<table><thead><tr><th></th><th>Exp.</th><th>Participants</th><th>Part.</th><th>Nb. mots</th><th>VIs WM</th><th>VIs LTM</th><th>Type de rappel</th><th>VDs</th></tr></thead><tbody><tr><td rowspan="4">Chapitre 5</td><td rowspan="2">Exp 1</td><td>33 adultes</td><td>a</td><td rowspan="2">2 à 6<br>par essai</td><td rowspan="2">Coût cognitif<br>(couleur vs.<br>pair-impair)</td><td rowspan="2">Similarité<br>sémantique</td><td rowspan="2">Immédiat<br>+<br>Avec<br>enregistrement<br>des temps de<br>rappel</td><td rowspan="2">Performances<br>+<br>RT / ACC<br>+<br>Types<br>d'erreurs<br>+<br>Temps de<br>rappel</td></tr><tr><td>36 enfants</td><td>b</td></tr><tr><td rowspan="2">Exp 2</td><td>33 adultes</td><td>a</td><td rowspan="2">4<br>par essai</td><td rowspan="2">Vitesse de<br>présentation</td><td rowspan="2">Similarité<br>sémantique</td><td rowspan="2"></td><td rowspan="2"></td></tr><tr><td>32 enfants</td><td>b</td></tr></tbody><tfoot><tr><td colspan="9">Suppression articulatoire : Répétition des syllabes "babibou"</td></tr></tfoot></table>

<table><tbody><tr><td rowspan="4">Chapitre 6</td><td>Exp 1</td><td>25 adultes</td><td>a</td><td>6<br>par essai</td><td>Vitesse de<br>présentation</td><td>/</td><td>Rappel immédiat</td><td rowspan="4">Performances<br>+<br>Positions<br>sérielles<br>+<br>Trichotomous</td></tr><tr><td rowspan="2">Exp 2</td><td rowspan="2">60 adultes</td><td>a</td><td>6<br>par essai</td><td rowspan="2">Vitesse de<br>présentation</td><td rowspan="2">Similarité<br>sémantique</td><td>Rappel immédiat</td></tr><tr><td>b</td><td>72<br>au total</td><td>Rappel différé</td></tr><tr><td rowspan="2">Exp 3</td><td rowspan="2">60 adultes</td><td>a</td><td>6<br>par essai</td><td rowspan="2">Vitesse de<br>présentation</td><td rowspan="2">Similarité<br>sémantique</td><td>Rappel immédiat</td><td rowspan="2"></td></tr><tr><td>b</td><td>72<br>au total</td><td>Rappel différé</td></tr><tr><td rowspan="2">Exp 4</td><td rowspan="2">32 adultes</td><td>a</td><td>6<br>par essai</td><td rowspan="2">Vitesse de<br>présentation</td><td rowspan="2">Similarité<br>sémantique</td><td>Rappel immédiat</td><td rowspan="2"></td></tr><tr><td>b</td><td>72<br>au total</td><td>Rappel différé</td></tr></tbody><tfoot><tr><td colspan="9">Suppression articulatoire : Lecture des mots et des chiffres à haute voix</td></tr></tfoot></table>

<table><caption>Tableau 2. Récapitulatif des études présentées, avec leurs nombres d'expériences, leurs participants, matériels, variables indépendantes et dépendantes, types de rappel ainsi que modalités de suppression articulatoire impliqués. L'impact de ces facteurs est observé sur les performances de rappel et de reconnaissances et d'autres VDs complémentaires.</caption><tbody><tr><td rowspan="2">Chapitre 7</td><td rowspan="2">Exp 1</td><td rowspan="2">96 adultes</td><td>a</td><td rowspan="2">4<br>par essai</td><td>Vitesse de<br>présentation</td><td>Similarité<br>sémantique</td><td>Reconnaissance</td><td rowspan="2">Performances<br>+<br>Modèle CR</td></tr><tr><td>b</td><td>Vitesse de<br>présentation</td><td>Similarité<br>phonologique</td><td>Reconnaissance</td></tr><tr><td colspan="9">Suppression articulatoire : Lecture des mots et des distracteurs à haute voix</td></tr></tbody></table>
<figure><img src="image_26.png" /></figure>

# PARTIE EXPÉRIMENTALE

<figure><img src="image_27.png" /></figure>
<figure><img src="image_28.png" /></figure>
# Chapitre 5. Role of attention in the associative relatedness effect in verbal working memory: Behavioral and chronometric perspectives

**Titre en français** : Rôle de l'attention dans l'effet d'association sémantique en mémoire de travail : Perspectives comportementales et chronométriques.

Ce chapitre a été publié précédemment selon la référence « Rosselet-Jordan, F. L., Abadie, M., Mariz-Elsig, S., & Camos, V. (2022). Role of Attention in the Associative Relatedness Effect in Verbal Working Memory: Behavioral and chronometric perspective. *Journal of Experimental Psychology: Learning, Memory and Cognition*. Online preview. https://doi.org/10.1037/xlm0001102 ». La mise en page a été adaptée à celle de ce travail de thèse.

Cette étude a été soutenue par le Fond National Suisse de la Recherche (grant n° 100019_175960) accordé à la Prof. Valérie Camos et a reçu l'approbation du comité d'examen institutionnel de l'éthique de la recherche de l'Université de Fribourg. Les listes de mots et de données sont disponibles sur https://osf.io/sqz52/.
Résumé

La mémoire à long-terme (LTM) sémantique est connue pour affecter le rappel
durant des tâches de mémoire de travail (WM). Cependant, la manière dont la LTM
intervient en WM reste inconnue. De plus, les résultats disponibles sont inconstants
concernant la façon dont l'attention module l'influence de la LTM sur la WM. Afin
d'examiner cette question, l'implication de représentations à long-terme dans une tâche
d'empan complexe a été manipulée au travers la variation de l'association sémantique
entre items à mémoriser, pendant que la demande attentionnelle de la tâche concurrente
était variée. Enfants et jeunes adultes ont également été comparés, car les enfants sont
moins efficaces que les adultes dans leur utilisation du rafraichissement pour le maintien.
Malgré l'impact de ces trois facteurs principaux sur les performances de rappel, lesquelles
étaient meilleures pour les mots reliés que pour les mots non-reliés, au terme d'une tâche
concurrente à faible demande plutôt que forte ; et chez les adultes en comparaison aux
enfants, il n'y avait aucune interaction entre association sémantique et demande
attentionnelle, ni chez les enfants, ni chez les adultes. Nous avons répliqué ces résultats
dans une seconde expérience avec une plus forte demande attentionnelle de la tâche
concurrente. De plus, les analyses des temps de rappel ont montré que les adultes étaient
plus rapides que les enfants dans le rappel des mots, et que les deux groupes d'âges étaient
plus rapides pour les mots reliés (vs., non-reliés). Finalement, les erreurs observées
étaient majoritairement des omissions et des transpositions, mais plus prédominantes en
cas de forte demande attentionnelle concurrente. Ces résultats suggèrent que la
disponibilité attentionnelle ne module pas l'effet de la LTM sur la WM. La manière dont
les modèles existant de WM peuvent rendre compte de ces résultats et de la manière dont
la LTM peut agir sur le fonctionnement de la WM sont discutés.
Long-term semantic memory (LTM) is known for affecting recall during working
memory (WM) tasks. However, the way LTM intervenes in WM remains unknown.
Moreover, the available findings are incongruent concerning how attention modulates the
impact of LTM on WM. To examine this issue, the involvement of LTM representations in
a complex span task was manipulated through variations of the associative relatedness of
memory items, while the attentional demand of the concurrent task was varied. Children
and young adults were also compared, because children are less efficient in using
refreshing for maintenance than adults. Despite the impact of the three main factors on
recall performance, which was better for related than unrelated words, with the low
rather than the high demanding concurrent task and for adults than children, there was
no interaction between associative relatedness and attentional demand, neither in
children nor in adults. We replicated these results in a second experiment with a more
attention-demanding concurrent task. Moreover, analyses of recall latency showed that
adults were faster than children at recalling words and both age groups were faster for
related (vs. unrelated) words, but there was no effect of the concurrent attentional
demand on recall latency and no interaction. Finally, errors were mostly omissions and
transpositions, both more prevalent under high concurrent attentional demand. The
present findings suggest that the availability of attention does not modulate the effect of
LTM on WM. We discuss how WM models can account for this finding and how LTM can
act on WM functioning.
# 1. Introduction

The contribution of semantic long-term memory (LTM) is well known for improving recall performance in working memory (WM) tasks (Hulme et al., 1991; Schweickert, 1993). However, the way in which LTM intervenes in a WM task remains a topic of debate (see Thorn & Page, 2009, for a review). The present study aimed at examining the role of attention as a potential moderator of the effect of semantic LTM on WM functioning. For this purpose, we manipulated both the availability of attention during WM maintenance and the associative relatedness of the memoranda. Associative relatedness, which depends on semantic LTM, is known for impacting the ease of retrieving information from LTM (Collins & Loftus, 1975; Nelson & Zhang, 2000), whereas attention is depicted in many models as having a central role in WM maintenance (Barrouillet, Bernardin, et al., 2004; Barrouillet & Camos, 2015; Cowan, 2001; Engle, 2002; Unsworth & Engle, 2007a). However, there are inconsistent findings in the current literature that support different theoretical conceptions of how attention moderates LTM impact on WM. Hence, the present study aimed at examining this question.

## 1.1. The role of attention in mediating semantic LTM impact on WM

Several studies have shown that factors known for improving retrieval from LTM, such as lexicality, familiarity, frequency and relatedness, also impact recall performance in WM tasks (Hulme et al., 1991; Hulme et al., 2003; Poirier et al., 2011; Saint-Aubin et al., 2005; Saint-Aubin & Poirier, 1999a). The past five years showed the emergence of studies dedicated to the role of attention in mediating the LTM impact on WM. Many, if not all, of them were triggered by the interest in the functioning of attentional refreshing. Attentional refreshing is conceived as one of the main mechanisms to maintain verbal information in WM (Camos, 2015, 2017). Among the WM maintenance mechanisms, the
most studied one is verbal rehearsal that maintains verbal information through
phonological traces by using of language processes. Beside this domain-specific
maintenance mechanism, attentional refreshing is thought to use (so-called executive)
attention to reactivate any types of memory traces. This domain-general mechanism
increases the activation of recently presented, encoded, or retrieved information to keep
it in an accessible state from moment-to-moment, thereby preventing it to fade out from
immediate awareness (Camos et al., 2018). Directing attention to memory
representations boosts, prolongs, and strengthens their activation, leading to better recall
performance for the refreshed relative to non-refreshed representations. Although there
is some consensus on the definition of attentional refreshing, its functioning remains
unclear (Camos et al., 2018), and to explore its links with LTM, several studies examined
how LTM effects were modulated by variations on its availability.

Rose et al. (2014) reported that the level-of-processing effect, one of the best-
known LTM effect (i.e., better retrieval of deeply processed rather than shallowly
processed words) affects recall in a WM task if attention is distracted by a concurrent task,
but not when items can be maintained in primary (short-term) memory (see also, Rose et
al., 2015). Other studies contrasted LTM effect in simple vs. complex span tasks, the
difference between the two tasks being indicative of the involvement of refreshing.
Indeed, authors like McCabe (2008) or Loaiza and McCabe (2012) have put forward that
the difference between the simple and the complex span tasks relies on the fact that
complex span tasks provide refreshing opportunities, but not simple span tasks.
According to these authors, refreshing is thought to use pauses between processing
episodes in complex span tasks to retrieve into the focus of attention information that was
displaced in secondary (long-term) memory during the processing episodes. There are no
refreshing opportunities in a simple span task since there is no concurrent task and information is immediately recalled. Within this framework, Loaiza, Duperreault, et al. (2015) tested the lexicality effect in a simple and an operation span task. While words were better recalled than non-words in both span tasks, the lexicality effect was reduced in the operation span task compared to the simple span task. Similarly, Abadie and Camos (2018) reported a smaller word frequency effect in a complex than in a simple span task. The same interaction between word frequency and task has been previously reported by Engle et al. (1990), when comparing the frequency effect in a simple vs. an operation span task. Overall, these studies reported a reduction of the impact of LTM effects on WM performance in complex span task, a task in which there are more refreshing opportunities than in simple span task according to McCabe (2008).

As suggested by Rose et al. (2014), such an interaction can find an explanation within the primary-secondary memory framework (James, 1890; Unsworth & Engle, 2007a, 2007b), in which items are primarily maintained in primary memory. When the current task exceeds the capacity of the primary memory (e.g., when the items to be maintained are too numerous or when refreshing opportunities are reduced like in simple span tasks or through distraction of attention), memory items are displaced from primary memory into secondary memory, and they must be retrieved from LTM for further recall through a cue-dependent search. Consequently, effects known for impacting LTM should be particularly evident when recall relies on retrieval from LTM, i.e., when refreshing is prevented (Rose et al., 2014). This could explain the findings reported by Engle et al. (1990), Rose et al. (2014), Loaiza, Duperreault, et al. (2015) and Abadie and Camos (2018). However, besides these studies that reported an interaction between a LTM effect and the availability of attention for maintenance, others failed to observe any interaction
(Camos et al., 2019; Campoy, et al., 2015; Loaiza & Camos, 2018). Examining the concreteness effect (i.e., more imageable words are better recalled than less imageable words) in immediate recall tasks, Campoy, et al. (2015) varied the availability of attentional resources by asking participants to concurrently perform either a simple tapping or a highly demanding random tapping task. As expected, the increase in attentional demand reduced recall performance, but the concreteness effect was not moderated by the concurrent tasks. Similarly, Camos et al. (2019) showed that the frequency and lexicality effect were not modulated by the attentional demand of the concurrent task in complex span tasks, although both LTM effects affected recall performance. These authors also showed that frequency and lexicality effects did not moderate refreshing speed, which supports some independence of refreshing functioning from LTM. Using different types of manipulation, Loaiza and Camos (2018) provided further evidence of the independence of refreshing from LTM. In complex span task, the presentation of semantic cues during the recall phase had a beneficial effect on word recall performance compared to phonological cues. However, this benefit of the semantic cues did not vary as a function of the attentional demand of the concurrent task or even the intention to learn. Indeed, when participants were submitted to surprise recall test, semantic cue benefit remained the same as when they were instructed to actively maintain words for recall.

As proposed by Camos et al. (2019), the absence of interaction between LTM effect and the availability of attention can find an account within Cowan (1999b) Embedded-Processes (EP) model. In this model, WM is defined as the currently activated part of LTM, WM being a subset of LTM. Among this subset, 3-4 chunks of information are maintained in a high state of accessibility by a limited capacity focus of attention (Cowan et al., 2005).
In this framework, one can expect that factors known for facilitating the retrieval from LTM (what we call here LTM effects) would also ease the encoding of items in WM. In other words, items easily retrievable from LTM have a higher probability to be activated and to constitute the content of WM. However, performing a concurrent task would more or less distract attention depending on its nature. This would lead to forgetting memory items. To counteract forgetting, memory traces are refreshed or reactivated through a rapid scanning of the central component of WM (Vergauwe & Cowan, 2014; 2015), akin to the mechanism proposed by Sternberg (1966; see also, Sternberg, et al., 1978). The sequential scanning would be performed on the set of memory items that constitute the central capacity-limited component of WM and would reactivate the memory traces of these items. As a consequence, LTM effects should impact recall performance, because they affect the probability to enter into WM, but LTM effects should not vary with the availability of attention, which should impair the efficiency of the scanning among the subset of items that were in WM.

To summarize, there is some discrepancy within the literature about the
interaction between LTM effects and the availability of attentional refreshing, and two
streams of findings can be depicted. While some studies reported that LTM effects were
amplified when refreshing opportunities were reduced (Abadie & Camos, 2018; Engle et
al., 1990; Loaiza et al., 2015; Rose et al., 2014), others failed to report any interaction
between LTM effects and manipulation of attention (Camos et al., 2019; Campoy et al.,
2015; Loaiza & Camos, 2018). These contradictory findings contribute to the fact that the
functioning of attentional refreshing is still unclear (Camos et al., 2018), but also support
different theoretical conceptions on how attention mediates LTM effects in WM based on
either the primary-secondary framework or the embedded-processes model. Hence, the
present study aimed to re-examine this question by testing how variations in attentional
refreshing moderate the relatedness effect. Like the previously mentioned studies, we
examined recall performance. However, to help clarifying this discrepancy, we seek
through other measures of cognitive functioning indexes of the role of attention on LTM
effect in WM.

## 1.2. Recall latency and errors as indexes of memory functioning

The most obvious index of memory functioning is recall performance, and as such, it
is the most commonly studied. However, examining recall latency and recall errors can be
adequate indicators of the way memory is functioning. For example, in free recall tasks, it
has been shown that recall probability and recall latency are independent measures
(Rohrer & Wixted, 1994), while recall latency and recall errors account for unique variance
(Unsworth, 2009).

Recall latency in WM span tasks has been the subject of several papers (Cowan, 1992;
Cowan et al., 1994; Cowan et al., 2003; Cowan et al., 1998; Hulme et al., 1999; Towse et al.,
2008); after a seminal study by Cowan (1992). These papers resulted on a set of congruent
findings. First, the duration between the recall signal and the utterance of the first recalled
word is systematically longer than the recall latencies for the other words. Authors referred
often to this duration as a preparatory pause during which participants recapitulate the
word list before they start recalling the words. As a consequence, this latency should
increase with the list length, something that is sometimes observed (Cowan et al., 1994,
1998), but not systematically (Cowan, 1992). Moreover, preparatory intervals can be more
complex to interpret than other inter-word pauses as they can reflect not only the retrieval
of the memory list, but also some articulatory planning needed to output responses (Jarrold
et al., 2000). Second, recall latency for the serial positions after the first one also increases with list length (Cowan, 1992; Cowan et al., 1994, 1998, 2003), an increase that is stronger in younger children (Cowan et al., 1998). These inter-word pauses are considered as an index of retrieval processes that contribute to recall performance (Cowan, 1992; Towse & Cowan, 2004). According to Rose et al (2014), when recall is rapid, this suggests that words were “reported directly from the focus of attention” (p. 695) and recall did not involve retrieval processes from secondary memory. On the contrary, when recall is slow, this suggests that it “involved cue-dependent search and retrieval from LTM” (p. 695). In addition, it has been shown that differences in the size of the search set could also be responsible for increasing recall latencies with list length (Rohrer & Wixted, 1994).

Finally, children are slower than adults (Cowan et al., 1994), which is considered as resulting from their slower scanning pace (Keating et al., 1980). It can also be suggested that children have larger search sets than adults resulting in longer recall latencies and a greater impact of list length on latencies. Studies within the primary-secondary memory framework that examined individual differences in WM showed that individuals with low WM capacity exhibited longer recall latencies due to larger search set size (Unsworth, 2009). Similar results can be expected in children as they have lower WM capacity compared to adults.

The impact of LTM effect on recall latency was rarely examined, and the two available studies used the lexicality effect (Cowan et al., 2003; Hulme et al., 1999). This effect impacts recall latency, with longer inter-word pauses to recall lists of non-words compared to lists of words, and whatever the serial position (i.e., first and other serial positions). These findings are explained by a LTM support to WM performance. Because familiar items (like words) have stronger lexical representations in LTM, they are more easily reconstructed
than unfamiliar items (non-words) during the pauses between recalling successive items,
leading to shorter inter-word pauses and better recall (Hulme et al., 1999).

Concerning recall errors, they are not systematically reported in studies using WM
span tasks. Nevertheless, in two experiments using lists of related words, Atkins and Reuter-
Lorenz (2008) observed that semantic errors (i.e., recalling an unpresented theme word or
an associated word) exceeded other types of errors, in particular they occurred more often
than phonological intrusions and other unrelated intrusions. These semantic errors were
greater when participants had to complete a high-demanding concurrent task while
maintaining memory words than when there was no concurrent task.

Except this study, when errors were examined in WM tasks, this was mostly in
recognition test (e.g., Abadie & Camos, 2019; Flegal et al., 2010; McBride et al., 2019;
Shulman, 1972). Because the nature of the test has a direct impact on the types of errors
(for example, recall test elicited obviously more omissions than recognition test in which it
is not possible), it is difficult to use previous data on recognition test to support the
examination of errors in recall test. However, we can take advantage of the work done in a
related task, the simple span task, for which errors were reported in several studies using
recall test (e.g., Murdock, 1976; Saint-Aubin et al., 2005; Tse, 2009; Tse et al., 2011).

Well replicated findings are that transpositions (i.e, recalling a word in a different
serial position than the one in which it was presented) and intrusions (i.e., recalling an item
from another list), especially semantic intrusions (i.e., recalling semantically related extra-
list item) are more frequent in related than in unrelated word lists (Murdock, 1976; Saint-
Aubin et al., 2005; Tse, 2009). However, it should be noted that intrusions are rare, often
less than 1% when lists were not only semantically related word lists (Tse et al., 2011). These findings were often considered as evidence for the item redintegration theory. This theory predicts higher number of order errors for related lists, because degraded memory trace of an item has higher probability to overlap with features of another item in related lists. Hence, recall errors, as recall latency, can give insights about WM functioning.

**1.3. The present study**

In the present study, we conducted two experiments aimed at examining the mediating role of attention on the associative relatedness effect in WM. Semantic relatedness affects recall performance in serial recall task, with semantically related word lists better recalled than semantically unrelated word lists (Poirier et al., 2011; Poirier & Saint-Aubin, 1995; Saint-Aubin & Poirier, 1999a; Tse, 2009). Two effects can be distinguished among the semantic relatedness effects: categorical relatedness that links words belonging to the same semantic category (e.g. *apple-banana-pear*) and associative relatedness, which is characterized by a high probability of associations between two or more words despite the fact that they do not belong to the same semantic category (e.g., rabbit-carrot-ear; Tse et al., 2011). This latter type of associations is mainly based on the recurrent use or appearance of these words together. Two words can be highly associated while being semantically dissimilar (e.g. *rabbit-carrot*), or weakly associated and yet semantically similar (e.g. carrot - cabbage; Thompson-Schill et al., 1998). The present study focused on the latter type of associative relatedness.

