# Impact of immersive virtual reality compared to a digital static approach in word (re)learning in post-stroke aphasia and neurotypical adults: Lexical-semantic effects?

Julie Franco, Bertrand Glize, Marina Laganaro

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## ► To cite this version:

Julie Franco, Bertrand Glize, Marina Laganaro. Impact of immersive virtual reality compared to a digital static approach in word (re)learning in post-stroke aphasia and neurotypical adults: Lexical-semantic effects?. Neuropsychologia, 2025, 208, pp.109069. 10.1016/j.neuropsychologia.2025.109069. hal-04937846

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# Impact of immersive virtual reality compared to a digital static approach in word (re)learning in post-stroke aphasia and neurotypical adults: Lexical-semantic effects?

Julie Franco<sup>a,*</sup>, Bertrand Glize<sup>b</sup>, Marina Laganaro<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Immersive virtual reality

Word learning

Aphasia

Speech and language therapy

Word production

## ABSTRACT

**Background:** Word production difficulty is one of the most common and persisting symptoms in people suffering from aphasia (i.e., anomia). However, there is a considerable variability in patients' responses to treatment, leading to the need of new effective approaches. Also, the mechanisms underlying word (re)learning is little known in production even in neurotypical adult native language and especially in relationship with the lexical-semantic integration of (re)learnt words. The lexical-semantic network being highly contextual and multimodal, new technologies such as immersive virtual reality (iVR) may become pertinent approaches, but still need scientific proof, especially as past studies have found no advantage over a control method and have never used an immersive version of VR. Yet, the immersion has been identified as being a key factor of positive outcomes in learning. Therefore, the aim of the present study was to investigate whether iVR provides a benefit in word learning in neurotypical adults (Study 1) and in the treatment for anomia in people suffering from aphasia following stroke (Study 2).

**Method:** In study 1, 32 neurotypical adults learned two matched lists of 30 rare words each in their native language (French) during a one-week protocol alternating test and learning sessions with iVR and a digital static learning method. Study 2 followed the same design over a two week-period with 16 people with aphasia (re) learning two matched lists of 28 frequent words.

**Results:** Neurotypical adults demonstrated a higher accuracy rate in word production for words learned with iVR in comparison to those learned with the digital static learning method. For people suffering from anomia, the iVR did not differ from the control method on total accuracy but led to a greater reduction of lexical errors.

**Conclusion:** iVR has a potential of use for learning new words but also for the treatment of word production difficulties, especially in people with aphasia that produce mainly lexical errors. These two main results lead to the hypothesis that iVR promotes lexical-semantic processes.

## 1. Introduction

Damage to brain areas responsible for language can lead to language difficulties called aphasia. After a stroke, between a third to a quarter of stroke survivors develop aphasic symptoms (Ali et al., 2015), with anomia being the main difficulty (Goodglass, 1980; Laine and Martin, 2013). Aphasia in general and anomia in particular affect many areas of the patient's life, including social, family and work, and can lead to reduced quality of life (Berg et al., 2022; Spaccavento et al., 2013). The mainstream of rehabilitation for aphasia is behavioural intervention (Brady et al., 2016, 2022), targeting the language deficit in terms of

modality and levels of processes (Galletta and Barrett, 2014). The same is true for anomia rehabilitation, that can take different forms depending on the underlying deficit (Howard and Gatehouse, 2006; Nickels et al., 2002). Recent research has highlighted that (re)learning may underlie the rehabilitation of people suffering from aphasia and, in particular, word production difficulties (Middleton et al., 2015; Penaloza et al., 2022). One of the key factors influencing (re)learning is the learning method and new technologies such as virtual reality hold promises, as they may go beyond, targeting the deficit and promote (re)learning in multimodal contexts close to real life communication. To the best of our knowledge, the immersive version of virtual reality (iVR) has never been

<footnote><sup>a</sup> Faculty of Psychology and Educational Sciences, University of Geneva, 40 Boulevard du Pont d'Arve, 1205, Geneva, Switzerland</footnote>

<footnote><sup>b</sup> Handicap Activité Cognition Santé, BPH U1219 Inserm, Université de Bordeaux, F-33000, Bordeaux, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: julie.franco@unige.ch (J. Franco), betrand.glize@chu-bordeaux.fr (B. Glize), marina.laganaro@unige.ch (M. Laganaro).</footnote>
used in the field of learning or re-learning, neither with neurotypical adults for L1 word learning, nor for rehabilitation in people with an aphasia. Therefore, the aim of the present study is to investigate the effects of immersive virtual reality on word learning with neurotypical adults learning infrequent words in their mother tongue and relearning in people suffering from aphasia in the context of rehabilitation for anomia.

In the following, we will first review the relationship between L1 word learning in neurotypical individuals and (re)learning in people with aphasia. We will then consider the immersive virtual reality as a (re)learning method.

## 1.1. Word learning and relearning in anomia

Anomia is the difficulty in finding or producing words leading to high production latencies and the production of errors. Some people with aphasia produce errors mainly at the word level (i.e. lexical errors), that may include a semantic link between the error and the target (i.e. semantic paraphasias) (Schwartz, 2014). This type of error is thought to originate from lexical mis-selection (Levelt et al., 1999; Nozari, 2019). The activation of several lexical entries by semantic features requires a lexical selection process which can be impaired in people with aphasia, leading to a greater number of errors and in particular the production of another activated word (see Levelt et al., 1999; Roelofs, 1997 for word production model). By contrast, some other patients tend to produce errors mainly at the word form level, involving phoneme substitutions, omissions, metatheses, but the target word is generally correctly selected. In such cases, the impairment has been attributed to lexical-phonological encoding (Schwartz, 2014). The impairment lies inactivating or assembling phonemes, but the lexical selection is correctly processed. These distinctions in the underlying impairments have led to the development of different therapies depending on the underlying deficit. Therapy for anomia can take different forms depending on the underlying lexical-semantic or lexical-phonological impairment (Howard and Gatehouse, 2006; Nickels et al., 2002). Although the use of phonological or semantic approaches to facilitate access to mental representations has been shown to be effective (Efstratiadou et al., 2018; Quique et al., 2019; Pierce et al., 2019; Sze et al., 2021; Wisenburn and Mahoney, 2009), all people who suffer from aphasia do not respond adequately to these treatments, resulting in a high variability in recovery (Best and Nickels, 2000; Mirman and Britt, 2014). This observation has led to an alternative hypothesis that anomia may be due to a deficit in activating lexical items from semantic features due to weak connections between semantic features and lexical representations (Nozari, 2019). In such cases, phonological cues will not be effective as it does not impact on the inhibition of the competitors nor increase the connection weights. As for semantic approaches, they may also prove ineffective in some people with aphasia who have difficulty inhibiting semantic competitors, as described by Nozari (2019). These differences in the treatments offered and the underlying deficits may be at the root of this important inter-individual variability in the response to treatment. Consequently, other approaches and therapies need to be explored. However, instead of targeting the underlying language processes, it could be effective to directly target the mechanisms involved in the therapy and recovery, such as (re)learning (Ferguson, 1999; Hopper and Holland, 2005; Ralph et al., 2007). In fact, (re)learning plays an inherent role in the language rehabilitation process (Peñaloza et al., 2022) and therapy itself may be considered as a learning experience (Hopper and Holland, 2005). Training specific aspects of language directly involves memory and learning (Helm-Estabrooks, 2002) and it also enables the development of strategies and skills that can help to restore or compensate impaired language functions (Nickels et al., 2002), through processes of brain plasticity (Kelly and Armstrong, 2009). Finally, it has been highlighted that a relationship exists between learning skills and recovery from aphasia (Dignam et al., 2016; Peñaloza et al., 2022; Tuomiranta et al., 2011).

