Benjamin Kowialiewski
email: bkowialiewski@uliege.be

Steve Majerus

Running head: SEMANTIC AND WORKING MEMORY

Keywords: Working Memory, Semantic Knowledge, Interference, Computational Simulations

Several studies have demonstrated an influence of semantic knowledge on verbal working memory (WM) performance, such as shown by the observation of semantic relatedness (related vs. unrelated words) and word imageability (high vs. low imageability words) effects in working memory. The present study extends these observations by examining in four experiments the extent to which semantic knowledge can protect WM representations against interference. We assessed immediate serial recall performance for semantically related vs. unrelated word lists and for high vs. low imageability word lists, with memory lists being followed by an interfering task after encoding or not. Results show that semantic relatedness leads to a stronger protective effect against interference than word imageability at the item level.

Furthermore, the semantic relatedness had a stronger impact on WM performance than word imageability; this was further supported by a meta-analysis of all relevant studies in the field.

These results suggest that inter-item associative semantic knowledge can protect WM content against interference, but less so item-level semantic knowledge. This protective effect may result from between-item recurrent reactivation or from reduced cognitive load via the compression of memoranda into conceptual units, as further supported by a series of computational simulations.

Semantic knowledge has been shown to influence WM performance: Semantically related words are better recalled as compared to semantically unrelated words (semantic relatedness effect) [START_REF] Poirier | Memory for related and unrelated words: Further evidence on the influence of semantic factors in immediate serial recall[END_REF], and concrete/highly imageable words are better recalled as compared to abstract or low imageability words (word imageability effect) (Walker & Hulme, 1999). The aim of this study is to examine the impact of semantic knowledge on WM performance in a more specific manner, by examining the extent to which semantic knowledge can protect memoranda against interference. In order to do so, we assessed the protective effect of semantic knowledge associated with semantic relatedness and word imageability when participants have to conduct an interfering task between encoding and recall of memoranda.

In the semantic relatedness effect, words related at the semantic level (e.g., leaf -threebranch) lead to higher recall performance as compared to lists composed of unrelated words (e.g., neck -brush -floor) [START_REF] Kowialiewski | The non-strategic nature of linguistic long-term memory effects in verbal short-term memory[END_REF][START_REF] Monnier | The semantic-similarity effect in children: influence of long-term knowledge on verbal short-term memory[END_REF][START_REF] Poirier | Memory for related and unrelated words: Further evidence on the influence of semantic factors in immediate serial recall[END_REF][START_REF] Tse | The role of associative strength in the semantic relatedness effect on immediate serial recall[END_REF][START_REF] Tse | The effect of semantic relatedness on immediate serial recall and serial recognition[END_REF]. In the word imageability effect (or word concreteness effect1 ), words with more concrete and less context-dependent semantic representations (e.g., leaf -hand -bear) lead to better recall performance than abstract words (e.g., phase -doubt -magic) [START_REF] Acheson | The Interaction of Concreteness and Phonological Similarity in Verbal Working Memory[END_REF][START_REF] Campoy | Automatic semantic encoding in verbal short-term memory: Evidence from the concreteness effect[END_REF][START_REF] Castellà | The (lack of) effect of dynamic visual noise on the concreteness effect in short-term memory[END_REF][START_REF] Chubala | Does dynamic visual noise eliminate the concreteness effect in working memory[END_REF][START_REF] Kowialiewski | The non-strategic nature of linguistic long-term memory effects in verbal short-term memory[END_REF][START_REF] Miller | The interaction of word frequency and concreteness in immediate serial recall[END_REF][START_REF] Romani | Concreteness effects in different tasks: implications for models of short-term memory[END_REF]Walker & Hulme, 1999). In the psycholinguistic domain, these two effects are thought to reflect the intervention of two different types of semantic knowledge on verbal WM.

While the semantic relatedness effect reflects the intervention of inter-item associative knowledge (e.g., "cat" and "dog" share more semantic features than do "cat" and "rope", see [START_REF] Dell | Lexical Access in Aphasic and Nonaphasic Speakers[END_REF], the imageability effect reflects the richness and consistency of semantic features associated to each individual item (i.e. high imageability words are associated with more, context-independent semantic features than low imageability words, see [START_REF] Pexman | The Impact of Feedback Semantics in Visual Word Recognition: Number of Features Effects in Lexical Decision and Naming Tasks[END_REF]Yap, Lim, & Pexman, 2015).

The intervention of semantic levels of representation on WM performance is also supported by other lines of evidence. Several studies have shown that so-called phonological WM effects, such as the phonological similarity and the word length effects, appear to be reduced when participants use a semantic coding strategy instead of a phonological one [START_REF] Campoy | Phonological and semantic strategies in immediate serial recall[END_REF][START_REF] Logie | Group aggregates and individual reliability : The case of verbal short-term memory[END_REF]. Furthermore, when participants are explicitly instructed to use these semantic coding strategies [START_REF] Hanley | Irrelevant speech, articulatory suppression, and phonological similarity : A test of the phonological loop model and the feature model[END_REF], or when required to perform a semantic judgement over to-be-remembered items at the moment of encoding [START_REF] Savill | Semantic categorisation of a word supports its phonological integrity in verbal short-term memory[END_REF], WM performance is increased. Similarly, newly acquired phonological word forms are better recalled in a subsequent WM task if these forms are also associated with semantic knowledge [START_REF] Savill | Newly-acquired words are more phonologically robust in verbal short-term memory when they have associated semantic representations[END_REF]. How semantic knowledge impact the ability to recall information over the short-term is therefore a critical question for our understanding of verbal WM maintenance.

One possibility is that semantic knowledge protects WM content against the deleterious impact of interference 2 . However, for now, only a few studies have examined semantic effects under interference-inducing dual-task conditions, and evidence supporting a protective effect of semantic knowledge in these conditions remains inconclusive. As regards the semantic relatedness effect, two studies found stronger relatedness effects under concurrent articulatory suppression, but only for recall measures that did not take into account serial order information [START_REF] Poirier | Memory for related and unrelated words: Further evidence on the influence of semantic factors in immediate serial recall[END_REF][START_REF] Saint-Aubin | Semantic Similarity and Immediate Serial Recall: Is There a Detrimental Effect on Order Information[END_REF]. Similarly, [START_REF] Neale | Age and redintegration in immediate memory and their relationship to task difficulty[END_REF] observed a progressive increase of semantic relatedness effect when the level of interference was parametrically modulated. However, in this latter study, the interaction between semantic relatedness and interference may be difficult to interpret given that half of the conditions were likely to suffer from ceiling effects as only 4 items needed to be maintained. Regarding the imageability effect, some studies found that concreteness effects were as strongly observed with and without a concurrent interfering task requiring participants to process visual distractors during the inter-item intervals [START_REF] Campoy | Automatic semantic encoding in verbal short-term memory: Evidence from the concreteness effect[END_REF][START_REF] Castellà | The (lack of) effect of dynamic visual noise on the concreteness effect in short-term memory[END_REF][START_REF] Chubala | Does dynamic visual noise eliminate the concreteness effect in working memory[END_REF]. Another study observed a stronger concreteness effect under articulatory suppression, but this result was not robust as it disappeared when the authors controlled for potential ceiling effects [START_REF] Romani | Concreteness effects in different tasks: implications for models of short-term memory[END_REF]. Overall, whether semantic effects are more strongly observed under interfering conditions and hence protect the content of WM is, at best, inconclusive.

In the present study, we determined the extent to which semantic knowledge can counteract the deleterious effect of interference on WM content. We used an immediate serial recall paradigm in which the potential impact of semantic knowledge was varied at the inter-item or individual item level, by assessing the impact of semantic relatedness in Experiments 1 & 2 and of word imageability in Experiments 3a & 3b. In the no interference condition, participants were instructed to directly recall the words in the correct serial order after the encoding phase. In the interference condition, participants were required to perform a backward counting task after the encoding phase, until being probed for serial recall of the encoding list. This task was used as the continuous calculation and updating of numerical information fully captures attentional resources and the verbal production of the numbers prevents rehearsal. According to decay-based models of WM [START_REF] Barrouillet | The time-based resource-sharing model of working memory[END_REF][START_REF] Cowan | An embedded-processes model of working memory[END_REF][START_REF] Martin | Recovery in deep dysphasia: Evidence for a relation between auditory-verbal STM capacity and lexical errors in repetition[END_REF], this would lead to strong degradation of WM representations as decaying WM traces cannot be refreshed, either via articulatory rehearsal or attentional refreshing mechanisms. Alternatively, according to interference-based models of WM [START_REF] Lewandowsky | Interference-based forgetting in verbal short-term memory[END_REF][START_REF] Oberauer | Modeling working memory: An interference model of complex span[END_REF], the stimuli of the interfering task will interfere with the verbal representations of the memoranda, and the attentional capture would prevent participants from removing distractors from WM, also leading to strong degradation of WM representations (see also [START_REF] Lemaire | A Computational Model of Working Memory Integrating Time-Based Decay and Interference[END_REF] for a combination of both accounts). We predicted that if semantic knowledge provides a protective effect against interference, lists composed of semantically related/high imageability words should be less affected by the interfering task, as compared to lists composed of unrelated/low imageability words.

In addition, both semantic relatedness and word imageability effects may also differ in their ability to protect memoranda from interference. Since the two effects are supposed to stem from different mechanisms (i.e. inter-item associative knowledge for the semantic relatedness effect and richness of semantic features for the imageability effect), one may expect that they might not be equivalent as regards their ability to protect memoranda against interference.

Semantically related items could be regrouped into one single conceptual unit, thereby allowing for compression of the whole list and freeing up WM resources, in addition to inter-item associative knowledge that will allow for reactivation of the different items. The additional, list-level knowledge may provide further protection of the memoranda against task-based interference. In contrast, high imageability words would only benefit from the richer semantic features associated to each individual item, without the possibility to compress the information at the whole-list level or reactivate items via inter-item associative knowledge. These predictions regarding possible different natures of semantic effects in WM tasks were further explored in a final part of this study using a computational modeling approach.