In both experiments, we manipulated the impact of LTM by presenting lists of either associatively related or unrelated words. In Experiment 1, these word lists were introduced in a complex span task in which adults and 11-year-old children had to perform either a low
(a color judgment task) or a high (a parity judgment task) attention-demanding concurrent task. These tasks have been previously used in WM tasks with adults and children to vary the concurrent attentional demand (Barrouillet, Bernardin, et al., 2004). Increasing the attentional demand of the concurrent task performed during the delay of retention reduces the availability of attention for maintenance purpose, and in particular for refreshing memory items. To ensure that the observed effects only rely on the use of attentional refreshing, we hindered the use of articulatory rehearsal by imposing a concurrent articulation during the retention interval. Indeed, articulatory suppression by a concurrent articulation prevents the use of this subvocal repetition by inner speech strategy (Baddeley, 1986), with no impact on attentional refreshing in adults (e.g., Camos et al., 2009) and children (Mora & Camos, 2015; Oftinger & Camos, 2016, 2017, 2018)

We compared two age groups, children and adults, because development is a particularly powerful, quasi-experimental, manipulation of ability level. Indeed, it has been shown that children have a less efficient use of refreshing (Barrouillet et al., 2009; Camos & Barrouillet, 2011; Gaillard et al., 2011; Portrat et al., 2009b). This was especially supported by the fact that WM recall performance in children was less affected than in adults by the increase of concurrent attentional demand. Indeed, by increasing the attentional demand of the concurrent task in complex span task, it has been shown that the decrease in children's recall performance was smaller than in adults; the slope related attentional demand to recall performance being steeper in adults and older children (14-year-old) than in younger children (8-, 10-, 12-years of age). Moreover, Barrouillet et al. (2009, Exp. 2) assessed the rate of refreshing (i.e., the number of items refreshed per second) of two groups of children, 8- and for 14-year-olds, in a complex span task. They showed that the rate of refreshing is twice as high for 14- than for 8-year-old children, which suggests a stronger or more
efficient use of refreshing in older children. In a previous experiment, the same authors showed that 14-year-old children have the same pattern of performance as young adults, with a similar impact of variation in concurrent attentional demand on recall performance, suggesting that age 14 is an end point in the development of the refreshing mechanism. Hence, testing children would allow us to assess LTM effect in WM in conditions of poor use of attentional refreshing, and according to one of the previously described sets of findings, this should strongly promote LTM effect.

Experiment 2 aimed at replicating Experiment 1 findings with younger children, a stronger manipulation of concurrent attentional demand and a more precise measure of recall latency. In both experiments, in addition to recall performance, we also assessed recall latency and recall errors, because these measures can also reflect how retrieval in a WM task can be influenced by LTM knowledge.

As previously explained, we expected to replicate well-known effects of age, with poorer recall performance in children than adults, of associative relatedness with better recall performance for related word lists than unrelated word lists, and of concurrent attentional demand, its increase leading to reduced recall performance. This latter effect should be stronger in adults than in children, because the former are more efficient in using attention for maintenance purposes than the latter. However, the main purpose of the current study is related to other interactions. The main point of interest of the present study concerned the effect of associative relatedness as a function of the availability of attentional refreshing, which can be impaired either experimentally through variations in attentional demand of the concurrent task or through the comparisons between age-groups as children spontaneously present a poorer use of refreshing. As previously
described, the literature reported two streams of results: either LTM effect is increased under the impairment of refreshing or LTM effect is immune to it. Each of these patterns of findings found an account within either the primary-secondary memory framework or the embedded-processes model, respectively. Moreover, because children are less affected by variation in attentional demand, it can be extrapolated that, if the increase of LTM effect under high attentional demand, as expected by the primary-secondary framework, was observed, it should be greater in adults than in children. Finally, and although this was never reported in the literature, it remains possible that LTM effect would be enhanced under low concurrent attentional demand, as suggested by the time-based resource sharing model (Barrouillet & Camos, 2015).

Contrasting with the numerous studies on recall performance in WM tasks, there have been fewer investigations of recall times and recall errors. Previously observed findings should be replicated. Concerning recall latency, children should be slower than adults, the first recall latency should be longer than the others, latency should increase with list length and in unrelated word lists. Finally, the increase of latency with list length should be larger in children than in adults. Concerning recall errors, semantic errors should be more frequent in condition with high concurrent attentional demand and in related word lists, and the occurrence of order errors should increase in related compared to unrelated word lists.

Concerning the predictions issued from the two streams of findings and their supporting model, we can only speculate, as no explicit predictions have been made on recall latency and errors. Nevertheless, this helps in organizing the report of the findings. We can first expect that the same interactions (or absence of interactions) predicted for
recall performance should affect the other aspects of memory behavior, like latency and error. Second, the impact of concurrent attentional demand can also be envisioned. On the one hand, within the primary-secondary memory framework, Rose et al.'s (2014) suggested items retrieved from primary memory should then be retrieved faster than those in secondary memory. Therefore, recall latency should be longer under high attentional demand. On the other hand, the embedded-processes model would expect that concurrent attention-demand does not affect recall latencies, because items are recalled from the central component of WM in which items are in a high state of accessibility. Finally, intrusions, and in particular semantic intrusions, can occur more easily when items are retrieved from secondary than primary memory, which should be more frequent under high concurrent attentional demand, according to Rose et al.'s (2014) proposal. Our predictions and main results are summarized in Table 3.

## 2. Experiment 1

In this first experiment, we manipulated the impact of LTM by presenting lists of either associatively related or unrelated words. Following an increasing length procedure, these word lists were introduced in a complex span task in which adults and 11-year-old children had to perform either a low (a color judgment task) or a high (a parity judgment task) attention-demanding concurrent task. These tasks have been previously used in WM tasks with adults and children to vary the concurrent attentional demand (Barrouillet, Bernardin, et al., 2004). To ensure that the observed effects only rely on the use of attentional refreshing, we hindered the use of articulatory rehearsal by imposing a concurrent articulation, i.e., continuously repeating three syllables aloud. As explained in the introduction, the role of attention in mediating the LTM impact on WM was examined in recall performance, as well as in recall latency and recall error.
<table><caption>Table 3: Summary of predictions and main results</caption><thead><tr><th>DVs</th><th>Replication of known effects</th><th>Based on primary-secondary memory framework</th><th>Based on embedded-processes model</th></tr></thead><tbody><tr><td rowspan="4">Recall Performance</td><td>Age: Poorer performance in children than in adults</td><td>✓ A stronger relatedness effect in children than in adults</td><td>(✓) No interaction between relatedness and age</td><td>(✓)</td></tr><tr><td>Concurrent attentional demand: Poorer performance in high than low attentional demand</td><td>✓ A stronger relatedness effect in high concurrent attentional demand than in low demand</td><td>x No interaction between relatedness and concurrent attentional demand</td><td>✓</td></tr><tr><td>Relatedness: Poorer performance in unrelated than related word lists</td><td>✓ A stronger increase of the relatedness effect in high (vs low) concurrent attentional demand in adults than in children</td><td>x No 3-way interaction</td><td>✓</td></tr><tr><td>Interaction Concurrent attentional demand by Age: a stronger effect in adults than in children</td><td>(✓)</td><td></td><td></td></tr><tr><td rowspan="4">Recall Latency</td><td>Age: Children are slower than adults</td><td>✓ Age: Longer latency in children due to their poor use of refreshing or to larger search set size</td><td>✓ Age: Longer latency in children due to their slower scanning pace</td><td>✓</td></tr><tr><td>Serial position: Longer latency for first serial position than the others</td><td>✓ Concurrent attentional demand: Longer latency in high than in low attentional demand</td><td>x No effect of concurrent attentional demand</td><td>✓</td></tr><tr><td>List length: Increased latencies with list length</td><td>(✓)</td><td></td><td></td></tr><tr><td>Relatedness: Longer latency in unrelated than related word lists</td><td>✓</td><td></td><td></td></tr><tr><td rowspan="4">Recall Errors</td><td>Interaction Age by List length: A stronger increase with length in children than in adults</td><td>x Interaction Age by List length: A stronger increase with length in children than in adults due to larger search set size</td><td>x</td><td></td></tr><tr><td>Relatedness: More semantic and transpositions in related than unrelated word lists</td><td>(✓) ✓</td><td></td><td></td></tr><tr><td>Concurrent attentional demand: More semantic errors in high than low attentional demand for related word lists</td><td>x</td><td></td><td></td></tr><tr><td>Concurrent attentional demand: More omissions and transpositions in high than low attentional demand</td><td>✓ ✓ Concurrent attentional demand: More intrusions in high than low attentional demand</td><td>*</td><td></td></tr><tr><td></td><td>Relatedness: More omissions in unrelated than related word lists</td><td>✓</td><td></td><td></td></tr></tbody></table>

Note: Effects in Italics are not predictions based on the literature but observed in the current study. ✓ indicates that the effect was observed in both experiments, (✓) indicates that the effect was observed in one of the two experiments or descriptively present in recall errors, x indicates that the effect was not observed, and * indicates an inconclusive effect.
## 2.1 Method

### 2.1.1. Participants

Participants included 33 undergraduate students of the University of Fribourg (28 females, $M_{age}$ = 20.9 years, $SD$ = 1.83) and 36 children from local schools (18 females, $M_{age}$ = 11.1, $SD$ = .82). All of them were French native speakers. They gave either a signed informed consent or a legal tutor agreement for children. Adults received course credits for participating. Children did not receive any reward for their participation, but they voluntarily participate to ensure their motivation. None of them took part in the pre-test of the material (see Supplementary Material in Appendix E).

### 2.1.2. Material

To our knowledge, no study in French ever created lists of several words to test the associative relatedness effect in WM tasks. Thus, we created our own lists of words using verbal association norms by De La Haye (2003) and by Duscherer et al. (2009). From these norms, 160 one to three syllable words were selected and divided into two sets of 80 words. In each set, we created four lists of associatively related words for each length (from two to six). The words were then mixed to create an equivalent number of lists of two to six unrelated words (see Supplementary Material for the pre-test of the material). Each participant saw each word only once in the experiment, either in a related or unrelated list. The related lists of one set and the unrelated lists of the other set were administrated to each participant. For the concurrent tasks, series of blue and red digits ranging from one to eight were randomly generated with a rate of 50% of each color and 50% of each parity.
### 2.1.3. Procedure

The task was programmed on E-Prime 2.0 (Psychology Software Tools Inc. [E-Prime 2.0], 2012). Participants performed a complex span task during which they maintained lists of words while performing a concurrent task, either the color (low attentional demand) or parity (high attentional demand) judgment task on series of digits (Figure 13).

<figure><img src="image_29.png" /></figure>

*Figure 13.* Example of a trial with a related list of four words in Experiment 1 in which participants had to remember the word list, while performing either a color-judgment (low demanding) or a parity judgment (high demanding) concurrent task. Each trial began by an indication of the concurrent task (color or parity) to be performed on digits and the number of memory words to be maintained. Starting at the asterisk ready signal, participants repeatedly uttered ba-biboo until the appearance of a question mark and a beep prompted aloud recall of the words in the order of presentation.

Each trial began with the presentation of an asterisk for 2000 ms indicating the start of the concurrent overt articulation. Throughout each trial, participants repeated aloud the syllables *ba-bi-boo*, the experimenter tracking their continuous utterance. This concurrent articulation hinders articulatory rehearsal (Camos et al., 2009; Cowan et al., 1998), and favors the use of attentional refreshing (Camos et al., 2011). After the ready signal, the first to-be-remembered word appeared. Each word was presented during 1500 ms, followed by four digits presented for 1200 ms each with 300-ms inter-stimuli interval (ISI), during which a fixation cross kept participant fixating the screen. Word lists were
presented in increasing length from two to six with four lists of related and four lists of unrelated words in each length. Within each length, the eight lists were randomly displayed, with the constraints that four lists were assigned to each concurrent task, with half of them being related word lists and the other half unrelated word lists. In a counterbalanced way, participants studied each length, starting either by the color or parity judgement task, and completed the secondary tasks by pressing on / (for red or odd digits) and s (for blue or even digits) keys on a Swiss keyboard. At the end of a trial, a question mark with a beep prompted the oral recall of words in their order of presentation. The experimenter took note of the recall on protocol sheets. Participants were also asked to press the spacebar on the keyboard each time they recalled a word to record recall latencies. Because complying with this latter instruction could be difficult for children, the experimenter was in charge of pressing the key each time a child recalled a word. The length of word lists as well as the concurrent task to be performed (color or parity judgment task) were indicated on screen at the beginning of each trial.

The experiment started with two training phases. The first one was dedicated to the color and parity judgment tasks performed each on 20 digits, after an example on three digits. The second training phase was analogous to the complex span task, using lists of forenames instead of words. Participants performed four lists of length two, half with the color judgment task and half with the parity judgment task; the order of the two conditions being counterbalanced across participants. Finally, in a post-test at the end of the experiment, participants judged the associative relatedness of the word lists that they had not studied in the experiment (see Supplementary Material in Appendix E for the results of this post-test).
## 2.2. Results

To control that participants complied with the instructions and paid enough attention to the concurrent tasks, data from participants with an accuracy rate lower than 65% were excluded. This lead to exclude the data from two children. Moreover, data from participants with a span score differing from the average span of their age group by more than 3SDs were also excluded. This resulted in the exclusion of the data of one adult. The data from one additional child were excluded due to a computer error and another one refused to finish the experiment. Additional participants replaced these missing participants in order to have an equal sample size (N=32) in children and adults. The reported analyses were performed on this sample.

All analyses were performed with JASP. (Version 0.8.3.1, Love et al., 2019). For each dependent variable, a Bayesian Analysis of Variance (ANOVA) was performed using the default settings. The BF<sub>10</sub> of each model (e.g., main effects only, main effects + interaction effects) was obtained by comparing it to the null model. For each dependent variable, we first report the best model, i.e., the model with the largest BF<sub>10</sub>. Then, we report the BF<sub>inclusion</sub> value for each factor in the model (i.e., a main effect or an interaction effect), which indicates the likelihood of the data under models that included a given factor compared to all models stripped of the factor. Table 4 provides the mean and SD for each dependent variable and Table 5 summarizes the BF<sub>inclusion</sub> of main effects and interactions. Before reporting the results on recall performance, latency and error, we performed analyses on the concurrent task performance.
<table><caption>**Table 4.** Descriptive statistics of all dependent variables (DVs) in Experiment 1 as a function of age group (adults vs. children), the concurrent task (color vs. parity judgment task) and associative relatedness of word lists (related vs. unrelated words).</caption><thead><tr><th rowspan="3"></th><th rowspan="3">DVs</th><th colspan="4">Adults</th><th colspan="4">Children</th></tr><tr><th colspan="2">Color</th><th colspan="2">Parity</th><th colspan="2">Color</th><th colspan="2">Parity</th></tr><tr><th>Related</th><th>Unrelated</th><th>Related</th><th>Unrelated</th><th>Related</th><th>Unrelated</th><th>Related</th><th>Unrelated</th></tr></thead><tbody><tr><td rowspan="3">Recall</td><td>Span</td><td>4.0 (0.9)</td><td>2.9 (0.8)</td><td>3.8 (1.1)</td><td>2.69 (0.9)</td><td>3.2 (1.1)</td><td>2.2 (0.9)</td><td>3.1 (1.1)</td><td>1.7 (0.7)</td></tr><tr><td>Serial recall</td><td>73.4 (15.3)</td><td>56.7 (17.2)</td><td>68.7 (17.2)</td><td>51.10 (20.5)</td><td>60.4 (19.4)</td><td>41.1 (20.5)</td><td>53.8 (21.5)</td><td>36.1 (17.4)</td></tr><tr><td>Free recall</td><td>90.2 (7.4)</td><td>75.8 (12.6)</td><td>90.6 (8.5)</td><td>75.10 (13.5)</td><td>82.1 (10.0)</td><td>61.6 (15.2)</td><td>79.5 (12.8)</td><td>54.1 (16.7)</td></tr><tr><td rowspan="2">Concurrent task</td><td>ACC</td><td>93.1 (5.8)</td><td>93.3 (5.5)</td><td>89.0 (6.0)</td><td>87.8 (7.7)</td><td>89.0 (6.2)</td><td>88.4 (7.9)</td><td>76.1 (12.2)</td><td>74.1 (12.6)</td></tr><tr><td>RT</td><td>586 (81.2)</td><td>610 (86.3)</td><td>664 (75.1)</td><td>681 (77)</td><td>652 (71.7)</td><td>672 (66.3)</td><td>743 (74.7)</td><td>748 (67.6)</td></tr><tr><td rowspan="4">Recall latency</td><td>Latency 1</td><td>1965 (485)</td><td>2043 (611)</td><td>1951 (413)</td><td>2270 (719)</td><td>4616 (3204)</td><td>4904 (3315)</td><td>4030 (1698)</td><td>6502 (5845)</td></tr><tr><td>Latency 2</td><td>1052 (371)</td><td>1241 (554)</td><td>999 (387)</td><td>1306 (559)</td><td>1073 (549)</td><td>2948 (1853)</td><td>1092 (695)</td><td>2285 (2369)</td></tr><tr><td>Latency 3</td><td>855 (408)</td><td>1655 (998)</td><td>1953 (356)</td><td>1883 (1385)</td><td>1710 (2177)</td><td>2642 (3343)</td><td>2012 (2285)</td><td>2786 (3136)</td></tr><tr><td>Latency 4</td><td>1718 (1278)</td><td>2396 (2489)</td><td>1666 (1280)</td><td>2281 (1928)</td><td>2412 (3735)</td><td>3666 (5389)</td><td>2198 (3402)</td><td>3963 (5385)</td></tr><tr><td rowspan="5">Type of errors</td><td>Omission</td><td>5.4 (6.4)</td><td>18.0 (10.1)</td><td>5.9 (6.2)</td><td>19.9 (11.3)</td><td>15.4 (9.6)</td><td>36.5 (15.6)</td><td>17.9 (11.7)</td><td>39.8 (17.8)</td></tr><tr><td>Transposition</td><td>17.4 (9.8)</td><td>18.4 (8.8)</td><td>22.9 (14.3)</td><td>22.6 (13.1)</td><td>21.5 (12.7)</td><td>17.2 (11.2)</td><td>24.5 (14.2)</td><td>18.8 (10.7)</td></tr><tr><td>Semantic</td><td>3.1 (3.9)</td><td>1.8 (2.4)</td><td>2.1 (2.2)</td><td>1.3 (1.6)</td><td>1.8 (2.8)</td><td>1.8 (2.7)</td><td>2.7 (4.4)</td><td>1.4 (2.0)</td></tr><tr><td>Phonological</td><td>0 (0)</td><td>0.3 (0.8)</td><td>0 (0)</td><td>0.4 (0.9)</td><td>0 (0)</td><td>0.2 (0.6)</td><td>0.3 (0.9)</td><td>0.3 (1.0)</td></tr><tr><td>Intrusion</td><td>0.6 (1.6)</td><td>4.1 (4.3)</td><td>0.4 (0.9)</td><td>3.7 (4.7)</td><td>0.3 (1.1)</td><td>2.9 (4.4)</td><td>0.6 (1.6)</td><td>2.4 (2.8)</td></tr></tbody></table>

*Note.* Recall performance is expressed as span and as percentage of correct serial and of free recall. The concurrent task performance is expressed as percentage of correct responses (ACC). RT stands for reaction time in milliseconds in the concurrent task. Omission, transposition, semantic, phonological, and intrusion refer to the percentage of recall errors for each type of errors. Standard deviations are in brackets.
<table><caption>Table 5. BF<sub>inclusions</sub> of all variables of interest for recall performance (span, percentage of serial and free recall), recall latency, recall error, percentage of accuracy (ACC) and reaction time (RTs in ms) in the concurrent tasks in Experiment 1.</caption><thead><tr><th>Effect</th><th>Span</th><th>Serial recall</th><th>Free recall</th><th>Recall latency</th><th>Recall error</th><th>ACC</th><th>RTs</th></tr></thead><tbody><tr><td>Concurrent task</td><td>43.3</td><td>313</td><td>1.97</td><td>0.15</td><td>7.98</td><td>1.45×10<sup>25</sup></td><td>3.34×10<sup>38</sup></td></tr><tr><td>Relatedness</td><td>1.26×10<sup>31</sup></td><td>2.10×10<sup>37</sup></td><td>1.63×10<sup>71</sup></td><td>1.90</td><td>1.22×10<sup>9</sup></td><td>0.36</td><td>69.7</td></tr><tr><td>Age</td><td>195</td><td>79.0</td><td>4447</td><td>1.09</td><td>78.4</td><td>2477</td><td>116</td></tr><tr><td>Concurrent task x Relatedness</td><td>0.27</td><td>0.12</td><td>0.26</td><td>0.19</td><td>0.13</td><td>0.32</td><td>0.42</td></tr><tr><td>Concurrent task x Age</td><td>0.19</td><td>0.09</td><td>2.87</td><td>0.20</td><td>0.16</td><td>1.34×10<sup>8</sup></td><td>0.29</td></tr><tr><td>Relatedness x Age</td><td>0.21</td><td>0.13</td><td>93.5</td><td>0.28</td><td>0.21</td><td>0.23</td><td>0.25</td></tr><tr><td>Concurrent task x Relatedness x Age</td><td>0.46</td><td>0.12</td><td>0.23</td><td>0.39</td><td>0.18</td><td>0.24</td><td>0.28</td></tr><tr><td>List length</td><td>-</td><td>3.93×10<sup>88</sup></td><td>4.69×10<sup>12</sup></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>List length x Concurrent task</td><td>-</td><td>0.004</td><td>0.008</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>List length x Relatedness</td><td>-</td><td>0.62</td><td>4311</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>List length x Age</td><td>-</td><td>0.003</td><td>1.37</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>List length x Concurrent task x Relatedness</td><td>-</td><td>0.14</td><td>0.04</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>List length x Concurrent task x Age</td><td>-</td><td>0.01</td><td>0.03</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>List length x Relatedness x Age</td><td>-</td><td>0.02</td><td>0.03</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>List length x Concurrent task x Relatedness x Age</td><td>-</td><td>0.03</td><td>0.14</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position</td><td>-</td><td>-</td><td>-</td><td>1.57×10<sup>7</sup></td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Concurrent task</td><td>-</td><td>-</td><td>-</td><td>0.06</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Relatedness</td><td>-</td><td>-</td><td>-</td><td>0.16</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Age</td><td>-</td><td>-</td><td>-</td><td>4.11×10<sup>5</sup></td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Concurrent task x Relatedness</td><td>-</td><td>-</td><td>-</td><td>0.12</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Concurrent task x Age</td><td>-</td><td>-</td><td>-</td><td>0.11</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Relatedness x Age</td><td>-</td><td>-</td><td>-</td><td>1.26</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Concurrent task x Relatedness x Age</td><td>-</td><td>-</td><td>-</td><td>0.19</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Types of error</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.05</td><td>-</td><td>-</td></tr><tr><td>Types of error x Concurrent task</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.18</td><td>-</td><td>-</td></tr><tr><td>Types of error x Relatedness</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1.45×10<sup>20</sup></td><td>-</td><td>-</td></tr><tr><td>Types of error x Age</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2.22×10<sup>11</sup></td><td>-</td><td>-</td></tr><tr><td>Types of error x Concurrent task x Relatedness</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.23</td><td>-</td><td>-</td></tr><tr><td>Types of error x Concurrent task x Age</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.41</td><td>-</td><td>-</td></tr><tr><td>Types of error x Relatedness x Age</td><td>-</td><td>-</td><td>-</td><td>-</td><td>77.30</td><td>-</td><td>-</td></tr><tr><td>Types of error x Concurrent task x Relatedness x Age</td><td>-</td><td>-</td><td>-</td><td>0.29</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
### 2.2.1. Performance in Concurrent Tasks

Percentage of correct responses (ACC) and reaction times (RTs) in the concurrent tasks were submitted to default Bayesian mixed ANOVA, with concurrent task (color vs parity judgement task) and relatedness (related vs. unrelated lists) as within-subject factors, and age group (adults vs. children) as a between-subject factor.