If we consider that recovery is close to learning, it is worth reviewing the complex cognitive processes underlying word learning. In neurotypical adults, word learning is generally conceptualised as involving two stages from the first exposure to an unfamiliar word to the lexical-semantic integration. This refers to the integration of the newly learned word into the mental lexicon (Davis and Gaskell, 2009; Dumay and Gaskell, 2007; Gaskell and Dumay, 2003; Kapnoula et al., 2015; Tamminen et al., 2010). To enable the production and recognition of newly learned words (Gupta and Tisdale, 2009), connections are created in the lexical-semantic system throughout the interconnected semantic system (Howard and Gatehouse, 2006; McClelland and Rogers, 2003). The semantic system is not a simple lexical entry, but it implies the interaction of many domains (sensorimotor, visual, auditory, and so on). In fact, it has been described as being interactive, multimodal and contextual (Doedens and Meteyard, 2022). This means that word meanings are linked to conceptual and sensorimotor properties that are integrated through experience and interactions (Binder et al., 2009; Price, 2012). Fostering and stimulating this multimodal dimension could lead to better learning and/or recovery for instance with immersive virtual reality.

## 1.2. Immersive virtual reality

Virtual reality (VR) is an emerging technology that has the potential advantage of integrating multiple dimensions. Indeed, VR is defined as 3D interactive environments computer generated in which the person is immediately immersed in a virtual environment that mimics the human experience through auditory, visual and tactile feedback (Bryant et al., 2020; Burdett and Bowen, 2013). It allows the creation of interactive environments that may be closer to everyday life communication while allowing total control of the variables involved.

VR-based treatment has already been investigated for aphasia rehabilitation for many reasons such as its ecological validity, increased intensity or dose, independent use even at home, and practice in a multimodal simulation (Devane et al., 2023). Recent systematic reviews (Cao et al., 2021; Devane et al., 2023; Repetto et al., 2021) have shown that most studies showed positive outcomes of VR-based aphasia rehabilitation in terms of linguistic measures and well-being. However, most of the studies are case experiments only assessing pre- and post-therapy outcomes without a control method. This makes it impossible to determine whether VR is more effective than another approach. In fact, studies using a control method, such as conventional face-to-face treatment, no longer found an advantage for VR on linguistic outcome measures, such as word finding difficulties (Cherney et al., 2021; Giachero et al., 2020; Grechuta et al., 2019; Maresca et al., 2019; Thompson et al., 2010). VR is therefore relevant for the treatment of people with aphasia due to the increased dose and the independency at home, but effects are the same as conventional therapies.

However, in addition to the control method issue, the possibility of recreating rich environments by embedding situated language use closer to everyday communication offered by VR (Memar et al., 2013; Rodriguez-Mier et al., 2016) has not been really explored in previous studies. In fact, they have only replicated clinical settings with familiar therapies delivered in VR (Cherney et al., 2021; Giachero et al., 2020; Thompson et al., 2010) and most of the software involve limited pre-programmed tasks with static images, without developing the multimodal and integrative aspect of VR. Last but not least, none of the previous studies have ever used an immersive version of VR.

Immersive virtual reality (iVR) is a type of VR that allows users to manipulate or interact with the environment as seamlessly as in real life, with the highest level of immersion. Moreover, immersion is an essential variable of the positive outcomes of iVR. Indeed, a recent theoretical model, the Cognitive Affective Model of Immersive Learning (CAMIL, Makransky and Petersen, 2021), makes specific predictions of the iVR technical advantages on learning. The CAMIL states that the potential benefits of iVR arise from three defining characteristics: immersion,
control factors, and fidelity of representations. These 'technological factors' directly influence two psychological affordances (Jerald, 2015; Milgram and Kishino, 1994): the sense of presence, which refers to the feeling of 'being there' (Riva, 2008), and the sense of agency, which refers to the feeling of generating and controlling actions (Johnson-Glenberg, 2019; Moore and Fletcher, 2012). The sense of presence and agency leads to the capital feeling of immersion. These two senses have received empirical evidence in favour of their positive influence on educational learning (Petersen et al., 2022), but also on memory (Cadet and Chainay, 2020; Dehn et al., 2018; Harman et al., 2017; LaFortune and Macuga, 2018; Makowski et al., 2017; Smith, 2019). This effect is thought to be mediated by several affective and cognitive factors (interest, motivation, self-efficacy, embodiment, cognitive load, and self-regulation) that are known to facilitate learning (for more details see Makransky and Petersen, 2021). According to this model, immersion promotes cognitive and affective factors known to be involved in learning. Finally, the factor of immersion has been validated as being essential in the empirical domain with studies on second language (L2) vocabulary learning (Altun and Lee, 2020; Dhimolea et al., 2022). Indeed, technologies with the higher amount of immersion lead to the better outcomes (Dhimolea et al., 2022; Halabi, 2020) and the immersive virtual reality is better for L2 vocabulary learning than a non-immersive version (Legault et al., 2019). However, to the best of our knowledge, an immersive version of virtual reality has never been used to test word learning in neurotypical adults and neither (re)learning in people with aphasia.

## 1.3. Present studies

Therapy for anomia has been shown to be effective, but the decontextualized approaches do not work for all patients and all underlying impairments. In particular, as language and semantics are conceptualised as interactive, multimodal and contextual systems (Doedens and Meteyard, 2022), virtual reality seems to have relevant potentials of use in word learning and (re)learning. Unfortunately, previous studies on VR (Cao et al., 2021; Devane et al., 2023) have only reproduced the clinical setting with familiar treatments or have not necessarily compared VR with a control method. Most importantly, previous studies have never used immersive version of VR. The present study will therefore attempt to fill these gaps by investigating the effects of immersive virtual reality, by comparing it to a digital static control method, first with neurotypical adults to determine the relevance of this method in word learning contexts to then investigate the iVR as a method of anomia rehabilitation through relearning in people with aphasia. The use of immersive virtual reality, by increasing the sense of immersion with an enhanced sense of presence and agency, will result in better performance in the production of newly learned words in neurotypical adults and of common/frequent words in people with anomia in aphasia.

## 1.4. Study 1: iVR in neurotypical adults

The aim of the first study was to investigate the relevance of using iVR compared to a digital static learning method for learning (rare) words in the mother tongue, with the outcome assessed with a referential word production task.

# 2. Method

## 2.1. Participants

32 native French speakers (19 women; 13 men) participated. They were between 20 and 35 years old (M = 24 years, SD = 4.10). Their mother tongue was French (L1), but all of them also spoke another language (L2, L3, etc.). Most of the participants were university students. They were all right-handed according to the Edinburgh

Handedness Scales (Oldfield, 1971, M = 89.90).<fnref n="1" /> They reported normal or corrected-to-normal vision and had no neurological, learning or developmental disabilities. All participants gave written informed consent and were paid for their participation. The study protocol was approved by the local research ethics committee of the University of Geneva (N° PSE. PSE20201003.06).

## 2.2. Overall study design

Fig. 1 illustrates the within-subject design of the present study which allows the two methods to be compared while controlling the learning effects of each participant. The study took place over a period of one week, alternating between assessment and learning sessions in which participants had to learn the names of real but very rare objects in French (L1) with two sequentially methods. On the first day (Monday, Day 1), a pretest was administered with a colour picture naming task corresponding to 60 rare words (see Material). Immediately after the pretest session (Day 1), a first list of 30 rare words was studied either with iVR or with a digital static learning (DSL) method on a tablet for about 30 min. On Day 2 (Tuesday), learning continued with the same method and list as on Day 1 for 1 h, giving a total of one and a half hour of learning with the first list of rare words. On Day 3 (Wednesday), a first post-test was given with the picture naming task, followed by a learning session with the second matched list of 30 rare words and the second method for 30 min. On Day 4 (Thursday), learning continued for 1 h using the same method and list as on Day 3. On Day 5 (Friday), a second post-test was carried out with the same task. Finally, the following week, on Day 9 (Tuesday), a final post-test was administered to assess long-term learning with the picture naming task. The lists of new words and the learning methods were counterbalanced across subjects to control for the order effect.