Experiment 1

In Experiment 1, we assessed the impact of semantic relatedness on immediate serial recall performance with and without interference. More specifically, participants were required to attend to lists of words that were semantically related (e.g., leaf -tree -branch -cloud -skyrain) or unrelated (e.g., knife -rice -dog -chair -wall -lake). After the encoding stage, participants were invited to either directly recall the words in correct serial order (immediate recall condition) or to perform a backward counting task (interference condition) before being invited to recall the items. In the backward counting task, a number was displayed on the screen (e.g., 35) and the participants were invited to count backwards by steps of 3 (i.e., 35, 32, 29, 26…) until being probed for recall of the memory list.

Method

Participants. Twenty-nine undergraduate students (20 females and 9 males) aged between 18 and 31 years (M = 21.14, SD = 2.45), recruited from the university community, took part in the experiment. All participants were native French speakers with no history of neurological disorders or learning difficulties. All participants gave their written informed consent prior to their inclusion in the study. The study had received approval from the local ethics committee.

Materials.

The material consisted of a set of 180 word stimuli, drawn from a pool used in a previous study (see [START_REF] Kowialiewski | The non-strategic nature of linguistic long-term memory effects in verbal short-term memory[END_REF]. All stimuli were monosyllable words with a mean frequency of Mlog = 1.536 and SDlog = .695 (Lexique 3 database; [START_REF] New | Lexique 3: Une nouvelle base de données lexicales[END_REF]. They formed 60 different triplets of semantically related words. The semantic relationships included taxonomic (e.g., gold -iron -lead) or thematic (e.g., tree -branch -trunk) categories. Semantic relatedness had been determined via an online survey in which participants were invited to judge the semantic relatedness of words by resampling them in pairs of items, on a scale ranging from 0 (not related at all) to 5 (strongly related) (for more details, see [START_REF] Kowialiewski | The non-strategic nature of linguistic long-term memory effects in verbal short-term memory[END_REF].

In average, each pair had been judged 75.25 times. The word triplets used in this study all had an average semantic relatedness rating of at least 3.5. All the stimuli were recorded by a French native male speaker in a neutral voice. Background noise was removed via the noise reduction tool implemented in Audacity®. The length of each item was normalized to 375 ms without altering the pitch.

To create semantically related memory lists, two triplets were pseudorandomly sampled from the whole set of triplets for each list, by ensuring that two triplets from the same list could not belong to the same semantic category (e.g., leaf -tree -branch -cloud -sky -rain). The unrelated lists were created by randomly combining the words taken from the same stimulus set.

Each sequence was checked to ensure that each word within a list could not have a specific relationship with another word in the list. Each word appeared twice throughout the entire experiment; once in a related list, and once in an unrelated list, and each time in a different serial position. We also assessed to what extent our lists differed according to the lexical co-occurrence dimension, by first extracting from each list the pairs of words that shared adjacent positions (e.g., extracting "hand -sky" and "sky -cat" from "hand -sky -cat"). For semantically related lists, this was performed by extracting only those pairs that were embedded in a similar semantic group (e.g., extracting "leaf -tree", "tree -branch", "cloud -sky" and "sky -rain" from the sequence "leaf -tree -branch -cloud -sky -rain"). Lexical co-occurrence values were then computed using the Latent Semantic Analysis (LSA) (http://lsa.colorado.edu/). Results show that related and unrelated lists did indeed differ in terms of LSA values (M = .218, SD = .183 and M = .11, SD = .107 for related and unrelated lists, respectively, BF10 > 100).

There were sixty trials in total, fifteen in each experimental condition. We generated 16 different versions of memory lists in order to neutralize any possible stimulus list effects. We first generated 4 different versions by using the rules described above. For each version, a new version was then created by re-ordering the items within each list in a random order, resulting in 8 different versions. For related lists, this was done by re-ordering the items within each semantic triplet (e.g., leaf -tree -branch; branch -leaf -tree). Order of presentation of lists was pseudorandomized with no more than three successive presentations of the same stimulus condition. Furthermore, the succession of interference and no-interference trials was pseudorandomized following the same constraints. Lists presented in interference versus nointerference conditions were in addition randomized between participants.

Procedure.

Each trial began with an on-screen countdown starting from 3, announcing the beginning of the trial. This countdown was followed by a blank screen and the presentation of the memory list. Each of the six items was presented at a pace of 750 milliseconds per item, with each item lasting for 375 milliseconds and an inter-stimulus interval of 375 milliseconds. In the no-interference condition, the end of each list was signaled by a brief (100 milliseconds) sinusoidal tone of 440 Hz, prompting the subject to recall aloud the sequence. Participants were instructed to recall the sequence in the order in which each word appeared, and to substitute any item they could not remember with the word "blanc" ("blank" in French). In the interference condition, the end of the list was followed by a number displayed on the screen. This number was a two-digit number randomly chosen on each trial from a uniform distribution, ranging from 19 to 99. Participants were required to subtract three to this number, and then to subtract three to the result of this subtraction, and so on during 5 seconds until a tone prompted the participants to recall aloud the encoding sequence. Participants were required to say aloud the numbers which were recorded for subsequent scoring and analysis. When participants had finished recalling all items they could remember, they were invited to press the spacebar of the keyboard to initiate the next trial. Participants performed 3 practice trials of each interference condition before the beginning of the main experiment. None of the stimuli used in the practice phase were used in the main experiment. The experiment was divided into 2 blocks composed of 30 trials each to allow participants to take a very short break and refocus on the task if they needed to.

Task presentation and timing was controlled using OpenSesame [START_REF] Mathôt | OpenSesame : An open-source , graphical experiment builder for the social sciences[END_REF] running on a desktop computer. The auditory stimuli were presented at comfortable listening level via headphones connected to the computer in a soundproof booth.

Participant's responses were recorded using a digital recorder for later transcription and scoring.

Scoring procedure. In order to assess recall performance, two scores were computed.

The item recall score included all items recalled, regardless of serial position. The strict serial recall score included all items recalled at the correct serial position. Finally, we also assessed performance on the backward counting tasks by counting the number of digit output by participants before recalling the memoranda, regardless whether the response was correct or not.

Note that considering only correct responses did not change the results across all experiments.

Statistical analysis. We performed a Bayesian analysis as it reduces Type-1 false error probabilities relative to frequentist statistics [START_REF] Schönbrodt | Sequential Hypothesis Testing With Bayes Factors : Efficiently Testing Mean Differences[END_REF]. The Bayesian approach has the further advantage of computing continuous values against or in favour of a given model, rather than deciding for the presence of an effect based on an arbitrary statistical threshold. Evidence in favour of a model is given by the Bayesian Factor (BF), reflecting the likelihood ratio of a given model relative to other models, including the null model. Both the null model and the effect of interest can be simultaneously tested, by directly comparing the alternative hypothesis against the null hypothesis, and vice versa. The BF10 is used to determine the likelihood ratio for the alternative model (H1) relative to the null model (H0), and the BF01 to determine the likelihood ratio for H0 relative to H1. We use the classification of strength of evidence proposed in previous studies [START_REF] Jeffreys | The theory of probability[END_REF]Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011): A BF of 1 provides no evidence, 1 < BF < 3 provides anecdotal evidence, 3 < BF < 10 provides moderate evidence, 10 < BF < 30 provides strong evidence, 30 < BF < 100 provides very strong evidence and 100 < BF provides extreme/decisive evidence. In Bayesian ANOVAs, we performed Bayesian model comparisons using a top-down testing procedure, which first computes the BF value for the most complex model possible (i.e. the model including all main effects and all possible interactions). The BF value for each term is then assessed by directly comparing the full model against the same model, but by dropping the term under investigation. In order to minimize error of model estimation, the number of Monte Carlo simulations generated was set to Niterations = 1,000,000. In each graph, we furthermore computed Bayesian credible intervals using the 95% highest density interval on the sampled posterior distribution of the model under investigation (Niterations = 100,000). All the analyses were performed using the BayesFactor package [START_REF] Morey | BayesFactor: Computation of Bayes factors for common designs (Version 0[END_REF] implemented in R (R Development Core Team, 2008) using the default wide Cauchy prior distribution of r = √ .

Results and discussion

We first assessed recall performance as a function of interference condition (no interference, interference), semantic relatedness (related, unrelated) and serial position (1 through 6) using a Bayesian Repeated Measures ANOVA.

Item recall criterion. The main effects of interference, semantic relatedness and serial position were associated with decisive evidence. As can be clearly seen in Figure 1, upper panel, items were more poorly recalled in the interference condition, as compared to the no-interference condition. In addition, recall performance was higher for related lists, as compared to unrelated lists. Importantly, the interaction between interference and semantic relatedness was supported by decisive evidence. This interaction was characterized by the fact that the relatedness effect was observed to a stronger extent in the interference condition (Mdiff = .32, d = 2.67) compared to the no interference condition (Mdiff = .224, d = 1.867), and this difference was supported by decisive evidence (BF10 = 448.728, CI99% = [-1.384; -.262]). See Table 1 for a full report of BF values.

Strict serial recall criterion. The same analysis was performed using the strict serial recall criterion. Overall, all main effects of interference, semantic relatedness and serial position were associated with decisive evidence. However, this time we observed moderate evidence against the interaction between interference and semantic relatedness (BF10 = 0.301; BF01 = 3.322). In other words, the semantic relatedness effect was observed to a similar extent under the interference (Mdiff = .248, d = 2.078) and no interference conditions (Mdiff = .218, d = 1.825), and this absence of difference was only slightly supported (BF01 = 1.661, CI99% = [-.741; .191]), as can be seen in Figure 1, lower panel.

< INSERT FIGURE 1 AND TABLE 1 ABOUT HERE >

We also assessed whether the semantic condition had an impact on the interfering task.

We compared the participant's performance on the backward counting task as a function of the type of words to be maintained (related, unrelated) using a Bayesian Paired Samples T-Test. We observed no impact of the semantic condition on the participant's ability to perform the backward counting task (Mdiff = -.011, CI99% = [-.473; .432], BF01 = 5.002).