Concerning the percentage of correct responses, the best model included the main effects of concurrent task and age group and the interaction between the two factors ($BF_{10} = 3.57 \times 10^{36}$). As expected, the percentage of correct responses was higher in the color ($M = 91\%$, $SD = .06$) than in the parity judgement task ($M = 82\%$, $SD = .12$), and in adults ($M = 91\%$, $SD = .05$) relative to children ($M = 82\%$, $SD = .09$). Moreover, follow-up Bayesian paired t-tests showed that the impact of concurrent task was larger in children ($BF_{10} = 5.07 \times 10^6$) than in adults ($BF_{10} = 1551$).

Concerning RTs, the additive model including the main effects of the three factors was the preferred model ($BF_{10} = 1.27 \times 10^{41}$). As expected, participants were faster when performing the color ($M = 630$ ms, $SD = 76$) than the parity judgment task ($M = 709$ ms, $SD = 74$). They were also faster to perform the concurrent task when maintaining lists of related ($M = 661$ ms, $SD = 76$) than unrelated words ($M = 678$ ms, $SD = 74$). Unsurprisingly, children were slower than adults ($M = 704$ ms, $SD = 70$ vs. $M = 635$ ms, $SD = 80$, respectively).

### 2.2.2. Recall Performance

Recall performance was first scored as span, with span being equal to $1 + (1/4 \times N)$ with N the total number of trials in which all words were correctly recalled in their correct
serial position (e.g., Barrouillet et al., 2009; Smyth & Scholey, 1992). Because this measure
may not be sensitive enough to grasp small recall differences, recall performance was also
scored as percentage of serial recall and of free recall, i.e., irrespective of serial position.

Bayesian mixed ANOVA with concurrent task and relatedness as within-subject
factors and age group as between-subject factor indicated that the additive model
including the main effects of the three factors was the best model to account for span (BF₁₀
= 7.26×10³³). As can be seen in Table 3 and as predicted, span was higher when
participants performed the color (*M* = 3.07, *SD* = .11) rather than the parity judgment task
as concurrent task (*M* = 2.81, *SD* = .10), when they maintained related rather than
unrelated word lists (*M* = 3.50, *SD* = .12 and *M* = 2.37, *SD* = .09, respectively) and in adults
(*M* = 3.34, *SD* = .93) than in children (*M* = 2.54, *SD* = .92). However, the analysis provided
substantial evidence against the interactions of interest between relatedness and
concurrent task, and between relatedness and age. There was also weak evidence against
the interaction between the three factors.

A similar analysis was performed on the percentage of correct serial recall with
one additional within-subject factor, the list length (2 to 6). As for span, the best model
included the main effect of the four factors (BF₁₀ = 1.07×10¹¹⁸). However, this model was
only weakly preferred to the second-best model, which also included the four main effects
and the interaction between relatedness and list length (BF₁₀ = 6.58×10¹¹⁷). The effect of
age group, relatedness, and concurrent task were similar as those in span (Tables 4 and
5). The percentage of serial recall decreased with list length (*M* = 80%; *SD* = 24.5, *M* =
71%; *SD* = 25.2, *M* = 57%; *SD* = 28.1, *M* = 49%; *SD* = 26.7 and *M* = 41%; *SD* = 25.8 for list
length 2 to 6, respectively). The post-hoc analyses showed that the list length effect was
substantially stronger for the unrelated word lists (decreasing from 73 to 33% for lists 2 to 6) than for the related word lists (from 85 to 49%; BF₁₀ = 1.86×10³⁶).

The same analysis on the percentage of free recall led to a slightly different pattern of findings. The best model included the four main effects and interactions between concurrent task and age group and between relatedness and age group, as well as interactions between list length and relatedness and list length and age group (BF₁₀ = 1.31×10⁹⁰). The four main effects were similar as those in span and serial recall analyses. Although the evidence for the concurrent task x age group interaction was weak (Table 4), we nevertheless conducted follow-up Bayesian paired t-tests in each age group. Evidence was gathered for an effect of concurrent task in children (BF₁₀ = 20.5), while there was substantial evidence against this effect in adults (BF₁₀ = 0.19). Concerning the relatedness by age group interaction, follow-up Bayesian t-tests indicated that the effect of relatedness was larger in children (BF₁₀ = 2.06×10¹¹) than in adults (BF₁₀ = 9.56 ×10⁸). Moreover, as shown by another follow-up Bayesian paired t-tests, list length had no effect when recalling related words (BF₁₀ = .04), but impacted recall of unrelated words (BF₁₀ = 1.72×10⁶). Lastly, follow-up Bayesian ANOVA was performed in each age group showing that children were far more impacted by list length (BF₁₀ = 7.03×10⁹) than adults (BF₁₀ = 1.91). As in span and percentage of serial recall, there was substantial evidence against the interaction of interest between concurrent task and relatedness and also against the interaction including concurrent task, relatedness and age group.

### 2.2.3. Recall Latency

Recall latency was collected for each recalled word when recalled in correct serial position, and, for each participant, averaged per position in each condition for each length.
Data diverging from more than 3 SDs from the mean (1.99% of the recall latencies) were replaced by the average of this condition for the same position and list length in the same age group. Moreover, because recall performance decreased across list lengths and to have a high percentage of RTs per presented words in adults as well as in children, we restricted our analyses to list lengths 2 to 4 (97%, 93%, and 80% in adults, and 90%, 84% and 69%, in children, respectively).

The Bayesian ANOVA with relatedness, concurrent task and serial positions (1 to 4 averaged across list lengths 2 to 4) as within-subject factors and age group as a between-subject factor showed that the best model included the main effects of relatedness, serial position and age group, as well as the serial position x age group interaction (BF₁₀ = 7.02×10¹²). However, this model was only weakly preferred to the second-best model which did not include the relatedness effect (BF₁₀ = 3.71×10¹²). Accordingly, there was weak evidence that related words were faster recalled than unrelated words (M = 1933 ms, SD = 1009 and M = 2741 ms, SD = 1511, respectively). Serial position had a decisive impact on recall latencies. Post-hoc comparisons indicated that participants took far more time to recall the first word (M = 3535 ms, SD = 2454) than the second (M = 1368 ms, SD = 667; BF₁₀ = 1.88×10⁶), the third (M = 1821 ms, SD = 307; BF₁₀ = 3.26×10⁵), and the fourth (M = 2454 ms, SD = 2773; BF₁₀ = 3.16). Except these differences, there was weak evidence of differences between the other serial positions (BFs₁₀ < 1.69). Although there was weak evidence that children (M = 3926 ms, SD = 1145) were slower than adults (M = 1647 ms, SD = 403) to recall words, the analysis showed decisive evidence for an interaction between serial position and age group. Bayesian independent t-tests provided decisive evidence that children (M = 5689 ms, SD = 2741) were slower than adults (M = 2100 ms, SD = 417) at recalling words at each position (BF₁₀ = 1.05 x 10⁵, 5.75, and 14.75 for
positions 1, 2 and 3, respectively), except for position 4 (BFs₁₀ = .95). There was substantial evidence against the main effect of concurrent task, and the interactions between relatedness and concurrent task, and weak evidence against the interaction between relatedness, concurrent task and age group.

To complete the analyses of recall latencies, two additional Bayesian ANOVAs with relatedness, concurrent task and list length (two to four words) as within-subject factors and age group as a between-subject factor were performed on recall latency for the first word and on the recall latency for the other words (i.e., averaged across serial positions two to fourth). The analysis for the first word indicated a preference for the model showing the main effects of relatedness and age group (BF₁₀ = 258), whereas the same analysis for the subsequent words showed also the main effects of relatedness, age group, and list length, but highlighted an interaction between relatedness and age group (BF₁₀ = 3.64 x 10⁶). For the subsequent words (BF₁₀ = 1042), related words (M = 1202 ms, SD = 748) were faster recalled than unrelated words (M = 1899 ms, SD = 1860), and the same pattern was observed on the first word (BF₁₀ = 1.34, M = 2904 ms, SD = 1872 and M = 3492 ms, SD = 2783, respectively). For both first and subsequent words, children were slower than adults (M = 4366 ms, SD = 3976 and M = 2030 ms, SD = 679, BF₁₀ = 1.62×10¹³ vs. M = 1783 ms, SD = 1853 and M = 1318 ms, SD = 755, BF₁₀ = 9.78, respectively). The preferred models showed that list length had no effect on recall latencies for the first words (BF_inclusion = .08), but did for subsequent ones (BF_inclusion = 2275). Indeed, the more the list length increased, the more the latencies for recalling the subsequent words increased (M = 1313 ms, SD = 1283 for length of two words, M = 1267 ms, SD = 944 for three, and M = 2071 ms, SD = 1685 for four). Finally, for relatedness and age group interaction in subsequent word latencies, Bayesian independent t-tests showed that
children (*M* = 2291 ms, *SD* = 2780) were slower than adults (*M* = 1506 ms, *SD* = 941) at recalling unrelated words (BF₁₀ = 7.99), the age difference being smaller for the related words (*M* = 1276 ms, *SD* = 925 and *M* = 1129 ms, *SD* = 570, for children and adults, respectively; BF₁₀ = 8.69). This interaction was not observed for the first words (BFₘₐₜ = .44). As in the previous analysis, there was substantial evidence against the main effects concurrent task (BFₘₐₜ = .13 and .11 for first and subsequent words, respectively), the interactions between relatedness and concurrent task (BFₘₐₜ = .16 and .15, respectively), and between relatedness, concurrent task and age group (BFₘₐₜ = .19 and 1.23, respectively).

### 2.2.4. Recall Errors

Recall errors were classified into five types: transpositions, omissions, intrusions, phonological and semantic errors. Errors were defined as a transposition when a word was recalled but in a different position than the one it was presented (e.g., last presented word recalled as first), as an omission when a word was not recalled, and as an intrusion when the recalled word was studied in a previous list. When the recalled word differed from only one syllable or swapped two syllables from the original studied item and had no semantic link with the items in the list, it was classified as a phonological error. Recalled words having a semantic connection with the items presented in the list were classified as semantic errors. Based on these definitions, three independent judges classified errors. We used a very stringent criteria; when there was no full agreement between the judges on the error category, errors were treated as unclassifiable and were therefore excluded from the analysis. This represented only 2.15% of the errors, showing the high agreement rate between the judges.
Examination of Table 4 showed that omissions and transpositions were the two most frequent errors, while semantic, phonological and intrusion errors were quite rare. Although the low rates of the latter types of errors precluded statistical analysis, it is worth noting that semantic, phonological and intrusion errors exhibited specific patterns, showing that our three variables of interest (relatedness, concurrent task and age groups) impacted differently their production. Semantic errors were more frequent in related than unrelated lists, in color than parity task and in adults than in children. While both phonological and intrusions errors were less frequent in related than unrelated lists, the phonological errors were lower in color than parity task, and intrusions were conversely more frequent in color than in parity task. Finally, adults produced more intrusions than children, but their phonological error rates were quite similar.

For the omissions and transpositions, the preferred model in the Bayesian ANOVA with error types, concurrent task, and relatedness as within-subject factors, and age group as between-subject factor included main effects of the four factors, and three two-way interactions (error types x relatedness, error types x age group, and relatedness x age group), as well as the interaction between error types, relatedness and age group (BF<sub>10</sub> = 2.24x10<sup>39</sup>). Concerning the main effects, the percentage of omission and transposition errors was similar (see Table 5 for BFs). There was substantial to decisive evidence that participants made more errors in the parity (*M* = 43%, *SD* = 18) than color judgment condition (*M* = 38%, *SD* = 17, BF<sub>inclusion</sub> = 7.98), when maintaining unrelated (*M* = 48%, *SD* = 18) than related lists (*M* = 33%, *SD* = 17), and that children (*M* = 48%, *SD* = 16) produced more errors than adults (*M* = 33%, *SD* = 13).
Concerning the two-way interactions, because the relation between their factors
were qualified by a three-way interaction, we chose to decompose the higher order
interaction between error types, relatedness and age group, for which we gathered strong
evidence. Hence, we conducted analyses on the relatedness by age group interaction in
each error type. There was weak evidence for the interaction in transpositions (BF<sub>inclusion</sub>
= 2.66) and substantial evidence in omissions (BF<sub>inclusion</sub> = 1065). Children did more
transpositions (M = 23%, SD = 12, and M = 18%, SD = 9, respectively, BF<sub>10</sub> = 20.25) and
less omissions (M = 16%, SD = 10, and M = 38%, SD = 16, respectively, BF<sub>10</sub> = 1.26×10<sup>23</sup>)
in related than unrelated words lists. The percentages of transpositions were not affected
by relatedness in adults (M = 20%, SD = 11 for related and M = 21%, SD = 9 for unrelated
word lists, BF<sub>10</sub> = .20) while, as children, they did less omission in related (M = 6%, SD = 5)
than unrelated (M = 19%, SD = 10) word lists (BF<sub>10</sub> = 1.56×10<sup>15</sup>).

## 2.3. Discussion

In summary, the pattern of findings was rather similar across the different recall
scores (see Table 4). Our manipulations were successful in impacting recall performance
in the expected way and independently of the scoring, with poorer recall in children than
in adults, in parity than color judgment task, and in unrelated than related word lists.
However, whatever the score we examined, no evidence supported two of the main
interactions of interest, that are the interactions between concurrent task and
relatedness, and between the concurrent task, relatedness and age group. The interaction
between age and relatedness was not supported, except when recall was scored without
taking into account serial position (i.e., free recall score).
Findings in recall latency and error went in a similar direction, strengthening the
results in recall performance, as none of the previously mentioned interactions received
support. However, in response latency as well as in error, known findings were replicated.
As expected, children were slower than adults, and recall latency increased with list
length. As reported in previous analyses on response latency (e.g., Cowan et al., 1994,
2003), the first word of the lists took longer to be recalled than the following words. This
could reflect the recapitulation of the entire word list before recalling each word in serial
order. In addition, the shorter response latency for related word lists was weakly
supported, indicating that as soon as memory words are maintained enough in WM to be
recalled, the semantic links between these words does not speed up their retrieval.
However, manipulation of the concurrent attentional demand did not affect response
latency. These findings shed light on WM functioning, especially how refreshing is
working and its relationships with LTM knowledge. These issues are discussed in the
general discussion.

Concerning recall errors, transpositions and omissions were by far the most
common errors, both in adults and children. Their rate of occurrence increased in a
condition with a higher concurrent attentional demand. Unrelated word lists gave rise to
more omissions, which shows that recall benefitted from the semantic links between the
related words. This echoed what was observed in recall performance. Finally,
transpositions were more frequent in related than unrelated word lists in children. This
is congruent with previous findings showing that associative relatedness between items
increases the proportion of order errors (Saint-Aubin et al., 2005) In addition, Poirier et
al. (2015) demonstrated that items for which the semantic activation was heightened by
items within the same list are more likely to migrate toward earlier positions at recall.
These findings suggest that associative relatedness can produce perturbations of representations of item order. Before discussing the theoretical implications of these findings, we reported a second experiment that aimed at strengthening our results.

### 3. Experiment 2

Experiment 2 has the same aim as Experiment 1, i.e., to examine the mediating role of attention on the relatedness effect in WM. However, three changes were implemented to strengthen our manipulations and measures. First, Experiment 2 involved younger children (aged 9) than those in Experiment 1. The lack of support for the expected interactions with age in Experiment 1 could result from smaller (than expected) age-related differences between 11-year-old children and young adults in the efficiency of refreshing. Hence, with younger children in Experiment 2, we aimed at increasing these age-related differences to assess whether or not we replicated the absence of interactions with age. Second, following the same idea, we implemented in Experiment 2 a stronger manipulation of the concurrent attentional demand by varying the pace of presentation of digits (either slow or fast) on which a parity judgement task was performed. Indeed, in Experiment 1, and contrary to what was previously observed, recall performance in adults was not more strongly affected than in children by the variation in concurrent attentional demand. We chose then another way to vary the concurrent attentional demand. It has been extensively shown that increasing the pace of presentation of distractors in complex span task has a detrimental effect on recall performance because it induces a stronger capture of attention needed for memory maintenance (e.g., Barrouillet et al., 2007; see Barrouillet & Camos, 2015, for a review). Finally, we also improved our measures of recall latency by audio-recording the entire sequence of responses and measured the oral inputs based on the sound oscillographic display.
**3.1. Method**

**3.1.1. Participants**

Thirty-three students of the University of Fribourg (26 females, *M*<sub>age</sub> = 20.6 years, *SD* = 1.90) and 32 children recruited in local schools (12 females, *M*<sub>age</sub> = 9.22, *SD* = .71) took part in the experiment after providing either their informed consent or a legal tutor agreement for children. They were all native French speakers. As in Experiment 1, adults received course credits for participating and children participated voluntarily.

**3.1.2. Material**

Thirty-two new lists of four associatively related or unrelated words (16 lists each) were created following the same procedure as for the material in Experiment 1 (see Supplementary Material). The associative relatedness of the lists was pre-tested on 120 students at the University of Fribourg. Each list included one to three syllables neutrally valenced words, with a maximum of one verb per list.

**3.1.3. Procedure**

We used the same complex span task as in Experiment 1, except that concurrent task was always a parity judgement task for which the attentional demand was varied by changing the pace of presentation of the digits (Figure 14). Digits were all black and presented for 1700 ms in the slow pace condition and for 700 ms in the fast pace condition, both with a 300-ms ISI. Another change compared to Experiment 1 was that word lists were of a constant length of four. Finally, to improve the measure of recall
latency, we used version 2.3.3 of Audacity® recording and editing software<fnref n="6" /> to record each participant's oral recall and extracted recall latency from oscillographic display on screen. The training phase preceded the task was akin to the one in Experiment 1, except that in the second phase with the complex span task one list was presented for each pace condition.

<figure><img src="image_30.png" /><figcaption>Figure 14. Example of two trials with a list of four related words in Experiment 2. Participants had to remember the word list, while performing a parity judgment task on digits that were presented either at a slow or fast pace. At the beginning of the trial, an indication mentioned the pace of the concurrent task. Starting at the asterisk ready signal, participants repeatedly uttered ba- biboo throughout the trial. Prompted by a question mark and a beep, participants orally recalled the words in the order of presentation at the end of each trial.</figcaption></figure>

## 3.2. Results

We excluded data from participants with performance in the concurrent and recall tasks that differed from more than 3SDs of their age group's mean. This resulted in data exclusion for one adult due to poor performance in the concurrent task, and one adult and one child for poor recall performance. The following analyses were then performed on 31

<footnote n="6">⁶ Audacity® software is copyright © 1999-2019 Audacity Team. The name Audacity® is a registered trademark of Dominic Mazzoni.</footnote>
adults and 32 children. Table 6 shows mean and SD for each dependent variable and Table 7 the BF<sub>inclusion</sub> value for each main effect and interaction.

### 3.2.1 Performance in Concurrent Task

Bayesian ANOVAs on percentage of correct responses and RTs with pace and associative relatedness as within-subject factors and age group as between-subject factor lead to the same preferred model, which included main effects of pace and age group and a pace by age group interaction (BF<sub>10</sub> = 1.57 × 10<sup>52</sup> and BF<sub>10</sub> = 2.55 × 10<sup>60</sup>, respectively).

As expected, accuracy was higher and RTs longer in the slow (*M* = 85%, *SD* = 14, and *M* = 697 ms, *SD* = 135, respectively) than in the fast pace condition (*M* = 75%, *SD* = 17.4, and *M* = 541 ms, *SD* = 52.2, respectively). Adults (*M* = 92%, *SD* = 6.19) outperformed children (*M* = 68%, *SD* = 15.4) and they were also faster (*M* = 580 ms, *SD* = 86.8, vs. *M* = 658 ms; *SD* = 151, respectively). Follow-up Bayesian paired-t-tests performed in each age group revealed decisive effect of pace in both age groups, but with a larger pace effect in children (BF<sub>10</sub> = 1.07 × 10<sup>10</sup> and BF<sub>10</sub> = 1.85 × 10<sup>11</sup>) than in adults (BF<sub>10</sub> = 1.10 × 10<sup>6</sup> and BF<sub>10</sub> = 353016) both for accuracy and RTs.

