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British Journal of Psychology (2014), 105, 226–242 © 2013 The British Psychological Society www.wileyonlinelibrary.com

Binding and content updating in working memory tasks Caterina Artuso* and Paola Palladino University of Pavia, Italy Working memory updating can involve processing of either a specific memory content or a binding. So far, research has focused mainly on single contents as objects of updating, via recall accuracy measures. Here, we have addressed more direct measurement of the updating process (i.e., response times), assessing individually the role of single contents, as well as bindings. To this end, we compared two updating tasks from separate research traditions: a RT-based computer task and a classical accuracy-based task. The former consisted of trials where measures of content and binding updating were obtained, allowing a dissociation between these two components. The latter measured recall accuracy and intrusion rate for lists of words under different conditions of maintenance/ inhibition. These results enable a better understanding of the updating process for the dual components of binding and content updating, and their potential role in an accuracy-based task. An overlap between the underlying components of updating tasks was demonstrated, specifically between binding updating RT and intrusion rate. Notably, binding updating appears to be a more sensitive measure in explaining results in the classical updating task.

Working memory (WM), the limited-capacity system maintaining and processing information, is likely to work largely through the mechanism of updating. Historically, WM updating was conceptualized by Morris and Jones (1990) as the replacement of current memory content by new material. Some parts of the current content remain untouched, while other parts change, thus giving WM simultaneous stability and flexibility (Kessler & Meiran, 2008). Within this theoretical framework, the classical task used to evaluate updating accuracy has been the running memory span task, devised by Pollack, Johnson and Knaff (1959), and then developed by Morris and Jones (1990). In this task, participants listen to lists of letters, varying in length, and then are asked to recall a fixed number of the last items of the list. Given that the list length is unknown to participants, the assumption is that every item is initially held in memory and updated later, until it appears no longer relevant to the task. Interpretation of Morris and Jones’ results presents a highly important first issue in updating research. That is, the serial position curves reported by Morris and Jones (1990) were characterized by marked recency, but not primacy, effects, differing from those in a *Correspondence should be addressed to Caterina Artuso, Department of Humanistic Studies- Psychology Section, University of Pavia, 27100 Pavia, Italy (email: [email protected]). DOI:10.1111/bjop.12024

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standard immediate serial recall (see Bunting, Cowan & Saults, 2006; Palladino & Jarrold, 2008). If individuals were holding all updated items in immediate memory, then we would expect both primacy and recency effects to be observed. In their study, Palladino and Jarrold (2008) concluded that typically, participants do not adopt a strategy of actively updating the memory list, but instead, tend to wait passively until the list ends, before trying to recall the most recently presented items. Ideally, tasks would be constructed so as to prevent participants from adopting this strategy. Among these, the task devised by Palladino, Cornoldi, De Beni and Pazzaglia (2001) is particularly suitable. In this task, participants listen to lists of words, both concrete and abstract, and afterwards are asked to recall a number of these that reflect a semantic criterion (i.e., the smallest items presented, objects or animals). Thus, the task is not based on a temporal criterion, nor subject to recency effects (i.e., recall of the last three items), but hinges instead on a semantic relevance criterion. In fact, during the task, participants have to process each piece of incoming information, maintaining it actively in WM. At the same time, participants have to inhibit items that are no longer relevant, reducing their activation level. That is, they have to inhibit dominant or automatic responses, specifically here, words that are not congruent with task requests (see also Friedman et al., 2008). A critical variable in updating is the effort required to inhibit the information. Therefore, an inhibitory response should be easier for items immediately excluded from WM, such as, for example, abstract words, which can be discarded according to the semantic criterion. Moreover, updating should be more difficult with an increase in the memory load (i.e., in the number of to-be-maintained items); so, for example, maintaining five words would be more difficult than maintaining three (see Palladino et al., 2001). A second relevant issue concerns the method of measuring updating accuracy. Traditionally, the running memory span task and other tasks derived from it (see e.g., Carretti, Cornoldi & Pelegrina, 2007; Palladino et al., 2001; Ruiz, Elos
ua & Lechuga, 2005) have measured updating indirectly, through indexes of recall accuracy and intrusion rate. In particular, recall accuracy tends to combine the effects of all processes that are active during updating and to mask their separate contributions. In contrast, intrusion rates show the ability to inhibit no-longer relevant information to encode new goal-relevant stimuli. Thus, these have been interpreted as an index of inhibitory efficiency (see e.g., Palladino, 2006; Palladino et al., 2001). Indeed, both recall accuracy and intrusion rate reflect non-specific memory success (accuracy) or failure (intrusion). For this reason, a less ambiguous measurement of the process would be useful. To go beyond issues of recency strategies and indirect measurement, contributions from some authors (see e.g., Artuso & Palladino, 2011b; Artuso, Palladino & Ricciardelli, 2012; Kessler & Meiran, 2008; Oberauer & Vockenberg, 2009) have emphasized the usefulness of measuring response times (RTs) directly. Response times are thought to represent both a sensitive index of the process occurring during the task, and a clear-cut measurement of the updating process. Specifically, self-paced RTs have been shown to track the updating process most effectively (see Artuso & Palladino, 2011b; Kessler & Meiran, 2008). In the traditional running memory span task (and similar tasks), the pace of the task is fixed by the experimenter. However, it has been observed that fixing stimulus presentation pace may affect task processing, by participants taking a strategic approach aimed to evade highly demanding task requests (see Palladino & Jarrold, 2008). In contrast, allowing the participant to proceed at her/his own pace might enable a less biased performance. A third important issue that deserves particular attention, concerns the object of updating. As noted earlier, classical tasks focus on single WM contents such as letters or