In sum, the semantic relatedness effect was robustly observed, and this more strongly in the interference condition as compared to the no interference condition. This was only observed when the item recall criterion was used but not when performance was in addition scored at the serial order recall level, supporting the assumption that the benefit of semantic knowledge mainly operates at the level of the retention of item information. It could be argued that the semantic relatedness effect was more strongly observed under interference due to the fact that words were in addition presented in two three-item groups of semantically related words;

grouping effects have been shown to improve recall performance overall [START_REF] Henson | Short-term memory for serial order[END_REF][START_REF] Hitch | Temporal Grouping Effects in Immediate Recall: A Working Memory Analysis[END_REF]. Hence, the protective effect of semantic relatedness on item recall performance may at least partially reflect the intervention of grouping effects. In Experiment 2a, we aimed at replicating the results of Experiment 1 while avoiding grouping effects.

Experiment 2a

In Experiment 2a, we manipulated semantic relatedness in immediate serial recall tasks with or without an interfering task by presenting lists of six words that were either all related at the semantic level (e.g., foot, leg, arm, hand, elbow, thigh) or completely unrelated. This manipulation allowed us to avoid sublist semantic grouping effects while varying the impact of associative semantic knowledge.

Method

Participants. Twenty-nine undergraduate students (21 females and 8 males) aged between 18 and 27 years (M = 21.9, SD = 2.91), recruited from the university of Liège, took part in the experiment. All participants were native French speakers with no history of neurological disorders or learning difficulties. All participants gave their written informed consent prior to their inclusion in the study. The study had received approval from the local ethics committees.

Materials. We used a set of 192 words (one to three syllables long) with a mean frequency of Mlog = 1.164 and SDlog = .732 (Lexique 3 database; [START_REF] New | Lexique 3: Une nouvelle base de données lexicales[END_REF]. The stimuli were drawn from thirty-two different semantic categories, with six words per category. All the stimuli were recorded by a French native male speaker in a neutral voice. Background noise was removed via the noise reduction tool implemented in Audacity®. The stimulus set had a duration of M = .454 and SD = .063.

Unrelated lists were created by pseudorandomly combining the words from the semantically related lists, such that each word within a list could not share an obvious semantic relationship with another word in the list. Like in Experiment 1, each word appeared twice in different serial positions: Once in a related list, and once in an unrelated list. Again, both kind of lists did differ in terms of LSA values, after extraction of each adjacent words within each list (M = .165, SD = .193 and M = .06, SD = .08 for related and unrelated lists, respectively, BF10 > 100).

The entire experiment was comprised of 64 trials, with 16 trials per experimental conditions. To avoid stimulus list effects, we generated 6 different versions of the memory lists, by randomly recombining the words for unrelated lists as described above, and by re-ordering the serial position of words for related lists. The lists within each version were then assigned to recall conditions with or without an interfering task. These pairings were reversed for half of participants, resulting in 12 different list versions. Finally, for a third of participants, the order of items within a list was reversed (i.e. the first items of each lists were shifted to position 6). For another third of participants, the order of the trials was reversed (the first presented list was now the last one), leading to a total of 36 different list versions.

The experiment was divided into 2 blocks composed of 32 trials each to allow participants to take a very short break and refocus on the task if they needed to. All other aspects of the method, including the task procedure (presentation rate, interfering task…), scoring procedure and statistical analysis were identical to Experiment 1.

Results and discussion

Recall performance as a function of interference (no interference, interference), semantic relatedness (related, unrelated) and serial position (1 through 6) was first assessed using a Bayesian Repeated Measures ANOVA. Experiment 2a replicated the main results of Experiment 1: Semantic knowledge, as assessed by semantic relatedness effects, had a protective impact on item recall performance but not when performance was scored using a strict serial order recall criterion. We however need to examine whether the interaction observed in Experiment 2a could have been affected by a potential ceiling effect in recall performance for serial positions 1 through 3 in the no interference condition. We therefore ran an additional analysis by restricting recall measures to serial positions 4 through 6 not affected by ceiling effects. This additional analysis did no more support an interaction between semantic relatedness and interference, with anecdotal evidence against the interaction (BF01 = 1.906 when running a Bayesian Repeated-Measures ANOVA). At the same time, limiting analysis to a reduced number of serial positions may not eliminate the potential impact of a ceiling effect as performance for the later serial positions, even if not at ceiling, may still be facilitated by the highly efficient and seemingly effortless maintenance and/or recall of items associated with the earlier serial positions.

Item recall criterion. As can be clearly seen in

Experiment 2b

We administered an additional WM condition to a new group of participants which should lead to a performance profile of an intermediate level as compared to the no interference and interference conditions in Experiment 2a. This was achieved by inviting the participants of Experiment 2b to perform a 2-second shadowing task before recalling the words. We reasoned that there would be a drop of performance in word list recall performance in this condition as compared to the no interference condition in the previous experiment and should allow for a more valid comparison with the performance drop in the interference condition of Experiment 2a. In the digit shadowing condition of this new experiment, time-based forgetting should be weaker as compared to the counting backward task of Experiment 2a, as the digit shadowing task was significantly shorter (2 seconds) compared to the counting backward task (5 seconds). In addition, the digit shadowing task should also occupy the focus of attention for a shorter duration as participants will have the opportunity to refresh the items during the inter-digit interval [START_REF] Oberauer | Modeling working memory: a computational implementation of the Time-Based Resource-Sharing theory[END_REF][START_REF] Vergauwe | The Impact of Storage on Processing: How Is Information Maintained in Working Memory[END_REF], or the remove the digits from WM [START_REF] Oberauer | Modeling working memory: An interference model of complex span[END_REF], leading to less degradation of WM representation compared to the backward updating task which requires constant updating of one's WM content.

Method

Participants. Twenty-nine undergraduate students (25 females and 4 males) aged between 18 and 32 years (M = 21.21, SD = 4.23), recruited from the university of Grenoble, took part in the experiment. All participants were native French speakers with no history of neurological disorders or learning difficulties. All participants gave their written informed consent prior to their inclusion in the study. The study had received approval from the local ethics committees.

Materials.

All the stimuli were identical to those of Experiment 2a, including words, audio files and lists.

Procedure. After the presentation of the memory lists, the participants had to read out aloud a sequence composed of two numbers that appeared on the screen. The numbers appeared at the rate of one number every second. The experimenter stayed in the room during the whole session to ensure that participants complied with task requirements.

The entire experiment was comprised of 64 trials, with 32 trials per experimental conditions; 32 trials in the semantically related condition, and 32 trial in the semantically unrelated condition. Performance of the participant group of Experiment 2b was compared to performance of participants in Experiment 2a for the no interference and backward counting, interference condition.

Results and discussion

First, and very importantly, when compared to the immediate recall condition of Experiment 2a, participants in Experiment 2b recalled semantically unrelated lists more poorly (M = .68 and M = .516, respectively, BF10 = 52541 for a Bayesian independent samples T-Test).

As can be seen in Figure 3, there was now room for performance improvement across all serial positions, showing that we have eliminated the ceiling effect for earlier positions that characterized the no interference condition in Experiment 2a.

Next, we compared the participant group of Experiment 2b with participant group of Experiment 2a for the interference condition as a function of semantic relatedness (related, unrelated) and serial position (1 through 6) using a mixed Bayesian ANOVA.

Item recall criterion. All main effects of group, semantic condition and serial position were supported by decisive evidence, as can be seen in Figure 3 and Table 3. Overall, recall performance was poorer in the participant group of Experiment 2a for the interference condition as compared to the participant group in Experiment 2b, and semantically related lists led to higher recall performance than unrelated lists. Most importantly, an interaction associated with decisive evidence was observed between participant group and semantic relatedness, (BF10 = 145.181). More specifically, we observed that the semantic relatedness effect was still stronger in Strict serial recall criterion. When using the strict serial recall criterion, all main effects of participant group, semantic condition and serial position were supported by decisive evidence.

There was also anecdotal evidence supporting an absence of interaction between participant group and semantic relatedness (BF10 = .505, BF01 = 1.982). The magnitude of the semantic relatedness effect in the participant group of Experiment 2b (Mdiff = .088, d = 1.187) was comparable to the one observed in participant group of Experiment 2a for the interference condition (Mdiff = .123, d = 2.011); this absence of difference was associated to ambiguous evidence (BF10 = 1.218, CI99% = [-1.093; .216]). This result mirrors the results observed for the strict serial recall measure in Experiment 2a.

< INSERT FIGURE 3 AND TABLE 3 ABOUT HERE >

In sum, when comparing the participants of Experiment 2a in the interference condition to a new participant group performing a recall condition under intermediate interference levels, we still observed an increased semantic relatedness effect for recall in the interference condition using an item recall criterion.

Experiment 3a

Experiments 1 and 2 have shown that semantic knowledge, in the form of inter-item associative knowledge, can protect memoranda against task-based interference, and this specifically for item recall. The aim of Experiment 3a was to determine whether the same protective effects can be observed when semantic knowledge operates on individual item characteristics rather than on inter-item associative knowledge. This question was examined by varying the imageability dimension of memoranda, based on the assumption that high imageability words are associated with richer and more context-independent semantic features than low imageability words [START_REF] Evans | What's in a word? A parametric study of semantic influences on visual word recognition[END_REF][START_REF] Muraki | Mapping semantic space: property norms and semantic richness[END_REF][START_REF] Pexman | The Impact of Feedback Semantics in Visual Word Recognition: Number of Features Effects in Lexical Decision and Naming Tasks[END_REF]Yap et al., 2015). All other aspects of the experiment were identical to Experiment 2a.

Method

Participants. Thirty undergraduate students (20 females and 10 males) aged between 18 and 25 years (M = 21.34, SD = 2), recruited from the university community, took part in the experiment. All participants were native French speakers with no history of neurological disorders or learning difficulties. All participants gave their written informed consent prior to their inclusion in the study. The study had received approval from the local ethics committee.