### 3.2.2. Recall Performance

Because we presented constant length trials, recall performance was scored as percentages of correct serial and free recall. The preferred model for percentages of serial recall included the three main effects of pace, relatedness and age group and the interactions pace by age group and relatedness by age group (BF<sub>10</sub> = 1.56 × 10<sup>43</sup>). However, this model was only weakly preferred to the second-best model, which did not include the relatedness by age group interaction (BF<sub>10</sub> = 1.13 × 10<sup>43</sup>).
As expected, recall performance was decisively better in the slow ($M = 54\%, SD = 11.9$) than in the fast pace condition ($M = 45\%, SD = 14.0$). There was also decisive evidence for the relatedness effect, with related words better recalled than unrelated words ($M = 55\%, SD = 12.7$ and $M = 43\%, SD = 13.3$, respectively). Moreover, there was decisive evidence that adults recalled more words ($M = 71\%, SD = 12.9$) than children ($M = 28\%, SD = 13.1$). Regarding the pace by age group interaction, follow-up paired t-tests conducted separately in each age group provided decisive evidence for the pace effect in adults ($BF_{10} = 45508$), but weak evidence against this effect in children ($BF_{10} = 0.47$). Although the evidence for the relatedness by age group interaction was weak, the follow-up t-tests indicated that the relatedness effect was stronger in children ($M = 36\%, SD = 14.1$, $M = 19\%, SD = 10.2$, for related and unrelated word lists respectively; $BF_{10} = 5.86 \times 10^6$) than in adults ($M = 77\%, SD = 9.82$, $M = 66\%, SD = 12.8$, respectively; $BF_{10} = 2794$). Finally, as in Experiment 1, the analysis provided weak evidence against the interaction between pace and relatedness and substantial evidence against the three-way interaction between pace, relatedness and age group.

Similar analyses were conducted on the percentage of free recall and confirmed the findings observed in percentage of serial recall with the same preferred model including the main effects of the three factors and the interactions pace by age group and relatedness by age group ($BF_{10} = 1.46 \times 10^{65}$). The $BF_{inclusion}$ values for each main effects and interactions are given in Table 7.
<table><caption>Table 6. Descriptive statistics of all dependent variables (DVs) in Experiment 2 as a function of age group (adults vs. children), pace of the concurrent task (slow vs. fast) and associative relatedness of word lists (related vs. unrelated words).</caption><thead><tr><th rowspan="3"></th><th rowspan="3">DVs</th><th colspan="4">Adults</th><th colspan="4">Children</th></tr><tr><th colspan="2">Slow Pace</th><th colspan="2">Fast Pace</th><th colspan="2">Slow Pace</th><th colspan="2">Fast Pace</th></tr><tr><th>Related</th><th>Unrelated</th><th>Related</th><th>Unrelated</th><th>Related</th><th>Unrelated</th><th>Related</th><th>Unrelated</th></tr></thead><tbody><tr><td rowspan="2">Recall</td><td>Serial recall</td><td>82.3 (10.3)</td><td>71.5 (11.2)</td><td>70.1 (13.5)</td><td>58.8 (16.5)</td><td>38.9 (13.9)</td><td>23.0 (11.9)</td><td>31.8 (12.6)</td><td>17.0 (12.6)</td></tr><tr><td>Free recall</td><td>91.6 (6.7)</td><td>77.5 (9.6)</td><td>85.0 (8.7)</td><td>66.1 (12.9)</td><td>59.2 (15.2)</td><td>31.4 (14.0)</td><td>57.7 (15.1)</td><td>28.8 (13.0)</td></tr><tr><td rowspan="2">Concurrent task</td><td>ACC</td><td>95.5 (3.3)</td><td>94.5 (3.6)</td><td>89.4 (5.8)</td><td>87.5 (7.3)</td><td>74.9 (14.1)</td><td>74.4 (15.1)</td><td>59.9 (13.7)</td><td>59.5 (13.8)</td></tr><tr><td>RT</td><td>624 (98.2)</td><td>624 (98.3)</td><td>534 (45.3)</td><td>541 (47.7)</td><td>769 (128.0)</td><td>764 (131.0)</td><td>543 (72.7)</td><td>532 (72.0)</td></tr><tr><td rowspan="4">Recall latency</td><td>Latency 1</td><td>3232 (2234)</td><td>3094 (1656)</td><td>3732 (2682)</td><td>3761 (2893)</td><td>2808 (1188)</td><td>3827 (1615)</td><td>3058 (1643)</td><td>3599 (1689)</td></tr><tr><td>Latency 2</td><td>1633 (839)</td><td>2100 (1310)</td><td>2003 (1149)</td><td>2087 (948)</td><td>2973 (1476)</td><td>3970 (2696)</td><td>3179 (1533)</td><td>4154 (1581)</td></tr><tr><td>Latency 3</td><td>2000 (950)</td><td>2818 (2785)</td><td>2193 (1224)</td><td>3378 (3518)</td><td>2971 (1313)</td><td>4828 (3248)</td><td>2884 (2043)</td><td>4159 (2710)</td></tr><tr><td>Latency 4</td><td>1481 (959)</td><td>1908 (1059)</td><td>1696 (1059)</td><td>2152 (1519)</td><td>2711 (1802)</td><td>3422 (1952)</td><td>2990 (2060)</td><td>2703 (1504)</td></tr><tr><td rowspan="5">Type of errors</td><td>Omission</td><td>6.1 (6.3)</td><td>20.1 (9.9)</td><td>12.2 (7.6)</td><td>32.3 (14.2)</td><td>35.6 (15.3)</td><td>61.0 (14.1)</td><td>37.2 (16.4)</td><td>64.0 (13.7)</td></tr><tr><td>Transposition</td><td>9.1 (7.4)</td><td>5.1 (4.9)</td><td>14.7 (7.7)</td><td>7.5 (6.7)</td><td>19.4 (18.3)</td><td>9.5 (11.0)</td><td>24.7 (17.6)</td><td>12.1 (13.4)</td></tr><tr><td>Semantic</td><td>2.2 (2.4)</td><td>1.0 (1.7)</td><td>2.8 (3.0)</td><td>0.4 (1.1)</td><td>4.1 (3.7)</td><td>1.1 (1.5)</td><td>3.9 (3.9)</td><td>1.1 (2.0)</td></tr><tr><td>Phonological</td><td>0 (0)</td><td>0.7 (1.5)</td><td>0 (0)</td><td>0 (0)</td><td>0.7 (1.6)</td><td>2.2 (2.9)</td><td>0.5 (1.2)</td><td>1.8 (3.0)</td></tr><tr><td>Intrusion</td><td>0.3 (1.2)</td><td>1.0 (2.6)</td><td>0.1 (0.6)</td><td>0.5 (1.4)</td><td>0.3 (0.9)</td><td>1.0 (2.7)</td><td>1.22 (1.8)</td><td>2.0 (2.9)</td></tr></tbody></table>

*Note.* Recall performance is expressed as percentage of correct serial and of free recall. The concurrent task performance is expressed as percentage of correct responses (ACC). RT stands for reaction time in milliseconds in the concurrent task. Omission, transposition, semantic, phonological, and intrusion refer to the percentage of recall errors for each type of errors. Standard deviations are in brackets.
<table><caption>Table 7. BF<sub>inclusions</sub> of all variables of interest for recall performance (span, percentage of serial and free recall), recall latency, recall error, percentage of accuracy (ACC) and reaction time (RT) in concurrent task in Experiment 2.</caption><thead><tr><th>Effect</th><th>Serial recall</th><th>Free recall</th><th>Recall latency</th><th>Recall error</th><th>ACC</th><th>RT</th></tr></thead><tbody><tr><td>Pace</td><td>4.47×10<sup>5</sup></td><td>3295</td><td>0.15</td><td>4.25 x 10<sup>3</sup></td><td>1.54×10<sup>33</sup></td><td>3.32×10<sup>40</sup></td></tr><tr><td>Relatedness</td><td>2.43×10<sup>17</sup></td><td>5.13×10<sup>41</sup></td><td>2.90×10<sup>5</sup></td><td>1.43 x 10<sup>4</sup></td><td>0.44</td><td>0.12</td></tr><tr><td>Age</td><td>1.62×10<sup>21</sup></td><td>5.04×10<sup>18</sup></td><td>118</td><td>4.26×10<sup>14</sup></td><td>1.37×10<sup>11</sup></td><td>127</td></tr><tr><td>Pace x Relatedness</td><td>0.45</td><td>0.41</td><td>0.19</td><td>0.17</td><td>0.21</td><td>0.18</td></tr><tr><td>Pace x Age</td><td>123</td><td>14.4</td><td>0.56</td><td>0.53</td><td>8.76×10<sup>7</sup></td><td>4.39×10<sup>16</sup></td></tr><tr><td>Relatedness x Age</td><td>1.94</td><td>3.76×10<sup>4</sup></td><td>0.89</td><td>0.21</td><td>0.27</td><td>0.31</td></tr><tr><td>Pace x Relatedness x Age</td><td>0.11</td><td>0.35</td><td>0.13</td><td>0.24</td><td>0.28</td><td>0.30</td></tr><tr><td>Serial position</td><td>-</td><td>-</td><td>2.19×10<sup>6</sup></td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Pace</td><td>-</td><td>-</td><td>0.001</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Relatedness</td><td>-</td><td>-</td><td>1.55</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Age</td><td>-</td><td>-</td><td>1.20×10<sup>4</sup></td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Pace x Relatedness</td><td>-</td><td>-</td><td>0.02</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Pace x Age</td><td>-</td><td>-</td><td>0.04</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Relatedness x Age</td><td>-</td><td>-</td><td>0.08</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Serial position x Pace x Relatedness x Age</td><td>-</td><td>-</td><td>0.07</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Types of error</td><td>-</td><td>-</td><td>-</td><td>5.23 x 10<sup>30</sup></td><td>-</td><td>-</td></tr><tr><td>Types of error x Pace</td><td>-</td><td>-</td><td>-</td><td>0.24</td><td>-</td><td>-</td></tr><tr><td>Types of error x Relatedness</td><td>-</td><td>-</td><td>-</td><td>3.34×10<sup>30</sup></td><td>-</td><td>-</td></tr><tr><td>Types of error x Age</td><td>-</td><td>-</td><td>-</td><td>1.07×10<sup>22</sup></td><td>-</td><td>-</td></tr><tr><td>Types of error x Pace x Relatedness</td><td>-</td><td>-</td><td>-</td><td>0.64</td><td>-</td><td>-</td></tr><tr><td>Types of error x Pace x Age</td><td>-</td><td>-</td><td>-</td><td>0.59</td><td>-</td><td>-</td></tr><tr><td>Types of error x Relatedness x Age</td><td>-</td><td>-</td><td>-</td><td>27.41</td><td>-</td><td>-</td></tr><tr><td>Types of error x Pace x Relatedness x Age</td><td>-</td><td>-</td><td>-</td><td>0.43</td><td>-</td><td>-</td></tr></tbody></table>
### 3.2.3. Recall Latency

Participants' recall was audio-recorded, and recall latencies (in ms) were extracted from the oscillographic display between the recall signal (a beep) and the beginning of the first word for the first recall latency, and from the utterance of the previous word to the beginning of the following word for the other recall latencies. Recall latencies were collected for each word when recalled in correct serial position and, for each participant, averaged per position in each condition. Data from one adult and three children were excluded due to recording technical issues. The following analyses included then 30 adults and 29 children. Data diverging from more than 3 SDs from the mean of the condition in each position were replaced by the average of this position in this condition in the concerned age group (i.e., for 4.5% of the recall latencies).

The best model in the Bayesian ANOVA with associative relatedness, pace and serial positions (first to fourth) as within-subject factors and age group as between-subject factor highlighted the main effects of relatedness, serial position and age group, and the interactions between age group and serial position, as well as between relatedness and serial position (BF₁₀ = 3.40 × 10¹⁷). However, this model was only weakly preferred to the second-best model, which did not include the relatedness by serial position interaction (BF₁₀ = 3.03 × 10¹⁷). There was decisive evidence that related words (*M* = 2590 ms, *SD* = 1697) were faster recalled than unrelated words (*M* = 3232 ms, *SD* = 2313). Recall latencies were also decisively impacted by serial position. Indeed, post-hoc comparisons indicated that participants took far more time to recall the first word of the lists (*M* = 3378 ms, *SD* = 2034) than the second (*M* = 2749 ms, *SD* = 1753; BF₁₀ = 92.6) and the fourth (*M* = 2373 ms, *SD* = 1642; BF₁₀ = 1.64×10⁶), but not the third (*M* = 3144 ms, *SD* = 2531; BF₁₀ = 0.14).
There was weak evidence that recalling the third word took more time than recalling the second ($BF_{10} = 1.61$) and decisive evidence that it took more time than recalling the fourth word ($BF_{10} = 767$). Finally, there was substantial evidence that the fourth word was faster recalled than the second one ($BF_{10} = 3.63$). As in Experiment 1, there was decisive evidence that children were slower ($M = 3390$ ms, $SD = 2020$) for recalling words compared to adults ($M = 2448$ ms, $SD = 1979$).

To examine the age group by serial position interaction, Bayesian independent t-tests compared age groups in each serial position. This provided substantial evidence that children and adults did not differ in recall latencies for recalling the first word ($M = 3323$ ms, $SD = 947$ and $M = 3432$ ms, $SD = 2067$, respectively; $BF_{10} = .272$). However, children were systematically slower than adults at recalling the following serial positions ($M = 3569$ ms, $SD = 1219$ and $M = 1956$ ms, $SD = 1062$; $BF_{10} = 42985$, for the second position, $M = 3711$ ms, $SD = 1482$ and $M = 2597$ ms, $SD = 2119$; $BF_{10} = 6.57$ for the third position and $M = 2957$ ms, $SD = 1073$ and $M = 1809$ ms, $SD = 1149$; $BF_{10} = 869$ for the fourth position, respectively). Although the evidence for the relatedness by serial position interaction was weak, we nevertheless conducted Bayesian analyses to test the relatedness effect in each serial position. Relatedness had no effect in the first and last serial positions ($BF_{10} = .37$, and $BF_{10} = .45$, respectively), but related words tended to be recalled faster than unrelated ones in the second and third serial position ($BF_{10} = 3.09$ and $BF_{10} = 12.9$, respectively). Akin to our results in Experiment 1, the analysis provided substantial evidence against the interaction between relatedness and pace, and between relatedness, pace, and age group.
### 3.2.4. Recall Errors

As in Experiment 1, we classified errors as transposition, omission, semantic, phonological and intrusion errors. Three independent judges did the classification. Only 3.18% of errors did not satisfy the classification criteria and were therefore excluded from the analyses. As in Experiment 1, omissions and transpositions on which we performed statistical analysis were the most frequent errors (Table 6). Semantic, phonological and intrusion errors were rare with children producing more errors of each type than adults. As also observed in Experiment 1, semantic errors were more frequent in related than unrelated lists, while it was the reverse for phonological and intrusion errors. Finally, semantic and phonological errors were slightly more produced under the low than high pace task. It was the same for the intrusions in adults but the reverse for children.

A Bayesian ANOVA was conducted on the percentage of omissions and transpositions with error types, pace and associative relatedness as within-subject factors and age group as between-subject factor. The best model included the main effects of the four factors and the interactions, error types by relatedness, error types by age group, relatedness by age group and the three-way interaction between error types, relatedness and age group ($BF_{10} = 6.37 \times 10^{91}$). This model did not strongly differ from the second-best model, which included the same effects and interactions with the addition of the pace by age interaction ($BF_{10} = 3.55 \times 10^{91}$).

There was decisive evidence for a main effect of error types, omissions ($M = 31\%$, $SD = 19$) being more frequent errors than transpositions ($M = 12\%$, $SD = 10$). There was strong evidence that participants made more errors in the fast ($M = 24\%$, $SD = 11$) than in the slow pace condition ($M = 19\%$, $SD = 12$), decisive evidence that there were more
errors in unrelated (M = 25%, SD = 12) than related word lists (M = 18%, SD = 10) and in
children (M = 33%, SD = 5) than adults (M = 13%, SD = 5).

As in Experiment 1, because most of the two-way interactions were qualified by a three-way interaction, we chose to decompose the higher order interaction between error types, relatedness, and age group by examining the relatedness by age group interaction in each error type. The pattern of findings was rather similar to what was observed in Experiment 1. There was decisive evidence for the interaction between relatedness and age group in omissions and strong evidence for the interaction in transpositions (BF_inclusion = 162.70 and 32.90, respectively). Both adults and children did less omissions in related (M = 9%, SD = 6 and M = 36%, SD = 14, respectively) than unrelated lists (M = 26%, SD = 11, and M = 62%, SD = 12, respectively), but the strength of evidence was larger in adults (BF_inclusion = 6.58×10¹⁹) than children (BF_inclusion = 5.18×10¹⁶). By contrast, both adults and children did more transpositions (M = 12%, SD = 6, and M = 22%, SD = 17, respectively) in related than unrelated word lists (M = 6%, SD = 5, and M = 10%, SD = 12, respectively), and the strength of evidence was larger in children (BF_inclusion = 5.81×10⁶) than adults (BF_inclusion = 1.09x10⁵).

**3.3. Discussion**

To summarize, Experiment 2 replicated the pattern of findings obtained in
Experiment 1 for recall performance, latency and error. Across the two types of recall
performance scoring, we replicated that recall was better in the slow than fast pace
condition, with related than unrelated word lists, and in adults rather than in children. As
we expected, our manipulation of the concurrent attentional demand and the involvement
of younger children did result on an age group by pace interaction, with the expected
stronger pace effect in adults. We also observed a stronger relatedness effect in children
than in adults. Despite these two interactions that gave better conditions for assessing our
predictions than in Experiment 1, no evidence was gathered in favor of any of the two
interactions of interest (relatedness by pace, and relatedness by pace by age group).

Similarly, for recall latency, results in Experiment 2 mimicked those in Experiment
1, with a longer time for the first than the following words, for children than adults, and
for unrelated than related words. Nevertheless, the interactions of interest remained
unsupported by the data. For recall errors, omissions and transpositions were still the
most common error types in both age groups. There were more omissions and
transpositions in the fast than in the slow pace condition, and unsurprisingly, children
made more errors than adults. Associative relatedness impacted the number of
transpositions and omissions. Overall, the different manipulations had the expected
effects, and many well-known effects were replicated, but nevertheless, the interactions
of interest remained unsupported. This strengthened the findings of Experiment 1 in
confirming the absence of such interactions in different aspects of recall. The general
discussion exposed how these findings impact the current theoretical conceptions of WM
maintenance, and of its relationships with LTM.

**4. General discussion**

The aim of this study was to investigate whether attention modulates the
contribution of LTM knowledge in WM. The current state of affairs showed divergent
findings that was interpreted among two distinct conceptions of WM, based either on the
primary-secondary framework or the embedded-processes model. To vary the
implication of LTM knowledge, we asked participants to maintain lists of either
associatively related or unrelated words. We also manipulated the availability of attention
to refresh memory traces by either implementing in complex span tasks concurrent tasks
that differed in their attentional demand or by comparing two age groups, young adults
and children, the latter being less efficient in refreshing. The different theoretical
proposals were contrasted in their predictions and we analyzed the impact of the above-
mentioned variables on recall performance, latency and error.

To summarize the main findings, our manipulations lead to the expected findings,
replicating some well-known effects (see discussion of each experiment). When focusing
on the predictions, the current findings are more in line with the previous studies that
reported an absence of interactions between LTM effect and variation in attentional
refreshing (Camos et al., 2019; Campoy, et al., 2015; Loaiza & Camos, 2018). In the present
study, while the increase in concurrent attentional demand and relatedness had the
expected impact in recall performance, they did not interact with each other, in adults and
children. This absence of interaction was also observed in recall latency, which was
affected by the relatedness of the memory words, but not by the demand of the concurrent
task. Finally, relatedness and concurrent attentional demand influenced the occurrence
of the two most committed errors, omissions and transpositions. We discuss below the
implications of these findings in light of the different WM models, and especially what the
absence of interaction between associative relatedness and variation in the availability of
attention implies for the role of attention in WM. Finally, we discuss how recall latency
and errors can bring insights on WM functioning and in particular on its relationships
with LTM. We discuss these two points in turn.
4.1. Attention and relatedness impact different WM components

Our results revealed a strong effect of associative relatedness on recall
performance, both in children and adults. As LTM is well known for impacting WM
retrieval, this relatedness effect was expected. Indeed, it is assumed that relatedness could
intrinsically activate associated LTM parts and then increased the accessibility to these
memory traces. LTM then contributes to the maintenance of WM traces by activating new
information that could be retrieved from the items to be maintained. Encoding related
items should then lead to superior retention performance. The manipulation of the
concurrent attentional demand also showed a significant impact on performance, with a
poorer recall in the high demanding condition. This result was also expected by any WM
models that give to attention a major role in the maintenance of information in WM (e.g.,
Barrouillet & Camos, 2015; Cowan, 1995; Engle, 2002). As a consequence, any depletion
of the attentional resources by a concurrent task would reduce their availability for
maintenance purpose. The stronger demand of attention (by the parity judgment task and
the fast pace condition in Experiments 1 and 2, respectively) led to a stronger reduction
of attention for maintenance and to poorer recall performance. Moreover, as expected,
the effect of concurrent attentional demand was stronger in adults than in children in
Experiment 2 in which children were younger and the manipulation of the concurrent
attentional demand stronger. This replicates previous findings (e.g., Barrouillet et al.,
2009) and indicates that adults were more efficient than children in using attention for
maintenance purpose. The fact that the relatedness of memory items and the attentional
demand of the concurrent task have both the expected effect on WM performance
confirms the soundness of our manipulation. This was particularly important for the
relatedness effect because, due to the absence of existing material in French, we had to
create especially for this study the lists of memory items (see Supplementary Material).
The main interest of our study was to test the contrasted predictions concerning the interactions between LTM effect (associative relatedness) and variation in the availability of refreshing (through variations in concurrent attentional demand and contrasted age groups). The current findings are clear. Bayesian analysis strongly supported the absence of interactions. This departs from reported interactions between a LTM effect and refreshing availability (Abadie & Camos, 2018; Engle et al., 1990; Loaiza et al., 2015; Rose et al., 2014), and brings some support to an embedded-processes conception of the links between LTM and WM.

