs. #1). If SCP amplitude would be relatively more negative for target change than for target repetition, this would suggest an increased task-related activation when shifting the focus from one association to another within the same associative network, probably

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reflecting a higher retrieval effort. The increased retrieval effort can be either due to interference from lingering activation of the previous target, necessitating higher cognitive control, or due to increased effort when a previously inhibited retrieval competitor needs to be accessed and therefore be released from inhibition. As a control, we should not see any of such effects when the cue–target network changes (condition #3 vs. #4), or the effects should be at least strongly reduced, with possible residual facilitatory target effects emerging through conceptual priming of the target category across cues. Second, parietal regions (i.e., intraparietal sulcus, superior parietal lobe) are assumed to be involved in the modulation of attention, i.e., in the case of internal attention towards memory representations (Cabeza et al., 2008; Chun et al., 2011; Ciaramelli et al., 2010; De Brigard, 2012). Thus, we expect to find parietal ERP amplitude differences related to when attention is directed towards the target association. The cue and target repetition condition (condition #1) is expected to involve the least attentional resources, and should thus produce the smallest parietal SCP effect (because attention does not have to be directed towards a different association). In comparison, shifting the attentional focus from one target to another associated with the same cue (condition #2), should be more difficult and evoke higher parietal activation. Again, as a control, we should see no or at least strongly reduced effects when the cue–target network changes. To conclude, the different retrieval-related ERPs can support the behavioral data in delineating the kind of process that is generally involved in trial-by-trial memory retrieval dynamics, and, in addition, can be used to specify the possible sub-processes that modulate memory retrieval when relevant and irrelevant targets change their status from trial to trial. 2. Material and methods 2.1. Participants Twenty-two students (seven male) of the Philipps-University of Marburg (18–34 years; median = 22, SD = 3.8) participated in the study. All of them were right-handed by self-report, native speakers of German, and had normal or corrected-to-normal vision. They were naïve with regard to the aims of the study and gave written informed consent to the procedure. Participants were compensated by either course credit or money. Four had to be excluded, because of an insufficient number of correct responses and artifact-free EEG segments per condition. The remaining 18 subjects (five male) were between 18 and 31 years old (median = 22, SD = 3.0). 2.2. Material Color photos of 25 animals were associated with three symbolized characteristics each, forming ‘‘miniature’’ associative networks for later retrieval (body weight, the distance of their natural habitat to the city of our university, i.e., Marburg, Germany, and sociability, e.g., whether they live solitary or in herds, etc., see Fig. 1A). Existent, wildlife animals were chosen that were mostly familiar to the participants (e.g., lion, cheetah, bat, penguin) to fit in the cover story of a zoo to-be-built in Marburg. The values of the characteristics were based on the real average values pertaining to the animals, but were transformed into categories of a 7-point ordinal scale. Weight was represented by differently sized weight symbols, distance of habitat by arrows of different length (drawn on a flat Europe-centered map), and sociability by different numbers of dots in circular configuration. Because the to-be-learned associations built on existing knowledge about wildlife animals and supplemented existing semantic knowledge,