Materials. The stimuli were taken from a previous study [START_REF] Kowialiewski | The non-strategic nature of linguistic long-term memory effects in verbal short-term memory[END_REF] in which they produced reliable imageability effects in standard immediate serial recall conditions. The stimuli were two sets composed of ninety unisyllabic words, selected from the Lexique 3 database [START_REF] New | Lexique 3: Une nouvelle base de données lexicales[END_REF]. One of the sets scored high on the imageability dimension (M = 6.661, SD = .122, min = 6.5, max = 6.9) and the other one scored low on the imageability dimension (M = 2.908, SD = .379, min = 1.9, max = 3.4) [START_REF] Content | Brulex . Une base de données lexicales informatisée pour le français écrit et parlé[END_REF]. The entire experiment was comprised of 60 trials, with 15 trials in each experimental conditions. Six different list versions were created using an algorithm programmed under MATLAB®. The algorithm ensured that a word could not be repeated within the same list, or within the same serial position throughout the experiment. Furthermore, two stimulus conditions (high, low imageability words) or interference conditions (no interference, interference) could not be repeated on more than three consecutive trials. Six further list versions were created by inverting the pairings of the stimulus lists and interference conditions, resulting in 12 different versions. The lists of each of these versions were furthermore reversed as regards the serial position of items in each list (i.e. the first item of each lists was shifted to position 6) and then flipped up to down (the first presented list was now the last one), resulting in a total of 24 versions. Throughout the experiment, each word was repeated twice in different serial positions:

Once in the no interference condition, and once in the interference condition. Finally, we checked that high and low imageability lists were also equivalent in terms of lexical cooccurrence values using the same procedure as described in Experiments 1 and 2. When averaged across all versions, high and low imageability lists showed very similar LSA values (M = .118, SD = .047 and M = .123, SD = .059 for high and low imageability words, respectively, BF01 = 6.529).

Procedure. Contrary to Experiments 1 and 2, items within lists were presented at a rate of 1.5 seconds/items. This slower presentation rate was used because the imageability effect is maximized for slow stimulus presentation rates [START_REF] Campoy | Automatic semantic encoding in verbal short-term memory: Evidence from the concreteness effect[END_REF]. The experiment was divided into 2 blocks composed of 30 trials each to allow participants to take a very short break and refocus on the task if they needed to.

Results and discussion

We first assessed recall performance as a function of interference (no interference, interference), imageability (high, low imageability) and serial position (1 through 6) using a Bayesian Repeated Measures ANOVA Item recall criterion. The main effect of interference condition was supported by decisive evidence. Recall performance strongly decreased in the interference condition. The imageability effect was also associated with decisive evidence, with high imageability words leading to higher recall performance than low imageability words. Likewise, the serial position effect was also supported by decisive evidence (see Figure 4, top panel, and Table 4). Critically, there was moderate evidence supporting the absence of interaction between the interference condition and

< INSERT FIGURE 4 AND TABLE 4 ABOUT HERE >

In line with the previous experiments, we found evidence slightly favouring the absence of a stimulus condition effect on performance for the backward counting task (Mdiff = -.064, CI99% = [-.643; .266], BF01 = 2.945).

In this experiment, we observed that the imageability dimension had no obvious protective effect against interference; high and low imageability words were similarly affected by interference. This is in contrast with Experiments 1 and 2 where the impact of the semantic relatedness variable did interact with the interference condition. This might suggest that different semantic dimensions do not protect against interference in the same way.

Experiment 3b

In this final experiment, we aimed at replicating the results of Experiment 3a.

Furthermore, we presented the interference -no-interference conditions in blocks in order to rule out the possibility that the unpredictability of the interference conditions may have led to the negative results observed in Experiment 3a. Materials & procedure. The material, testing and scoring procedure were identical as in Experiment 3a, except that the interference and no interference lists were presented in a fully blocked manner. The block conditions were counterbalanced across subjects, such that odd subjects received the interference condition first, while even subjects received the no interference condition first. Participants received 3 practice trials before each block.

Method

Results and discussion

Recall performance as a function of interference, imageability and serial position were assessed using a Bayesian Repeated Measures ANOVA.

Item recall criterion.

As can be seen in Figure 5, upper panel, and Table 5, all main effects were supported by decisive evidence: Recall performance dropped in the presence of task interference and high imageability words were better recalled than low imageability words. We also reproduced the main result of Experiment 3a by observing moderate evidence for the absence of an interference-by imageability interaction (BF10 = 0.12; BF01 = 8.333). The Strict serial recall criterion. Under the strict serial recall criterion, the overall conclusions were consistent: All main effects were supported by decisive evidence. In addition, there was moderate evidence supporting the absence of interaction between interference and imageability (BF10 = 0.15; BF01 = 6.667). Again, the imageability effect was of similar magnitude both in the no interference (Mdiff = .075, d = .851) and in the interference condition (Mdiff = .091, d = .894), leading to moderate evidence against an absence of difference (BF01 = 4.044, CI99% = [-.57;

.332]).

< INSERT FIGURE 5 AND TABLE 5 ABOUT HERE >

Again, we observed evidence against an impact of the stimulus condition on performance during the backward counting task (Mdiff = -.013, CI99% = [-.494; .402], BF01 = 4.97).

This final experiment shows that the absence of an imageability-by-interference interaction is a robust finding and was not caused by the blocked versus random order of presentation of task conditions. One possibility is that the word imageability dimension protects less against interference because its impact on WM performance is smaller than the impact of the semantic relatedness dimension. This possibility is explored in the next section.

Are the semantic relatedness and imageability effects equivalent?

We explored the possibility that the semantic relatedness dimension is affecting more strongly recall performance than the imageability dimension, by directly comparing the mean difference between the two stimulus conditions of Experiments 2a and 3a (related and unrelated in the semantic relatedness study; high and low imageability in the imageability study) using a

Bayesian Independent Samples T-Test, and this in the no interference condition only. As can be shown in Figure 6, left panel, the impact of the semantic relatedness manipulation was nearly twice as bigger (Mdiff = .163, d = 2.327) as compared to the imageability manipulation (Mdiff =

.088, d = 1.232), and this difference was supported by decisive evidence (BF10 = 162.978, CI99%

= [-1.699; -.26]). We took one step further by assessing the impact of the semantic variables across multiple studies reported in the literature, by performing a random effect meta-analysis, and this using the "metafor" package [START_REF] Viechtbauer | Conducting meta-analisys in R with metafor package[END_REF] We also assessed the magnitude of both effects when a strict serial recall criterion was considered (see Figures 9 and10). Using the data from Experiments 2a and 3a, the magnitude of the semantic relatedness effect was now clearly reduced (Mdiff = .107, d = 1.205), and was comparable to the one observed for the imageability effect, which remained unchanged (Mdiff =

.091, d = 1.091). Accordingly, moderate evidence supported the absence of difference between both effects (BF01 = 3.41, CI99% = [-.731; .501]). This is also reflected by the meta-analysis, with the semantic relatedness effect for a strict serial order recall criterion being substantially reduced (Cohen's d = 1.29), while the imageability effect remained virtually unchanged (Cohen's d = .82). Overall, this closer inspection confirms that the semantic relatedness effect is reduced using a strict serial recall criterion, suggesting that associative semantic knowledge allows participants to substantially recall more items, but not necessarily the order in which they were presented, as previously observed [START_REF] Poirier | Order recall in verbal short-term memory : The role of semantic networks[END_REF][START_REF] Tse | The effect of semantic relatedness on immediate serial recall and serial recognition[END_REF].

< INSERT FIGURES 6, 7, 8, 9 and 10 ABOUT HERE > Hence, Experiments 1 through 3 suggest that the semantic relatedness and the imageability effect in WM stem from different mechanisms, as both effects did not resist to interference to the same extent. In the next section, we investigated more directly the nature of these mechanisms by using a computational modeling approach.

Modeling the nature of semantic effects in immediate serial recall tasks

The general assumption of the present study is that items associated with richer linguistic representations will receive stronger feedback from higher (semantic) levels of linguistic representations. One way by which this feedback could occur is by considering continuous interactive activation feedbacks between levels of representations [START_REF] Dell | Lexical Access in Aphasic and Nonaphasic Speakers[END_REF][START_REF] Mcclelland | An interactive activation model of context effects in letter perception: Part 1. An account of basic findings[END_REF]. Interactive activation models are widely used for modeling various aspects of language processing, especially in reading [START_REF] Ginestet | Modeling the length effect for words in lexical decision: The role of visual attention[END_REF][START_REF] Mcclelland | Integrating probabilistic models of perception and interactive neural networks: A historical and tutorial review[END_REF][START_REF] Snell | OB1-Reader : A Model of Word Recognition and Eye Movements in Text Reading[END_REF], and therefore provide an excellent starting point for also modeling language effects in the context of WM tasks. In order to connect the language and WM domains, we implemented an adaptation of Dell's interactive activation model initially developed for modeling naming errors in aphasic and non-aphasic speakers [START_REF] Dell | Lexical Access in Aphasic and Nonaphasic Speakers[END_REF], and integrated it with the Start-End Model, an influential model for accounting WM recall performance as a function of serial position of items [START_REF] Henson | Short-Term Memory for Serial Order : The Start-End Model[END_REF]. We used the Start-End Model because it is a simple, yet efficient mathematical implementation of WM which produces realistic serial position curves and is able to simulate several serial order phenomena [START_REF] Hurlstone | Functional similarities and differences between the coding of positional information in verbal and spatial short-term order memory[END_REF]. The Start-End Model, by assuming that the order of a sequence is coded according to the start and the end of a sequence, is further supported by recent empirical finding suggesting that serial order coding in WM is supported by a left-right spatial dimension [START_REF] Abrahamse | Finding the answer in space: the mental whiteboard hypothesis on serial order in working memory[END_REF]. Finally, like the theoretical position adopted throughout this entire study, the Start-End Model is also neutral as regards the mechanisms that are responsible for forgetting in WM. The resulting model is a three-layer network composed of a sublexical biphone layer (e.g., the biphones /kae/ and /aet/ will represent the word /kaet/ at the sublexical level) [START_REF] Hannagan | Spoken word recognition without a TRACE[END_REF], a lexical layer and a semantic layer, with each adjacent layer being connected via bi-directional excitatory connections. The general linguistic architecture is represented in Figure 11.