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Figure 1. An example of a ‘binding updating sequence’, with one update, ending with a positive probe.

words (see e.g., both the running memory span task and the task used by Palladino et al., 2001). However, associations between contents (i.e., bindings) play a primary role in the WM system economy. For example, the ‘episodic buffer’ component introduced by Baddeley (2000) is thought to represent a link between the separate systems of WM and long-term memory, which are bound together through it. For the purposes of this study, a useful distinction can be made between inter-item bindings and item-position bindings (see e.g., Piekema, Rijpkema, Fern
andez & Kessels, 2010). The former are associations between the stimulus and the context in which it is embedded (for example a letter and the adjacent letters); the latter are associations between the stimulus and the spatial position in which it has been presented. In a previous study investigating the role of binding updating in WM, Artuso and Palladino (2011a,b) distinguished different objects of WM updating, by the amount of information which had to be updated and integrated in memory. They devised a computer task where triplets of consonants were presented, with each item being bound to the other two items (e.g., inter-item binding). Thus, each item was identified with a content and each item-to-item association was identified with a binding. In every trial, participants were presented with three letters to remember and were asked to either update all three letters with a new set (content updating trials), or to update one of the items while remembering the other two original letters (binding updating trials). For example, if the initial to-belearned set was TRB, and the final updated set was SRB, then the participant had to replace the binding between T and RB with another between S and RB. This entailed substituting the previous binding with the new binding between S and RB. See Figure 1 for an example of trial requiring binding updating. Alternatively, if the initial set was TRB and the final CNS, participants had to completely discard previous information (e.g., TRB), and to create a new representation (e.g. CNS) through substitution of the entire triplet. Thus, in binding updating trials, participants were asked to substitute a binding within the trial, that is, a binding between an item and the adjacent items, whereas in content updating trials, participants had to presumably re-encode the triplet and the new bindings between the three letters. Of note, in this task, participants were not explicitly requested to study or remember the position of letters, as in fact, they were tested only on item recognition, not item position. By comparing these two conditions, Artuso and Palladino (2011a,b) found longer RTs for binding updating compared to content updating. In addition, longer RTs were found

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where updating was needed, compared with either memory or maintenance steps. Importantly, this difference was absent in content updating trials, indicating that the two processes can be distinguished. Overall, binding costs were interpreted as consequence of the additional cognitive operations required during binding updating, but unnecessary in content updating (see Artuso & Palladino, 2011b). We believe, exploration of these three issues (i.e., the potential recency strategy bias, measurement of updating, and the specific object of updating) is timely and worthy of investigation in current WM updating research. In particular, the object of updating and its measurement are likely to be crucial when dealing with updating, both theoretically and empirically. To address these issues in the current study, we administered participants with the two updating tasks mentioned above, i.e., a RT-based task (Artuso & Palladino, 2011a, 2011b) and an accuracy-based task (Palladino et al., 2001), which thus far have been separately studied in literature. In the RT-based task, we assessed both content and binding updating components directly, through RTs. However, for the accuracy-based task, where there is no clear conceptualization of the updating components involved, we evaluated the overall task indirectly through recall accuracy and intrusion rate. Importantly, both tasks are resistant to recency bias. In the accuracy-based task, although participants have to recall items within the list, this does not include the last presented item. Alternatively, in the RT-based task, they have to recognize a stimulus, in self-paced trials ending with a maintenance step only (see Method section for further details). For consistency with the original task (Artuso & Palladino, 2011a, 2011b) and other studies which emphasize the importance of considering inter-item binding in updating (see e.g., Bopp & Verhaeghen, 2009), we maintained this distinction, keeping content and binding updating conditions separate. Although we recognize that both inter-item bindings (i.e., focus on item recognition) and item-position bindings (i.e., focus on item position) might play a role in updating tasks, in the present study we focused our investigation on inter-item bindings only, leaving item-position bindings for dedicated future studies. For this purpose, we manipulated inter-item bindings to create an opposition between content updating and binding updating conditions. Through this study, we also aimed to support the dissociation previously found between content and binding updating processes, with longer RTs for binding updating conditions processing (see above). Overall, a dissociation between content and binding updating would contribute considerably to a better understanding of updating processes. Furthermore, this could be related to the existing literature and traditional accuracy-based tasks, broadening theoretical knowledge on the updating concept overall and the debate on its link with those tasks used currently. Most relevantly, although updating appears to be primarily a process of content substitution (e.g., Morris & Jones, 1990), the binding process has not been systematically investigated as yet. Therefore, our objective is as much to study the two components of binding updating and content updating, as to explore them in the accuracy-based task. With this aim in mind, we have investigated the relation between these two updating tasks. We expected that performance in the RT-based task would be similar to the low maintenance condition (with high inhibition of information) in the accuracy-based task. In fact, as the highest memory maintenance requirement is three items in the RT-based task, and is likely to be within participants’ average span (see e.g., Cowan, 2001), it is possible to suggest that this task involves low maintenance demand. Similarly, the accuracy-based task in low maintenance conditions also requires a maintenance of three items. In contrast, the RT-based task is mainly focused on inhibitory ability, rather than maintenance ability, especially where updating is needed specifically, for example, when