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the novel information could be integrated into established memory structures to facilitate learning (Van Kesteren, Ruiter, Fernández, & Henson, 2012). During the retrieval phase, we used the animals’ names as cues and colored markings on dimensional scales to indicate the to-be-retrieved characteristic (Fig. 1B). The presentation format was changed from learning to test in order to exclude stimulus and perceptual priming effects in the test trials, which are not related to memory retrieval in the strict sense. 2.3. Procedure Stimuli were presented on a light gray background on a 2100 CRT monitor with a refresh rate of 85 Hz. Participants sat in a dimly lit room at a distance of ca. 75 cm in front of the screen. Responses were made with a standard computer keyboard. Two learning tasks preceded the retrieval phase. 2.3.1. Learning phase, Part I: Memorization First, participants were instructed to memorize animals and their characteristics for a later memory test, which took, on average, 31 min (SD = 12 min). Each trial was self-paced (participants could proceed by pressing Enter) and consisted of two screens: a picture of an animal together with its name, followed by a picture of the animal together with its characteristics (Fig. 1A). The trial sequence was randomized for each subject. After all animals were shown together with their characteristics once, participants were instructed to imagine the characteristics of an animal on viewing the first screen before disclosing the second screen with the true values of the characteristics. 2.3.2. Learning phase, Part II: Multiple-choice quiz The second learning task was a multiple-choice quiz to test the memorized associations and to offer another opportunity for memorization (mean duration = 47 min, SD = 25 min). In each trial, an animal was presented together with three possible values of each characteristic arranged in a 3  3 array. Participants had to choose the correct value of each characteristic by pressing a spatially corresponding key on a number pad. After three values were chosen, feedback was given by green (correct value) and red (incorrect value) borders surrounding the chosen symbols. When an incorrect value had been chosen, the correct value was encircled in green in order to strengthen the correct cue–target association. Trial sequence was randomized, as were the positions of the randomly chosen distractor values and the target value. The quiz was terminated after a minimum of three runs in which the number of errors fell below a predefined criterion of 6.66% (i.e., 5 errors per block of 75 trials). This criterion yielded enough trials for the analysis of RTs and ERPs during testing, but also allowed for an analysis of error rates. 2.3.3. Retrieval phase: Familiarization with the task The retrieval phase with electroencephalogram (EEG) recording was preceded by a practice phase to familiarize the participants with the retrieval task (mean duration = 32 min, SD = 10 min). Each trial began with the presentation of a fixation cross for 1000 ms, followed by the name of an animal shown for 600 ms. Then three scales appeared that indicated the value range of the three characteristics (Fig. 1B). This display was shown until a response was given, but at least for 3000 ms. The presentation of the cue, indicating the relevant network, and the scales, indicating the retrieval target, were temporally separated to allow for a dissociation of the involved cognitive processes in the ERPs. A duration of 600 ms for cue presentation was chosen to be long enough to ensure that our subjects finished reading the cue word and recalling the animal, but too short to consciously retrieve all associated targets. This assumption is based on studies from the field of verbal

processing, which have shown that humans need about 400 ms to fully process the visual input and access LTM to process the semantic meaning of the presented word (Kutas & Federmeier, 2011) (see Supplementary material, p. 4, for a more detailed discussion of this issue). The participants’ task was to decide whether the highlighted value on a scale matched the remembered value by responding with the right or left index finger placed on the Ctrl buttons (match and non-match assignment to the buttons was counter-balanced). Of the three scales, only one segment of the relevant scale was marked (covering two values of the 7-point feature scale) while the other two were colored completely.1 The incorrect values could be any other value lower or higher than the correct values. To balance the difficulty across trials, each possible incorrect marking appeared with the same probability. After each trial, feedback was given by means of the German words for ‘‘correct’’ in green font color and ‘‘incorrect’’ in red in the center of the screen. One block consisted of 42 trials, i.e., 14 sequences of three consecutive trials with the same cue. After each block, participants were informed about the total number of errors made in that block. The practice session lasted until less than 9.5% of errors (i.e., less than 4 errors in one block) were committed. 2.3.4. Retrieval phase: EEG-recording session The testing phase followed after applying the electrodes for EEG recording, which lasted between 35 and 50 min. The task consisted of 750 trials, including 18 short breaks following each block (mean duration = 112 min, SD = 8 min). Trial structure matched that of the practice phase except that no feedback was given at the end of each trial, but only after a block. All of the phases described above were completed in direct succession. In total, the whole experiment lasted four to five hours, depending mostly on the individual learning speed. 2.4. Design As illustrated in Fig. 2, for each three consecutive trials, the cue was repeated while changes and repetitions of the to-be-retrieved target were varied in a way that all possible target repetitions within each trial triplet (from the 1st to the 2nd trial, from the 1st to the 3rd trial, from the 2nd to the 3rd trial, and no repetition) occurred equally often, i.e., with a probability of .25. This ensured that it was unpredictable whether and when a repetition of the to-be-retrieved association would occur. The main analyses were focused on the first two trials of a triplet in order to avoid the number of cue repetitions to become a confounding factor (see the Supplemental material for an analysis of the 3rd trials to evaluate whether facilitatory or inhibitory effects would still have an impact across an intermediate trial, i.e., from trial i  2 to i): the first trial represented a cue change and the second trial a cue repetition. The probability of a target repetition in cue-change sequences was .5. The programmed probability of the occurrence of cue and target changes/repetitions led to an average of 127 trials with cue change and target change (SD = 6.9), 122 trials with cue change and target repetition (SD = 6.9), 189 trials with cue repetition and target change (SD = 4.5), and 60 trials for cue and target repetition (SD = 4.8). The probability for the repetition of cue and target, a relatively salient condition, was held relatively low to avoid triggering certain strategies in the participants, like generally keeping in mind which target value had been retrieved in the previous trial. As a consequence, the probability of cue repetition and target change 1 We presented all three scales in each trial even though only one target had to be retrieved to ensure comparable visual stimulation for all conditions and comparability with a previous study in which either one or three characteristics had to be retrieved.