## 2.3. Material & procedure

### 2.3.1. Rare words

60 rare words (Appendix A.1) with very low lexical frequency (<0.38 per million words) were selected from the French database Le xique.org (New et al., 2005) on the basis of a pretest with 66 stimuli. To ensure that the selected words were unfamiliar, a pretest was carried out online with twenty participants; each of the 66 pictures corresponding to the preselected rare words was presented on the screen and the participants had to write the name of the object if they knew it or give a no-response if they did not know the name. After the pre-test, three stimuli with a percentage of correct responses higher than 30% were discarded and three other stimuli were removed to achieve an appropriate match, leaving a total of 60 stimuli that could be divided into two lists of 30 items, matched on relevant psycholinguistic variables (i.e., number of syllables, number of phonemes, lexical frequency, phonological and orthographical neighbourhood and sonority values of the first phoneme). The 60 stimuli belong to three semantic categories (fruits/vegetables, tools and musical instruments, with 20 rare words in each category). For each word, four different coloured pictures were selected from free internet databases and cut out to be placed on a white background. Four different pictures allowed for different representations of the same concept in the assessment and learning sessions.

### 2.3.2. Test sessions

Participants were tested individually in a soundproof, dimly light room and sat 60 cm in front of a computer screen. For all tasks, items were presented at constant size on a grey screen in a pseudo-randomised order (no more than two consecutive items from the same semantic

<footnote n="1">¹ The fact that all the participants were right-handed is due to electroencephalographic recordings made during the word production tasks. These data are not presented here.</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. Experimental within-subject design.</figcaption></figure>

Note. Assessment sessions refer to the testing of each rare word learned with the picture naming task. Two lists of rare words were learned in 2x2 learning. The approach refers to the two methods (iVR or DSL) used in a cross-over design across participants.

category) and in separate blocks, preceded by two warm-up filler trials. Four different orders were created for each task and counterbalanced across participants and assessment sessions to avoid any effect of order of stimulus presentation. The E-prime 2 (E-studio) software presented the trials and recorded the vocal responses. Spoken responses were digitised and recorded for later verification of response latency and accuracy using speech analysis software.

Each trial began with a blank screen for 300 ms, followed by a fixation cross in black Arial font, size 24, presented in the centre of the screen for 500 ms. After the fixation cross, a screen was presented for 100 ms. The stimulus then appeared for 3000 ms, followed by a black screen for 500 ms before the next trials. Participants were informed that they would see pictures of unusual or common objects and were asked to name the picture aloud if they knew the name, or to say "no" as accurately and quickly as possible. Each stimulus was presented once in two separate pseudorandomised blocks, separated by a self-paced break, for a total of 90 stimuli (60 rare words and 30 common words).

### 2.3.3. Learning sessions

The learning procedure was conducted twice during the week (days 1 and 2 and days 3 and 4), once with each method (on tablet for DSL or on iVR) on a list of 30 rare words.

### 2.3.4. Virtual reality

The virtual reality application consists of a market where participants have to look for 30 items on the list to learn. It was developed in Unity (2019.4.1) and Blender for the creation of 3D objects. The participants were equipped with a virtual reality headset (Oculus Rift S) connected to a computer (Acer Predator Triton 300 and placed in an isolated room. This system involves the use of a helmet that sits at eye level and displays the 3D virtual reality world, isolating people from the real outside world. Interaction with the virtual world is possible thanks to two controllers. The first is a shopping list (see Fig. 2a) indicating the object to be found/requested and the second allows the participants to move or select an object (see Fig. 2b). They move around a market with

three stalls that presents objects of a different semantic category: tools, musical instruments and fruits/vegetables. Once the item has been located and selected, two options are offered.

1. They can point at the item and ask for the name of the item.

2. You can ask for a specific item by saying its name.

The avatar will first check if the selected item matches the item in the shopping list. If the selected item does not match, the avatar will reply: "This is not the item you are looking for". Otherwise, in case n°1, the avatar will audibly give the name of the selected item. In case n°2, the avatar will give the object to the participants if it matches the object on the shopping list and if the participants have pronounced the name correctly (if it is recognised by the speech recognition algorithm, see below). If not, the avatar will pronounce the correct item name. The item name is also displayed orthographically on the shopping list. When a specific object was selected, an exclamation mark appeared, which not only allowed the selected object to be manipulated with the right hand, but also displayed a video on how to use the tools and presented the specific sound of the instruments. Finally, when all the objects had been encountered, the participants moved on to a final stand representing the cash register. In order to make purchases, the participants had to select each of the objects present at random from the list they had learnt and name them aloud once again.

The duration of the learning sessions was approximately 30 min for the first learning day (Day 1 or Day 3) and 1 h for the second learning session (Day 2 or Day 4). Participants completed the application twice on the first day (four exposures of each word) and four times on the second day (eight exposures of each word).

### 2.3.5. Digital static learning method (tablet)

The application on the tablet was developed using Unity (2019.4.1) and Android Studio. It consists of a typical picture naming task. All objects are presented to the participants one by one in a random order, in the form of a picture (see Fig. 2b). At each presentation of an object,

<figure><img src="image_4.png" /><figcaption>Fig. 2. Print screen of an example of an iVR trial for the item "sapotille" and of a tablet trial for the item "tabla".</figcaption></figure>

Note. (a) The first picture refers to the shopping list with the item to choose/ask for and the second picture is the fruit and vegetable stall with the laser pointer to select the item.
participants are offered two choices (corresponding to two different button presses).

1. I do not know the name of the object

2. I know the name of the object

After choosing button n°1, they can listen to the auditory presentation of the object name. The written word will also be presented. If you choose button n°2, you will be asked to say the name of the object and, if it is correctly recognised by the speech recognition algorithm (see below), the next object will be displayed. Otherwise, the result is the same as in case 1.

To allow comparisons between the two methods, the number of exposures for each word was controlled to be exactly the same for both methods. In fact, seven exposures were made for each word in each method. In addition, both systems included the written form of the words, required oral production of the stimuli, presented the stimuli visually and in colour, and finally provided feedback on the participant's response. The inherent features of iVR, such as communicative context, object manipulation and semantic enrichment around the word, could not be reproduced in the DSL.

### 2.3.6. Speech recognition algorithm

In both applications, a speech recognition algorithm was integrated to analyse the participants' pronunciation of the words and to provide feedback. A new speech recognition model was trained using the IBM Watson API from the basic en-FR_BroadbandModel, based on audio recordings made by 20 subjects (different from those participating in the study and from those who completed the pretest for the selection of the stimuli). The algorithm was trained with 1560 audio files containing the rare words in isolation and 6530 audio files containing the rare words in sentences. An efficiency test run with 640 audio files (8 different sentences for each rare word) from an additional subject on four different APIs resulted in 94.99% correct recognition.

### 2.3.7. Data analysis

Each verbal response from the picture naming task was checked using the CheckVocal software (CheckVocal 2.2.6, Protopapas, 2007) to categorize it as a correct, 'no' or incorrect response. Correct responses corresponded to the target word with a tolerance of phonemic transformations on a maximum of two phonemes for plurisyllabic newly learned words that had to remain intelligible (example: chenilloir for échenilloir). Rare words that were named correctly on Day 1 were excluded from the analyses (1% of correct responses on Day 1 for rare words across all participants).

To determine whether iVR has an advantage over digital static learning, an analysis of accuracy was performed with learning method (iVR or DSL) and test day (Day 1, Day 3, Day 5 and Day 9) as independent factors on the picture naming task. Accuracy was analysed with a generalised linear mixed effects model (Baayen et al., 2008) using R software (Venables and Smith, 2004) with participant and item as random effects. The model is described in detail in the "Results" section and in Appendix B.