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participants have to discard no-longer relevant information and substitute it with new, placing less reliance on maintenance (i.e., they have to substitute one letter at a time within the trial). Similarly, in the low maintenance condition of the accuracy-based task, maintenance is less effortful, involving few resources, as the greater effort is in inhibiting irrelevant and potentially interfering information (i.e., potential target words which instead have to be excluded). In other words, participants’ resources are used mostly to inhibit no-longer relevant information in both tasks. Therefore, the process of low maintenance and high inhibition of irrelevant information is thought to characterize both the RT-based and the accuracy-based tasks and to relate these tasks together. In summary, the aim of the current study is to disentangle further components of the WM updating process. This is undertaken by providing within participants comparison of two memory updating tasks, previously examined separately: a computer RT-based task and a classical accuracy-based task. To this end, a series of correlation and regression analyses were conducted on the various performance measures of the two tasks. Before examining the relations existing between the tasks, we also considered each task individually to explore the differential contributions of the updating process.

Method Participants Fifty-four undergraduates from the University of Pavia (mean age = 24.01 years; ranging from 20 to 28 years) volunteered in the experiment. Thirty-eight were female.

Materials and Procedure Participants were presented with two memory updating tasks: a classical (Palladino et al., 2001) and a computer task (Artuso & Palladino, 2011b). Half of the participants were administered the classical task first, followed by the computer task; the other half were administered in the reverse order. The whole session lasted approximately 1 hr.

Classical updating task Lists of words were presented, comprising familiar nouns which referred to either concrete objects or animals measurable by size, or abstract filler concepts. Participants were instructed to listen carefully to each list of words and, only for concrete objects/ animals, to select the smallest. Therefore, at the end of the list, their task was to recall the indicated numbers of items from each list. Lists were divided into four conditions, according to the number of relevant items to be maintained (low or high maintenance) and the number of items to be inhibited; that is, items whose level of activation had to be updated (low or high inhibition). For half the list, the participant had to maintain (and then recall) the three smallest words (low maintenance), and for the other half, the five smallest (high maintenance). In the same way, for half the list, each participant had to inhibit two words (low inhibition), and for the other half, five words (high inhibition). Twenty-four lists of twelve words each were administered orally, for a total of 288 words. Thus, six lists were administered for each of the four conditions and, overall, 96 words had to be recalled across all conditions. An example trial for the low maintenance-low inhibition condition would be the following list: meeting- sense- woodpecker- passion- law- cow- happiness- amount-

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caterpillar- lamb- frog- feast. Of these 12 words, each participant had to recall the three smallest ones: woodpecker, caterpillar, frog. Two had to be inhibited: cow and lamb. The experiment was designed with Maintenance (low, high) and Inhibition (low, high) as within-participants factors. Maintenance represented the memory requirement or the numberof items measurableinsize to-be-maintained (e.g., inthe exampleabove,five animal words have to be maintained). Inhibition represented the number of potential target words, (objects/animals) which had to be excluded from the selected pool of words, as not being the smallest ones. Recall was a general index of both maintenance and inhibition, whereas intrusion rate represented the ability to control the activation of irrelevant information (see Palladino et al., 2001). As dependent measures, we recorded both recall accuracy and intrusion rate, which were scored as tallies of the respective responses, i.e., the number of correctly recalled words and the number of wrongly recalled words, respectively.

Computer updating task The task was administered on a standard pc running the SuperLab software (Cedrus Corporation, 1999). Stimuli were 10 high frequency consonants from the Italian alphabet. Each consonant appeared inside a rectangular frame (i.e., as a separate element), distinct from adjacent items throughout the task. Only new consonants were presented; when a consonant did not change, the symbol plus ‘+’ indicated this, to encourage the active maintenance of previously presented information (see Artuso & Palladino, 2011a, 2011b). The task consisted of five-step sequences. These always started with an initial stimulus study phase, followed by maintenance steps alternated with updating steps, and ending with a final maintenance. This was implemented to minimize the use of recency-based strategies, which could bias performance (see Palladino & Jarrold, 2008). At the end of the sequence, participants received a probe recognition task, that is, a single consonant was displayed in the centre of the screen. Study, maintenance and updating represented the on-line tracking of the task, whereas recognition probe was considered off-line, as it was required once the task was completed. Participants had to memorize a triplet of consonants, maintaining it unchanged or modifying it, that is updating it, in a self-paced fashion. Thus, participants needed to press the spacebar when they were ready to see the next screen, and RT at the key press was measured. In the probe recognition task, participants had to recognize if the probed letter belonged or not to the most recent studied triplet. They answered by pressing one of two keys from the keyboard (i.e., M for a ‘Yes’ response, Z for ‘No’). Letters belonging to the final triplet of updated consonants required a positive response (positive probes), whereas letters presented previously, but then discarded (lure probes) or letters that were not presented (negative probes), required a negative answer. We designated positive as relevant probes, and lure/negative as irrelevant probes. See Figure 1 for an example. Three within-subjects factors were manipulated: Updating Type, Updating Load and Process. Updating Type had two levels: content or binding updating. In content updating sequences, participants had to update the entire content of the triplet, recoding a new one. In binding updating sequences, only one consonant varied at a time, and participants had to update a single content together with its binding with the other contents, while keeping these contents’ identity unaltered (see examples in the previous section). As described throughout the introduction, with respect to binding, we manipulated specifically an inter-item association, that is, an association between adjacent letters. Updating Load represented the number of updating steps required in each sequence, and had three levels: zero updates (control condition), one or two updates. Finally, there were