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was larger. However, the overall probability of a trial-to-trial cue and target repetition within each triplet was always .5. The probability of a presented test value of a characteristic being correct was always .5, as was the probability of a response switch. Note that even when both the cue and the target were repeated, the position of each scale in the display as well as the range in which the relevant scale was marked had a probability of .5 to switch, which ensured that repetition effects were not confounded with repetitions of the stimulus display or the motor response. Thus, all effects of the experimental manipulations can be attributed to processes affecting memory representations and not to stimulus or response priming. Moreover, the stimulus– response mapping (‘‘task set’’) always remained the same (as described to be a critical point by Mayr, Awh, and Laurey (2003). 2.5. EEG recording and analysis EEG was recorded in direct-current (DC) mode with a sampling rate of 500 Hz from 61 Ag/AgCl scalp electrodes arranged according to the extended 10–20-system (Jasper, 1958), from one inferior ocular channel (IO), and both earlobes (A1, A2). All electrodes were recorded in reference to FCz. Impedance was kept below 5 kX. All recording and analysis software, EEG caps, and amplifiers were provided by BrainProducts GmbH (Gilching, Germany). EEG was re-referenced offline to averaged earlobes and low-pass filtered at 30 Hz (24 dB/oct). The electrooculogram was calculated by rereferencing IO to Fp1. DC drift artifacts were corrected by a regression method (Hennighausen, Heil, & Rösler, 1993). The EEG was segmented into epochs of 3700 ms length, beginning 100 ms before the onset of the cue. Segments including blinks, eye movements, muscle potentials, and other artifacts were excluded by means of a semi-automatic filtering procedure.2 ERPs were extracted by averaging baselinecorrected epochs separately for participants, electrodes, and experimental conditions. Cue- and target-related ERPs were computed relative to their specific baselines (100-ms long) preceding the cue or the target, respectively. As the cue-related ERPs only revealed the same effect of cue repetition as was found in our previous ERP study with the same material, i.e., a word-repetition effect (Van Petten, Kutas, Kluender, Mitchiner, & McIsaac, 1991), we will not elaborate on this effect here any further (see Kizilirmak et al., 2012, for further discussion). However, this temporally and spatially extended general difference between cue change and cue repetition prevailed during the time interval defined as a baseline for the target-related ERPs and it may even work into the time epoch after target presentation. Using the pre-target-epoch as a baseline to be subtracted from post-target amplitude changes would therefore affect amplitudes differently for cue change and cue repetition trials. Using a pre-cue epoch as baseline for amplitude measures taken after target presentation would not solve the problem either if the cue effects are still present, at least to some degree, after target presentation. Thus, target-related ERPs will always depend on whether they follow a cue repetition or a cue change. To control for this, the ERP effects related to target change vs. repetition were analyzed separately for cue changes and cue repetitions. As we outlined in Section 1, we hypothesized that differences between target repetition and target change should be larger within an associative network, i.e., in cue repetition, than when switching to another network, i.e., in cue change. After evaluating ERP amplitude differences for factor TARGET for each level of CUE separately, we will be able to compare the TARGET effects for cue repetition vs. cue change. We thereby circumvent the problem 2

The following criteria were used for artifact detection and rejection: Maximum allowed difference between two adjacent voltage values = 20 lV/ms; maximum allowed amplitude difference in any interval of 200 ms length = 150 lV; minimum allowed amplitude difference in any interval of 100 ms length = 0.5 lV.