<INSERT FIGURE 11 ABOUT HERE>

The linguistic sub-architecture. For each simulation, we created a network whose lexicon was restricted to the items actually used in each experiment (i.e. 192 items in Experiment 2, 180 items in Experiment 3). We observed that a lexicon of this size was largely sufficient to produce a natural variability within the activation level of the nodes without adding any noise, while allowing for time-efficient simulations. For each word included within the lexicon, their biphones were defined in order to create the sublexical layer. Each unique biphone was represented by a single node within the sublexical layer, with each node being connected to its lexical representation via a bi-directional connexion weight. Each lexical representation is connected to a set of semantic nodes representing the semantic features associated to each item (e.g., if the word "cat" is represented at the semantic level by "has a tail", "is an animal" and "meows", it will be connected to 3 semantic nodes). In order to set the connection weights between each level, we applied a matrix multiplication rule between each adjacent levels [START_REF] Kosko | Bidirectional Associative Memories[END_REF]:

(1) = .

where is a matrix resulting from the multiplication of the vectors that represent units and after activating the nodes corresponding to that item. Throughout all simulations, we defined the connection weights between each adjacent levels as fixed parameters. Note that the weights that connect the sublexical nodes to the lexical nodes are divided by the number of biphones contained in each word to avoid that long words would receive more activation than short words.

Simulations started by feeding the network with input values of .2 at the sublexical level during 10 iterations. As soon as the network receives inputs at the sublexical level, the activation propagates between levels according to:

(2) , = , + ∑( . , ) +

In this formula, , is the activation of unit j at time t, , is the activation of unit j at time t-1, and is the decay rate (fixed at a value of .5). The network also receives the sum of all activations coming from all units at time t-1, scaled by the connection weights . Finally, is the external input received by the network during stimulus presentation (which is only relevant for the sublexical layer, because external input is sent only at this level). In this type of implementation, the activated units behave as leaky integrators: their activation level is strictly dependent on the external input, and as soon as the network is no longer fed, their activation level rapidly drops off to a stable resting level, as can be seen in Figure 12.

< INSERT FIGURE 12 ABOUT HERE >

To facilitate the integration of the linguistic architecture within the Start-End Model, we consider that the encoding strength Ω at position i for an item j is the product of the activation accumulated (cumulative sum) over that specific position:

(3) Ω = ∑ , , for = 1, 2, … , where = 1 and = index the first and last iterations over position i, respectively. Hence, if we consider a sequence of N items, Ω will be computed for each position, resulting in a N-by-N matrix of summed activation. Note that for simplicity, each item is considered to be encoded correctly on each occasion. In addition, since we are dealing with word items, encoding is assumed to happen at the lexical level of processing. Computing Ω allows compressing a complex temporal information within a single vector, which can then be used at the moment of retrieval as a weighting parameter in the Start-End Model, as we will see below.

The working memory sub-architecture. The whole point of the Start-End Model is to consider that items are represented across two dimensions: a start (s) and an end (e) vector. Each vector gives a stronger weight to the start or the end of the to-be-remembered sequence for each i-th position, computed as such:

(4) = =

where and are two weighting parameters controlling the maximum values of each vector, and and control the distinctiveness of the positions. As in the original Start-End Model, and were fixed to 1 and .8, respectively, and and were treated as free parameters. The state of activation of each item for a given position is then defined by combining the two vectors:

(5

) ! = "# . # $ / exp{-(∑(# -# ) ) / }
where # is a 2-by-N matrix that combines the start and end vectors, and # . # is the inner product between the ,-th and --th columns of #. The resulting matrix represents the activation level but also the intrinsic competition between items for a given position ,, which almost entirely drives recall performance. At the moment of retrieval, an item is then selected from:

(6) . = ! Ω (1 -/) + 0(1, 2)
Hence, the WM state ! at position , in the Start-End Model is weighted by the encoding strength Ω through dot product, plus Gaussian noise drawn randomly from a continuous distribution with 1 = 0 and 2 treated as a free parameter. Selection is done by taking the item associated with the maximum value in .. In many mathematical implementations of WM, an item needs to be suppressed after being recalled to avoid repetition errors, which is also called response suppression [START_REF] Duncan | The time course of response suppression: No evidence for a gradual release from inhibition[END_REF]. This is done through the vector / for each ,-th position:

(7) / = 3 0, max(.) < 8 1, max(.) ≥ 8

Put simply, if the value of the selected item exceeds a retrieval threshold 8, this item is considered to be recalled and its activation is supressed (i.e. / is set to 1). If the value of this item is above 8, the model produces an omission. Since omissions are particularly critical in psycholinguistic effects of WM, 8 is treated as a free parameter.

Quantitative fit. In order to fit the Start-End Model, we used a chaining algorithm which combines some principles of a Markov Chain and of gradient descent, and this in order to minimize the model's error against the empirical data using the Root Mean Squared Error (RMSE). This algorithm was found to be effective to fit the Start-End Model, which rapidly converged to a global minimum in almost every case. The details of this algorithm are explained in Appendix C. The model was always fitted to the weakest psycholinguistic condition (i.e. the condition that produced the lowest performance levels in WM tasks). Then, using the parameters that minimized the RMSE in the weakest condition, the encoding strength produced by the strongest condition was used as a new weighting parameter within the Start-End Model. For each simulation presented below, the model was run 100,000 times per trial in order to minimize the model's error predictions as much as possible. In Table 6, we summarise the different parameters used in each simulation. The code has been made available on the Open Science Framework: https://osf.io/9fut6/.

< INSERT TABLE 6 ABOUT HERE >

Simulation 1a -The semantic relatedness effect -shared semantic features. According to language-based models of WM, the recall advantage observed for semantically related items can be explained by assuming that related words share common semantic features, as it is the case for "cat" and "dog" [START_REF] Dell | Lexical Access in Aphasic and Nonaphasic Speakers[END_REF]. Accordingly, in this simulation, we varied the number of semantic features shared between semantic categories, while keeping constant the absolute number of semantic features associated to each item. Unrelated items always shared 0 semantic nodes. The lexicon used in this simulation was composed of 192 words drawn from 32 different semantic categories. The model was presented with 32 lists composed of completely unrelated items, and 32 lists composed of items drawn from the same semantic category. As can be seen in

Figure 13, the model correctly predicted the recall advantage for semantically related items. This is explained by the fact that related items reactivate each other via their shared semantic features.

When for instance the word "branch" is presented, this item will already pre-activate the word "tree". Hence, "tree" will already receive a boost of activation before being presented to the model, thereby rising its activation level at the moment of encoding. Therefore, when the word "tree" will be activated in turn, this item will receive a much stronger boost than it would normally if it was preceded by an unrelated item. Overall, there was little effect upon order memory; order recall performance (i.e. the proportion of items recalled in correct position, divided by all items recalled regardless of their position) was M = .91 for related lists, and M = .898 for unrelated lists.

< INSERT FIGURE 13 ABOUT HERE >

Simulation 1b -The semantic relatedness effect -inter-item excitatory connections. The previous simulation is restricted to semantic relatedness effects based on shared features; it however does not provide a good account for semantic relatedness effects based on direct associations, such as "hammer" and "nail". Therefore, we ran an additional simulation in order to show that, if semantic relatedness is explained by considering shared semantic features or interitem associations, these two different mechanisms produce sensibly the same outcomes. Instead of modeling semantic relatedness via shared features, we got rid of the semantic layer and replaced it by inter-item excitatory connections between lexical nodes stemming from the same semantic category. The outcomes of this simulation are shown in Figure 14. Again, there was a clear advantage on recall performance for semantically related lists, and little effect on memory for order information (M = .915 and M = .922 for related and unrelated lists, respectively). This is overall congruent with studies showing that semantic relatedness effects, assessed either through associative or taxonomic relationships, produce very similar outcomes in WM tasks [START_REF] Tehan | Associative relatedness enhances recall and produces false memories in immediate serial recall[END_REF][START_REF] Tse | The role of associative strength in the semantic relatedness effect on immediate serial recall[END_REF].

< INSERT FIGURE 14 ABOUT HERE >

Simulation 2 -The imageability effect. Finally, we used the same architecture in order to model the imageability effect, by considering that high imageability words are connected to a larger number of semantic nodes compared to low imageability words. This assumption is based on the observation in the psycholinguistic domain that high imageability words are associated to a larger number of semantic features compared to low imageability words [START_REF] Muraki | Mapping semantic space: property norms and semantic richness[END_REF][START_REF] Pexman | The Impact of Feedback Semantics in Visual Word Recognition: Number of Features Effects in Lexical Decision and Naming Tasks[END_REF]. The lexicon that composed the model contained the 180 words included in Experiment 3. The model was presented with 15 lists composed of high imageability word and 15 lists composed of low imageability words. As can be seen in Figure 15, the model produced higher recall performance for words connected to a higher number of semantic nodes, and the impact on order memory was non-existent (M = .9 and M = .898 for high and low imageability words, respectively). This pattern of results is the consequence of the higher number of semantic nodes connected to each lexical unit. Once these semantic features are activated, they send more activation back to the lower levels of representations via interactive activation, thereby producing a recall advantage for high imageability words.

< INSERT FIGURE 15 ABOUT HERE >

To sum up, we implemented a linguistic architecture based on interactive activation principles within a WM architecture, and we have shown that the semantic relatedness and imageability effect can be explained by different mechanisms within this general linguistic architecture. As our simulations show, the semantic relatedness may stem from reactivation via shared semantic features or via inter-item associations, and the imageability effect may stem from the fact that high imageability words are connected to a larger number of semantic features.

Critically, this may explain why both semantic effects do not resist to interference effects in WM to the same extent, as we will discuss in the General discussion.

General discussion

In this study, we investigated the extent to which semantic knowledge can protect memoranda against task interference in WM tasks. We observed in two experiments a protective effect of semantic relatedness against interference caused by a secondary task presented between the WM encoding and recall phases: Task-based interference had a lesser detrimental effect on semantically related versus unrelated word lists, and this was observed specifically at the item level. However, in two further experiments assessing the same question by manipulating word imageability rather than semantic relatedness, we observed no differential impact of task interference: the secondary task reduced recall performance for high and low imageability word lists to the same extent. In a final experiment, we showed via computational modeling that both semantic effects can be explained based on interactive activation principles within a linguistic architecture, but also that both effects may stem from different mechanisms, in line with the empirical results observed in Experiments 1 through 3.