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three main Processes required in each sequence: study, maintenance and updating. At study, participants encoded the triplet, and at maintenance, they had to actively maintain it in memory. Lastly, at updating, they had to change the memorized triplet; this entailed a) discarding the old triplet and substituting the new in content updating sequences, or b) updating a single item of the triplet together with its binding with the other two items, in binding updating sequences. Moreover, in the present study, we also introduced a control for probe position across trials, and verified the time point they originated from. This was to monitor possible differences due to temporal distance between study and recognition steps. Consider the following sequence with two updates: study [RBN]; maintenance [+ + +]; first update [+ + S]; second update [+ D +]; maintenance [+ + +]. Here, the final triplet to keep in mind is RDS. Therefore, the possible positive probes would be R, D or S. Thus, if the recognition probe were R, it would originate from the study step. If it were S, from the first update, and with D, it would originate from the second update. In fact, we manipulated the time point from which the probe originated to observe whether the time elapsing (i.e., the temporal distance), between encoding of each potential positive probe and its recognition, was informative about the updating process. With load 1, we included probes from the initial study step or from the first update, and with load 2, from study or the first/second update. One hundred and twenty sequences were presented, divided into two blocks. Each block contained equal numbers of content and binding updating trials, and their order of presentation was randomized within blocks. Each sequence appeared once per block. Similarly, different load conditions were equally presented and randomized within blocks. Fifty per cent of the probe cases were positive response probes, with the other 50% equally shared between lure and negative probes. After receiving instructions, each participant was presented with a practice block and subsequently, two experimental blocks. As dependent measures, we considered the online and off-line RTs individually. To obtain on-line updating process measures, the RTs of self-paced responses in each step were recorded separately (i.e., study, maintenance, updating). In addition, accuracy and RTs in the probe recognition phase were also collected to obtain off-line measures.

Results Accuracy In the classical task, participants recalled a mean of 82 words out of a maximum of 96 (SD = 5.3 words), across all conditions, and distributed among the 24 lists. These patterns replicated those reported in Palladino et al. (2001). In the computer task, participants performed and recognized correctly a mean of 79 sequences out of 122 (SD = 6.8 sequences). Only on-line RTs for sequences that ended with correct probe recognition were analysed. Then, trials with RTs below 150 ms, or exceeding a participant’s mean RT for each condition by more than 3 intra-individual standard deviations were considered outliers, and therefore excluded from analyses (1.87%). These patterns were comparable to those found by Artuso and Palladino (2011a, 2011b).

Classical updating task An initial ANOVA with Maintenance and Inhibition as within-subjects factors was run on percentages of correctly recalled words. A main effect of Maintenance, F(1, 53) =

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5

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Figure 2. Classical accuracy-based task: Intrusion rate in high and low Maintenance conditions as a function of Inhibition (high vs. low).

7,532.09, partial g² = .99, p < .001, was found. In the high maintenance condition, participants recall performance was lower (72% of words), than the low maintenance condition, where participants recall performance was 78% of words. A second ANOVA with Maintenance and Inhibition as within-subjects factors was run on intrusion rate. A significant interaction between Maintenance and Inhibition, F(1, 53) = 1,214.02, partial g² = .96, p < .001, was found. Post hoc planned comparisons showed that when both maintenance and inhibition were high, an increase in intrusion rate was found, t(53) = 52.17, p < .001. However, when inhibition was low, intrusion rate did not differ significantly across high or low maintenance conditions, t(53) = .13, p = .89 (see Figure 2). Overall, results were consistent with those found in Palladino et al. (2001).

Computer updating task Off-line: Positive probes analyses An initial 2 9 3 ANOVA, Updating Type (content, binding) 9 Updating Load (0, 1, 2), was conducted on RTs of correctly recognized positive probes. The main effect of Updating Type reached significance, F(1, 53) = 4.06, partial g² = .07, p = .048, thus showing a higher cost in binding updating trials compared with content updating trials (binding updating: M = 1150 ms, SD = 29 ms; content updating: M = 1116 ms, SD = 34 ms). As with the binding condition, we distinguished between different types of positive probes on the basis of their encoding position in the sequence. This enabled us to control for the potential confounding effect of the time elapsing between the initial study step and final recognition. Therefore, this necessitated two further analyses. Here, a 2 9 2 ANOVA, Probe Position (study, updating) 9 Updating Load (1, 2), was run. No main effects or interactions reached significance, Fs < 1. A second one-way ANOVA with Probe Position (first update, second update) as the single factor was conducted on Updating Load 2. Again, no main effects or interactions reached significance, Fs < 1. In summary, no differences emerged between the varying distances from encoding point and recognition of positive probes. In other words, the recognition RTs were similar for both letters most recently updated and those presented previously. Therefore, updating is likely to have been completed through the on-line steps of the task.