## 3. Results

On Day 1, only 1.17% of the items were correctly named (range: 0–5%). These items were excluded from the analysis. On Day 3, the average of accuracy was 73.10% (range: 36%–96%). On Day 5, participants produced on average 71.11% of the rare words correctly (range: 42%–90%), and on Day 9, the accuracy rate was 73.06% (range: 35%–92%). The results by learning method and test day are shown in Fig. 3.

The model showed a main effect of the learning method (X2(1) = 35.97, *p* < .001) with a higher accuracy rate for the iVR than the DSL and a main effect of test day (X2(2) = 704.70, *p* < .001) with an increase in accuracy from Day 1 to Day 3, Day 5 and Day 9, but no difference

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Learned words at Day 3, Day 5 and Day 9 for the picture naming task comparing iVR and DSL method.
*Note.* Accuracy (words learned, in %) on each list of rare words according to learning method and test day.</figcaption></figure>

between Day 3, Day 5 and Day 9 (see detailed statistical results in Appendix B).

## 4. Discussion

The aim of the present study was to determine whether learning concrete rare words in the L1 with a word production paradigm using iVR provides an advantage over a digital static learning method.

Word production accuracy was better for words learned with iVR than for those learned with DSL immediately after the learning period, several days after learning and one week later. The overall learning advantage of iVR was 14% of 30 newly learned words per list for Day 3, 6% for Day 5 and Day 9. Before discussing the possible implications for (re)learning, we will discuss the results of the present study in relation to the literature on foreign language learning, as to our knowledge no previous study has examined native language rare word learning to assess the potential advantage of immersive virtual reality. Recent systematic reviews have found a positive benefit of iVR in second language learning (Altun and Lee, 2020; Dhimolea et al., 2022). However, most studies using immersive virtual reality in L2 word learning have not compared the iVR method with a control method (Berg et al., 2022; Garcia et al., 2019; Xie et al., 2019). Only two studies have compared iVR with another learning method (Legault et al., 2019; Tai et al., 2022). They reported gains ranging from 6% for 30-item lists (Legault et al., 2019) to 40% for 18 unfamiliar words (Tai et al., 2022). It has also been shown that the advantage tends to diminish over time (Tai et al., 2022). However, those studies have assessed learning with word recognition or comprehension tasks, and to the best of our knowledge, the present study is the only one that assesses learning with word production tasks, and it found a persistence of iVR gains over time. Furthermore, the control method is an important factor in assessing the iVR advantage and several strived to control for some factors that were not controlled in the mentioned studies on L2 as the number of exposures between the different methods and the type of learning to which iVR was compared. For instance, in Legault and colleagues (2019), the control method was a passive learning method while iVR is an active method. The present study tried to control those factors as the number of exposures was balanced across methods, and also to use a control digital method that contains the same information as the iVR, but in a static form, without interaction with a virtual environment.

In summary, as mentioned in the Introduction, the learning of rare words in the mother tongue in Study 1 was a proxy to assess whether immersive virtual reality is relevant in the context of learning or relearning. And indeed, it appears that immersive virtual reality does
have a real impact on word learning, as it results in better production performance than a control method. Since learning mechanisms can also be at play in the rehabilitation of people with (see Introduction), iVR may be a relevant tool in the treatment of anomia in people with aphasia.

## 4.1. Study 2: iVR in people with aphasia

The aim of the second study was to investigate whether immersive virtual reality also has an advantage over a digital static approach in the treatment for anomia (for common/frequent words) in people with aphasia.

# 5. Method

## 5.1. Participants

Sixteen people with aphasia participated in this study (12 male; mean age: 65, SD: 11). They were recruited at the University Hospital of Bordeaux and gave written informed consent, which was validated by the local research ethics committee.

All were right-handed native French speakers and were selected according to the following criteria: suffering from mild to moderate aphasia following a left hemisphere stroke (ischaemic or haemorrhagic, range months after onset: 3 to 96), without previous dementia or psychiatric illness. They presented word-finding difficulties with good comprehension and all the patients had spared semantic system according to a standard aphasia assessment battery (BECLA, Macoir et al., 2016) administered before the experimental protocol.

Table 1 summarises the demographic data, the results of the BECLA assessment, and the types of errors in the experimental picture naming task (see material) on Day 1.

## 5.2. Overall study design

The design was very similar to that of Study 1, but it took place over a two-week period to allow longer treatment periods. Fig. 4 shows the within-subject design of the Study 2. On the first day (Day 1), a pretest was administered with a colour picture naming task with the 56 items corresponding to common words in French (see Material). On the following three days, three therapy sessions with a list of 28 were carried

out either with iVR or with DSL on a computer for about 1 h per day. On Day 5, after one week of therapy with one list of frequent words and one method, a first post-test was administered with the picture naming task. The second week consisted of three additional therapy sessions with another list and method (iVR or DSL) that was not used in the first week. To assess the effect of the method, the number of exposures was identical between the two methods. On Day 12, a second post-test was performed with the same naming task. Finally, the following week, on Day 19, a follow-up post-test was administered to evaluate the long-term therapy effect with the picture naming task. The lists and learning methods were counterbalanced between participants.

## 5.3. Material & procedure

### 5.3.1. Picture naming task

56 concrete words (Appendix A.2) from three semantic categories (fruits/vegetables, tools and musical instruments) were selected from the French database: Lexique.org (New et al., 2005). All the words were common French words (mean frequency: 5.4 per million, SD: 5.9). The 56 words were divided into two lists of 28 items, matched on relevant psycholinguistic variables (i.e. number of syllables, number of phonemes, phonetic and orthographic neighbourhood, lexical frequency and sonority values of the first phoneme). For each word, four different coloured pictures were selected from free internet databases and cut out to be placed on a white background. Four different pictures allowed for different representations of the same concept in the assessment and learning sessions.

### 5.3.2. Test sessions

The procedure was exactly the same as the Study 1 except that the speech and language therapist was next to the patients and the stimulus appeared for 6000 ms followed by a black screen manually checked by the therapist.

### 5.3.3. Learning sessions

The learning procedure was the same software as in Study 1, but with the frequent words and with all manual controls adapted to the left hand, due to the frequent hemiparesis of the right hand in people with aphasia.

<caption>**Table 1**</caption>

Demographic data, standard aphasia assessment and error types at Day 1 on the picture naming task of the 16 people with aphasia.