More specifically, our results are in line with the last suggestion on refreshing made within the embedded-processes model. Vergauwe and Cowan (2015) suggested that refreshing acts as the scanning of the central component of WM that reactivates the memory traces stored in this component. The idea that attentional refreshing is a reactivation has been favored by several authors (Barrouillet et al., 2004; Cowan, 1995; Johnson, 1992; McCabe, 2008; see Camos et al., 2018, for a review and discussion on the different conceptions about refreshing). Hence, several models that share the idea that memory traces are stored in a central buffer in which they can be reactivated via attention can account for the current findings. Within Cowan's embedded-processes model, memory items stored in the focus of attention benefit from a heightened accessibility. Refreshing would act as a sequential scanning or search within this central component of WM, leading to their sustained maintenance. Such a description of the refreshing fits also with Oberauer's (2002) concentric model that distinguishes a single-item focus of attention within a capacity-limited region of direct access. In this model, refreshing would act as a rapid switching of the one-item focus of attention among the region of direct access. Similarly, in Barrouillet and Camos' time-based resource-sharing model, items
encoded and maintained in the executive loop would be refreshed sequentially by attention. In the most recent description of Baddeley's multicomponent model, the authors accepted the idea that an attentional refreshing mechanism involves *"the focusing of attention on the representation of material within the episodic buffer"* (Baddeley et al., 2021). Overall, our finding strengthens this view, as the reactivation of WM traces would not depend on LTM, but on the availability of attention for maintenance. LTM effects would then affect WM recall performance by modulating either encoding or retrieval processes at recall.

Conversely, our results are for the most part at odds with the Rose et al.'s (2014) findings. According to Rose et al.'s (2014) proposal, LTM would be more strongly involved in WM recall in high attentional demand condition, because the to-be-remembered items would have a higher probability to be retrieved from secondary memory than from primary memory when attention is less available (but see Rose, 2020, for a modified version of this model). In line with this proposal, in Experiment 2 (and in Experiment 1 when recall is scored as free recall), the relatedness effect was stronger in children. This finding seems to contradict the overall pattern of results, which supports the absence of interaction between LTM and refreshing. However, if the relatedness effect was modulated by the availability of attention, it should have been stronger in adults than in children in the high attentional demanding condition, because children were less affected by the variation of attention. This was not the case in any of our experiments. Hence, our results rather suggest that LTM is similarly involved in the retrieval of the memory items whatever the availability of attention. Nevertheless, our findings can be understandable within the primary-secondary memory model as originally depicted by Unsworth and Engle (2007). In this model, any concurrent task, whatever its attentional demand, would
lead to the displacement of memory items in secondary memory, from which items are
retrieved through a cue-dependent search. As a consequence, LTM knowledge are
similarly involved as soon as a concurrent task is involved and whatever its attentional
demand. In the case of the relatedness effect, maintaining semantically related items
would provide more efficient cues (e.g., the common gist underlying the list of items) to
search for the items in secondary memory, leading to the observed benefit in recalling the
related items (see Poirier et al., 2011; Saint-Aubin & Poirier, 1999). This echoes
Oberauer's (2009) study in which the nature of the distractors that were presented after
a memory item was manipulated in a complex span task. The distractors were either
semantically related (presenting *Silk, Wool, Velvet* and *Linen, after Cotton*) or unrelated
(presenting *Queen, Duke, Prince, and Knight* after *Cotton*). Despite the fact that the
semantically related distractors improved recall performance of the memory items,
probably by giving more efficient cues to search of the items, this semantic relatedness
effect was not affected by variations in the attentional demand of the concurrent task.

## 4.2. Insights from recall latency and error on LTM impact in WM

As exposed in the introduction, it is quite rare to examine recall latency and error
to get insights about WM functioning. However, previous studies provided interesting
findings papers (Cowan, 1992; Cowan et al., 1994; Cowan et al., 2003; Cowan et al., 1998;
Hulme et al., 1999; Towse et al., 2008), which showed that this is an avenue of research to
further investigate. Hence, and contrary to most previous studies dedicated to the
question of the role of attention in the LTM effect on WM, the present study included
analysis of recall latency to inform us about the ease to retrieve items from memory. As
suggested by Rose et al. (2014), items retrieved directly from primary memory would be
faster to be recalled than items retrieved from secondary memory. Therefore, according
to these authors' proposal for the moderating role of attention, a high concurrent
attentional demand or a poor use of attention for maintenance (as in children) should lead
to longer recall latency. Alternatively, within the embedded-processes model, recall
latency of items retrieved from WM should not be sensitive to the concurrent attentional
demand, which conversely determines whether the items are present or not within WM.
The analysis of the duration of the recall latency showed three main findings.

First, the attentional demand of the concurrent task had no impact on recall
latency. In line with the embedded-processes model, we assume that memory traces are
retrieved from a central component of WM, and as such, any items stored in this
component would be retrieved and recalled at the same speed. As put forward by the
time-based resource sharing model, the availability of attention would affect maintenance
and thus, whether the items are still stored in this central component (Barrouillet &
Camos, 2015). As a consequence, manipulating the attentional demand of the concurrent
task affects the number of items stored (Barrouillet & Camos, 2014), and for these items,
retrieval would be similar. Second, associative relatedness had a great impact on recall
latency, related word lists being faster recalled. Accordingly, previous studies showed
that LTM knowledge actually impacts recall latency, the retrieval of non-words producing
longer inter-words pauses during recall than words (Hulme et al., 1999). This can be
conceived like priming effects in LTM: retrieving and recalling the first item of a list could
provide a kind of boost to utter the remaining items of the list when items are linked to
each other. In this case, recall latency for the first item should not differ with relatedness
conditions, and only the following items would show a beneficial effect of relatedness.
However, it is not what we observed in Experiment 1, in which relatedness weakly
impacted the first four positions. In Experiment 2, we observed the predicted pattern with
a relatedness effect occurring only in second and third positions, but not on the first
position. Although this seems to be a divergence between our two experiments, it should
be noted that there was evidence against the relatedness by serial position interaction in
Experiment 1 and support for this interaction in Experiment 2 was weak. Hence, there
might be another reason why relatedness impacts recall latency. One can envision that
related items have stronger memory traces because they benefit from a semantic network
in LTM (Ralph et al., 2017). Finally, congruently with Cowan et al. (2003), children took
more time before recalling the first word in a list than adults in Experiment 1. We
interpreted this effect as evidence of longer recapitulation time in children. Participants
tend to recapitulate the word list before uttering the words. Children need then more time
to build the list due to the time to retrieve each word, the larger size of the search set
among which words have to be retrieved, or the difficulty in organizing their utterance.
Indeed, Cowan and collaborators (1994, 1998) proposed that recall times provides an
index of memory search. To prepare a response, a participant has to scan through all the
stored items like in a Sternberg (1966) task in which individuals determine whether an
item was previously presented, children being slower to do so. It can also be envisioned
that children have larger set of candidates among which they have to search for the words
to recall (Rohrer & Wixted, 1994; Unsworth, 2009, for studies in adults). Alternatively,
Jarrold et al. (2000) proposed that these times reflect speech planning processes needed
to utter the word list. However, this effect was not replicated in Experiment 2 and more
importantly, recall latencies in both experiments were longer in children than in adults
for subsequent positions. Although further studies are needed to examine what trigger
these age-related differences, a simple explanation in terms of processing speed
differences could account for this finding and was often mentioned as a source of WM
development (e.g., Camos & Barrouillet, 2018; Gaillard et al., 2011).
In addition to recall performance and latency, we analyzed recall error. Despite the fact that participants mostly omitted items when recalling lists of words, the impact of the relatedness on errors enlightens the mechanisms that sustain maintenance of verbal information at short term, and especially the role played by LTM. Indeed, participants produced less omissions when they had to recall related than unrelated words. This suggests that relatedness has a protective effect against forgetting. Related words may have stronger memory traces that better resist from representation-based interference and temporal decay.

Relatedness also descriptively reduced the number of intrusions, that is the production of words from a previously presented list. When participants retrieved a word presented in previous trials, this word could be more easily rejected as a candidate for recall for lists of related words because these words share a common gist that differ from the gist of the other word lists. For example, it would be easier to reject the word “car” when the just-studied list is *Rabbit, Ear, Carrot* in the related condition than after the list *Rabbit, Line, Sport* in the unrelated condition). On the contrary, relatedness did not moderate the production of phonological errors, which can be expected as it would depend on the phonological similarity between the memory words. However, we expected that semantic errors would be enhanced by the presentation of related words, because the activation of a common gist would automatically activate other related words, as observed in the emergence of false memory in LTM (Reyna & Brainerd, 1995). This was not the case, but the rate of intrusions, phonological and semantic errors was so low that *further* studies should be dedicated to examine how false memory could emerged in short-term recall tasks, and what is triggering the production of errors.
Finally, it should be noted that the increase in concurrent attentional demand resulted on more omissions. This was particularly expected by the time-based resource sharing model because the number of items maintained in the episodic buffer is a direct function of the attentional demand of the concurrent task, that is the amount of time during which attention is available for refreshing activity (Barrouillet & Camos, 2015). Increasing the attentional demand of the concurrent task directly reduces the availability of attention for maintenance, resulting in a smaller number of maintained items.

## 5. Conclusion

The relationships between LTM and WM has been and still is source of intense theoretical debates in the literature. Although it is well known that knowledge stored in semantic LTM improves recall performance from WM, the exact pathway of such an effect remains under investigation. Within this line of research, the present study proposed to examine the moderator role of attention on the effects that semantic LTM had on WM functioning. In the past five years, an interest on this specific question emerged to highlight the functioning of one particular WM maintenance, attentional refreshing. This leads to a rather divergent pattern of findings on how attention may or not moderate LTM effects in WM.

In the present study, we have been able to replicate the beneficial effect of associative relatedness between memory items and the availability of attention for maintenance on WM performance. However, the vast majority of our pieces of evidence tends to support an independent impact of these two factors on WM functioning. This finding is in line with previous studies (Camos et al., 2019; Campoy, et al., 2015; Loaiza & Camos, 2018), and brings some support to a class of WM models in which attention is used
to sequentially reactivate memory traces stored in a central component of WM (Baddeley
et al., 2021; Barrouillet et al., 2004; Cowan, 1995; Oberauer, 2002). Moreover, from an
age-related perspective, the present study showed that children exhibited rather similar
pattern of findings than adults, besides expected poorer performance. This could evidence
that children’s use of LTM knowledge to support WM did not qualitatively differ from
adults, contrary to what was reported in other types of tasks. Finally, we believe that
examining recall latency and error helped uncovering the role played by LTM in WM, and
could be more frequently examined when studying WM.
Résumé et implications du Chapitre 5

Le but des deux expériences présentées au Chapitre 5 était d'observer si l'attention
modulait la contribution des effets de LTM en WM. Nous avons manipulé les opportunités
de rafraichissement attentionnel en impliquant deux niveaux de demande attentionnelle
au sein d'une tâche d'empan complexe. Les enfants utilisant le rafraichissement de
manière moins efficace que les adultes, la comparaison entre ces deux groupes d'âges
servait le même objectif. Parallèlement, le maintien de listes de mots sémantiquement
reliés ou non-reliés variait la présence d'effet de LTM dans la tâche. Nos résultats
répliquent les effets principaux connus des trois variables de rafraichissement
attentionnel, de similarité sémantique et d'âge ; ainsi que l'absence d'interaction entre les
deux premiers facteurs tant sur les performances que sur les temps de rappels.

Cette absence d'interaction est conforme à la vision du rafraichissement
attentionnel selon le modèle à processus emboîtés (Vergauwe & Cowan, 2015) et
privilégiée par plusieurs autres champs théoriques suggérant l'action du rafraichissement
comme un balayage du contenu de la WM et réactivant alors les traces stockées. Nos
analyses des temps de rappels confirment cette conclusion. De plus, les enfants
présentent des patterns de résultats comparables à ceux des adultes, démontrant leurs
capacités d'utilisation des connaissances de LTM pour soutenir leur WM. Ces différentes
observations démontrent l'effet indépendant des manipulations propres à la WM et celles
de la LTM sur le fonctionnement de la WM et apportent un soutien certain aux modèles
théoriques dans lesquels l'attention est utilisée pour la réactivation des traces contenues
en WM.

Cependant, si cette étude démontre que les effets de LTM n'interviennent pas en
WM au travers des processus de maintien, ces effets favorisent tout de même les
performances en WM d'une manière ou d'une autre. Les hypothèses les plus probables
sont que la LTM favorise les performances soit en améliorant l'encodage des informations,
soit en améliorant le rappel au travers d'un processus de reconstruction des traces
dégradées. Pour l'étude suivante, nous avons suivi cette dernière hypothèse en appliquant
un modèle permettant d'observer les effets de nos facteurs et leur potentielle interaction
sur les processus de reconstruction et d'accès direct durant le rappel des traces en WM.
<figure><img src="image_31.png" /></figure>
# Chapitre 6. Recollective and non-recollective processes
## in working memory retrieval

**Titre en français** : Processus de recollection et de reconstruction (non-recollection) dans la récupération en mémoire de travail

Cette étude a été soutenue par le Fond National Suisse de la Recherche accordé à
Valérie Camos (grant n°100019_175960) et à Pierre Barrouillet (grant
n°IZK0Z1_160676/1) et a reçu l'approbation du comité d'examen institutionnel de
l'éthique de la recherche de l'Université de Fribourg. Les listes de mots et de données sont
disponibles sur https://osf.io/v4z2c/.<fnref n="7" />

<footnote n="7">⁷ Merci à Carlos Gomez, Pierre Barrouillet et Charles Brainerd pour la collecte des données de l'Expérience 1, Stéphanie Mariz Elsig pour son aide dans la collecte de données et le dépouillement des résultats des Expériences 2, 3 et 4, ainsi qu'à Marlène Abadie et Valérie Camos pour leur implication générale tout au long de cette étude.</footnote>
Résumé

Cette étude avait pour but d'investiguer la nature des processus impliqués dans la
récupération en mémoire de travail (WM) en fonction de la disponibilité du
rafraichissement attentionnel et des connaissances de mémoire à long-terme (LTM). Dans
cet objectif, la théorie trichotomique du rappel (Trichotomous theory of recall, Brainerd et
al., 2009) a été appliquée aux performances de rappel de jeunes adultes au terme de
tâches d'empans complexes en trois essais d'études et de rappels successifs. Quatre
expériences manipulaient la disponibilité du rafraichissement attentionnel pour
maintenir différents items en variant le coût cognitif de la tâche concurrente, l'implication
des connaissances de LTM et les modalités de présentation des essais successifs afin d'en
observer les effets sur les processus de rappel. Le modèle distinguant le processus de
recollection en rappel (i.e., accès direct) du de non-recollection (i.e., reconstruction) a
montré (1) une prédominance importante du processus d'accès direct par rapport à la
reconstruction des traces en mémoire, (2) que la disponibilité attentionnelle n'affectaient
que les capacités d'encodage, mais jamais la reconstruction ; étant donné qu'une forte
demande cognitive concurrente (vs., plus basse) augmentait la proportion d'items non-
appris et reconstruits mais non rappelés, mais n'affectait jamais la proportion d'items en
accès direct ou reconstruits et rappelés correctement. Finalement (3), nos résultats
montraient que les connaissances à long-terme (i.e., l'association sémantique)
augmentaient l'accès direct aux traces, mais pas leur reconstruction. Ceci suggère que
l'accès direct soutient fortement le rappel à court-terme, mais quand l'utilisation de la
reconstruction est encouragée par la présence de connaissances à long-terme dans la
tâche ou pas une procédure imposant le ré-encodage des items à chaque nouvel essai.
Abstract

This study aimed at investigating the nature of the processes involved in working
memory (WM) retrieval as a function of the availability of attentional refreshing and of
long-term memory (LTM) knowledge. For this purpose, the trichotomous theory of recall
(Brainerd, Reyna, & Howe, 2009) was applied to recall performance of young adults in
complex span task in three successive study-test trials. Four experiments manipulated the
availability of attentional refreshing to maintain memoranda by varying the cognitive load
of the concurrent task, the implication of LTM knowledge and the way of presentation of
the three successive trials to observe the impact on retrieval processes. The two-stage
model distinguishing recollective (direct access) and non-recollective (reconstruction)
recall (1) revealed a strong predominance of direct access over reconstruction of memory
traces, (2) that attentional availability variation only affected encoding capacities, but
never reconstruction, as higher cognitive load variation (vs., lower) increased the rate of
unlearned and reconstructed-but-not-recalled items but never affected the proportion of
recollected and reconstructed-and-recalled items. Finally (3), our results showed that
LTM knowledge (i.e., associative relatedness) enhanced the recollective process, but not
non-recollective processes. This suggests that recollection strongly underpins short-term
recall, even when the use of the reconstruction process is encouraged by the presence of
long-term knowledge in the task or by a procedure which imposes the re-encoding of
items on each new trial.
# 1. Introduction

Memory is one of the most important components of human cognition. Over the years, it has been analyzed and divided into several elements, each one having their own properties. Among these components, long-term memory (LTM), which allows the maintenance of information over long periods, is frequently the best known by a large audience, but working memory (WM) also plays a decisive role. WM is essential to the proper functioning of human cognition as it allows the maintenance and manipulation of information in a given period of time (Baddeley, 1986; Baddeley & Hitch, 1974). Nonetheless, this on-line processing requiring a rapid access to relevant information, it has accordingly been suggested that retrieval is one of the basic function that WM fulfils (Unsworth & Engle, 2007a). The aim of this study was then to investigate the nature of the processes governing retrieval from WM, as function of attentional availability and the nature of representations involved in the tasks.

## 1.1 Retrieval from LTM

Several models assume that WM consists of LTM items activated above threshold, the accessibility of which depends on their level of activation (Anderson, 1993; Anderson et al., 1996; Cowan, 1999a, 2016; Oberauer, 2002). For example, Cowan (1999a) embedded-processes model argued that, because memory items will not stay activated long, retrieval must race against forgetting. Accordingly, several theoretical models assume a hierarchical organization of WM in which only three or four highly activated elements held in a focus of attention (Cowan, 2016) or a region of direct access (Oberauer, 2002) are highly activated and directly accessible. If the activated memory representations have disappeared, their retrieval remains nonetheless possible if sufficient episodic memory traces have been stored. Indeed, the central attentional region
such as described by Cowan (1999a) and Oberauer (2002) is surrounded by an activated
LTM area containing less activated items that can nonetheless be retrieved, but only
indirectly through associations with items in the more central regions. Therefore, several
theories converge toward the idea that WM performance also depends on items that are
less directly accessible, but still retrievable. This conception is best exemplified by
Unsworth and Engle (2007a) who suggest that WM limitations arise from two
components. One is a dynamic attention component able to actively maintain a maximum
of about four items in a primary memory akin to the focus of attention or the region of
direct access evoked above (Unsworth et al., 2010). However, when more than four items
are present in complex task, items currently within primary memory are displaced into
secondary memory from which they must be recalled through a cue-dependent search
process.

The same process occurs in complex span tasks situations such as the reading span
(Daneman & Carpenter, 1980) or operation span tasks (Turner & Engle, 1989) in which
the secondary task of reading sentences or solving equations occupies primary memory,
thus displacing items to secondary memory. According to Unsworth and Engle (2007a),
whereas traces in primary memory are directly accessible, retrieval from secondary
memory requires a complex process involving the generation of a search set from some
retrieval cue plus a discrimination process to differentiate between relevant from
irrelevant items that can enter this search set through spreading activation or proactive
interference. Thus, retrieval from WM goes beyond direct access of items in a heightened
state of activation to involve more complex search processes among relevant and
irrelevant items. However, more factors external to the WM may contribute to the
improvement or deterioration of the recall. This is particularly the case of LTM
knowledge.
1.1.1. How can LTM influence WM performance?

Studies showing the beneficial impact of LTM knowledge on recall performance are numerous (see Hulme et al., 1991; Hulme et al., 2003; Poirier et al., 2011; Saint-Aubin et al., 2005; Saint-Aubin & Poirier, 1999a). Our interest was primarily in associative relatedness effect. This effect arises when presenting a material of words to remember composed of associatively related words. Many words are considered associatively related when they have a high probability of language association with each other (Thompson-Schill et al., 1998). LTM factors allow then a redintegration process, which facilitates the repair of degraded memory traces prior to recall (Hulme et al., 1999) by reconstructing items thanks to their lexical representation. This effect is strong and therefore easily replicable because of its high stability. A certain advantage also lies in its resistance to articulatory suppression (Shivde & Anderson, 2011). Therefore, LTM knowledge associated with related words would significantly increase the recall of these traces during WM tasks. However, if this fact is proven and has been demonstrated through numerous studies, the nature of the retrieval processes involved when recalling such LTM knowledge associated items is not clear. However, recollective and non-recollective retrieval paradigm allow us to examine this question.

1.2. Recollective and non-recollective retrieval

As research progressed, it became clear that there are two distinct processes involved in learning items to be remembered for recall: Recollective and nonrecollective retrieval. This distinction was first introduced and studied within the old/new item recognition paradigm (Mandler, 1980) through procedures like Remember / Know paradigm (Tulving, 1985b), Confidence Judgment model (Yonelinas, 1994) or the Process Dissociation Procedure (PDP; Jacoby, 1991, 1998). However, the dual-process retrieval
approach has been recently extended to recall, which would rely on two dissociated
retrieval operations, direct access, and reconstruction (Barnhardt et al., 2006; Brainerd,
Payne, et al., 2003; Brainerd, Wright, et al., 2002; Reyna & Mills, 2007). In the vision of
such dual-traces design, the two types of encoded traces correspond to two different
episodic traces. Recollective traces correspond to an episodic encoding of the precise and
fast trace of an item, a direct access which retrieves verbatim traces of individual items
from a study list (Barnhardt et al., 2006). It proceeds by reinstating the surface form of
the item that can be recalled by "merely reading out this surface information that echoes
in the mind's ear or flashes in the mind's eye" (Brainerd et al., 2009, p. 786) This direct
access, which typifies one of the components of WM hypothesized by Oberauer (2002), is
clearly the process by which information is retrieved from primary memory in Unsworth
and Engle's (2007a, 2007b) model or the focus of attention in Cowan's (2005) model.