Off-line: Irrelevant probes analyses An ANOVA with factors of Updating Type (content, binding) 9 Irrelevant Probes (lure, negative) 9 Updating Load (1, 2) was run on RTs of correctly recognized irrelevant probes (i.e., negative answers). This produced main effects for the factors Updating Type,

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1,300 1,200 1,100 1,000 Content updating

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Figure 3. Computer RT-based task: Off-line mean RTs for lure and negative probes as a function of Updating Type. Bars represent standard error of the mean.

F(1, 53) = 10.58, partial g² = .17, p = .01, and Irrelevant Probes, F(1, 53) = 43.16, p < .001, partial g² = .45. A two-way interaction between Updating Type and Irrelevant Probes, F(2, 46) = 6.46, partial g² = .22, p = .018, also reached significance. Post-hoc planned comparisons showed that lure probes had a cost associated with their rejection in the binding updating condition only, t(53) = 2.69, p = .035 (See Figure 3). No other effects were significant, Fs < 1.

On-line analyses On the basis of preliminary omnibus analyses, we ran two ANOVAs to test specific effects with reference to updating type, the central focus of our study. A first ANOVA, comprising Updating Type (content, binding) and Updating Load (0, 1, 2) was conducted on RTs resulting from the self-paced on-line process. This produced the significant interaction between Updating Type and Updating Load, F(2, 106) = 16.44, partial g² = .24, p < .001. Post-hoc planned comparisons showed longer RTs for binding updating sequences, both with one update, t(53) = 2.94, p = .005 and two updates, t(53) = 6.70, p < .001, but no differences for sequences with zero updates, t(53) = .85, p = .40. Furthermore, sequences with two updates required longer RTs than those with one update, t(53) = 8.80, p < .001, and with no updates, t(53) = 12.83, p < .001, in binding updating trials. Moreover, sequences with one update needed longer RTs than sequences with no updates, t(53) = 9.48, p < .001. Similarly, content updating trials sequences with two updates required longer RTs than sequences with one update, t(53) = 4.26, p < .001, and no updates, t(53) = 9.78, p < .001. In addition, sequences with one update needed longer RTs than sequences with no updates, t(53) = 7.55, p < .001 (see Figure 4). 1,300

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Figure 4. Computer RT-based task: On-line mean RTs for content and binding updating sequences as a function of Updating Load. Bars represent standard error of the mean.

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Figure 5. Computer RT-based task: On-line mean RTs for content and binding updating sequences as a function of Process. Bars represent standard error of the mean.

A second ANOVA, comprising Updating Type (content, binding) and Process (study, maintenance, updating), was conducted on RTs resulting from the self-paced on-line process. This produced a significant interaction between Updating Type and Process, F(2, 106) = 10.07, partial g² = .16, p < .001. Post-hoc planned comparisons showed the absence of differences between content and binding updating conditions, both at the study step, t(53) = 1.77, p = .08, and maintenance step, t(53) = 1.27, p = .209. However, at the updating step, binding sequences required longer RTs, relative to content ones, t(53) = 5.18, p < .001. In addition, in content updating trials, study steps required longer RTs compared with maintenance steps, t(53) = 8.07, p < .001, but did not differ from updating steps, t(53) = .07, p = .95. Maintenance and update processes showed different patterns, with longer RTs needed to update than to maintain, t(53) = 11.10, p < .001. Moreover, in binding updating trials, study steps required longer RTs compared with maintenance steps, t(53) = 8.95, p < .001, but shorter RTs when compared with updating steps, t(53) = 3.44, p < .001. Finally, maintenance steps were quicker than updating steps, t(53) = 13.77, p < .001 (see Figure 5).

On-line: Serial position analyses Through the off-line analyses on positive probes RTs, we did not see any evidence of effects due to serial position, nor any due to the distance between study and recognition. To test more directly what happened during repeated updating, we ran two further analyses to see if there was an increase in RTs during the sequence. For binding and content updating, we compared the first and second updating for Load 2 conditions, via paired-sample planned comparisons. Differences in both content updating and binding updating failed to reach significance, Fs < 1.

Comparison between updating tasks Preliminary analysis: Correlation analyses To analyse the relation between the two tasks, we computed correlations between all representative measures of both tasks. We expected a significant correlation between content/binding updating for the computer task and the low maintenance-high inhibition condition of the classical task (i.e., that was more similar to the computer task, requiring low maintenance effort; see Introduction). For the classical task, we examined the dependent variables of recall accuracy and intrusion rate for both high/low maintenance and inhibition conditions. For the computer task, we created two composites measures