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that ERP amplitude modulations by factor TARGET would automatically depend on factor CUE – even when the intention was to look at main effects of TARGET – if directly compared with the same baseline. Although it would be legitimate to use either the pre-cue or the pretarget baseline to look at the effects of factor TARGET for each level of factor CUE separately, we chose the pre-target baseline to eliminate any random amplitude variance during the cue display from the analysis of the TARGET effects. All segments of trials with errors, or where no response was made within 6600 ms after scale onset (minimum duration from one target onset to another), were excluded. Averages were calculated with a minimum of 29 trials per condition for each participant. The interval from 250 to 3000 ms was analyzed in differently sized epochs, oriented on the typical memory-related ERP effects found in the literature (see Section 1): 250–500 ms, typically capturing early familiarity- or semantic-priming-related mid-frontal effects (Mecklinger et al., 2012; Paller et al., 2012; Rugg & Curran, 2007), 500–1000 ms, an epoch in which recollection-related memory ERP effects typically occur (Rugg & Allan, 2000; Vilberg et al., 2006), and 1000–3000 ms, an interval in which SCPs associated with processing effort can be observed (Birbaumer, Elbert, Canavan, & Rockstroh, 1990; Rösler, Heil, & Röder, 1997). These epochs were analyzed separately for cue changes and cue repetitions with repeatedmeasures ANOVAs comprising factor TARGET (repetition, change) and factor ELECTRODE (19 standard electrodes Fp1, Fp2, F3, F4, F7, F8, Fz, C3, C4, T7, T8, Cz, P3, P4, P7, P8, Pz, O1, and O2). Degrees of freedom were corrected according to Huynh and Feldt (1976). Main effects of ELECTRODE will not be reported. In case of a significant TARGET  ELECTRODE interaction, post hoc electrode-wise t tests were calculated to determine the principal electrode sites (mentioned if p 6 .05) and the polarity of the effects. 3. Results 3.1. Behavioral data 3.1.1. Response time RTs and error rates were analyzed with repeated-measures 2  2 ANOVAs with factors CUE and TARGET change vs. repetition. Incorrect trials and those that deviated more than ±2.5 SD from a participant’s mean value in each condition were excluded from the RT analysis (average proportion of incorrect trials: 13%, average proportion of removed outliers, i.e., ±2.5 SD: 1.9%). For the RTs, we found main effects of both CUE [F(1, 17) = 19.53, p < .001, g2 = .535] and TARGET [F(1, 17) = 76.66, p < .001, g2 = .818], as well as an interaction [F(1, 17) = 42.40, p < .001, g2 = .714]. As can be seen in Fig. 3, the interaction resulted from differently sized TARGET effects for the two levels of factor CUE (cue repetition, cue change). This observation was corroborated by post hoc t tests. The TARGET effect, i.e. the difference between target repetition and target change, was significant for both cue repetition [condition #1 vs. #2, t(17) = 9.26, p < .001] and cue change [condition #3 vs. #4, t(17) = 3.31, p = .004], but significantly larger for cue repetition [t(17) = 6.51, p < .001]. Additionally, while RTs for target-repetition trials were significantly different for cue repetition and cue change (condition #1 vs. #3) [t(17) = 6.07, p < .001], as can be seen in Fig. 3, RTs for target-change trials did not differ significantly (condition #2 vs. #4) [t(17) < 1, p = .884]. That is, when a different target association had to be retrieved in trial i than in trial i  1, it made no difference whether they were associated with the same cue or with different cues. 3.1.2. Error rate For the error rates, a main effect of CUE was found (see Fig. 3), indicating less errors when the cue was repeated compared to when it changed [13.0% vs. 14.7% errors; F(1, 17) = 5.32, p = .034,

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effects with a higher negativity for target repetition. No main effect of TARGET was observed. Post hoc t tests revealed that the effect was highly focused on right frontal to temporal electrodes (F4, F8, and T8; at O2 with reversed polarity) which can best be seen in Fig. 4B, map 3. (It should be noted that only the electrode with the largest amplitude difference for this effect is depicted in Fig. 4A, i.e., F4, while a better overview of its true extent is given by Fig. 4B, map 3.)