<table><thead><tr><th colspan="4">Demographic Data</th><th colspan="3">Clinical Assessment (BECLA)</th><th colspan="5">Distribution of responses at Day1 in the picture naming task/112</th></tr><tr><th>Patients</th><th>Gender</th><th>Age</th><th>Post-Stroke onset months</th><th>Picture semantic matching/20</th><th>Word repetition/15</th><th>Picture-word matching/20</th><th>Correct responses</th><th>Lexical errors</th><th>Phonological errors</th><th>No response</th><th>Other</th></tr></thead><tbody><tr><td>P101</td><td>male</td><td>69</td><td>3</td><td>100</td><td>100</td><td>100</td><td>65%</td><td>23%</td><td>6%</td><td>5%</td><td>0%</td></tr><tr><td>P102</td><td>female</td><td>55</td><td>6</td><td>95</td><td>100</td><td>100</td><td>38%</td><td>28%</td><td>4%</td><td>28%</td><td>4%</td></tr><tr><td>P103</td><td>male</td><td>63</td><td>13</td><td>100</td><td>100</td><td>95</td><td>41%</td><td>10%</td><td>18%</td><td>28%</td><td>4%</td></tr><tr><td>P104</td><td>male</td><td>72</td><td>6</td><td>100</td><td>20</td><td>100</td><td>6%</td><td>2%</td><td>64%</td><td>28%</td><td>0%</td></tr><tr><td>P105</td><td>female</td><td>47</td><td>96</td><td>100</td><td>100</td><td>100</td><td>78%</td><td>13%</td><td>4%</td><td>6%</td><td>0%</td></tr><tr><td>P106</td><td>male</td><td>75</td><td>18</td><td>95</td><td>93,3</td><td>100</td><td>15%</td><td>14%</td><td>11%</td><td>32%</td><td>28%</td></tr><tr><td>P109</td><td>female</td><td>75</td><td>13</td><td>100</td><td>86,7</td><td>100</td><td>63%</td><td>14%</td><td>12%</td><td>11%</td><td>1%</td></tr><tr><td>P110</td><td>male</td><td>72</td><td>11</td><td>85</td><td>53,3</td><td>95</td><td>3%</td><td>3%</td><td>11%</td><td>13%</td><td>71%</td></tr><tr><td>P111</td><td>male</td><td>64</td><td>14</td><td>100</td><td>86,7</td><td>100</td><td>54%</td><td>23%</td><td>4%</td><td>13%</td><td>6%</td></tr><tr><td>P112</td><td>male</td><td>62</td><td>13</td><td>100</td><td>100</td><td>100</td><td>54%</td><td>27%</td><td>6%</td><td>13%</td><td>0%</td></tr><tr><td>P113</td><td>male</td><td>71</td><td>64</td><td>100</td><td>93,3</td><td>100</td><td>58%</td><td>20%</td><td>9%</td><td>10%</td><td>4%</td></tr><tr><td>P114</td><td>male</td><td>75</td><td>21</td><td>100</td><td>93,3</td><td>100</td><td>54%</td><td>20%</td><td>11%</td><td>13%</td><td>3%</td></tr><tr><td>P115</td><td>female</td><td>31</td><td>19</td><td>100</td><td>100</td><td>100</td><td>71%</td><td>11%</td><td>4%</td><td>13%</td><td>0%</td></tr><tr><td>P116</td><td>male</td><td>75</td><td>6</td><td>100</td><td>100</td><td>100</td><td>52%</td><td>26%</td><td>12%</td><td>8%</td><td>3%</td></tr><tr><td>P117</td><td>male</td><td>72</td><td>11</td><td>95</td><td>86,7</td><td>90</td><td>37%</td><td>43%</td><td>9%</td><td>8%</td><td>4%</td></tr><tr><td>P118</td><td>male</td><td>65</td><td>7</td><td>95</td><td>93,3</td><td>85</td><td>4%</td><td>3%</td><td>11%</td><td>79%</td><td>4%</td></tr></tbody></table>

Note. For the clinical assessment, the number under each task corresponds to the total number of points in the task. The results of each person with aphasia at each task are expressed in percentage of correct responses. Underlined scores indicate a score below the 10 percentile and those in bold below the 5 percentile of the BECLA normalisation sample according to each task and the number of school years. The error types on Day 1 were divided into four categories: lexical errors including semantic paraphasias, phonological errors including phonological, phonetic and "conduites d'approche" behaviours, non-responses and another category including perseverations, mixed and unrelated errors.
<table><caption>**Fig. 4.** Experimental within-subject design in people with aphasia.</caption><thead><tr><th>Day1</th><th>Day2-4</th><th>Day5</th><th>Day9-11</th><th>Day12</th><th>Day19</th></tr></thead><tbody><tr><td>Test session<br>56 items</td><td>Treatment<br>List 1 iVR (or List 2 DSL)</td><td>Test session<br>56 items</td><td>Treatment<br>List 2 DSL (or List 1 iVR)</td><td>Test session<br>56 items</td><td>Follow-up:<br>Test session<br>56 items</td></tr></tbody></table>

Note. Assessment sessions refer to the testing of each frequent word with the picture naming task. Two lists of common words were treated during 2x3 therapy sessions. Treatment refers to the two methods (iVR or DSL) used in a cross-over design across participants.

### 5.3.4. Data analysis

To determine whether iVR has an advantage over digital static learning, we performed an analysis of accuracy with the learning

method (iVR or DSL) and test day (Day 1, Day 5, Day 12, and Day 19) as independent factors in the picture naming task. The analyses were carried out on overall accuracy (correct responses) and on errors by type of

A. Total accuracy

<figure><img src="image_6.png" /></figure>

B. Change in errors by type of errors

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Results at Day 1, Day 5, Day 12 and Day 19 for the picture naming task comparing iVR and DSL method and on the error type analysis according to the learning method (iVR; DSL) and the test day.</figcaption></figure>

Note. (A) Accuracy (correctly produced words, in %) according to learning method and the test day. (B) The difference in error rates between Day 1 and 5, Day 1 and 12 and between Day 1 and Day 19 for each error type; NR = no-response, LEX = lexical errors and PHONO = phonological errors according to the therapy method (iVR; DSL).
error (lexical, phonological, no-responses and others). The distribution of errors at each test session is presented in *Appendix D*. Accuracy was analysed with a generalised linear mixed effects model (Baayen et al., 2008) using the R software (Venables and Smith, 2004) with participant and item as random effects. The model is described in detail in the "Results" section and in *Appendix C and D*. For errors, the difference between Day 1 and Day 5, Day 1 and Day 12 and between Day 1 and Day 19 was calculated for each type of error to obtain the change in errors for each patient. A linear mixed model was performed with the error change for each error type as the dependent variable. Methods (iVR or DSL) and the test days were included as fixed effects and patients as a random effect.

# 6. Results

On Day 1, the mean accuracy rate was 43,35% (range: 0–77%). On Day 5, the accuracy rate increased to 58,8% (range: 16–91%) with an improvement of 14% (range: 5–28%). On Day 12, the accuracy was on average 64.56% (range: 14–94%) with an improvement of 21% compared to Day 1 (range: 5–33%). On Day 19, the accuracy rate reaches 68,92% (range: 19–95%) with an improvement of 25% in comparison to Day 1 (range: 10–30%). The results for accuracy and error types are shown in Fig. 5a.

The model on accuracy showed a main effect of day (X2(2) = 310.80, p < .001) with an improvement from Day 1 to Day 5, Day 12 and Day 19. Further analysis revealed significant differences between Day 5 and Day 12 ($\beta$ = 0.42, $SD$ = 0.09, $z$ = 5.38, p < .001) as well as between Day 5 and Day 19 ($\beta$ = 0.42, $SD$ = 0.10, $z$ = 4.26, p < .001) with higher accuracy rate for Day 12 and Day 19 in comparison to the Day 5. No significant difference was observed between Day 12 and Day 19. The learning method showed no significant difference (see *Appendix D*).

In the analysis by error type, the statistical model for lexical errors showed a main effect of method (F(1; 65.58) = 20.02, p < .001) with a greater reduction in lexical errors for words treated with iVR than for DSL for the comparison between the Day 1 and all the other Days (Day 5, 12 and 19) (see *Appendix E*). Phonological errors and non-responses showed no difference across methods (*Appendix E*).

# 7. Discussion

The present study investigated whether immersive virtual reality also has an advantage over a static digital approach in the treatment of common word anomia in people suffering from post-stroke aphasia.