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representing the two updating types (i.e., the primary focus of this study). First, a measure of content updating was obtained by averaging the RTs of updating steps for each load in the content updating condition (i.e., one update in load 1, and two updates in the load 2 condition). Similarly, a measure of binding updating was obtained by averaging the RTs of updating steps for each load in the binding updating condition (i.e., one update in load 1, and two updates in the load 2 condition). Second, as the two measures were likely to be highly sensitive to individual differences in the overall processing speed, we adjusted both content updating and binding updating measures by a study measure RT. This study measure was calculated by averaging the RTs of each study step in every load and updating type condition. Then, the content updating and binding updating measures were divided by the study measure, obtaining composite measures of ‘content updating’ and ‘binding updating’. Before running correlations, we examined the reliability of both tasks’ measures using a split-half method corrected with the Spearman-Brown formula. We found robust values for both the recall and intrusion rate measures of the classical task (.77 and .85, respectively), and the accuracy and RT measures of the computer task (.73 and .88). Furthermore, for the computer task, we examined the relation between the content and binding updating composite measures. Of particular interest, we found a correlation between content updating and binding updating, r(54) = .88, p = .01. To analyse the relation between the two tasks, we correlated the measures of the classical task, i.e., recall accuracy and intrusion rate, with the measures of the computer task. We found both measures of ‘content updating’ and ‘binding updating’ to be highly correlated with the classical task condition of low maintenance-high inhibition. In addition, the ‘content updating’ measure correlated with recall accuracy, r(54) = .32, p = .05, and with intrusion rate, r(54) = .43, p = .01. Similarly, the ‘binding updating’ measure correlated with recall accuracy, r(54) = .27, p = .05, and with intrusion rate, r(54) = .47, p = .01 (see also Table 1). This result was consistent with task demands, as the computer task adopted here required very low maintenance effort, being focused mainly on inhibitory ability. In fact, it relied on the ability to discard no-longer relevant information and substitute it with new, with less reliance on maintenance (i.e., as only three items were to be maintained). To summarize, these correlations supported the link between the two updating tasks, classical and computer, in the components of both content and binding updating.

Regression analyses Following the results showing content and binding updating to be highly correlated, r = .88, p = .01, this suggested a certain degree of multicollinearity, albeit with the caveat that these are unlikely to be the same factor. To understand to what extent these factors were involved in, and predictive of, recall in a traditional updating task, we conducted two series of hierarchical regression analyses. This comprised recall accuracy and intrusion rate for low maintenance-high inhibition conditions as dependent variables, and both ‘content updating’ and ‘binding updating’ composite measures as predictors. The purpose was to observe whether the main updating operations explored in the computer task were involved in explaining the performance in a classical accuracy-based task. We distinguished two models: model A, in which we entered ‘binding updating’ as the first predictor, followed by ‘content updating’, and model B, in

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Table 1. Pearson correlations between the composites measures of ‘content updating’ and ‘binding updating’ of the computer task, and the classical task measures of ‘recall accuracy’ and ‘intrusion rate’. Low (L) and High (H), maintenance (m) and inhibition (i) are shortened. 1 1. Content Updating 2. Binding Updating 3. Hm-Hi Recall 4. Lm-Li Recall 5. Hm-Li Recall 6. Lm-Hi Recall 7. Hm-Hi Intrusion 8. Lm-Li Intrusion 9. Hm-Li Intrusion 10. Lm-Hi Intrusion

2

-

3

4

5

6

.22 .14

.07 .04 .13

.26 .12 .28* .01

.27* .32* .30* .07 .25

.88** -

-

7 . 24 . 26 .45** .20 .05 .43**

-

8

9

10

.01 .05 .28* .10 .10 .03 .33*

.17 .23 .09 .02 .12 .24 .14 .17

.47** .43** .39** .01 .02 .53** .41** .14 .11

-

-

Note. *Correlation is significant at the 0.05 level (all significance tests are 2-tailed). **Correlation is significant at the 0.01 level;

which these predictors were reversed, to control for multicollinearity in regression analyses (see Pedhazur, 1997). In model A, we found that ‘binding updating’ explained 10.5% of variance for recall accuracy, R2 = .105, B = .324, F(1, 52) = 6.08, p = .01, whereas the contribution of ‘content updating’ did not reach significance, R2 = .001, B = .048, F(1, 51) = .03, p = .86. We then performed a second regression of intrusion rate on ‘binding updating’ as first predictor and ‘content updating’ as second predictor. We found that ‘binding updating’ explained 19% of variance for intrusion rates, R2 = .190, B = .436, F(1, 52) = 12.6, p < .001, but ‘content updating’ was not significant, R2 = .037, B = .405, F(1, 51) = 2.41, p = .13. In model B, we performed a first regression analysis of recall accuracy on ‘content updating’ as first predictor and ‘binding updating’ as second. We found that ‘content updating’ explained 7.5% of variance for recall accuracy, R2 = .075, B = .274, F(1, 52) = 4.23, p = .045, whereas the contribution of ‘binding updating’ did not reach significance, R2 = .030, B = .366, F(1, 51) = 1.70, p = .19. We then performed a second regression of intrusion rate on ‘content updating’ as first predictor and ‘binding updating’ as second. Here, neither content updating, R2 = .029, B = .170, F(1, 52) = 1.55, p = .21, nor binding updating, R2 = .036, B = .402, F(1, 51) = 1.97, p = .16, explained variance. Overall, these results pointed to the contribution of binding updating as also being an important component of updating performance in the classical task, as it explained a significant portion of variance in both recall accuracy and intrusion rate. However, some caution should be exercised, as the role of the binding updating component, when entered after content updating, was no longer significant.