Fig. 3. Behavioral data. Mean RTs and error rates showing the effect of repetitions and changes of the relevant associative networks (cues) and target associations.

g2 = .238]. Importantly, the lower error rate for cue repetition with target change was not accompanied by longer RTs (Pearson’s r(18) = .25, p = .316), speaking against a speed-accuracy trade-off. Neither a main effect of TARGET, nor an interaction was found. To summarize, retrieval was faster for target-repetition than for target-change trials. This effect was even present, though reduced, when the cue changed, i.e., when the same characteristic had to be accessed, but for a different animal (condition #3 in Fig. 2). As expected, repetition of both cue and target resulted in the fastest RTs and highest accuracy (condition #1). Furthermore, the decreased error rate for target change in cue repetition compared to cue change suggests that memory performance is generally better within a network, even when the target changed. 3.2. Event-related potentials As discussed in Section 2, the effect of target repetition and target change on mean ERP amplitudes was analyzed separately for cue-repetition and cue-change conditions. As can be seen in Fig. 4, the largest TARGET effects emerged in cue-repetition conditions (blue lines), whereas ERPs of the cue-change conditions hardly differ. By comparing the difference maps in Fig. 4B, it can be seen that in cue-repetition trials the topography and polarity of the TARGET effect change over time. 3.2.1. Target-repetition effects in cue repetition In all three analyzed epochs (250–500 ms, 500–1000 ms, 1000– 3000 ms) we observed significant TARGET effects or (at least marginally) significant TARGET  ELECTRODE interactions. Between 250 and 500 ms, we found a significant main effect of TARGET [F(1, 17) = 7.13, p = .016, g2 = .295] and a marginally significant interaction with ELECTRODE [F(18, 306) = 2.21, p = .097, g2 = .115, e = .170]. Mean amplitude was significantly lower for target change as compared to target repetition, with a broad maximum over frontal to central electrodes (see Fig. 4B, map 1). This topography was corroborated by electrode-wise post hoc t tests which were significant at Fp1, Fp2, F3, C3, C4, P3, O1, F7, T7, P7, Fz, Cz, and Pz. From 500 to 1000 ms, the main effect of TARGET was significant [F(1, 17) = 11.19, p = .004, g2 = .397] as was the interaction with ELECTRODE [F(18, 306) = 3.33, p = .020, g2 = .164, e = .193]. The effect now had a central-to-parietal topographical maximum (see Fig. 4B, map 2); electrode-wise post hoc t tests were significant at C3, C4, P3, P4, O1, O2, T7, T8, P7, Cz, and Pz. SCPs between 1000 and 3000 ms revealed only a marginally significant interaction [F(18, 306) = 2.03, p = .088, g2 = .107, e = .259] that showed a reversed amplitude difference from the previous

3.2.2. Target-repetition effects in cue change Cue-change conditions revealed only a marginal main effect of TARGET between 250 and 500 ms [F(1, 17) = 4.25, p = .055, g2 = .200], but no interaction with ELECTRODE, suggesting that the amplitude difference was roughly equal across electrodes. To summarize, supporting the assumption that repeating or changing the target should have a higher impact when the cue was repeated, and consistent with the behavioral target effect in the RTs, the analyses revealed that factor TARGET (repetition, change) had an impact on amplitude differences in all analyzed epochs for cue-repetition conditions, while it had only a weak impact between 250 and 500 ms for cue-change conditions. Moreover, the topographical maximum and polarity of the effect differed between epochs, suggesting that distinct cognitive processes (and ERP generators) might be involved in dynamically switching between to-be-retrieved memory representations. 4. Discussion With the present study, we want to contribute to the question of how our cognitive system dynamically controls LTM retrieval of currently relevant associations without being distracted by other associations coactivated by the same retrieval cues. To tackle this question, we investigated whether selective retrieval in one trial has detrimental or facilitatory effects on selective retrieval in the following trial. More specifically, we manipulated trial-to-trial changes and repetitions of cue–target associations and whether consecutively to-be-retrieved targets belonged to the same or to different associative networks, i.e., were associated with the same or different cues. 4.1. Behavioral evidence of benefits for cue repetition and target repetition We expected that facilitatory processes should become manifest in two different effects. First, the full-repetition condition in which both the retrieval cue and to-be-retrieved target are repeated (Fig. 1, condition #1) should lead to the fastest and most accurate performance, speaking for positive priming of LTM representation as a facilitatory mechanism. Our behavioral results support this idea. Positive priming was observed in the condition in which cue and target were repeated, i.e., mean RT was significantly reduced. Interestingly, a smaller reduction of RT was also observed for target repetition without the repetition of the cue, suggesting additional priming of the whole target category (the concept of weight, distance, or sociability, respectively) across cues (see also Horner & Henson, 2008; Müller, Strumpf, Scholz, Baier, & Melloni, 2013; Segaert, Weber, de Lange, Magnus Petersson, & Hagoort, 2012, for corresponding effects). This effect is consistent with an index of conceptual priming (Paller, Voss, & Boehm, 2007). Second, in comparison with a control condition in which both cue and target changed (condition #4), facilitation should show up as a benefit compared to when only the target switched but the cue was repeated (i.e., the associative network stayed the same; condition #2), speaking for activation spread from the previous target within cue–targets networks. In contrast to this facilitatory scenario, the latter comparison between conditions #2 and #4