First, we will discuss the origin of the task-based interference in the light of several contemporary models of WM processing. Overall, most current models predict the deleterious impact of an embedded distractor task on WM performance when presented between encoding and recall. According to models attributing the origin of forgetting in WM to decay [START_REF] Baddeley | Working memory[END_REF][START_REF] Barrouillet | Time constraints and resource sharing in adults' working memory spans[END_REF], a longer retention interval will prevent the participants from reactivating the WM traces if refreshing mechanisms such as rehearsal or attentional refreshing are already occupied by another task, which our backward counting task did. Impaired recall performance after longer retention interval is also predicted by a temporal distinctiveness account [START_REF] Brown | A Temporal Ratio Model of Memory[END_REF]: as the delay between encoding and recall increases without the possibility to refresh items, the relative between-items distance will diminish and items are more likely to be confused or forgotten due to lower distinctiveness.

However, we think that a temporal distinctiveness account is not appropriate for explaining the range of results observed in the present study, because recall performance in the temporal distinctiveness account is also driven by physical isolation; dissimilar items, such as semantically unrelated items are supposed to be more discriminable, and hence should be better recalled than semantically similar items [START_REF] Chekaf | Chunk formation in immediate memory and how it relates to data compression[END_REF]. According to interference-based models [START_REF] Oberauer | Design for a Working Memory[END_REF], time per se does not provoke forgetting in WM. Rather, distractors are assumed to interfere with the content of WM, and attentional capture would prevent participants from removing distractors [START_REF] Oberauer | Modeling working memory: An interference model of complex span[END_REF]. In the case of the present study, the verbal format of the digits would have interfered with the phonological representations of the memoranda. The extent to which the digits could also have interfered with the semantic aspect of memoranda is less clear, given that digits activate only a very restricted and specific portion of nodes in semantic memory (den [START_REF] Heyer | Priming Single Digit Numbers: Automatic Spreading Activation Dissipates as a Function of Semantic Distance[END_REF]. Note that the decay and interferencebased forgetting accounts may not be mutually exclusive, as computational implementations of both accounts have proven to be able to simulate classical effects in complex span tasks, such as cross-domain interference and cognitive loads effects [START_REF] Lemaire | A Computational Model of Working Memory Integrating Time-Based Decay and Interference[END_REF]. Whatever the cause of forgetting caused by the interfering task, the important aspect of this study is that we kept interference constant when manipulating the semantic relatedness and imageability effects, and that this manipulation led to opposing conclusions.

Next, we turn to the potential mechanisms that explain the strong impact of semantic relatedness on WM recall performance, and the resulting protective effect against task-based interference. At the psycholinguistic level, the semantic relatedness effect can be explained by assuming that similar items share more features at the level of semantic representations in the language network [START_REF] Dell | Lexical Access in Aphasic and Nonaphasic Speakers[END_REF]. The simulations we performed largely confirmed this prediction: when presented with lists of words that shared semantic features, the model produced a clear recall advantage compared to lists composed of words sharing no semantic features.

When multiple words sharing similar semantic features are presented, they will reactivate each other via interactive activation between the lexical and semantic levels of representations. This will lead to overall high and robust activation levels for all items during encoding in a WM task, relative to semantically unrelated words. These redundant semantic representations will also protect the items against degradation by decay ( [START_REF] Barrouillet | Time constraints and resource sharing in adults' working memory spans[END_REF][START_REF] Barrouillet | The time-based resource-sharing model of working memory[END_REF]Camos & Barrouillet, 2014) or interference (Oberauer, Farrell, Jarrold, & Lewandowsky, 2016;[START_REF] Oberauer | Modeling working memory: An interference model of complex span[END_REF][START_REF] Oberauer | Modeling working memory: a computational implementation of the Time-Based Resource-Sharing theory[END_REF], 2013). Furthermore, at recall after an interfering secondary task, the availability of a small number of semantic features of the original memoranda is sufficient to re-activate most of the semantically related items due to their redundant semantic features. This will be different for semantically unrelated word lists as in these lists the few semantic features still available for a given item will provide no information about the semantic features associated with the other items. Note that in the present experiment, semantically related lists were also characterized by higher LSA-cosine values, and hence the relatedness effect could also stem from higher lexical co-occurrence. By assuming a simple

Hebbian learning rule, it logically follows that if the words "man" and "woman", or "hammer"

and "nail" occur frequently within the same linguistic context, they will be simultaneously activated and their connections will be reinforced, probably via lateral excitatory connections. As we have formally demonstrated in the modeling section, if such lateral excitatory connections are assumed, re-activation via these lateral connections can also occur from an interactive activation point of view, thereby producing similar effects. Hence, the overall recall advantage produced by the relatedness effect may stem from a general redundant reactivation principle.

Overall, the protective effect of semantic relatedness against task-based interference is congruent with the study by [START_REF] Neale | Age and redintegration in immediate memory and their relationship to task difficulty[END_REF]. These authors parametrically assessed the magnitude of the semantic relatedness effect across different difficulty levels, including different list lengths and different filled-retention intervals. A regression analysis on item recall performance showed an increased magnitude of the semantic relatedness effect as task difficulty increased. Interestingly, the same phenomenon has been observed as regards the phonological similarity effect. At the item level, phonologically similar lists are better recalled as compared to phonologically dissimilar lists when an open set of items is used and if a sufficient number of phonemes are shared [START_REF] Gupta | Reexamining the phonological similarity effect in immediate serial recall: The roles of type of similarity, category cuing, and item recall[END_REF]. This advantage for phonologically similar lists at the item level has been shown to increase under interfering conditions, as does the semantic relatedness effect [START_REF] Neale | Age and redintegration in immediate memory and their relationship to task difficulty[END_REF]. In fact, both effects could stem from the same phenomenon: It has been argued that interactive activation between the phonological and lexical levels of language processing could be responsible for the phonological similarity effect [START_REF] Acheson | Verbal Working Memory and Language Production : Common Approaches to the Serial Ordering of Verbal Information[END_REF]. Indeed, since phonologically similar words are also lexical neighbours (e.g., cat, fat, bat, rat…), they may reactivate each other through redundant activation via their shared phonological features.

These interactions between task-based interference and semantic effects were not modeled within our computational simulations. The original Start-End Model only provides a mathematical description of WM performance without explicitly assuming the underlying mechanisms that may cause forgetting; it has the advantage of being theoretically neutral as regards the decay versus interference debate. We used these simulations as a starting point to evaluate the plausibility of the existence of distinct mechanisms behind the semantic relatedness and imageability effects, as suggested by our results of a differential sensitivity of these effects to task-based interference. Future studies should model a linguistic system within a pure decaybased architecture or within a pure interference-based architecture, and evaluate the plausibility of both architectures to account for psycholinguistic effects and their resistance to task-based interference. This endeavour is beyond the scope of this study but future studies could achieve a deeper understanding of the interactions between WM and the linguistic system by carrying out this type of comparison of computational architectures. This comparison could also inform us about the cause of forgetting in WM, which is still a critical and highly debated question in the field of WM [START_REF] Dagry | The fate of distractors in working memory: No evidence for their active removal[END_REF][START_REF] Farrell | A test of interference versus decay in working memory: Varying distraction within lists in a complex span task[END_REF][START_REF] Lemaire | A Computational Model of Working Memory Integrating Time-Based Decay and Interference[END_REF][START_REF] Oberauer | Is Rehearsal an Effective Maintenance Strategy for Working Memory[END_REF][START_REF] Oberauer | What Limits Working Memory Capacity ?[END_REF].

Furthermore, another aspect not investigated in our simulations, is that given that items in the semantically related lists share an important number of semantic features, they can be grouped into a smaller number of semantic or conceptual units. A similar assumption has been proposed by [START_REF] Saint-Aubin | Semantic similarity and immediate serial recall: Is there an effect on all trials[END_REF], who assumed that item-specific semantic features that correspond to each word can act as a retrieval cue at the moment of recall. In the context of current WM models which assume a close interaction between LTM and attention [START_REF] Cowan | Attention and memory: An integrated framework[END_REF][START_REF] Cowan | The magical number 4 in short-term memory: A reconsideration of mental storage capacity[END_REF][START_REF] Oberauer | Access to information in working memory: Exploring the focus of attention[END_REF][START_REF] Oberauer | Design for a Working Memory[END_REF], this would result in retrieving more easily the content activated in LTM and putting it back into the focus of attention. However, we go beyond this assumption by considering that the broader semantic category will allow compression of information in WM [START_REF] Chekaf | Chunk formation in immediate memory and how it relates to data compression[END_REF][START_REF] Mathy | Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression[END_REF][START_REF] Thalmann | How Does Chunking Help Working Memory[END_REF] by activating a small number of supra-item contextual representations [START_REF] Atkins | False working memories ? Semantic distortion in a mere 4 seconds[END_REF] reducing overall WM load already during encoding and maintenance.

For instance, given the target sequence "leg -hand -arm -foot -elbow -thigh", participants could activate a broader semantic and/or conceptual unit, such as "body part", and rely on these supra-item representations for maintaining and retrieving the subordinate items at the moment of recall. In the case of items that do no strongly overlap in terms of semantic features but are nonetheless related, such as items embedded in a common theme, participants could still have the opportunity to activate and maintain a broader conceptual unit, such as the concept "weather" for the words "sky -rain -cloud". This phenomenon may explain why the semantic relatedness effect has a weaker impact when a strict serial recall criterion is considered: At the moment of recall, items will be retrieved mostly by decompressing the supra-item semantic representations and by accessing the associated items directly from the semantic knowledge base. During this decompression process, the arbitrary, serial position order that characterized the serial order of presentation of items during encoding is likely to get lost. Furthermore, once attentional control is monopolized by the interfering task, the mapping of item and serial order information cannot be refreshed anymore. [START_REF] Guérard | Assessing the Effect of Lexical Variables in Backward Recall[END_REF] observed a similar detrimental impact of semantic relatedness on order recall performance in an attentional demanding backward recall paradigm, but not when using a less demanding standard immediate serial recall paradigm. Note that the compression mechanism we describe here is likely to be different from a chunking process whereby participants activate in LTM a single unit that perfectly represents all the items at the same time, otherwise we would observe quasi perfect recall performance. For instance, when the acronym "FBI" is recognized and correctly retrieved from LTM, it is very unlikely that the letters "F", "B" and "I" would be recalled in a wrong serial order or that any letters will be lost [START_REF] Portrat | Promoting the experimental dialogue between working memory and chunking: Behavioral data and simulation[END_REF]. This is different if the category "weather" is maintained; in this case there will still be room for order confusion between "rain" and "cloud"

or even for omissions, because each individual item and their constituent order are not directly maintained.