Discussion Two separate research traditions have explored WM updating through different tasks. However, these two traditions have not yet been related to each other, and the findings obtained from each one were neither comparable nor extendable. Our results contribute to clarify this crucial issue, whilst at the same time controlling for potential recency

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strategy bias, measurement of updating and the specific object of updating. We will first discuss the results for the two updating tasks separately, and interpretation of their relation in light of the issues mentioned above. In the accuracy-based classical task, we found an increase in intrusion rate when either maintenance or inhibition requirements were higher. The higher the number of items to be processed (and temporarily maintained), the higher the number of potential targets, and therefore, also items to be inhibited. Consequently, unsuccessful updating allowed no-longer relevant information to remain active in WM, and appeared subsequently at recall as intrusion errors. In the RT-based computer task, our results showed that at updating steps, binding updating (i.e., requiring the update of the binding between a single item and the other items in the set), needed longer RTs to complete, compared with the content updating condition (i.e., which required the updating of all the three items for each set; see Figure 5). These results suggested that the need to maintain several items and update one at a time, forcing the update of bindings between items, produced the difference in processing time. Moreover, in binding updating sequences, study and updating steps differed significantly, showing that updating is not simple re-encoding (see Figure 4). Conversely, in content updating sequences, no differences emerged across the sequence of study and updating steps, thus suggesting that re-encoding alone is necessary here (see Figure 4). However, an alternative account can be proposed here. Longer RTs in binding updating sequences relative to content updating ones, can be partly due to the fact that the correct letter to be updated in the triplet has to be retrieved from WM, e.g., in the trial RBN, ++S, +D+, the participant has to exert effort to retrieve the missing letters. Conversely, in content updating trials, the whole information necessary to update the triplet is available on the screen, e.g., RBN, +++, FDS, thus the participant is able to do the task with no effort and no load effect on memory. To test this account, future manipulations, in which a condition of binding updating where all the information is available on the screen is included (e.g., RBN, RBS, RDS), will be very useful. No differences emerged at recognition level in the RT-based task, across analysis of the precise position of positive probes and throughout varying time sequences (see both ‘Offline positive probes analyses’ and ‘On-line serial position analyses’ in the Results section). The temporal distance from study and recognition for each probe had no effect on the overall updating accuracy. This supports the view that updating has been fully completed on-line, regardless of the distance between each probe study and recognition step. Furthermore, it showed that binding updating has longer latencies, due to the effect of additional processes needed in this operation. Indeed, in binding updating conditions, both the inhibition of no-longer relevant information and no-longer relevant bindings are required. In other words, we showed that content updating was not easier simply because the to-be-recognized letters were the most recently updated; rather, it was facilitated presumably because it required fewer cognitive operations. As shown by Bunting et al. (2006) and Palladino and Jarrold (2008), a limitation with the running memory span task and similar updating tasks arises from how demanding they are for participants. That is, while listening to lists of items of unknown length, they have to remember a fixed number of items among the last presented (i.e., a recency criterion). Given the task difficulty, it has been shown that participants try to identify strategies to bypass the task demands; so, they usually tend to avoid processing each incoming item to update the list of items. Instead, they listen passively to the list until it ends, and then

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attempt to recall the most recently presented items only (see Palladino & Jarrold, 2008). Our results clearly demonstrate that both tasks examined here are resistant to recency bias, as they showed high accuracy rates sensitive to the conditions pertaining to each task. In traditional updating tasks, performance is sensitive to updating as well as load conditions. Here, the computer task showed an off-line accuracy rate higher than 95%, suggesting that participants going through the task at their own pace can process each presented item-set successfully. Consequently, if the whole task is unbiased, we can have confidence that the on-line level of the computer task is really informative of the processing. Thus, having these two tasks apparently immune from potential recency biases has allowed a clear comparison between them. The issue of whether the two components of content and binding updating have a role in an accuracy-based classical updating task was addressed through hierarchical regression analyses. These demonstrated that binding updating was a more sensitive measure of the performance in the accuracy-based task, relative to content updating. Indeed, binding updating was shown to be the most sensitive predictor to emerge as significant, relative to content updating. This supported the idea that binding updating also has a role in accuracy-based tasks. However, this runs contrary to the traditional view, which focuses on content updating only, indicating that single contents need to be studied/updated separately from the context they are embedded in. Conversely, the computer task attempts to combine these two updating objects within trials; some trials required updating of an entire item-set (i.e., a content updating trial), others updating of a partial item-set; that is the binding between an item and the adjacent two items (i.e., a binding updating trial). By comparing these two tasks, we investigated whether the binding component, which traditionally has not been conceptualized or manipulated in classical tasks, might play a role in both instances. Of particular note, we would like to highlight that contrasting model A with model B, only binding updating appears an important predictor of both recall accuracy and intrusion rate (10.5% vs. 19%). This result, we assume, is of the main importance as intrusion rate is a critical variable in memory updating tasks, as previously shown in several studies (see for example De Beni, Palladino, Pazzaglia & Cornoldi, 1998; Palladino et al., 2001). On the contrary, from our analyses, content updating is never a predictor of intrusion rate. In fact, in the light of our regression analyses, intrusion rate is likely to represent a more sensitive component of the updating process in the computer task. Intrusion rate represents a memory failure; it has been interpreted as an index of interference and an inhibition marker (see e.g., De Beni et al., 1998; Palladino, 2006; Palladino & Ferrari, 2013; Ruiz et al., 2005). Moreover, it has been hypothesized that this measure represents an insufficient inhibition of irrelevant information. Similarly, in binding updating, the participants have to perform an update, that is, to substitute a part of the previous inter-item binding which has to be inhibited. This is a function, absent in content updating, where all the previous bindings have to be erased and substituted. Therefore, in the binding updating process, as well as in intrusion control, there is an effective contribution of inhibition processes; as our results suggest, both updating efficiency and the control of intrusion rate are indexes of inhibitory efficiency. As previously noted, it is important to emphasize that in the present study, we manipulated the inter-item bindings only, choosing to leave item-position bindings for future investigation. Thus, the correlation and regression analyses have to be taken in the light of this manipulation limitation. In fact, we cannot exclude the possibility that itemposition binding might also have played a role in our task. Consequently, the percentages of variance predicted cannot be accounted for by inter-item bindings only. Therefore, in