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Fig. 4. ERP data. (A) Time course of ERPs triggered by the retrieval-target-indicating display. Cue-repetition conditions are drawn in blue, cue-change conditions in red. Target repetitions are drawn in a brighter color (light blue, light red) than target changes (for color code reference and the numbers of the conditions, see Fig. 2). (B) Difference maps of the three target effects. The analyzed time windows of the effects are highlighted at the electrodes of their maxima in panel A.

should have lead to the opposite effect in the presence of a detrimental process, i.e., impaired retrieval would arise, because a previously relevant target acts as a retrieval competitor within the cue–targets network and interferes with the retrieval of the current target. Our behavioral results are again more in favor of the facilitatory account. In cue repetition, i.e., when the same set of associated targets was relevant consecutively, we observed that memory performance was enhanced, as indicated by fewer errors. However, there was no difference in RTs, i.e., neither a condition #2 > #4 RT difference, indicating interference, nor a #2 < #4 effect, indicating facilitation by spreading activation. This null result is difficult to interpret and we can currently offer only a tentative explanation. One possible explanation for the absence of the respective RT difference might be due to the required match/ non-match decision. The task was to compare the memory representation of a value (target association with the cue) with a value indicated on the screen. It could be that incongruent decisions in the two consecutive trials with the same cue lead to uncertainty, prolonging the response in cue repetition which neutralizes any

RT benefit from spreading activation. However, testing the influence of a response switch revealed no effects.3 An alternative explanation could be that there was indeed spreading activation as indicated by the increased accuracy for cue repetition and target change (condition #2), but also retrieval interference. These opposing effects might have cancelled out an RT advantage. This tentative interpretation should be carefully tested in future studies. Together, the behavioral data support trial-by-trial facilitation of LTM retrieval, which agrees with the notion that the activation of one target associated with a cue leads to an activation spread across other targets associated with the same cue (Anderson, 1983), The claim of a beneficial effect of repetition priming is substantiated by an additional analysis (reported in the Supplementary material, see Fig. S1), which shows that the repetition-priming effect

3 We calculated the ANOVA used for the analysis of the RTs with the additional factor response switch (switch, no switch). This has been done in case response switch had any influence at all. This led to neither a main effect nor interactions with that factor.