By contrast, nonrecollective traces rather correspond to an episodic encoding of the
semantic characteristics of the item. While the recall of items is crystal clear in the case of
recollective traces, nonrecollective traces regenerates targets from partial-identifying
information, and especially from their meaning content captured in gist traces (Brainerd
et al., 2009). However, this partial-identifying information does not point to a unique
target but generates a set of candidates (e.g., remembering that some fruit was presented
might recover candidates such as orange, banana, apple, and lemon, when only lemon was
studied). Thus, reconstruction is followed by a judgment step. It is assumed that
reconstructed items generate familiarity signals, a given item being output if the strength
of its familiarity signal exceeds some decision criterion. This reconstruction process
clearly corresponds to the cue-dependent search process that retrieves memory traces
from secondary memory in Unsworth and Engle's (2007a) model. Thus, although it could
have been imagined at first glance that retrieval from WM would mainly rely on direct
access and recollective processes, non-recollective processes could be more prevalent
than expected. Indeed, if the concurrent processing involved in complex span tasks
occupies primary memory and displaces its content into secondary memory from which
memory items have to be retrieved, as Unsworth et al. (2010) assume, retrieval in WM
span tasks should frequently rely on reconstructive and non-recollective processes.
Previous studies observed that recollection has a prominent role on WM retrieval, but
remain limited by their reliance on recognition (Hedden & Park, 2003; Oberauer, 2005)
or free recall paradigms (Unsworth & Brewer, 2009; Unsworth & Engle, 2007b).
Therefore, although retrieval is one of the main functions of WM, the processes involved
in this key mechanism remain largely unexplored. As these processes raised the origins
of Markov modelling, it is possible to estimate the recall parameters by determining the
probability of recollective or non-recollective retrieval, without any need to collect
metacognitive judgments.

1.2.1 *The trichotomous theory of recall: How to quantify recollective and non-recollective process*

According to Brainerd et al. (2009), it is possible to interpret the retrieval in
different parameters in a study (Sᵢ) test (Tᵢ) cycle paradigm S₁T₁, S₂T₂, SᵢTᵢ. At the beginning
of any paradigm, each item is in a no-recall U state, where it is estimated that its
probability of being correctly recalled before the first study phase S₁ is p = 0. After S₁, the
learned items transit from U-state to two possible new states: a partial recall state P in
which the probability of a correct retrieval is 0 < p < 1, or in the perfect recall-state L in
which the probability of correct retrieval is p = 1. This L state can be accessed directly
from U at T₁, but also at subsequent test Tᵢ from state P, as L and P are communicating.
The model we used, the trichotomous theory of recall, works like a two-stage absorbing Markov chain, once an item as escaped *U*, which corresponds to a total lack of knowledge of the item, it cannot fall back to this state in further trials. Conversely, *L* is an absorbing state, such as once an item enters *L*, it cannot fall back to previous states and its recall is perfect for all the following trials.

In the trichotomous theory of recall, which reproduces these different retrieval states presented above, Brainerd et al. (2009) argued that the state *P* corresponds to nonrecollective traces and *L* to recollective, highlighting the two distinct types of retrieval. The state *L* corresponds to the recollective process, which quickly and properly finds the verbatim trace of a given item (Brainerd et al 2006), while the nonrecollective process (*P* state) must reconstruct the trace by gathering partial-identifying information known about it to have a chance of retrieval. This information comes mainly from the gist, the fuzzy information that is related to the item (Brainerd et al., 2009). Thus, faced with a set of items candidates for recall generated by the reconstruction process, comes a judgment of familiarity step to which each item must submit. If one of them generates a sufficient familiarity signal to exceed the personal decision criterion, it can be recalled. The item thus reconstructed can then be correct or false, and thus be categorized as being respectively in a *P*<sub>*E*</sub> or *P*<sub>*C*</sub> state.

The trichotomous theory of recall model mathematically quantifies the mechanisms that allow retrieval in WM tasks (Brainerd et al., 2009) and estimates the probability of an item to be in one of these particular memory states at the first recall test and at the next ones, with the probability that items may transit from one state to another in successive trials. The model is included in a three-state Markov Chain that starts from a
starting vector *W*, which defines parameters corresponding to the probabilities that an
item *i* could be in each of the possible states (*U*, *P*<sub>*E*</sub>, *P*<sub>*C*</sub> or *L*). These probabilities can be
generated on several consecutive trials through an interstate transition matrix *M* nested
in *W* (Brainerd & Reyna, 2010). This model defines seven parameters that allow
estimating the contribution of recollective and non-recollective retrieval processes in a
WM task. The first two parameters are the *D* parameters that define the probability that
an item will be accessed directly. We speak of *D*<sub>1</sub> to define the probability of a correct
recall in case of recall in *T*<sub>1</sub>, and *D*<sub>2</sub> for the following tests *T*<sub>*i*</sub>. The next two parameters
concern items that must go through a reconstruction process, with *R*<sub>1</sub> and *R*<sub>2</sub> defining the
probability for the item to enter a *P* state, respectively after *T*<sub>1</sub> test and for those that
follow. Finally, it is possible to evaluate the probability that an item entered in state *P* can
be correctly reconstructed (*P*<sub>*C*</sub>) or not (*P*<sub>*E*</sub>), once again respectively after the first test *T*<sub>1</sub>
and for the following ones. This evaluation is made through a judgment of familiarity
process (*J*<sub>1</sub>, *J*<sub>2</sub> and *J*<sub>3</sub> parameters).

**1.3. The present study**

The trichotomous theory provides the machinery needed to assess the respective
role of direct access and reconstruction in WM tasks. For this purpose, the model was
applied in the present study to a complex span task in which participants studied series
of six words for further serial recall, each word being followed by a concurrent task
consisting of reading aloud a series of digits appearing successively on screen. To obtain
estimations of the parameters of the model, each series of words was presented for three
successive study-test cycles in a design of the form S<sub>1</sub>T<sub>1</sub>, S<sub>2</sub>T<sub>2</sub>, S<sub>3</sub>T<sub>3</sub>. This study being a first
attempt to apply the trichotomous model to the complex span task paradigm, its main aim
was to assess the relative contribution of recollective and non-recollective retrieval
processes in WM. However, we also investigated the impact on these processes of a factor
known to have a strong impact on WM performance, namely the cognitive load (CL) of the
secondary task and, later, the nature of LTM representations. Within the Time-based
Resource-Sharing (TBRS) model, CL is conceived as the proportion of time during which
the secondary task occupies attention, thus preventing the refreshing of decaying
memory traces (Barrouillet, Bernardin, et al., 2004). Several studies demonstrated that
increased CL results in poorer recall performance (Barrouillet & Camos, 2012, for
reviews; 2015). The question was then to determine what kind of retrieval (i.e.,
recollective or non-recollective) is more affected by CL variations that were introduced in
our study by presenting either three or six digits to be read in a 4000ms interword
interval for low and high CL, respectively. Concurrent CL should mainly affect the
recollective processes underpinning direct retrieval. Indeed, if refreshing memory items
consists of reinstating their verbatim traces, preventing refreshing should hinder the
retrieval process based on verbatim traces (i.e., direct access), while leaving gist
representations and non-recollective processes largely unaffected (Cowan, 1999).

Alternatively, if the primary-secondary framework (PSM; Unsworth & Engle, 2007a) is
correct in assuming that complex span tasks involve retrieval from secondary memory,
higher CL resulting in more frequent displacement of memory items in secondary memory
should be associated with a more frequent recourse to non-recollective recall through
reconstruction. In terms of short-term recall performance, we expected to observe the
classic WM effects in which CL effect already demonstrated by several studies
(Barrouillet, Bernardin, et al., 2004; Barrouillet et al., 2007; Barrouillet & Camos, 2014)
with better recall performance in the low CL condition, a learning effect from trial to trial
as words are studied in three successive trials; and the presence of primacy and recency
effects over serial recall positions (Craik & Levy, 1976)
Four experiments were conducted in which we included these *CL* variations, and, in the last three of them, we also investigated the effect of LTM knowledge accessibility by manipulating the associative relatedness between the presented words (related vs. unrelated) on both retrieval processes. Previous studies with delayed recall of long lists of words (e.g., Brainerd, Wright, et al., 2002) showed that associatively related word-lists increased reconstruction, relative to unrelated word-lists. This effect comes from the greater accessibility to related words due to their proximity in a same semantic network. The recollection of a single word activated thus automatically the other ones which are therefore more easily accessible. Unrelated words lack this semantic network. We expected that associatively related word lists compared to unrelated ones would also increase reconstruction in immediate recall performance. Since LTM knowledge is known to increase recall performance in WM, we also expected to find an associative relatedness effect with related words better recalled than unrelated (Hulme et al., 1991; Hulme et al., 2003; Poirier et al., 2011; Saint-Aubin et al., 2005).

In the first experiment, participants studied lists of unrelated words and the *CL* of the secondary task was varied. In the second experiment, lists of related vs unrelated words were presented in the same task. In the third experiment, we changed the classic cycle paradigm 12 × (*S*₁*T*₁, *S*₂*T*₂, *S*₃*T*₃) in a new 12 × *S*₁*T*₁, 12 × *S*₂*T*₂, 12 × *S*₃*T*₃ cycle, that is to say that instead of carrying out the three *S*₁*T*₁, *S*₂*T*₂, *S*₃*T*₃ cycle phases successively on the same group of words, the participants first studied the first *S*₁*T*₁ cycle on the twelve presented groups of words before moving on to the second *S*₂*T*₂ cycle and lastly to the *S*₃*T*₃ one. The aim of this experiment was to examine the impact of a different encoding strategy of the same material. Finally, in the fourth and last experiments the order of presentation of the words to be recalled at each new *S*ᵢ*T*ᵢ cycle was also disturbed. In addition, a delayed long-term recall test was introduced in the last three experiments.
Previous studies showed that associative relatedness facilitates the retrieval of information from LTM and their encoding in WM but does not impact WM maintenance (e.g., Rosselet-Jordan et al., 2022). Both the CL and associative relatedness effects would then affect immediate recall performance, but not interact (Barrouillet, Bernardin, et al., 2004; Camos et al., 2019; Campoy et al., 2015; Loaiza & Camos, 2018, see also Chapter 5). CL and associative relatedness effects should also be observable on correct recall in delayed test, as attentional refreshing, when available, helps to create links between memoranda (Camos & Portrat, 2015) and relatedness being a strong effect (Hulme et al., 1991; Hulme et al., 2003; Poirier et al., 2011; Saint-Aubin et al., 2005; Saint-Aubin & Poirier, 1999a). However, the fact that word list was learned in three successive trials could led to a such good learning that it may decreased these effects in delayed recalls.

The third and fourth experiments modified the way of presentation of the three successive trials and of word presentation at each trial to observe the effects of these new encoding processes compared to the first two studies, each time so that the participants were increasingly urged to resort to reconstruction process rather than direct access.

## 2. Experiment 1

We applied the trichotomous theory of recall to a complex span task in which participants studied 6 words, each one followed by a concurrent reading digits task before serial recall in three successive study-test cycles (cycle paradigm $S_1T_1$, $S_2T_2$, $S_3T_3$). This first experiment assessed then the application of the trichotomous model to WM tasks and the impact relative to the CL variations on retrieval processes.
## 2.1. Method

### 2.1.1. Participants

Twenty-five undergraduate students (19 females; mean age = 21.9 years; *SD* = 4.1) at the University of Geneva received a partial course credit or CHF 20 for participating.

### 2.1.2. Material

We selected from the database Lexique3 (New et al., 2001) 144 monosyllabic French nouns with homogeneous frequencies of language apparition (*M* = 8.57, *SD* = 3.20) from which were created 24 series of six words. We verified in a pretest that none of these words was unknown to undergraduate students. Half of the participants studied the words of 12 of these 24 series, whereas the other half studied the other 12 series. For each participant, half of the series were presented in the low *CL* condition and the other half in the high *CL* condition, the attribution of the series to the experimental conditions being counterbalanced across participants. The 12 series were presented in random order, participants being informed of the level of *CL* of the forthcoming series. The concurrent reading task presented a total of 18 vs. 36 random digits (from 1 to 9) per trial.

### 2.1.3. Procedure

Each series was presented in three successive study-test cycles for a total of 36 trials per participants. Each trial began by the presentation for 1500ms of the words “Rapide” or “Lent” (slow and fast in French), informing participants of the rate of presentation of the digits after each word for the high and low *CL* conditions, respectively. After a delay of 100ms, the first word was displayed on screen for 800ms followed by a blank screen of 200ms and the 4000ms interword interval. This interval was filled by either three
(1183ms on – 150ms off) or six digits (516ms on – 150ms off) for the low and high CL conditions, respectively. After the 4000ms interval, the second word appeared for 800ms, followed after 200ms by a next 4000ms interword interval until the presentation of the 6 words of the series. Participants were asked to read aloud the words and the digits in order to hinder articulatory rehearsal (Camos et al., 2009) and favor attentional refreshing use (Camos et al., 2011); and to remember the words for further recall. At the end of the series, an “?” was displayed on screen to prompted recall phase. Participants were instructed to orally recall the words in correct order, saying “Je ne sais pas” (I don’t know) for any word forgotten. The experimenter took note of potential errors or missing on this reading digit task. Before the experimental session, participants were familiarized with the task by performing two series in each CL condition, each of these series being repeated in three successive trials. Figure 15 illustrates procedure for slow and high pace conditions.

<figure><img src="image_32.png" /><figcaption>Figure 15. Illustration of a trial with slow pace (upper part) and fast pace (downer part). Each trial began by an indication of the task's pace. Participants read aloud all word and digits until the appearance of a question mark which marked the start of oral serial recall.</figcaption></figure>

## 2.2. Results

All recall analyses were performed with JASP version 0.11.1 (Love et al., 2019). A 2 (CL conditions: high vs. low) x 3 (trials) x 6 (serial positions) Bayesian repeated measures ANOVA was run on the rate of words recalled in correct position. The preferred model
included the main effects of CL, trial and serial position, and the interaction between trial and serial position, BF<sub>10</sub> = 4.13 x 10<sup>199</sup>. Mean and SD for each dependent variable are available on Table 8 and Table 9 displays the BF<sub>inclusion</sub> values. Not surprisingly, there was a strong effect of repetition among trials, rate of correct recall increasing from trial one to trial three, with a stronger increase in the high than in the low CL condition, an interaction probably due to a ceiling effect in the low CL condition. Recall performance was also lower for high than low CL (M = 67.9, SD = 20.0 and M = 74.5, SD = 17.6, respectively), replicating the CL effect previously observed in several studies. It is worth to note that despite the interaction with trials, the CL effect was still significant in the last trial. There was also a strong effect of serial position, which did not significantly interact with CL, but with trials, the effect of serial position progressively vanishing as recall performance became nearly perfect with repetitions. Overall, the effects of serial position and CL were conformed to what is usually observed in complex span task

2.2.1 *Trichotomous analysis*

More interesting for our purpose were the results of computational modeling.
Trichotomous analyses were all performed with multiTree v046 (see Moshagen, 2010),
which assess the fit of the chosen model to the data. This mathematical model, which is
shown in Equation 2, consists of a starting vector (W) and an interstate transition matrix
(M). The parameters of W are the probabilities of an item being in state L, P, or U on the
first recall test, whereas the parameters of M are the probabilities that an item transitions
from one state to another in subsequent trials. Among the different available versions of
the trichotomous model, we chose to use the model called *Alternative error*, which
assumes that a given item can only access the L state at any trial only after failure to recall,
that is when the item was in state U or PE at the previous trial, hence the *error* appellation.
In all our experiments, the *Alternative Error* model provided the best fit for serial recall data and was consequently chosen for sake of comparison (detailed in Chapter 2, part 3.2, and Appendix B). The parameters estimate reached a high reliability $G^2 = 3.07$ for all parameters. Parameters *D* reflecting the access to the *L* state and direct recollective recall, whereas parameters *R* and *J* reflected the state *P* for reconstruction and judgment of familiarity, with a distinction between all parameters in first trial (*i*₁) and parameters in subsequent ones (*i*₁).

As shown in Table 10, the proportion of reconstructed items was lower in both first and subsequent trials and in both *CL* conditions than recollected items, the rate of reconstructed items (correctly recalled or not) almost disappearing in subsequent trials. Moreover, parameter *D*₂ was systematically higher than *D*₁, indicating that, in both conditions, the probability for a given item to reach the level of learning allowing its direct access (i.e., *L*) was lower in the first than in the subsequent trials. We also observed an effect of *CL* in more recollection in parameters *D*₁ for low than high *CL*, indicating the former observed better recall in the low *CL* condition resulted from a greater direct accessibility of the memory traces.

We observed a marginal effect of *CL* on *R*₁, suggesting that more reconstructions were potentially made in the low *CL* condition. This marginal effect had to be clarified in following experiment. A *CL* effect was observed on *J*₂, where more familiarity judgments were made on the high *CL* condition than on the low. This result can be interpreted as the repercussion of the opposite effect observed in *D*₁: Anything that never have been recollected and reached the *L* state was then stuck up the states *P* or *U*, therefore with some failed judgments of familiarity. We observed no significant effect of *CL* on *D*₂, *R*₂, *J*₁ and *J*₃.
<table><caption>Table 8. Descriptive statistics of all dependent variables (DVs) in Experiments 1, 2, 3 and 4 as a function of cognitive load (CL) of the secondary task (low vs. high), associative relatedness between memory words (related vs. unrelated) and trials (1st, 2nd and 3rd).</caption><thead><tr><th rowspan="3">DVs</th><th colspan="3">Low CL</th><th colspan="3">High CL</th></tr><tr><th colspan="3">Related</th><th colspan="3">Unrelated</th></tr><tr><th>1<sup>st</sup> trial / 2<sup>nd</sup> trial / 3<sup>rd</sup> trial</th><th>1<sup>st</sup> trial / 2<sup>nd</sup> trial / 3<sup>rd</sup> trial</th><th>1<sup>st</sup> trial / 2<sup>nd</sup> trial / 3<sup>rd</sup> trial</th><th>1<sup>st</sup> trial / 2<sup>nd</sup> trial / 3<sup>rd</sup> trial</th><th>1<sup>st</sup> trial / 2<sup>nd</sup> trial / 3<sup>rd</sup> trial</th><th>1<sup>st</sup> trial / 2<sup>nd</sup> trial / 3<sup>rd</sup> trial</th></tr></thead><tbody><tr><td colspan="7"><em>Experiment 1</em></td></tr><tr><td><strong>Recall</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Serial recall</td><td></td><td>47.8 (23.5) / 80.6 (19.1) / 95.1 (8.47)</td><td></td><td></td><td>37.3 (24.7) / 76.8 (21.7) / 89.7 (13.6)</td><td></td></tr><tr><td colspan="7"><em>Experiment 2</em></td></tr><tr><td><strong>Recall</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Serial recall</td><td>66.0 (17.5) / 84.7 (13.8) / 95.3 (5.66)</td><td>46.9 (15.0) / 67.0 (18.8) / 79.7 (15.9)</td><td>59.0 (13.6) / 79.2 (15.1) / 93.4 (5.99)</td><td>39.7 (18.1) / 60.7 (20.2) / 74.1 (21.6)</td><td></td><td></td></tr><tr><td><strong>LTM</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Short delayed</td><td>78.7 (17.9)</td><td>63.4 (25.0)</td><td>81.1 (20.2)</td><td>66.1 (25.2)</td><td></td><td></td></tr><tr><td colspan="7"><em>Experiment 3</em></td></tr><tr><td><strong>Recall</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Serial recall</td><td>66.0 (17.49) / 84.7 (13.8) / 95.3 (5.66)</td><td>46.9 (15.0) / 67.0 (18.8) / 79.7 (15.9)</td><td>59.0 (13.6) / 79.2 (15.1) / 93.4 (5.99)</td><td>39.7 (18.1) / 60.7 (20.2) / 74.1 (21.6)</td><td></td><td></td></tr><tr><td>Free recall</td><td>86.5 (8.59) / 95.7 (4.74) / 99.5 (1.48)</td><td>64.2 (13.1) / 82.6 (11.2) / 89.0 (9.38)</td><td>83.5 (8.28) / 94.7 (5.76) / 98.1 (2.54)</td><td>59.4 (12.4) / 77.0 (13.4) / 83.5 (12.4)</td><td></td><td></td></tr><tr><td><strong>LTM</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Short delayed</td><td>82.3 (13.6)</td><td>57.8 (23.78)</td><td>85.1 (12.7)</td><td>56.0 (22.0)</td><td></td><td></td></tr><tr><td>Long delayed</td><td>65.2 (22.2)</td><td>29.9 (18.1)</td><td>59.9 (20.5)</td><td>29.6 (20.0)</td><td></td><td></td></tr><tr><td colspan="7"><em>Experiment 4</em></td></tr><tr><td><strong>Recall</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Serial recall</td><td>59.3 (13.8) / 86.2 (7.51) / 94.4 (6.86)</td><td>50.3 (22.7) / 85.1 (15.1) / 95.7 (6.15)</td><td>58.2 (14.7) / 80.9 (12.9) / 93.2 (8.43)</td><td>44.8 (23.2) / 75.3 (23.1) / 92.4 (9.12)</td><td></td><td></td></tr><tr><td>Free recall</td><td>83.0 (6.77) / 98.0 (2.82) / 99.5 (1.05)</td><td>66.2 (15.4) / 93.2 (7.71) / 98.6 (3.15)</td><td>80.1 (9.19) / 96.0 (5.19) / 99.3 (1.40)</td><td>60.9 (14.9) / 89.5 (10.4) / 97.1 (4.22)</td><td></td><td></td></tr><tr><td><strong>LTM</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Short delayed</td><td>32.0 (9.22)</td><td>18.6 (6.85)</td><td>31.5 (10.2)</td><td>17.3 (8.27)</td><td></td><td></td></tr><tr><td>Long delayed</td><td>20.0 (11.6)</td><td>7.45 (5.44)</td><td>21.9 (9.25)</td><td>7.04 (6.24)</td><td></td><td></td></tr></tbody></table>