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this first comparison between tasks, we have shown that an important role is played by bindings, across different updating tasks. Further experiments will be useful to manipulate and disentangle the specific effects of the two components of bindings, i.e., inter-item and item-position, and to observe which is more predictive of effective updating performance. A further advantage of comparing different research traditions was the opportunity to relate indirect measures (such as the number of correctly remembered items or intrusion errors rates), to on-line processing measures concerning how the participants have been studying/processing the items through the task. The RT-based task (Artuso & Palladino, 2011a, 2011b) allowed us to delve into background recall accuracy measures (albeit these were not informative, as shown by high recall accuracy rates), to focus clearly on the online processing. The amount of to-be-processed information was not important; in fact, here, a set of no more than three items had to be maintained. Rather, we aimed at examining, via RTs, the different types of processes, which might be involved during updating. Clearly, RTs represent a direct measure as they allow observation of the processing patterns in each trial, i.e., of the study, maintenance and updating phase (as we designed them), indicative of the underlying cognitive processes. In particular, in the computer task, on-line RTs were taken to represent an authentic and extremely sensitive measure of updating processes by virtue of this high degree of accuracy. This takes for granted off-line accuracy as a baseline measure of memory updating efficiency (see Artuso & Palladino, 2011a, 2011b). Of course, the task is novel and needs further replication, but, we believe, it suggests new avenues for future research. Finally, we were interested in separating the individual objects of updating. We believe, it is particularly timely to investigate binding updating, because it allows examination of the relational component of WM. Binding updating consists of partial modification of the to-be-remembered stimulus set, whereas content updating comprises complete modification of the to-be-remembered stimulus set. The cost of updating is likely to depend on the change in binding, but not in content; that is, in the construction, maintenance and dismantling of temporary associations between a stimulus/content and its context (see also Schmiedek, Hildebrand, L€ ovd
en, Wilhelm & Lindenberger, 2009). In the present study, we have first demonstrated that binding updating appears to be the cognitive process more related and sensitive to the classical updating task. What is particularly interesting and new, we believe, is that the specific costs we explicitly sought out in the RT-based task were related to the number of intrusions in the accuracy-based task. This result supports the relevance of intrusion errors as measures of updating processing efficiency, representing a failure in the control of irrelevant information, which then remains active in memory and interferes with the memory processing. To date, inhibition of irrelevant information and updating both represent the ‘unity and diversity’ of executive functions, which have been shown to be moderately correlated but separable (see Friedman et al., 2008; Miyake et al., 2000). Therefore, given that neither of them has been shown to subtend the other, they are indeed difficult to clearly identify and describe as separate functions. In fact, if we think of any everyday activity involving WM, such as reading comprehension or solving an arithmetical operation, it is most likely that we do not have to update individual contents (e.g., words), but instead, bindings between contents. Thus, our finding fits neatly with the idea that binding updating, more than content, can account for WM performance. Moreover, the present study might provide more effective measures, procedures and related theoretical framework to study applied fields, such as,

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for example, ergonomics (e.g., Morris, Milne, Jones & Quayle, 1991), learning disabilities (e.g., Carretti, Cornoldi, De Beni & Palladino, 2004) age-related differences (e.g., De Beni & Palladino, 2004; Van der Linden, Br
edart & Beerten, 1994) or WM training (e.g., Holmes, Gathercole & Dunning, 2009). In summary, this study makes significant headway in studying the cognitive processes involved in updating tasks, by unifying and comparing different research traditions, i.e., accuracy-based and RT-based tasks. Overall, we have found a link between the two different updating tasks examined, demonstrating the important role of the common underlying component of binding updating. Our results show that investigating the role of binding appears both important per se, and to offer a considerable contribution in clarifying understanding of memory updating.

Acknowledgements We thank Yoav Kessler and Neil Morris for suggestions on earlier versions of this manuscript, and three reviewers for comments useful to improve our present work. We also thank Monica Presazzi for her help with testing participants.

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