The situation will be very different for the word imageability effect. Concrete or high imageability words are supposed to be better recalled as compared to abstract words because they are represented by a larger number of semantic features at the individual item level. In the psycholinguistic domain, this leads to faster response times and more accurate performance in purely linguistic tasks, such as lexical and semantic decision [START_REF] Muraki | Mapping semantic space: property norms and semantic richness[END_REF][START_REF] Pexman | The Impact of Feedback Semantics in Visual Word Recognition: Number of Features Effects in Lexical Decision and Naming Tasks[END_REF]Yap et al., 2015). This is interpreted by assuming that once these semantic features are activated, they will send more activation back to lower levels of representations through redundant activation, leading to more robust item-level representations for highly imageable words. To the best of our knowledge, this is the first time that this hypothesis has been demonstrated within a formal computational simulation. A purely semantic origin of the imageability effect strongly contrasts with previous studies who associated this effect with mental imagery processes [START_REF] Campoy | Automatic semantic encoding in verbal short-term memory: Evidence from the concreteness effect[END_REF]. In fact, this latter interpretation has shown to be problematic because preventing the creation of mental images (for example, via the simultaneous presentation of visual interfering tasks) does not reduce the imageability effect in WM tasks [START_REF] Campoy | Automatic semantic encoding in verbal short-term memory: Evidence from the concreteness effect[END_REF][START_REF] Chubala | Does dynamic visual noise eliminate the concreteness effect in working memory[END_REF]. Hence, a plausible explanation for the presence of the imageability effect is to assume that high imageability words are associated to richer semantic representations. Contrary to the semantic relatedness effect, the richer individual item information will not be informative about the other items (when high imageability items are not also semantically related) and hence no between-item reactivation nor any list compression processes can occur. In other words, the semantic relatedness and the word imageability share the situation of more robust item level representations at encoding, but, unlike word imageability, semantic relatedness is in addition characterized by the opportunity for inter-item reactivation and/or list compression effects. The latter effects may be particularly powerful when individual item-level information is strongly degraded, explaining why semantic relatedness leads to a protective effect of WM performance when a secondary interfering task has to be carried out. These additional sources of long-term memory support for semantically related words as opposed to high imageability words is also likely to explain the overall more robust effect of semantic relatedness on WM performance.

Alternative account

From a different theoretical perspective, the presence of psycholinguistic effects in WM tasks can be explained by the involvement of reconstructive processes (i.e. "redintegration") through which degraded phonological traces of memoranda stored within a phonological buffer are completed at the moment of retrieval by accessing linguistic LTM representations that match the degraded traces [START_REF] Hulme | Memory for Familiar and Unfamiliar Words: Evidence for a Long-Term Memory Contribution to Short-Term Memory Span[END_REF][START_REF] Schweickert | A multinomial processing tree model for degradation and redintegration in immediate recall[END_REF]. According to this framework, the presence of an interfering task should lead to stronger psycholinguistic effects, because under interfering conditions, the phonological traces in the phonological store will be even more degraded and hence more redintegration is needed [START_REF] Hulme | Word-Frequency Effects on Short-Term Memory Tasks: Evidence for a Redintegration Process in Immediate Serial Recall[END_REF][START_REF] Neale | Age and redintegration in immediate memory and their relationship to task difficulty[END_REF]. This account could potentially explain the stronger semantic relatedness effect in the interference condition, but it is inconsistent with the imageability effect which does not increase in the interference condition. Indeed, the stronger degradation of phonological traces in the interference conditions should lead to stronger reconstructive processes, irrespective of the nature of semantic knowledge that needs to be accessed.

We should also note here that, overall, semantic effects are more difficult to explain within the original redintegration framework. Indeed, the clean-up process is supposed to operate at the lexical level of language processing [START_REF] Hulme | Word-Frequency Effects on Short-Term Memory Tasks: Evidence for a Redintegration Process in Immediate Serial Recall[END_REF][START_REF] Schweickert | A multinomial processing tree model for degradation and redintegration in immediate recall[END_REF][START_REF] Thorn | Redintegration and the benefits of long-term knowledge in verbal short-term memory: an evaluation of Schweickert's (1993) multinomial processing tree model[END_REF], which explains the recall advantage for words over nonwords, and for words of high versus low lexical frequency. In the initial redintegration account, no semantic reconstructive processes were considered. In order to palliate this problem, it has been proposed that maintenance in WM tasks could also involve semantic traces, and that redintegration could operate on partially degraded semantic features [START_REF] Ritchie | Redintegration, Task Difficulty, and Immediate Serial Recall Tasks[END_REF][START_REF] Saint-Aubin | Semantic Similarity and Immediate Serial Recall: Is There a Detrimental Effect on Order Information[END_REF]Walker & Hulme, 1999). However, in this case, the redintegration account would have predicted that high imageability words should be less affected by interference than low imageability words, because the larger number of semantic features that are considered to characterize high imageability words should have facilitated redintegrative processes and increased item recall relative to low imageability words. However, this prediction does not fit with the current observation.

Conclusions

In this study, we investigated the extent to which semantic knowledge not only supports the maintenance of memoranda in a WM task, but provides additional protection at the item level against interference when a secondary task is introduced after the WM encoding phase. We found that only semantic relatedness provided this protective effect, presumably due to inter-item reactivation and conceptual list compression effects that allow heavily degraded information to be reinstantiated at the moment of recall. Richer item-level semantic knowledge only, as involved in the word imageability effect, does not have a protective effect of WM recall after interference. This study highlights the complexity and multiplicity of linguistic long-term memory variables that determine WM performance. for Experiment 1 (semantic relatedness effect, two semantic groups) using an item recall (upper panel) and strict serial recall (lower panel) score. The right panels represent the mean difference between the two semantic conditions. Error bars represent 95% credible intervals, after correction for between-subject variability [START_REF] Cousineau | Erratum to Cousineau (2005): Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method[END_REF][START_REF] Morey | Confidence Intervals from Normalized Data: A correction to Cousineau[END_REF]. for Experiment 2a (semantic relatedness effect, one semantic group) using an item recall (upper panel) and strict serial recall (lower panel) score. The right panels represent the mean difference between the two semantic conditions. Error bars represent 95% credible intervals, after correction for between-subject variability [START_REF] Cousineau | Erratum to Cousineau (2005): Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method[END_REF][START_REF] Morey | Confidence Intervals from Normalized Data: A correction to Cousineau[END_REF]. for Experiment 3a (imageability effect, random interference) using an item recall (upper panel)

and strict serial recall (lower panel) score. The right panels represent the mean difference between the two semantic conditions. Error bars represent 95% credible intervals, after correction for between-subject variability [START_REF] Cousineau | Erratum to Cousineau (2005): Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method[END_REF][START_REF] Morey | Confidence Intervals from Normalized Data: A correction to Cousineau[END_REF]. for Experiment 3b (imageability effect, blocked interference) using an item recall (upper panel)

and strict serial recall (lower panel) score. The right panels represent the mean difference between the two semantic conditions. Error bars represent 95% credible intervals, after correction for between-subject variability [START_REF] Cousineau | Erratum to Cousineau (2005): Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method[END_REF][START_REF] Morey | Confidence Intervals from Normalized Data: A correction to Cousineau[END_REF].

Figure 2 (

 2 upper panel) and Table 2, all main effects of interference, semantic relatedness and serial position were supported by decisive evidence. Again, the interfering task reduced recall performance, and related lists were better recalled as compared to unrelated lists. Importantly, the interaction between interference and semantic relatedness variables was associated with decisive evidence: the relatedness effect was stronger in the interference condition (Mdiff = .278, d = 3.628) compared to the no interference condition (Mdiff = .163, d = 2.327), and this difference was supported by decisive evidence (BF10 = 48631.8, CI99% = [-1.817; -.536]) (see Figure1, upper right panel).Strict serial recall criterion. As already observed in Experiment 1, all main effects of interference, semantic relatedness and serial position were associated with decisive evidence.Similarly, as can be seen in Figure2(lower panel) we observed an absence of interaction between interference and the semantic relatedness variables, and this absence was associated with moderate evidence (BF10 = 0.143, BF01 = 6.993). More specifically, the semantic relatedness effect was at least as strong in the interference condition (Mdiff = .123, d = 2.011) as it was in the no interference condition (Mdiff = .107, d = 1.205), and this absence of difference was supported by moderate evidence (BF01 = 3.985, CI99% = [-.588; .323]).< INSERT FIGURE 2 AND TABLE 2 ABOUT HERE >Finally, we assessed the participants performance on the backward counting task as a function of semantic relatedness using a Bayesian Paired Samples T-Test, and found again evidence for the absence of difference (Mdiff = -.011, CI99% = [-.487; .418], BF01 = 4.969).

  the participant group of Experiment 2a in the interference condition (Mdiff = .278, d = 3.628) compared to participant group in Experiment 2b (Mdiff = .199, d = 3.474); this difference was supported by decisive evidence, as shown by a Bayesian independent samples T-Test (BF10 = 430.558, CI99% = [-1.807; -.329]).