The results showed an efficient overall improvement after the treatment period without significant difference in accuracy between words treated with iVR and those treated with DSL. The two methods used, involving production, error-less context and constant word retrieval (Middleton et al., 2015), produced good post-therapy results and good maintenance over time. However, iVR leads to a greater reduction of lexical errors than DSL. Thus, iVR leads to the same overall improvement as a digital static treatment method commonly used in the STL setting but has a specific positive impact on lexical and semantic difficulties, while DSL tends to reduce errors without targeting a specific difficulty. As stressed in the Introduction, the immersive version of virtual reality has never been used as a rehabilitation method for anomia; only the effects of non-immersive or semi-immersive virtual reality have been investigated and tend to show a positive effect when comparing pre-post therapy (Cao et al., 2021; Devane et al., 2023), but no advantage for virtual reality compared to a control therapy method, especially for word finding difficulties (Cao et al., 2021; Devane et al., 2023). Virtual reality seems to be as effective as conventional therapy methods, even face-to-face therapy with a therapist, for language measures. This is a result that is replicated here on overall improvement when iVR is compared to a static digital approach with the same intensity. In contrast to the results found with non-immersive or semi-immersive virtual reality, the results of the present study show

beneficial effects of immersive virtual reality compared to a control method on the reduction of lexical errors, which may suggest better results with iVR in people with aphasia who make numerous lexical-semantic errors in a context of a spared semantic system and arising from lexical-semantic impairment (Nozari, 2019). The possible reasons and factors behind this advantage of immersive virtual reality will be discussed in the General Discussion along with the results of learning rare words in neurotypical participants in Study 1.

## 7.1. General discussion

The aim of the first study was to determine if immersive virtual reality could lead to potential benefits in rare word learning in neurotypical adults, in order to establish whether it could be used in people with aphasia for the rehabilitation of words used in daily life through learning skills. In fact, learning abilities similar to those used by neurotypical adults to learn new words could be at the heart of recovery from anomia, meaning that people with aphasia engage learning processes during rehabilitation. However, there is a fundamental difference between these two contexts. Neurotypical adults had to create a new concept for the rare objects/words, whereas people with aphasia were trying to make links between pre-existing or re-created semantic representations and lexical nodes. For this reason, we did not fully compare the two studies, but rather explored the implications of these two different results for the cognitive and linguistic processes involved in immersive virtual reality. In Study 1, neurotypical adults showed a higher accuracy rate for words learned with the iVR methods than those learned with the digital static learning method, both immediately after learning and after a one-week follow-up. In contrast, people who suffer from aphasia reach the same improvement in terms of overall accuracy for words learned with both methods, but show a greater reduction of lexical errors for words learned with iVR than with the control method (Study 2).

In the following we will discuss the factors that may underlie the observed benefits of immersive virtual reality, and then the hypothesis of specific help from this method of lexical-semantic links.

## 7.2. iVR advantages

Virtual reality has received increased interest in the treatment of aphasia and in word learning in recent years. As stressed in the Introduction, VR has the great advantage of providing a more ecological context and encouraging communication in a variety of simulated contexts (Devane et al., 2023). It therefore leads to higher motivation and interest (Marshall et al., 2016). It also provides a safe context for communication that can be carried out independently at home, while allowing the implementation of factors known to be essential for learning or for addressing language difficulties in a controlled way.

However, no previous study has tested the immersive version of VR, they only replicate the clinical settings without exploring the real potential of integrative context of VR (Cao et al., 2021; Devane et al., 2023) even though it is well known that immersion is one of the most important factors for positive outcomes in learning (Maresca et al., 2019). Indeed, according to the CAMIL model, the immersion is one of the key factors in the benefits of iVR in educational learning, influencing senses of presence and agency, as well as psychological and affective factors (Kaplan-Rakowski and Wojdynski, 2018; Makransky and Petersen, 2021). In fact, the greatest positive effects on vocabulary learning (Dhimolea et al., 2022; Legault et al., 2019) and communication skills (Halabi, 2020) are generally found in the immersive virtual reality. The immersion experienced by participants in the present studies was enhanced by the use of a head-mounted display and object manipulation, as has been identified in L2 vocabulary learning (Legault et al., 2019). In addition to immersion, other factors can be identified as influencing learning and the treatment of aphasia. Indeed, the software created did not represent a clinical setting like the previous studies, but
represented a situation similar to everyday communication with a
marketplace and interactions with avatars. This more ecological context
is known to encourage learning (Newgarden et al., 2015; Verga and
Kotz, 2017, 2019) and is one of the crucial points in the use of VR
compared with standard methods allowing patients to communicate in a
variety of simulated contexts in a safe context (Devane et al., 2023).
Finally, the previous studies used traditional face-to-face therapy as the
comparison method, leading to major differences compared with the
virtual reality method used. The present studies have the distinct
advantage of having a digital comparison method used in a clinic
without the intervention of a therapist, with a certain amount of control
over the elements presented in order to make the two methods more
comparable. Consequently, the results of the present studies are in line
with studies on L2 vocabulary learning which have demonstrated a
better effect of immersive virtual reality than semi-immersive and
non-immersive as control methods (Legault et al., 2019) with the im-
mersion, the ecological context and the interactions as factors of main
influence.

### 7.3. A specific advantage for lexical-semantic processes?

The advantage of iVR has been suggested as having its origin in the
influence on cognitive and affective factors known to help learning
(CAMIL: Makransky and Petersen, 2021). However, the results of the
present studies may suggest a different underlying process. We studied
the effect of immersive virtual reality on the learning of rare words in
neurotypical adults and on the (re)learning/recovery of common words
in people with aphasia. The study of these two population groups
originated from the hypothesis that a learning or relearning process may
underlie recovery from anomia (Ferguson, 1999; Hopper and Holland,
2005; Ralph et al., 2007). Indeed, not only does (re)learning play an
inherent role in language rehabilitation (Peñaloza et al., 2022), but
therapy can be seen as a learning experience (Hopper and Holland,
2005). Training specific aspects of language directly involves memory
and learning (Helm-Estabrooks, 2002) and also enables the creation
and/or reinforcement of links in the lexical-semantic system involved in
word production. In addition, it has been pointed out that there is a
specific link between learning skills and language recovery in aphasia
(Dignam et al., 2016; Tuomiranta et al., 2011). A correlation has been
shown in particular between learning ability and recovery from
word-finding difficulties (Dignam et al., 2016; Tuomiranta et al., 2011).
This would mean that people with aphasia would use their learning or
relearning skills during therapy to improve their ability to produce
words. In addition to this recent hypothesis, the results of the present
study with neurotypical adults and people with aphasia led to a more
specific hypothesis of the effects of iVR on underlying language pro-
cesses. Indeed, word learning implies the creation of mental represen-
tations along the conceptual-phonetic system but especially, between
the lexical and the semantic systems with the establishment of connec-
tions between newly learned words and already known words. This re-
fers to the lexical-semantic integration and enables the production of
newly learned words. This means that lexical-semantic processes are
essential for learning a word, as also suggested by neuroimaging studies
on word learning (Franco and Laganaro, 2024). Moreover, as stressed in
the Introduction, a distinction may be done between patients that pro-
duce mainly lexical (semantic) versus phonological errors with distinc-
tive origins of the underlying impairments (Schwartz, 2014). Some
people with aphasia produce errors mainly at the word level (i.e., lexical
errors) including the production of one word for another with semantic
links between the two words (i.e., semantic paraphasias). This type of
error is thought to originate in lexical-semantic processes (Levelt et al.,
1999; Nozari, 2019). The activation of several lexical entries by se-
mantic features requires a competitive lexical selection and/or inhibi-
tion process which can be impaired in people with aphasia, leading to a
greater number of semantic errors due to the production of other acti-
vated words. As both, word learning and lexical errors produced by

people with aphasia involve lexical-semantic processes, immersive vir-
tual reality may have a positive effect on lexical-semantic processes. This
iVR facilitation can enhance both the creation of links when learning
rare words, as in neurotypical adults, or the reinforcement of existing
links, as in people with aphasia through relearning.

Thanks to the sense of immersion and the context that is closer to real
life, iVR would promote lexical-semantic integration both in the
learning of native language words in neurotypical adults and in people
with aphasia in the context of (re)learning for production difficulties.
Nevertheless, future research is needed to address this issue and assess
whether iVR can lead to better lexical-semantic integration than a
control method by testing lexical-semantic integration using specific
tasks, such as the picture-word interference task or semantic priming.