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outlasted even an intermediate trial, i.e., from i  2 to i. More specifically, we compared trials with a cue and target repetition from trial i  2 to i, a target repetition from trial i  1 to i, and trials with no target repetition within the preceding two trials. This analysis revealed a monotonic RT decrease with decreasing distance between repetitions, suggesting that the previous activation of a memory representation prevails for at least two consecutive trials. Note that the occurrence of a target-repetition benefit in RT for both cue repetition and cue change could be interpreted as selective retrieval having a bigger effect on the target category across associative networks than on the associations within networks. However, the RT benefit for target-repetition was significantly larger for cue repetition, i.e., within the same network, than for cue change. This and the significantly higher accuracy for cue repetition than for cue change corroborates the idea that the involved retrieval processes had a higher impact on the network level (within the mini associative networks between the cue and three targets) than on the level of the target category. This notion also receives strong support from the ERPs. 4.2. ERPs reveal temporally and spatially dissociable processes related to successful retrieval The conclusion that trial-by-trial retrieval dynamics are restricted to the links between cues and targets established during learning is strongly supported by the pronounced ERP amplitude differences that emerged between target repetition vs. change throughout all analyzed time windows when the cue was repeated. When the cue changed only a marginal difference was present in the earliest time window. The ERPs during target retrieval exhibited three distinct effects with different latencies and topographies. First, there was an early mid-frontal amplitude difference from 250 to 500 ms between target change and repetition which was found for both cue repetition and cue change, albeit larger for cue repetition, thus exactly mirroring the target effect in RTs. Importantly, the interpretation put forward based on the behavioral data that this effect might reflect conceptual priming of the target category is supported by the mid-frontal topography, polarity, and latency of the ERP effect, which has been closely associated with conceptual priming and/or familiarity in previous studies (e.g., Voss & Paller, 2006). Related to the notion of conceptual priming is an interpretation of the effect in terms of ‘‘retrieval orientation’’ (Rugg & Wilding, 2000). In several recognition memory studies it has been observed that ERPs show a relatively more positive deflection when the modality in which an item has been studied, e.g., as a picture or an auditory word, overlaps with the modality in which it is presented/cued during the memory test as compared to a study-test mismatch condition (Hornberger, Morcom, & Rugg, 2004; Stenberg, Johansson, & Rosén, 2006). If one assumes that retrieval orientations can be sufficiently specific to differentiate between different pictorial presentations during study, i.e., between the different symbols used to represent the different target categories (weight as differentially sized weight symbols, sociability as different numbers of dots in circular organization, and distance as different lengths of arrows on a world map), then this effect could also indicate the higher cognitive effort due to a change in retrieval orientations from one trial to the next. However, studies of retrieval orientation only reported ERP differences in tasks in which retrieval demands remain constant (Werkle-Bergner, Mecklinger, Kray, Meyer, & Düzel, 2005; Wilding & Nobre, 2001). Furthermore, retrieval orientation studies usually differentiate between modalities such as pictures, written words, or auditory words, but not as fine-grained as between different kinds of symbols. We therefore prefer the conceptual priming interpretation of the current data.

The next and more prominent effect occurred over posterior electrodes between 500 and 1000 ms after cue onset. Within this time window, target repetition evoked a relatively more positive amplitude than target change with a central-to-parietal maximum, but only when retrieval continued within the same network, i.e., when the cue was repeated. Latency, topography and polarity of this effect are similar to that of the parietal recollection effect from the recognition memory ERP literature (Rugg & Curran, 2007; Wilding, 2000). As hypothesized, and in agreement with the finding that this component is sensitive to the recency of the last encounter of an item (Grove & Wilding, 2009), the higher positivity for target repetition suggests a facilitatory process that enables fast and accurate retrieval when the same memory representation is repeatedly accessed. Note that although both the mid-frontal and posterior ERP effects that were observed in the current study seem to reflect well-known memory-related ERP components, i.e., the mid-frontal ‘‘familiarity’’ and the posterior ‘‘recollection’’ effect, our paradigm differs from those that have until now reported such effects. First, these components have previously been found in recognitionmemory paradigms with the requirement to dissociate between old and new items that are presented instead of cued, as was done here. Second, there are no new items in our paradigm, but only more (target repetition) or less (target change) recently relevant targets. The current findings therefore support the finding that these components are sensitive to relative ‘‘oldness’’, i.e., recency of occurrence, as proposed by Grove and Wilding (2009), and seem to affect memory retrieval even on a dynamic trial-by-trial basis. Nevertheless, further studies are needed to investigate the overlap between ‘‘classical’’ familiarity and recollection ERP components and our findings. A final ERP effect was found between 1000 and 3000 ms poststimulus which reaches its maximum between 1750 and 2500 ms over the right anterior scalp, with more negative amplitudes for target repetition compared to target change, but, again, only in cue-repetition trials. Note that the polarity of this effect is completely reversed in comparison to the earlier target effect from 500 to 1000 ms. Such late SCP amplitude shifts, which are optimally measured by means of DC recording, are usually interpreted in terms of a higher negativity reflecting higher task-related activation (e.g., Rösler et al., 1997). Increased SCP negativity probably reflects increased neural activation as corroborated by numerous studies that found the BOLD signal to correlate negatively with the SCP amplitude, i.e., the larger the negative-going slow amplitude shift, the stronger the BOLD responses (e.g., He & Raichle, 2009; Hinterberger et al., 2004; see Khader et al., 2008, for a review). In the present study, the condition which evoked the highest negativity was also the fastest to be responded to as well as one with the highest accuracy. In line with the usual SCP interpretation, the higher negativity is therefore likely to represent a higher involvement of task-related processes. This idea is consistent with the finding that the right lateral frontal cortex is involved in tuning attentional processing to maintain the relevance of an activated memory representation when attended input matches the current focus, or when detecting a signal match for a maintained target (Hampshire, Thompson, Duncan, & Owen, 2009; Levy & Wagner, 2011; Owen & Hampshire, 2009). The present effect pattern might therefore again reflect an increased involvement of a facilitatory rather than an inhibitory process, because in this full-repetition condition the SCPs turned out to have the most negative amplitude. Alternatively, this effect might also reflect processes that follow, rather than precede, the retrieval process, such as post-retrieval monitoring. This interpretation would be consistent with findings of Hayama et al. who found a late ERP component between 1200 and 1900 ms to vary with the amount of semantic judgment required on an item (Hayama,