*Note.* Performances are described in percentage and standard deviations are in brackets.
<table><caption>Table 9. BF<sub>inclusion</sub> of all variables of interest for recall performance (percentage of recall in immediate and delayed recall) in all experiments.</caption><thead><tr><th>Effects</th><th>Exp 1</th><th>Exp2</th><th>Exp3</th><th>Exp4 (serial)</th><th>Exp4 (free)</th></tr></thead><tbody><tr><td>CL</td><td>2.61 × 10<sup>6</sup></td><td>263478</td><td>5.01 × 10<sup>10</sup></td><td>3.27 × 10<sup>7</sup></td><td>593128</td></tr><tr><td>Relatedness</td><td>-</td><td>1.73</td><td>3038</td><td>.10</td><td>1.11 × 10<sup>30</sup></td></tr><tr><td>CL × Relatedness</td><td>-</td><td>.67</td><td>.08</td><td>.22</td><td>.68</td></tr><tr><td>Trial</td><td>2.25 × 10<sup>177</sup></td><td>+∞</td><td>1.70 × 10<sup>217</sup></td><td>3.02 × 10<sup>14</sup></td><td>3.87 × 10<sup>36</sup></td></tr><tr><td>Trial × CL</td><td>.57</td><td>.27</td><td>.19</td><td>.18</td><td>.015</td></tr><tr><td>Trial × Relatedness</td><td>-</td><td>1.07 × 10<sup>11</sup></td><td>.19</td><td>99.9</td><td>990</td></tr><tr><td>Trial × CL × Relatedness</td><td>-</td><td>.02</td><td>.06</td><td>.39</td><td>.02</td></tr><tr><td>Serial position</td><td>3.64 × 10<sup>38</sup></td><td>7.58 × 10<sup>128</sup></td><td>1.38 × 10<sup>163</sup></td><td>6.98 × 10<sup>89</sup></td><td>1.88 × 10<sup>6</sup></td></tr><tr><td>Serial position × CL</td><td>.007</td><td>.01</td><td>.01</td><td>.02</td><td>.002</td></tr><tr><td>Serial position × Relatedness</td><td>-</td><td>.01</td><td>872</td><td>6.84 × 10<sup>-4</sup></td><td>.01</td></tr><tr><td>Serial position × Trial</td><td>1.62 × 10<sup>7</sup></td><td>5.94 × 10<sup>44</sup></td><td>1.80 × 10<sup>27</sup></td><td>2.52</td><td>.002</td></tr><tr><td>Serial position × CL × Relatedness</td><td>-</td><td>.01</td><td>.09</td><td>.003</td><td>.003</td></tr><tr><td>Serial position × CL × Trial</td><td>.008</td><td>5.75 × 10<sup>-4</sup></td><td>.02</td><td>.004</td><td>.760 × 10<sup>-4</sup></td></tr><tr><td>Serial position × Relatedness × Trial</td><td>-</td><td>.01</td><td>.10</td><td>.003</td><td>.09</td></tr><tr><td>Serial position × CL × Relatedness × Trial</td><td>-</td><td>.002</td><td>.03</td><td>.049</td><td>.04</td></tr><tr><td>Short differed LTM - CL</td><td>-</td><td>.21</td><td>.20</td><td></td><td>.23</td></tr><tr><td>Short differed LTM - Relatedness</td><td>-</td><td>9.25 × 10<sup>6</sup></td><td>111460</td><td></td><td>689.9</td></tr><tr><td>Short differed LTM - CL × Relatedness</td><td>-</td><td>.20</td><td>.43</td><td></td><td>.26</td></tr><tr><td>Long differed LTM - CL</td><td>-</td><td>.23</td><td>.34</td><td></td><td>-</td></tr><tr><td>Long differed LTM - Relatedness</td><td>-</td><td>3.55 × 10<sup>6</sup></td><td>2.55 × 10<sup>6</sup></td><td></td><td>-</td></tr><tr><td>Long differed LTM - CL × Relatedness</td><td>-</td><td>.35</td><td>.43</td><td></td><td>-</td></tr></tbody></table>
<table><caption>**Table 10.** Response tracking pattern in percentage among the three successive trials in the low and high load cognitive load (CL) conditions as a function of associative relatedness between memory words for all experiments, with E for Errors in recall and C for Correct recall in right part of table. In left part of table, parameter estimates generated by the Alternative model.</caption><thead><tr><th></th><th></th><th>EEE</th><th>EEC</th><th>ECE</th><th>ECC</th><th>CEE</th><th>CEC</th><th>CCE</th><th>CCC</th><th>D<sub>1</sub></th><th>D<sub>2</sub></th><th>R<sub>1</sub></th><th>R<sub>2</sub></th><th>J<sub>1</sub></th><th>J<sub>2</sub></th><th>J<sub>3</sub></th></tr></thead><tbody><tr><td colspan="17"><em>Experiment 1</em></td></tr><tr><td>Low CL</td><td></td><td>4</td><td>13</td><td>1</td><td>35</td><td>0</td><td>2</td><td>0</td><td>45</td><td>.45 (.02)</td><td>.65 (.04)</td><td>.74 (.47)</td><td>0 (1.57)</td><td>.07 (.04)</td><td>.11 (.14)</td><td>.56 (.48)</td></tr><tr><td>High CL</td><td></td><td>8</td><td>14</td><td>2</td><td>39</td><td>0</td><td>1</td><td>1</td><td>36</td><td>.34 (.04)</td><td>.52 (.16)</td><td>.23 (.21)</td><td>.22 (.27)</td><td>.23 (.26)</td><td>.70 (.29)</td><td>.77 (.30)</td></tr><tr><td colspan="17"><em>Experiment 2</em></td></tr><tr><td>Low CL</td><td>Related</td><td>3</td><td>6</td><td>1</td><td>27</td><td>1</td><td>1</td><td>1</td><td>61</td><td>.61 (.02)</td><td>.71 (.02)</td><td>.32 (.10)</td><td>0 (const)</td><td>.19 (.05)</td><td>.11 (const)</td><td>0 (.27)</td></tr><tr><td>High CL</td><td>Related</td><td>4</td><td>8</td><td>1</td><td>24</td><td>0</td><td>3</td><td>0</td><td>58</td><td>.58 (.02)</td><td>.66 (.02)</td><td>.21 (.2.90)</td><td>.54 (3.19)</td><td>.07 (.46)</td><td>.12 (.09)</td><td>.08 (.20)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>5</td><td>11</td><td>1</td><td>33</td><td>0</td><td>1</td><td>0</td><td>49</td><td>.49 (.02)</td><td>.66 (.02)</td><td>.21 (.2.90)</td><td>.54 (3.19)</td><td>.07 (.46)</td><td>.12 (.09)</td><td>.08 (.20)</td></tr><tr><td>High CL</td><td>Related</td><td>4</td><td>8</td><td>1</td><td>24</td><td>0</td><td>3</td><td>0</td><td>58</td><td>.58 (.02)</td><td>.66 (.02)</td><td>.21 (.2.90)</td><td>.54 (3.19)</td><td>.07 (.46)</td><td>.12 (.09)</td><td>.08 (.20)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>8</td><td>14</td><td>1</td><td>32</td><td>0</td><td>2</td><td>0</td><td>42</td><td>.41 (.02)</td><td>.52 (.05)</td><td>.23 (.21)</td><td>.22 (.27)</td><td>.23 (.26)</td><td>.70 (.29)</td><td>.77 (.30)</td></tr><tr><td colspan="17"><em>Experiment 3</em></td></tr><tr><td>Low CL</td><td>Related</td><td>2</td><td>8</td><td>2</td><td>22</td><td>1</td><td>4</td><td>1</td><td>60</td><td>.58 (.02)</td><td>.58 (.07)</td><td>.90 (.09)</td><td>0 (const)</td><td>.22 (.08)</td><td>.35 (const)</td><td>.55 (.07)</td></tr><tr><td>High CL</td><td>Related</td><td>4</td><td>9</td><td>2</td><td>26</td><td>1</td><td>7</td><td>1</td><td>50</td><td>.50 (.02)</td><td>.59 (.03)</td><td>.91 (.11)</td><td>0 (const)</td><td>.28 (2.35)</td><td>.16 (const)</td><td>.71 (.09)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>12</td><td>13</td><td>3</td><td>25</td><td>2</td><td>6</td><td>3</td><td>35</td><td>.27 (.04)</td><td>.32 (.08)</td><td>.61 (.11)</td><td>.11 (.16)</td><td>.44 (.10)</td><td>.58 (.09)</td><td>.71 (.09)</td></tr><tr><td>High CL</td><td>Related</td><td>4</td><td>9</td><td>2</td><td>26</td><td>1</td><td>7</td><td>1</td><td>50</td><td>.50 (.02)</td><td>.59 (.03)</td><td>.91 (.11)</td><td>0 (const)</td><td>.28 (2.35)</td><td>.16 (const)</td><td>.71 (.09)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>18</td><td>16</td><td>5</td><td>22</td><td>1</td><td>5</td><td>2</td><td>32</td><td>.24 (.04)</td><td>.10 (.15)</td><td>.58 (.15)</td><td>.26 (.15)</td><td>.35 (.11)</td><td>.62 (.11)</td><td>.77 (.10)</td></tr><tr><td colspan="17"><em>Experiment 4 (serial recall)</em></td></tr><tr><td>Low CL</td><td>Related</td><td>11</td><td>8</td><td>7</td><td>14</td><td>6</td><td>14</td><td>11</td><td>29</td><td>.07 (.10)</td><td>0 (.17)</td><td>.92 (.05)</td><td>0 (.34)</td><td>.62 (.06)</td><td>.63 (.07)</td><td>.67 (.09)</td></tr><tr><td>High CL</td><td>Related</td><td>13</td><td>9</td><td>8</td><td>18</td><td>6</td><td>11</td><td>8</td><td>27</td><td>.10 (.10)</td><td>.06 (.20)</td><td>.89 (.07)</td><td>0 (.29)</td><td>.53 (.06)</td><td>.62 (.09)</td><td>.66 (.12)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>13</td><td>12</td><td>10</td><td>16</td><td>8</td><td>8</td><td>11</td><td>22</td><td>.14 (.04)</td><td>.20 (.07)</td><td>.42 (.03)</td><td>.73 (.09)</td><td>.96 (const)</td><td>.53 (.06)</td><td>.41 (.09)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>15</td><td>17</td><td>9</td><td>15</td><td>6</td><td>10</td><td>7</td><td>20</td><td>.08 (.05)</td><td>0 (.12)</td><td>.68 (.31)</td><td>.58 (.24)</td><td>.55 (.24)</td><td>.55 (.08)</td><td>.63 (.09)</td></tr><tr><td colspan="17"><em>Experiment 4 (free recall)</em></td></tr><tr><td>Low CL</td><td>Related</td><td>1</td><td>2</td><td>1</td><td>9</td><td>1</td><td>3</td><td>2</td><td>82</td><td>.81 (.02)</td><td>.69 (.07)</td><td>.97 (.20)</td><td>0 (const)</td><td>.35 (.08)</td><td>.37 (const)</td><td>.20 (.21)</td></tr><tr><td>High CL</td><td>Related</td><td>1</td><td>3</td><td>2</td><td>10</td><td>1</td><td>3</td><td>3</td><td>77</td><td>.75 (.02)</td><td>.60 (.80)</td><td>.85 (.16)</td><td>0 (const)</td><td>.41 (.08)</td><td>.48 (const)</td><td>.35 (.18)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>2</td><td>4</td><td>3</td><td>18</td><td>2</td><td>5</td><td>3</td><td>65</td><td>.64 (.02)</td><td>.67 (.06)</td><td>.64 (.20)</td><td>1 (0.001)</td><td>.42 (.08)</td><td>.31 (.08)</td><td>.05 (.30)</td></tr><tr><td>High CL</td><td>Unrelated</td><td>3</td><td>7</td><td>3</td><td>20</td><td>3</td><td>7</td><td>5</td><td>52</td><td>.49 (.04)</td><td>.55 (.08)</td><td>.69 (.86)</td><td>.48 (1.3)</td><td>.50 (.60)</td><td>.46 (.10)</td><td>.35 (.23)</td></tr></tbody></table>

Note: Standard deviations are indicated in parenthesis. The indication “const” stand for “constant”, indicating that parameter was fixed to the based model value.
## 2.3. Discussion

These results revealed that the dual-process model of recall provides a very good fit of the data, confirming that these two distinct processes govern retrieval from WM. The trichotomous model indicates that most of the recalled items in WM span are retrieved through recollective, and not reconstructive, processes. The recall from WM is therefore governed by the two recovery processes, but it is the direct access which constitutes the most privileged process. This finding is in line with the widespread conception of WM as a buffer maintaining, in view of online processing, a small amount of information in a state of activation allowing its direct and fast access (Baddeley, 1986; Miller et al., 1960; Newell, 1990). Recollection is affected by *CL*, which is therefore a determining factor in accessing and maintaining information, as described in the TBRS model (Barrouillet, Bernardin, et al., 2004). The marginal effect of *CL* on reconstruction requires further evaluation. In addition, the low presence of reconstruction processes can be challenged by the introduction of item benefiting from LTM support.

## 3. Experiment 2

In this experiment, the same successive study-test cycles cycle paradigm $S_1T_1$, $S_2T_2$, $S_3T_3$ and *CL* manipulations were applied, but we manipulated the associative relatedness of the memory items to vary the implication of LTM. As Experiment 1 showed the predominance of recollective direct-access process, increasing the implication of LTM knowledge should increase reconstruction (Brainerd, Wright, et al., 2002). Moreover, this second experiment aimed at replicating the *CL* effect that reduced retrieval from recollective process and at clarifying the marginal observations made on Experiment 1.
**3.1. Method**

3.1.1. *Participants*

Sixty students enrolled at the University of Fribourg took part in Exp. 2 (51 females, mean age = 20.9 years, SD = 1.97). All participants were native French speakers and received course credits for participating. None of them took part of the pre-test of the material or in the previous experiment.

3.1.2. Material

Twenty-six series of 6 associatively related words were created thanks to De La
Haye (2003) and Duscherer et al. (2009) French verbal association norms. The 12 best
rated series were kept as experimental material according to the judgment of ten extern
judges. Afterwards, 12 unrelated series were also created by selecting 72 random words.
As we wanted to only manipulate the relatedness as LTM factor, we were careful to create
series with homogeneous frequencies of language apparition (by using the French
linguistic database Lexique3 from New et al., 2001) between related (M = 44.6, SD = 9.46),
and unrelated series (M = 44.8, SD = 2.76, BF₁₀ = .29). Words were 1-to-3-syllables nouns
(76%, 54% of which being male grammatical gender in French), and verbs (24%) and
each series proposed between 0 to 2 verbs. The (non)associative relatedness strength of
these 24 series of 6 words were finally pre-tested by 50 participants (39 females, mean
age M = 29.0 years, SD = 9.79). Participants rated on a Likert scale from 1 (not related) to
7 (very related) and it resulted in a mean of M= 6.80, SD = 0.49 for related series and M =
1.37, SD= 0.87 for unrelated ones. Participant studied either the related words list (e.g.,
*bird, cage, feather, egg, beak, and wing*) or the unrelated one (e.g., *soul, cocktail, puppet,
outlet, puzzle* and *week*, all in French in study). We also choose 64 arithmetic equations
(aa × b types) to propose a 10min distraction task before a 10-minutes-differed recall.
### 3.1.3. Procedure

We used the software E-Prime 2.0 (Psychology Software Tools Inc. [E-Prime 2.0], 2012) to display the same complex span task as in Experiment 1 (Figure 15). Half of the studied related or unrelated series that were presented in the high CL condition, and the other half in the high CL condition. After completing the three successive Study-Recall trials of the twelve series of words, participants were asked to solve as many arithmetic equations as possible in 10 minutes. Afterwards, we instructed participants to recall the whole experimental word lists. This delayed recall lasted for at least 5 minutes, to ensure that participants really took enough time to search in memory. Moreover, we also asked for a 7 days-differed new long-term recall, made online through Qualtrics XM Platform software (Qualtrics, Provo, UT).

## 3.2. Results

Data diverging from more than 3SDs of the means of each condition per trials were replaced by the mean of this condition per trials (i.e., 15% of the data scored as serial recall and 7.8% of the free recall data). Mean and SD for each dependent variable are available on Table 8 and Table 9 shows the $BF_{inclusion}$ values for each main and interaction effects for the 60 participants on which the following analyses were carried out. No data nor participant were excluded due to bad performance in the concurrent task as it led to few errors ($M = .52%, SD = .84$).

### 3.2.1. Recall performance

We conducted a Bayesian analysis with *CL*, trial and serial position (first to sixth) as within-subjects' factors and associative relatedness as between-subject factor on the
percentage of words recalled in the correct serial positions. The analysis exhibited several models with a BF<sub>10</sub> of +∞ when compared to the null model, so we chose the highest BF (BF<sub>M</sub> = 144) as the best one. This best model included the four main effects of *CL*, trial, serial position, and relatedness, as well as the interactions between trial and serial position and trial and relatedness. Performances were still higher in low (*M* = 78.9%, *SD* = 19.0) than in high *CL* (*M* = 75.1%, *SD* = 20.7). Recall increased among trials (*M* = 55.0%, *SD* = 26.0; *M* = 83.2%, *SD* = 20.0 and *M* = 92.8%, *SD* = 13.6 for first, second and third trials, respectively). As in Experiment 2, performance of recall decreased among serial position from the first to the fourth (*M* = 92.6%, *SD* = 11.8; *M* = 86.7%, *SD* = 16.9; *M* = 76.7%, *SD* = 21.0 and *M* = 70.3%, *SD* = 22.5, respectively) until reaching a stable level after the fourth serial position (*M* = 66.7%, *SD* = 23.0 and *M* = 69.0%, *SD* = 23.7 for the fifth and the sixth position, respectively). Recalls at each position were strictly different from each other, except the fourth vs. the fifth (BF<sub>10</sub> = .11) and the fifth vs. the sixth serial positions (BF<sub>10</sub> = 1.31). We also observed the effect of relatedness, related words (*M* = 81.1%, *SD* = 16.7) being better recalled than unrelated ones (*M* = 72.8%, *SD* = 23.0). Associative relatedness interacted with trials the same way as the previous analyses.

As mentioned previously, recall tended to decrease among serial positions, but also increased among trials. The interaction between trials and serial position shows that the decrement among serial position was incrementally less important as trials progressed (lowest Bayes factors indicated by a follow-up Bayesian paired t-tests were higher to BF<sub>10</sub> > 7.36 × 10<sup>11</sup> for the difference between the first and the second trials, BF<sub>10</sub> > 1.39 × 10<sup>14</sup> by comparing the first and the third and BF<sub>10</sub> > 124 for the difference between the second and the third trials).
### 3.2.2. Short delayed recall performance

Participants were instructed to recall as much words as they remembered after completing for 10 minutes the arithmetic equations. They had no limit of time to complete the task. A Bayesian analysis with CL as within-subject factors and associative relatedness as between-subject factor on the percentage of words correctly recalled indicated that the single main effect of relatedness was the preferred model ($BF_{10} = 1.31 \times 10^7$, see Table 9). Indeed, related words were better recalled than unrelated ones ($M = 31.7\%, SD = 9.72$ and $M = 18.0\%, SD = 7.56$, respectively).

### 3.2.3. Long delayed recall performance

Seven days after their day of participation, participant received the same instruction as during the immediate LTM recall task. A Bayesian analysis with CL as within-subject factors and associative relatedness as between-subject factor on the percentage of words correctly recalled showed the same result as for the immediate long-term recall with the best model preferring the single main effect of relatedness, $BF_{10} = 5.33 \times 10^6$. Participant again recalled more words when having study related words ($M = 21.0\%, SD = 10.4$) than unrelated ones ($M = 7.25\%, SD = 5.84$).

### 3.2.4. Trichotomous analysis

The objective of this experiment was to verify that the traces in WM are recalled mainly by recollection and to rule out any ambiguity as to the presence or not of an effect of CL on the reconstruction. In addition, the addition of the associative relatedness component, known to improve recall performance in WM thanks to the LTM knowledge associated with it, may have an interesting impact on the recollection and reconstruction
processes, as it could both affect the encoding and the reconstruction of words during
recall.

As in the previous experiment, $G^2$ ratio was great enough to reject the null
hypothesis ($G^2$ = 6.65, *p* = .16). Table 10 displays all parameters values for the 1st and
subsequent study phases for the *CL* and relatedness conditions. As in Experiment 1, the
part of recollected item was much more important than the part of reconstruction in all
conditions and especially in subsequent trials. Moreover, $D_2$ was also higher than $D_1$. This
therefore confirmed that WM traces are mainly recollected. We also replicated the effect
of *CL* on $D_1$, with more recollection in low *CL*, but also on $D_2$, and the reverse CL effect on
$J_3$ instead of $J_2$. Results also confirmed here the lack of *CL* effect on the parameters *R*.
Associative relatedness influenced $D_1$, related words being better recollected than
unrelated ones, but has no effect on reconstruction. Moreover, parameters showed that
the *CL* effect was not significant on related words on $R_2$.

**3.3. Discussion**

Recollection being the only one affected by associative relatedness, we can therefore
suppose that it affects the encoding of traces in the episodic loop in WM. If LTM knowledge
favors the encoding of WM traces, this could minimize the *CL* effect. A high *CL* degrades
traces more quickly in WM, but the fact that these traces are associatively related
consolidates them. It remains to be determined whether this effect is indeed due to
improved encoding (item chucking rather than individual item encoding) or better
reconstruction during recall.
## 4. Experiment 3

In this experiment, the 12 successive study-test cycles paradigm $S_1T_1, S_2T_2, S_3T_3 \times 12$ was transformed into 12 successive presentations of the same study-test cycle (i.e., a "$12 \times S_1T_1$; $12 \times S_2T_2$; $12 \times S_3T_3$" paradigm). that a word group was no more studied three times in a row before, but only seen a second and a three time after having studied the 11 other word groups in the same way. This modified the learning of these words and then impacted the encoding strategy, compared to the block learning performed in Experiments 1 and 2. To remain comparable to Experiment 2, *CL* and associative relatedness variations were identical as previously.