### 7.4. Limitations

Firstly, it should be noted that neurotypical adults showed a high
variability in their learning skills. This means that in our sample, people
with aphasia may have variable learning skills before the stroke on
which we based our rehabilitation and may therefore lead to a high
variability in the recovery of people with aphasia. Furthermore, the
stroke itself may also have an impact on these learning skills. Finally,
this could explain why immersive virtual reality did not lead to
enhanced accuracy compared to the static digital method as identified
with neurotypical adults.

Moreover, for people with aphasia, no control list was involved in the
study in order to investigate the possible transfer effects. It has been
shown that semantic treatment may have an impact on other words in
the same semantic categories. It has not yet been established whether
learning-based therapies can make a similar transfer. Here a possible
transfer effect could be verified at Day 5 on the list that was not learnt
between Day 1 and Day 5, but which will be the list learnt in the second
week. Given that no improvement was observed at Day 5 on the un-
learned list (see Appendix F), retraining of a list of frequent words with
iVR had no effect on the production of another list of frequent words
from the same semantic category, suggesting that the treatment is rather
lexical-semantic than semantic.

## 8. Conclusion

Here we have been able to demonstrate a learning advantage of an
iVR word learning approach in neurotypical adults compared to a digital
static learning method (Study 1), and a greater reduction in lexical er-
rors in people with anomia for words treated with an immersive virtual
reality in comparison to a digital static method (Study 2), without dif-
ference between the two methods on overall improvement. So far, the
advantage of iVR have been ascribed to a variety of factors, such as the
degree of immersion resulting from the sense of presence and agency
(Kaplan-Rakowski and Wojdynski, 2018; Makransky and Petersen,
2021), the communicative context (Newgarden et al., 2015; Verga and
Kotz, 2017, 2019) and the object manipulation (Legault et al., 2019)
which promote cognitive and affective factors known to be important in
learning (Makransky and Petersen, 2021). The present results allow
enrich those hypotheses by suggesting that iVR facilitates a more effi-
cient creation or reinforcement of links between semantic and lexical
representations. Consequently, immersive virtual reality would promote
lexical-semantic integration, which would be of real benefit for patients
with production difficulties and specifically for patients with lexical
errors and semantic paraphasias.

### CRediT authorship contribution statement

**Julie Franco:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Formal analysis, Data curation, Conceptualization. **Bertrand Glize:** Writing – review & editing, Validation, Supervision, Resources. **Marina**
**Laganaro:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.

## Informed consent statement

Informed consent was obtained from all subjects involved in the study.

## Ethics approval

Were validated by the local research ethics committees in CUREG of the University of Geneva (N° PSE. PSE20201003.06) and in Bordeaux.

## Consent for publication

Not applicable.

## Availability of data and materials

The datasets generated and/or analysed during the current study are available in the Yareta repository: https://doi.org/10.26037/yareta:nd rkd3ubp5eydi32hbhizxmhv4.

## Declaration of Generative AI

During the preparation of this work the author(s) used Deepl write in

order to verify the grammaticality of the language. After using this tool/ service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

## Funding

This research was funded by the NCCR Evolving Language, Swiss National Science Foundation Agreement #51NF40_180888.

## Conflicts of interest

The authors declare no conflict of interest.

## Acknowledgements

The authors thank Valentin Bourdon and Antoine Provost (Human Neuroscience Platform, Fondation Campus Biotech Geneva) for their help with the iVR and the tablet creation as well as Marie Couvreu De Deckersberg and Sophie Arheix-Parras for the huge help during the recruitment. This work has been carried out within the framework of the National Centre of Competence in Research (NCCR) Evolving Language supported by the Swiss National Science Foundation. The authors acknowledge the financial support of the SNSF.

# Appendix

## Appendix A: stimuli.

### Appendix A.1

rare words in Experiment 1

<table><thead><tr><th colspan="7">Music instruments</th></tr><tr><th>French</th><th>English</th><th>French</th><th>English</th><th>French</th><th>English</th></tr></thead><tbody><tr><td>alboka</td><td>alboka</td><td>shékéré</td><td>shekere</td><td>mandore</td><td>mandore</td></tr><tr><td>dranyen</td><td>dramyin</td><td>sistre</td><td>sistrum</td><td>mirliton</td><td>mirliton</td></tr><tr><td>kalimba</td><td>kalimba</td><td>tabla</td><td>tabla</td><td>olifant</td><td>olifant</td></tr><tr><td>magrouna</td><td>mizwad</td><td>virginal</td><td>virginal</td><td>sorna</td><td>sorna</td></tr><tr><td>ocarina</td><td>ocarina</td><td>adungu</td><td>adungu</td><td>taiko</td><td>taiko</td></tr><tr><td>marimba</td><td>marimba</td><td>dulzaina</td><td>dulzaina</td><td>vibraphone</td><td>vibraphone</td></tr><tr><td>kacapi</td><td>kacapi</td><td>shamisen</td><td>shamisen</td><td></td><td></td></tr><tr><th colspan="7">Fruits and vegetables</th></tr><tr><th>French</th><th>English</th><th>French</th><th>English</th><th>French</th><th>English</th></tr><tr><td>pitanga</td><td>surinam cherry</td><td>rambutan</td><td>rambutan</td><td>longane</td><td>longan</td></tr><tr><td>calebasse</td><td>calabash</td><td>safou</td><td>safou</td><td>mangoustan</td><td>mangosteen</td></tr><tr><td>sapotille</td><td>sapodilla</td><td>mizuna</td><td>mizuna</td><td>nèfle</td><td>medlar</td></tr><tr><td>jamalac</td><td>java apple</td><td>pepino</td><td>pepino</td><td>radicchio</td><td>raddichio</td></tr><tr><td>lucuma</td><td>lucuma fruit</td><td>carambole</td><td>carambola</td><td>salacca</td><td>salak</td></tr><tr><td>margose</td><td>bitter melon</td><td>salicorne</td><td>glasswort</td><td>mineola</td><td>mineola</td></tr><tr><td>nashi</td><td>nashi</td><td>jujube</td><td>jujube</td><td></td><td></td></tr><tr><th colspan="7">Tools</th></tr><tr><th>French</th><th>English</th><th>French</th><th>English</th><th>French</th><th>English</th></tr><tr><td>anémomètre</td><td>anemometer</td><td>palan</td><td>block and tackle</td><td>baratte</td><td>butter churn</td></tr><tr><td>chevillette</td><td>chevillette</td><td>mandrin</td><td>chuck</td><td>doloire</td><td>doloire</td></tr><tr><td>serpette</td><td>billhook</td><td>écouvillon</td><td>swab</td><td>aérographe</td><td>airbrush</td></tr><tr><td>gradine</td><td>claw chisel</td><td>trusquin</td><td>marking gauge</td><td>chignole</td><td>hand drill</td></tr><tr><td>plantoir</td><td>dibber</td><td>batée</td><td>gold panning</td><td>drille</td><td>drille</td></tr><tr><td>semoir</td><td>seed drill</td><td>merlin</td><td>dulcimer guitar</td><td>échenilloir</td><td>pruning pole</td></tr><tr><td>grelinette</td><td>broadfork</td><td>taraud</td><td>tap</td><td></td><td></td></tr></tbody></table>
## Appendix A.2