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Johnson, & Rugg, 2008). They reported an increased positivity when in addition to an old/new judgment, semantic properties of the items needed to be evaluated. Accordingly, our right-frontal effect could be interpreted as the target-change condition only requiring additional cognitive effort or post-retrieval monitoring when a target change takes place within an associative network, i.e., in cue repetition. However, this interpretation lacks support from the behavioral data. On the one hand, there is an RT difference for target change vs. target repetition both in cue repetition and in cue change (though smaller). If this late component was associated with the cognitive process behind this behavioral effect, it is difficult to explain why there is no such amplitude difference in cue change. On the other hand, mean RT in cue repetition and target change (condition #2) was not larger in comparison to the control condition #4 which shows the higher positivity, but error rate was lower. We therefore prefer the first interpretation of the SCP effect representing facilitatory processes of maintained attention. Moreover, the latency of SCPs only loosely indicate the onset or duration of an underlying cognitive process, since SCPs often arise delayed to cognitive processing, similar to the BOLD response as measured by fMRI (see Khader et al., 2008, for a review). This makes it relatively difficult to relate this effect directly to behavior. 4.3. Conclusions How do we dynamically control the retrieval of just the currently relevant associations from LTM without being distracted by other associations activated by the same retrieval cues? The present study generated first results towards answering this question by showing enhanced memory performance when targets associated with the same cue were accessed in successive trials, with additionally highly reduced RTs when the very same cue–target association was repeatedly relevant, indicating faster memory access. Taken together, our results favor a facilitatory approach for trial-to-trial effects of selective retrieval, over an account in which selective retrieval leads to lingering detrimental effects for potential retrieval competitors within associative networks such as established in the current study. This interpretation is supported by three findings: First, we found an RT advantage for target repetition that was double the size for cue repetition, but also significant in cue change. This effect was mirrored by a mid-frontal target-repetition effect, also larger for cue repetition, which probably reflects a processing advantage due to conceptual priming or familiarity when the same type of target had to be accessed a second time. Second, we found a posterior ERP effect for target repetition vs. target change, restricted to cue-repetition conditions, that probably reflects the higher successful retrieval rate, i.e., accuracy, when accessing the very same set of cue–target association repeatedly. This and the preceding ERP target-repetition effect might reflect two distinct processes that led to the high behavioral advantage for cue and target repetition. Importantly, the contrast between condition #2 and #4, i.e., shifting between targets associated with the same cue vs. with different cues, for which we had the strongest predictions, was also in favor of the facilitatory account. Though there was no difference in RT that would have been in favor of either inhibition due to interference from retrieval competitors (2 > 4), or facilitation due to spreading activation within the network (2 < 4), accuracy during the memory test was higher for cue repetition (condition #2), indicating that there was a memory benefit although it did not reduce RT. Finally, a late right-frontal SCP effect was found that might reflect the attentional refocusing when the same cue–target association is repeatedly relevant or post-retrieval monitoring. Due to their timing, topography, and condition–specific amplitude variations, the three ERP effects seem to be manifestations of different cognitive processes that are relevant for facilitating LTM retrieval on a trial-by-trial

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basis. Taken together, the present study provides insight into the different processing stages of memory retrieval when fast switches between retrieval targets are required.

Acknowledgments This work was supported by Grant KH 235/1-1 of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) assigned to PHK and FR. We would like to thank Gerd Waldhauser for valuable discussions, Lotta-Lili Fiedel and Nicole Cruz de Echeverría Loebell for their help in data collection, and two anonymous reviewers for their helpful comments.

Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bandc.2014.04. 013.

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