  This difference was reliable, as shown by a Bayesian Independent Samples T-Test (BF10 = 2.907e+145). Both sets were matched for different psycholinguistic dimensions: Lexical frequency (Mlog = 1.04, SDlog = .449 and Mlog = 1.001, SDlog = .82 for high and low imageability words, respectively, BF01 = 5.745), subjective frequency (M = 4.044, SD = .837 and M = 3.943, SD = .92 for high and low imageability words, respectively, BF01 = 4.694)[START_REF] Content | Brulex . Une base de données lexicales informatisée pour le français écrit et parlé[END_REF], number of phonemes (M = 3.522, SD = .691 and M = 3.478, SD = .722 for high and low imageability words, respectively, BF01 = 5.697), neighborhood density (M = 16.444, SD = 9.7 and M = 16.922, SD = 10.802 for high and low imageability words, respectively, BF01 = 5.914) and neighborhood frequency (Mlog = .115, SD = .4 and M = .033, SD = .388 for high and low imageability words, respectively, BF01 = 2.472). The neighborhood density values were obtained by computing the Levenshtein distance[START_REF] Levenshtein | Binary codes capable of correcting deletions, insertions, and reversals[END_REF] between the phonological form of each word relative to all other entries of the Lexique 3 database. The neighborhood frequency values were obtained by computing the ratio of the log10-transformed frequency of each word against the summed frequency of all neighbors including the target word. All the stimuli were recorded by a French native female speaker in a neutral voice. Background noise was removed via the noise reduction tool implemented in Audacity®. The original stimuli were normalized to a duration of 375 ms.

  the imageability effect (BF10 = 0.243; BF01 = 4.115); the imageability effect was as strongly observed in the no interference condition (Mdiff = .088, d = 1.232) as in the interference condition (Mdiff = .063, d = .656), and this absence of difference was slightly supported (BF01 = 2.54, CI99% = [-.246; .661]).Strict serial recall criterion. Using a strict serial recall criterion, the overall conclusions did not changed (see Figure4, lower panel): All main effects of interference, imageability and serial position were associated with decisive evidence. Likewise, we found no clear evidence supporting the interaction between interference and imageability (BF10 = 1.332). The imageability effect was similarly observed in the no interference condition (Mdiff = .096, d = 1.091) and in the interference condition (Mdiff = .05, d = .49). This absence of difference was only slightly supported (BF01 = 1.471, CI99% = [-.176; .741]).

Participants.

  Thirty undergraduate students (21 females and 9 males) aged between 19 and 27 years (M = 22.03, SD = 1.81), recruited from the university community, took part in the experiment. All participants were native French speakers with no history of neurological disorders or learning difficulties. All participants gave their written informed consent prior to their inclusion in the study. The study had received approval from the local ethics committee.

  imageability effect was observed to a similar extent in the no interference (Mdiff = .09, d = 1.155) and in the interference condition (Mdiff = .088, d = .84), and this absence was supported by moderate evidence (BF01 = 5.129, CI99% = [-.433; 0.462]).

  implemented in the statistical software R. The detailed statistical procedure and studies inclusion criteria are reported in Appendix A and B, respectively. The results from Figures 7 and 8 largely confirm the observations drawn from the present study: When the item recall criterion was considered, the semantic relatedness effect was twice as larger (Cohen's d = 2.08) as compared to the imageability effect (Cohen's d = .96), without overlap between confidence intervals.

Figure

  Figure 1C illustrates how fast the model converged to a global minimum in one of the

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .

 2 Figure 2. Proportion of items correctly recalled (y axis) as a function of serial position (x axis)

Figure 3 .

 3 Figure 3. Proportion of items correctly recalled (y axis) as a function of serial position (x axis)

Figure 4 .

 4 Figure 4. Proportion of items correctly recalled (y axis) as a function of serial position (x axis)

Figure 5 .

 5 Figure 5. Proportion of items correctly recalled (y axis) as a function of serial position (x axis)

Figure 6 .

 6 Figure 6. Mean difference (y axis) between stimulus conditions for the semantic relatedness

Figure 7 .

 7 Figure 7. Forest plot of the meta-analysis performed on studies assessing the semantic

Figure 8 .

 8 Figure 8. Forest plot of the meta-analysis performed on studies assessing the imageability effect

Figure 9 .

 9 Figure 9. Forest plot of the meta-analysis performed on studies assessing the semantic

Figure 10 .

 10 Figure 10. Forest plot of the meta-analysis performed on studies assessing the imageability

Figure 11 .

 11 Figure 11. General linguistic architecture of the interactive activation model. The network has

Figure 12 .

 12 Figure 12. Timecourse of the activation produced by the interactive activation model.

Figure 13 .

 13 Figure 13. Recall performance across serial position for the semantic relatedness effect on

Figure 14 .

 14 Figure 14. Recall performance across serial position for the semantic relatedness effect on

Figure 15 .

 15 Figure 15. Recall performance across serial position for the imageability effect on empirical data

  Figure 1

Figure 15

 15 Figure 15

Tables 1236 Table 1 .

 12361 Statistical values for Bayesian analyses in Experiment 1.

	Effects

Table 2 .

 2 Statistical values for Bayesian analyses in Experiment 2a.

	Effects

Table 3 .

 3 Statistical values for Bayesian analyses in Experiment 2b.

	Effects

Table 4 .

 4 Statistical values for Bayesian analyses in Experiment 3a.

	Effects

Table 5 .

 5 Statistical values for Bayesian analyses in Experiment 3b.

	Effects

Table 6 .

 6 Parameters used in the simulations of Experiment 4. Note. The free parameters associated with the linguistic system are indicated in bold face. The free parameters used in the quantitative fit of the Start-End Model are highlighted.

	Parameter	Sim. 1a	Sim. 1b	Sim. 2
	Number of memoranda (0)	6	6	6
	Connection weights ( )
	Biphone → Lexical	.05	.05	.05
	Lexical → Biphone	.02	.02	.02
	Lexical → Semantic	.06	.06	.06
	Semantic → Lexical	.06	.06	.06
	Lexical -Lexical (lateral)	N/A	.056	N/A
	Number of semantic features	15	N/A	0
	Overlap of semantic features	0 or 5	N/A	5 or 20
	External input ( )	.2	.2	.2
	Decay rate ( )	.5	.5	.5
	Number of iterations per position	10	10	10
	Maximum strength of the start marker ( )	1	1	1
	Rate of change within the start marker ( )	.8	.8	.8
	Maximum strength of the end marker ( )	.6	.608	.497
	Rate of change within the end marker ( )	.383	.43	.299
	Standard deviation of the Gaussian noise (2)	.092	.085	.091
	Retrieval threshold (8)	.194	.185	.202

Note that although the word imageability and word concreteness effects refer to two different theoretical constructs[START_REF] Richardson | Imagery, concreteness, and lexical complexity[END_REF], they are nevertheless highly correlated. In the present study, we will only refer to the term "imageability effect" as the corpus for French words used in this study was based on imageability (the visual dimension of a concept) rather than on concreteness (the sensory dimension of a concept).

Note that the term "interference" is used throughout the manuscript as a generic term that refers to forgetting following a distracting task. We are not explicitly stating that interference is the WM mechanism that causes forgetting. Instead, we remain neutral in this study as regards the mechanism responsible for forgetting in WM.

Acknowledgements

This study was supported by grant EOS 30446199 from the Belgian National Fund for Scientific Research FRS-FNRS and grant FSR-S-SH-17/04 from Fédération Wallonie-Bruxelles (Belgium). We thank Delchambre, R., Mossay, K. and Pauls, M. for their help in data collection and all the participants for their time devoted to this study. Wagenmakers, E.-J., Wetzels, R., Borsboom, D., & van der Maas, H. L. J. (2011). Why psychologists must change the way they analyze their data: the case of psi: comment on Bem (2011). https://doi.org/10.1037/a0022790 Walker, I., & Hulme, C. (1999). Concrete Words Are Easier to Recall Than Abstract Words : Evidence for a Semantic Contribution to Short-Term Serial Recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(5), 1256-1271. Yap, M. J., Lim, G. Y., & Pexman, P. M. (2015). Semantic richness effects in lexical decision:

The role of feedback. Memory & Cognition, 43(8), 1148-1167. https://doi.org/10.3758/s13421-015-0536-0

Appendix A

To compute Cohen's d for repeated measures in each study, we transformed F-statistics into T-values and then divided the T-value by the square root of the sample size [START_REF] Lakens | Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs[END_REF].

The variance of Cohen's d was computed using this formula:

where is the sample size and < is Cohen's d raised to the power of 2 (see also [START_REF] Hedges | Distribution Theory for Glass's Estimator of Effect Size and Related Estimators[END_REF].

Appendix B

We included in the meta-analysis only experiments that used a repeated-measures design and an immediate serial recall or similar procedure in healthy adult participants. In cases where the authors combined the psycholinguistic effect manipulation with another variable (e.g., forward and backward recall, see [START_REF] Guérard | Assessing the Effect of Lexical Variables in Backward Recall[END_REF]; or phonological overlap in [START_REF] Acheson | The Interaction of Concreteness and Phonological Similarity in Verbal Working Memory[END_REF], results of the standard condition (i.e. forward recall) were included when available. In addition, we report here only results based on the item recall score. We also included the no-interference conditions of the experiments of the present study. This led to a total sample of 8 experiments for the semantic relatedness effect and to 25 experiments for the imageability effect.

Appendix C

The algorithm we used in order to fit the model to the empirical data is a mix between the principles used in Markov Chains and gradient descent, following these steps:

1. Place a lower and upper bound (lb and ub, respectively) to each parameter to estimate.

2. Compute a standard deviation 2 from these parameters, set as (ub-lb) * .2886, which corresponds to one standard deviation around 1(ub-lb) if those parameters are drawn from a uniform distribution.

3. Set a stop criterion 8 beyond which the fitting will stop (5,000 in the present study).

4. Sample the parameters value at random between lb and ub from a continuous uniform distribution.

5. Compute the error produced by the model using 1,000 iterations:

6. Sample the new parameters around the old parameters from a Gaussian continuous distribution, with a standard deviation of 2 * >? . As the error decreases, the sampling will be more restricted to the old parameters.

7. Compute the error following (5), and if these new parameters minimize the error, accept them. If not, re-estimate the old parameters.

8. Iterate over ( 6) and ( 7) until the number of failure to minimize the error exceeds 8. 9. Take the parameters that produced the 50 lowest error values and re-estimate them with a much higher number of iterations (10,000). From these new parameters, select the one that minimizes the most the error.