The previous experiment suggested that the associative relatedness allowed an easier encoding, its effects being visible only on the very first trial recollection ($D_1$) and not on the following ones ($D_2$). In addition, recall performance also showed that relatedness only boosted recall on the first viewing of trials and not on subsequent ones. To defy the track of encoding, the paradigm was changed in this experiment to ensure that participants no longer studied the trials directly three times in a row, but now must re-encode them on subsequent delayed viewing. If the relatedness really affects encoding, we should thus observe its effect also in $D_2$ and not in $D_1$ only.

### 4.1. Method

#### 4.1.1. Participants

Sixty students at the University of Fribourg took part in our study (51 females, mean age = 21.5 years, *SD* = 2.27). Recruitment followed the same rules as in the previous experiment.
### 4.1.2. Material and Procedure

We used the exact same material as in Experiment 2. The procedure was very similar as in Experiments 1 and 2, one point apart. Instead of studying the whole twelve series in three successive trials, participants in this experiment did a first Study-Recall phase of the whole 12 essays before moving to a second and a third Study-Recall phases. To summarize, the Study (S) and Recall I phases in Experiments 1 and 2 procedures can be written "$S_1T_1$; $S_2T_2$; $S_3T_3 \times 12$" when Experiment 3 is "$12 \times S_1T_1$; $12 \times S_2T_2$; $12 \times S_3T_3$".

## 4.2. Results

We replaced data diverging from more than 3SDs of the means of each condition per trials were replaced by the mean of this condition per trials (i.e., 1.1% of the data scored as serial recall). Mean and SD for each dependent variable and $BF_{inclusion}$ values for each main and interaction effects for the 60 participants are again respectively displayed on Table 8 and 9. No data were exclude based on the concurrent task performance because, as in previous Experiment, few errors were made ($M = .46\%$, $SD = .67$).

### 4.2.1. Recall performance

Recall performance was also analysed according to serial positions from first to sixth. A Bayesian analysis with CL, trial, and serial position (first to sixth) as within-subjects' factors and associative relatedness as between-subject factor on the percentage of words correctly recalled in the correct serial positions showed several models with a $BF_{10}$ of +∞ when compared to the null model, so we chose the highest BF ($BF_M = 175$) as the best one. This best model included the main effects of CL, trial, serial position, and relatedness, as well as the interactions between serial position and trial and serial
position and relatedness. CL impacted recall (M = 73.2%, SD = 19.2 and M = 67.8%, SD = 20.6 for low and high CL, respectively), as well as trial (M = 53.0%, SD = 21.4, M = 73.1%, SD = 21.4 and M = 85.5%, SD = 16.5 for first, second and third trials) and relatedness (M = 79.0%, SD = 17.3 and M = 62.1%, SD = 22.6 for related and unrelated words, respectively).

Until the fourth serial position, recall performance decreased and got stable at the fifth and sixth serial position (M = 88.4%, SD = 13.5; M = 82.1%, SD = 17.5; M = 72.2%, SD = 21.7; M = 61.9%, SD = 22.0; M = 58.3%, SD = 22.4; and M = 60.1%, SD = 22.5, for first to sixth serial positions, respectively). Serial positions recall all differed from each other (BF₁₀ > 20.4), apart from the sixth one which did not differ from the fourth and the fifth (BF₁₀ = .18 and BF₁₀ = .32, respectively). Serial positions interacted with associative relatedness. Related words were better recalled at each serial position, but this associative relatedness effect became less important at the third and fourth serial position (BF₁₀ = 29.0 and BF₁₀ = 16.7, respectively) according to follow-up Bayesian paired t-tests.

Serial positions also interacted with trial. We conducted another follow-up Bayesian t-test and observed that both effects acted like the main effects described above, but trial effect was weaker in the third trial at each serial position (BF₁₀ > 102).

### 4.2.2. Short delayed recall performance

Participants received the same instructions as in Experiment 2. We conducted a Bayesian analysis with CL as within-subject factors and relatedness as between-subject factor on the percentage of words correctly recalled. Best model indicated only the main effect of relatedness, BF₁₀ = 110770. Related words were indeed still better recalled than unrelated ones (respectively M = 83.7%, SD = 13.2 and M = 56.9%, SD = 22.9).
### 4.2.3. Long delayed recall performance

Seven days after their day of participation, participant received the same instruction as during the immediate LTM recall task and as in Experiment 2. The same Bayesian analysis was performed on the percentage of correct recall and highlighted the same preferred model, with only the main effect of relatedness, $BF_{10} = 2.70 \times 10^6$. As in the previous analysis, related words ($M = 65.6\%$, $SD = 21.4$) were better recalled than unrelated ones ($M = 29.7\%$, $SD = 20.0$).

### 4.2.4. Trichotomous analysis

The model provided a ratio statistic $G^2$ sufficient to reject the null hypothesis, $G^2 = 12.5$. Table 10 shows all parameters values for the 1st and subsequent study phases for the CL and relatedness conditions. Replicating Experiments 1 and 2 results, we observed more recollection than reconstruction, but $D_2$ was equivalent to $D_1$ whereas $D_2$ was always higher than $D_1$ in previous studies, consequence of the greater learning effect among trials. There was significantly more recollection for low than high CL and for related versus unrelated words in $D_1$ and no effect was observed on $R_1$ and $R_2$. We also observe a reverse effect of relatedness on $J_1$ and on $J_3$. However, contrary to Experiment 2, CL effect was not significant in $D_2$, but associative relatedness was, as predicted by our hypothesis following the procedure changes.

## 4.3. Discussion

Since the trials were no longer presented directly one after the other but in a deferred manner, the items must be re-encoded for each new trial. The relatedness effect therefore is present as much on $D_1$ as on $D_2$. In this experiment where we forced a re-encoding on each trial, related words were therefore rather recollected while unrelated
ones were also reconstructed, even if the reconstruction rate remained low compared to
the recollection. However, even though study-recall trials were more distant from each
other unlike in the first two experiments, participants could still use contextual cues,
which might partly explain the predominance of the recollection process. Thus, in the next
experiment, we tackled removing these contextual cues and thus bring together many
conditions favoring the use of reconstruction.

**5. Experiment 4**

This last experiment kept the new $12 \times S_1T_1; 12 \times S_2T_2; 12 \times S_3T_3$ proposed in Experiment 3, but also distorted the order of appearance of words at each trial to induce proactive interference effect and thus, a heavier re-encoding from participants compared to previous experiments. Moreover, associative relatedness became a within-subject factors in such a way that LTM strategies of maintenance could be more spontaneously be used by participants even when studying unrelated words.

**5.1. Method**

5.1.1. *Participants*

Thirty-two students at the University of Fribourg took part in our study (24 females, mean age = 21.3 years, SD = 1.79). Recruitment followed the same rules as in the previous experiments.

**5.1.2. Material**

For this experiment, associative relatedness was within-subjects. We therefore
resumed our previous related and unrelated series to create two new lists, each made of
six related series and six unrelated ones. We chose to work with two lists instead of just
one to avoid a too long experiment but still allowing to observe results in a large panel of
different words. Because of this reunification, 36% of unrelated words had to be replaced
and words series were pre-tested again by 58 participants (30 females, mean age M =
27.97 years, SD = 8.96) and it resulted in a mean of M = 6.65, SD = 0.67 for related series
and M = 1.67, SD = 1.01 for unrelated ones. In the experiment, participants studied only
one of these two lists, so they were equally balanced in terms of frequencies between
related (M = 45.1, SD = 9.34), and unrelated series (M = 44.9, SD = 2.85), BF₁₀ = .29, and
grammatical status (76% of nouns, 54% of which being male grammatical gender in
French. The concurrent reading task and the arithmetical equations were the same as in
Experiments 2 and 3.

5.1.3. *Procedure*

The procedure was close to the one used in Experiment 3, aside from three aspects:
Participants studied both associative related and unrelated series. Moreover, there were
still three study-recall trials 12 × S₁T₁; 12 × S₂T₂; 12 × S₃T₃ as in Experiment 3, but this time
the order of appearance of words within the series was randomly changed at each trial,
whereas in Experiments 2 and 3, words were presented in the same order at each trial.
Instruction given to participants remained to recall words in their serial order of
presentation.

**5.2. Results**

Less than 1% (0.52%) of the data diverging from more than 3SDs of the means of
each condition per trials were replaced by the mean of their condition per trial. Mean and
SD for each dependent variable and BF<sub>inclusion</sub> values for each main and interaction effects
for the 32 participants are respectively displayed on Tables 8 and 9. Again, as participants
made few errors in the concurrent task (M = 1.23%, SD = 2.12), no data were excluded for
this reason.

5.2.1. Recall performance

We analyzed recall performance including serial position from first to sixth as
within-subjects' factors in a Bayesian Analysis, with our three other factors of interest (CL,
trial, and relatedness) as within-subjects' factors on the percentage of correct recalled
words, firstly on serial recall as in previous studies, and in free recall in a second same
Bayesian analysis. This last analysis was exploratory because task instructions asked for
serial recall.

Best model for classic serial recall highlighted the four main effects of *CL*, trial, associative relatedness, and serial position, as well as an interaction between trial and relatedness and between serial position and trial, BF<sub>10</sub> = 2.73 × 10<sup>108</sup>. Recall was better in low than in high *CL* condition, (*M* = 53.6%, *SD* = 31.6 and *M* = 60.9%, *SD* = 32.5, respectively), always increased among trials each (*M* = 50.7%, *SD* = 32.4, *M* = 57.8%, *SD* = 31.9 and *M* = 63.2%, *SD* = 31.8 for the first, second and third trials), but associative relatedness was weak (*M* = 57.9% *SD* = 32.3 for related words and *M* = 56.5%, *SD* = 31.8 for unrelated words, with no significant effect according to post-hoc comparisons, BF<sub>10</sub> = .071). The presence of associative relatedness main effect in preferred model might result from its interaction with trials. Indeed, follow-up Bayesian t-test on the interaction between trial and relatedness highlighted that performance continued to increase when maintaining unrelated words (BFs<sub>10</sub> < 16.9), but performance in related condition stayed the same among trials (BFs<sub>10</sub> >.74). Recall decreased gradually among serial positions (*M*
= 77.3%, SD = 28.5; M = 70.0%, SD = 29.3; M = 57.9%, SD = 33.1; M = 49.7%, SD = 33.4; M = 44.2%, SD = 32.9; and M = 44.3%, SD = 35.2, for serial positions first to sixth, respectively), until the last two positions no longer result in significantly different recalls (BF<sub>10</sub> = .057 according to post-hoc comparisons). Finally, preferred model showed an interaction effect between trial and serial positions indicating that the decrease of recall as serial position progress became less and less important over trials from fourth serial position. This effect disappeared in second Bayesian preferred model and was probably enhanced by the two main effects of serial position and trials.

For free recall performance, Bayesian analysis indicated the same preferred model as for serial recall, BF<sub>10</sub> = 6.76 × 10<sup>76</sup>, with the main effects of CL, trial, associative relatedness, and serial position, as well as an interaction between trial and relatedness, but without the interaction between serial position and trial. Recall remained better in low than in high CL condition, (M = 88.3%, SD = 18.7 and M = 83.6%, SD = 21.6), increased among trials each (M = 78.1%, SD = 24.1, M = 88.3%, SD = 19.3 and M = 91.4%, SD = 16.9 for the first, second and third trials) and was better for related than unrelated words (M = 91.0% SD = 16.3 and M = 80.9%, SD = 24.0 respectively). Recall followed a U-shaped curved among serial positions (M = 90.7%, SD = 18.4; M = 88.6%, SD = 18.5; M = 82.1%, SD = 23.5; M = 85.8%, SD = 20.3; M = 83.6%, SD = 21.0; and M = 84.7%, SD = 19.2, for serial positions first to sixth, respectively). Post-hoc comparisons indicated that there was no significant difference between first to second positions and third to sixth positions. Recall performance was therefore better on the first two positions and decreased from the third. Follow-up Bayesian t-test on the interaction between trial and relatedness showed that, if the associative relatedness was each time still significant, its strength decreased among trials.
### 5.2.2. Short delayed recall performance

Participants were instructed to recall as much words as they remembered 10 minutes after the end of the WM part of the experiment. They had no limit of time to complete the task. We conducted a Bayesian ANOVA with CL and relatedness as between-subjects' factors on the percentage of recall in the immediate long-term memory task. Best model indicated the main effect of relatedness, BF₁₀ = 691. Related words were better recalled than unrelated ones (M = 79.9%, SD = 19.0 and M = 64.8%, SD = 25.1 respectively). In this experiment, we did not propose a long delayed recall as in previous experiments, because the same participants participated in many other memory studies in the same time period, which greatly increased the risk of interference.

### 5.2.3. Trichotomous analysis

This experiment greatly incited the use of the reconstruction process over the recollection. Disrupting the order of the words appearing on each new trial therefore destroys all contextual cues that might remain to aid a recollection. We analyzed firstly serial recall performance.

The model's ratio statistic $G^2$ sufficient to reject the null hypothesis, $G^2 = 2.23$, $p = .14$. Table 10 for Experiment 4 for serial recall shows all parameters values for the first and subsequent study phases for the CL and relatedness conditions. Contrary to all previous results, reconstruction prevailed over recollection. In addition, parameters for first trial were higher than those for subsequent ones, especially for reconstruction parameters. We only observed CL and relatedness effect on $R_1$ with more reconstruction in low load condition and for related words, and the reverse CL effect on effect on $R_1$. We also observed no effect of CL on unrelated words and no relatedness effect in low CL condition on $J_1$.
This pattern of result was the reason why we also explored free recall in this experiment. In serial recall, the proportion of not recalled and unlearned recall was high, and we feared an illusory reconstruction effect due to proactive inference generated by changes in the order of presentation of words. When looking closer, we observed that correct / error patterns for serial recall in this experiment were slightly different than previously observed pattern. CEC (i.e., correct-error-correct successive recall, see Table 10) pattern for example is analyzed in the trichotomous model as a first correct recall not attributable to recollection, because these items were not successfully recalled on the next trial. Values for *D*₁ parameter then dropped drastically. This effect could be attribute to the serial curve of recall rather than to a real favoring of reconstruction processes. As memory performance analysis on serial and free recall were identical, we chose to explore free recall within the Trichotomous model.

Model's ratio statistic $G^2$ was sufficient to reject the null hypothesis, $G^2 = 1.03$, $p = .90$ and all patterns of recall and parameters values are displayed in Table 10. As expected, previously observed predominance of reconstruction in serial recall was an illusion due to serial misattributions rather than absence of recall. We therefore observed in free recall the same predominance of recollection over reconstruction like in Experiments 1, 2 and 3. As in Experiment 3, $D_1$ and $D_2$ were very close, which corresponds to the introduction of the procedure involving a more differed learning among trials rather than the three trials of a same essay carried out directly following each other. Low *CL* and related words increased $R_1$ but had no effect on other parameters.
### 5.3. Discussion

In this experiment like in Experiment 3, trials were no longer directly presented one after each other as they were in Experiments 1 and 2. Moreover, order of word presentation within lists changed at each trial. Our goal with this procedure was to impair contextual cues as much as possible, and then implying both the re-encoding of items at each new trial and the management of proactive interference generated by the change of words orders at each trial. Potential long-term representations created at the first trial became therefore irrelevant at the second trial, and again at the third. If participant had bound items and previous serial positions, this binding interfered in successive trials. Participants following a serial recall instruction, this proactive interference effect was felt in their weaker performance in serial analysis and in the observation of the predominance of $D_1$ parameter over $D_2$ contrary to all other experiments. Recollecting memory traces at first trial was not more difficult than usual, but in successive trials, recollection became a "false friend" because of these serial positions binding interferences. Bartsh and Shepherdson (2021) observed this kind of proactive interferences in a WM task. Their task directly induced unreliable long-term representations, which has deleterious effect on WM performance. Results of this study also demonstrated a decreased ability to efficiently reallocate attentional resources when participants were busy inhibiting proactive interferences.

We observed in free recall that recollection in WM always prevailed over reconstruction process and replicated the previously observed *CL* and relatedness effects on $D_1$. Removing all contextual cues ended then to the same observation of predominance of recollection predominance. It would be very interesting to reconduct this last experiment with a free order recall instruction to confirm the observed results.
## 6. General discussion

Our first experiment aimed at observing recollective and reconstruction processes in WM and the effect of *CL* variation on these retrieval parameters. As refreshing reinstates verbatim traces, we predicted that hindering refreshing should decrease verbatim-based retrieval process (i.e., recollection) but would not affect reconstruction (see Table 11 for all effects on processes). Results for this first study confirmed our prediction as high *CL* only affected direct access at first trial (*D*₁). This reinforces us in the idea that traces can be retrieved directly in WM and that attentional availability is a determining factor both in maintaining and accessing information, as described in the TBRS model (Barrouillet & Camos, 2015). However, we also observed a marginal *CL* effect on reconstruction at first trial *R*₁. We therefore conducted a second experiment favoring the use of reconstruction process by adding memoranda items that can benefit from LTM knowledge effects. This second experiment reproduced the *CL* effect only on direct access and remove ambiguity regarding reconstruction. LTM knowledge (i.e., associative relatedness) affected also only direct access, with related words enhancing *D*₁. Relatedness can therefore affect either the encoding or the reconstruction in recall. As reconstruction processes were not affected by relatedness an encoding in episodic buffer facilitated by LTM knowledge constituted our privileged track in the next experiment. Moreover, we observed no *CL* effect on related words, probably because related words could have been easily hold in chunks, leading to better recollection. The third experiment sought to further strengthen the use of LTM representations by postponing the presentation of the three successive trials (in the manner of differed learning rather than massed), to create a re-encoding of items at each new trial. Notice here that the Trichotomous theory of recall is calibrated for immediate recall and that Experiments 3 and 4 therefore slightly modified the presentation procedure.
This third experiment replicated previous *CL* and relatedness effects on *D*₁.

Relatedness also affected *D*₂, demonstrating the re-encoding effect in this experiment.

Lastly, we only observed a marginal relatedness effect in low *CL* condition on
reconstruction, which might be explained by the fact that unrelated words are very few
recollected in *D*₁, especially in high *CL* condition, and therefore had to be reconstructed.

LTM effect affected then encoding in episodic buffer while *CL* affected recollection.

Unrelated words not benefiting from LTM knowledge are less likely recollected. Our last
experiment achieved the impairment of all contextual cue by disturbing the order of
words presentation at each new trials in addition to the previously introduced postponing
of the succession of trials. Serial recall led to illusory reconstruction processes, so we
analyzed free recall processes. Recollection processes was still predominant and both
relatedness and *CL* factors had the same impact as previously described on *D*₁.

In summary, our results revealed five main findings. First, the dual-process model of recall provides a good fit of the data, confirming that two distinct processes govern retrieval from WM. Second, the trichotomous model indicates that most of the recalled items in WM span tasks are retrieved through recollective, and not reconstructive, processes. Third, this predominance of recollective processes does not depend on the difficulty of the task as it is not affected by variations in *CL*. The fourth main finding concerns associative relatedness, which always affected more first trials than subsequent one, and never reconstruction parameters, supporting an encoding hypothesis. Finally, the evolution of the retrieval processes over trials revealed by relative values of *D*₁ and *D*₂ parameters. Parameter *D*₂ always exceeded *D*₁ in our studies, a finding that could shed light on the retrieval processes involved in WM span tasks when compared with other recall tasks. These five points are discussed in turn below.
<table><caption>Table 11. Summary of simple effects for all parameter estimates for cognitive load (CL) and associative relatedness effect in all Experiments.</caption><thead><tr><th></th><th>D<sub>1</sub></th><th>D<sub>2</sub></th><th>R<sub>1</sub></th><th>R<sub>2</sub></th><th>J<sub>1</sub></th><th>J<sub>2</sub></th><th>J<sub>3</sub></th></tr></thead><tbody><tr><td>Experiment 1</td><td></td><td>&lt;D<sub>1</sub></td><td>&lt;D<sub>1</sub></td><td></td><td></td><td></td><td></td></tr><tr><td>CL</td><td>↗ in low CL</td><td>×</td><td>(×</td><td>×</td><td>×</td><td>↗ in high CL</td><td>×</td></tr><tr><td>Experiment 2</td><td></td><td>&lt;D<sub>1</sub></td><td>&lt;D<sub>1</sub></td><td></td><td></td><td></td><td></td></tr><tr><td>CL</td><td>↗ in low CL (↗ for unrelated)</td><td>↗ in low CL</td><td>×</td><td>×</td><td>×</td><td>×</td><td>↗ in high CL</td></tr><tr><td>Relatedness</td><td>↗ for related</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td></tr><tr><td>Experiment 3</td><td></td><td>= D<sub>1</sub></td><td>&lt;D<sub>1</sub></td><td></td><td></td><td></td><td></td></tr><tr><td>CL</td><td>↗ in low CL</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td></tr><tr><td>Relatedness</td><td>↗ for related</td><td>↗ for related</td><td>(×</td><td>×</td><td>↘ for related</td><td>×</td><td>↘ for related</td></tr><tr><td>Experiment 4 - Serial</td><td></td><td>= D<sub>1</sub></td><td>&gt;D<sub>1</sub></td><td></td><td></td><td></td><td></td></tr><tr><td>CL</td><td>×</td><td>×</td><td>↗ in low CL</td><td>↘ in low CL</td><td>⊗ in unrelated</td><td>×</td><td>×</td></tr><tr><td>Relatedness</td><td>×</td><td>×</td><td>↗ for related</td><td>×</td><td>⊗ in low CL</td><td>×</td><td>×</td></tr><tr><td>Experiment 4 - Free</td><td></td><td>= D<sub>1</sub></td><td>&lt;D<sub>1</sub></td><td></td><td></td><td></td><td></td></tr><tr><td>CL</td><td>↗ in low CL (↗ for unrelated)</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td></tr><tr><td>Relatedness</td><td>↗ for related</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td><td>×</td></tr></tbody></table>

Note: × indicates the absence of effect; ↗ and ↘ respectively indicate an increase or a decrease of the parameter in an experimental condition in particular; and ⊗ indicates the absence of effect in the described condition. Indication in parenthesis stands for marginal effects.