frequent words in Experiment 2

<table><thead><tr><th colspan="4">Music instruments</th></tr><tr><th>French</th><th>English</th><th>French</th><th>English</th></tr></thead><tbody><tr><td>batterie</td><td>drums</td><td>banjo</td><td>banjo</td></tr><tr><td>cor</td><td>horn</td><td>carillon</td><td>chime</td></tr><tr><td>cymbales</td><td>cymbals</td><td>clarinette</td><td>clarinet</td></tr><tr><td>flûte</td><td>flute</td><td>lyre</td><td>lyre</td></tr><tr><td>métronome</td><td>metronome</td><td>guitare</td><td>guitar</td></tr><tr><td>violoncelle</td><td>cello</td><td>piano</td><td>piano</td></tr><tr><td>orgue</td><td>organ</td><td>saxophone</td><td>saxophone</td></tr><tr><th colspan="4">Fruits</th></tr><tr><th>French</th><th>English</th><th>French</th><th>English</th></tr><tr><td>abricot</td><td>apricot</td><td>ananas</td><td>ananas</td></tr><tr><td>cerise</td><td>cherry</td><td>fraise</td><td>strawberry</td></tr><tr><td>framboise</td><td>raspberry</td><td>mangue</td><td>mango</td></tr><tr><td>kaki</td><td>kaki</td><td>pamplemousse</td><td>grapefruit</td></tr><tr><td>myrtille</td><td>blueberry</td><td>mirabelle</td><td>mirabelle</td></tr><tr><td>orange</td><td>orange</td><td>tomate</td><td>tomato</td></tr><tr><td>pruneau</td><td>prunes</td><td>figure</td><td>fig</td></tr><tr><th colspan="4">Vegetables</th></tr><tr><th>French</th><th>English</th><th>French</th><th>English</th></tr><tr><td>artichaut</td><td>artichoke</td><td>asperge</td><td>asparagus</td></tr><tr><td>aubergine</td><td>eggplant</td><td>brocoli</td><td>broccoli</td></tr><tr><td>céleri</td><td>celery</td><td>courgette</td><td>zucchini</td></tr><tr><td>épinard</td><td>spinach</td><td>haricot</td><td>green bean</td></tr><tr><td>patate</td><td>potato</td><td>olive</td><td>olive</td></tr><tr><td>radis</td><td>radish</td><td>poireau</td><td>leek</td></tr><tr><td>roquette</td><td>arugula</td><td>potiron</td><td>pumpkin</td></tr><tr><th colspan="4">Tools</th></tr><tr><th>French</th><th>English</th><th>French</th><th>English</th></tr><tr><td>agrafeuse</td><td>stapler</td><td>compas</td><td>compass</td></tr><tr><td>ciseaux</td><td>scissors</td><td>cuillère</td><td>spoon</td></tr><tr><td>perceuse</td><td>drill</td><td>équerre</td><td>square</td></tr><tr><td>scalpel</td><td>scalpel</td><td>scie</td><td>saw</td></tr><tr><td>truelle</td><td>trowel</td><td>tournevis</td><td>screwdriver</td></tr><tr><td>arrosoir</td><td>watering can</td><td>fourchette</td><td>folk</td></tr><tr><td>chalumeau</td><td>blowtorch</td><td>escabeau</td><td>step</td></tr></tbody></table>

## Appendix B

summary of accuracy analysis in Experiment 1 with emmeans posthoc analysis

Model: glmer(Accuracy ~ Method*Day + (1|Subject) + (1|Stimuli), family = "binomial")

<table><tbody><tr><td colspan="5">Test = Day 1:</td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>-0.00387</td><td>0.459</td><td>-0.008</td><td>0.9933</td></tr><tr><td colspan="5">Test = Day 3:</td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>-0.93121</td><td>0.204</td><td>-4.556</td><td>&lt;0.0001</td></tr><tr><td colspan="5">Test = Day 5:</td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>-0.42364</td><td>0.123</td><td>-3.434</td><td>0.0006</td></tr><tr><td colspan="5">Test = Day 9:</td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>-0.42969</td><td>0.126</td><td>-3.418</td><td>0.0006</td></tr><tr><td colspan="5">Method = DSL:</td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>Day 1 – Day 3</td><td>-6.3007</td><td>0.355</td><td>-17.749</td><td>&lt;0.0001</td></tr><tr><td>Day 1 – Day 5</td><td>-6.3683</td><td>0.343</td><td>-18.557</td><td>&lt;0.0001</td></tr><tr><td>Day 1 – Day 9</td><td>-6.4981</td><td>0.344</td><td>-18.892</td><td>&lt;0.0001</td></tr><tr><td>Day 3 – Day 5</td><td>-0.0677</td><td>0.149</td><td>-0.455</td><td>0.9687</td></tr><tr><td>Day 3 – Day 9</td><td>-0.1974</td><td>0.150</td><td>-1.320</td><td>0.5502</td></tr><tr><td>Day 5 – Day 9</td><td>-0.1298</td><td>0.119</td><td>-1.089</td><td>0.6965</td></tr><tr><td colspan="5">Method = iVR:</td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>Day 1 – Day 3</td><td>-7.2280</td><td>0.374</td><td>-19.309</td><td>&lt;0.0001</td></tr><tr><td>Day 1 – Day 5</td><td>-6.7881</td><td>0.353</td><td>-19.230</td><td>&lt;0.0001</td></tr><tr><td>Day 1 – Day 9</td><td>-6.9239</td><td>0.354</td><td>-19.564</td><td>&lt;0.0001</td></tr><tr><td>Day 3 – Day 5</td><td>0.4399</td><td>0.175</td><td>2.519</td><td>0.0570</td></tr><tr><td>Day 3 – Day 9</td><td>0.3041</td><td>0.176</td><td>1.731</td><td>0.3072</td></tr><tr><td>Day 5 – Day 9</td><td>-0.1358</td><td>0.126</td><td>-1.080</td><td>0.7016</td></tr></tbody></table>
## Appendix C

### summary of accuracy analysis in Experiment 2

<table><thead><tr><th colspan="6">glmer(Accuracy ~ Method + Day + (1|Subject) + (1|Stimuli), family = "binomial")</th></tr><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(&gt;|z|)</th><th></th></tr></thead><tbody><tr><td>(Intercept)</td><td>-0.456</td><td>0.432</td><td>-1.056</td><td>0.291</td><td></td></tr><tr><td>MethodiVR</td><td>0.025</td><td>0.065</td><td>0.381</td><td>0.704</td><td></td></tr><tr><td>DayDAY 5</td><td>0.950</td><td>0.090</td><td>10.484</td><td>&lt;0.001</td><td></td></tr><tr><td>DayDAY 12</td><td>1.450</td><td>0.091</td><td>15.892</td><td>&lt;0.001</td><td></td></tr><tr><td>DayDAY 19</td><td>1.380</td><td>0.100</td><td>13.760</td><td>&lt;0.001</td><td></td></tr></tbody></table><table><thead><tr><th colspan="6">glmer(Accuracy ~ Method + Day + (1|Subject) + (1|Stimuli), family = "binomial")</th></tr><tr><th></th><th>Estimate</th><th>Std. Error</th><th>z value</th><th>Pr(&gt;|z|)</th><th></th></tr></thead><tbody><tr><td>(Intercept)</td><td>0.493</td><td>0.433</td><td>1.140</td><td>0.254</td><td></td></tr><tr><td>MethodiVR</td><td>0.025</td><td>0.065</td><td>0.381</td><td>0.704</td><td></td></tr><tr><td>DayDAY 1</td><td>-0.950</td><td>0.090</td><td>-10.484</td><td>&lt;0.001</td><td></td></tr><tr><td>DayDAY 12</td><td>0.499</td><td>0.092</td><td>5.4387</td><td>&lt;0.001</td><td></td></tr><tr><td>DayDAY 19</td><td>0.429</td><td>0.100</td><td>4.263</td><td>&lt;0.001</td><td></td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Appendix D</figcaption></figure>

**Appendix D.1.** changes in number of lexical and phonological errors in people with aphasia between the Day 1 and Day 5, Day 1 and Day 12 and Day 1 and Day 19
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>**Appendix D.2.** Percentage of change in word (re)learning per people with aphasia according to the method.</figcaption></figure>
# Appendix E. summary of error analysis in Experiment 2

## Appendix E.1

lexical errors

<table><thead><tr><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Day = Day1-5:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>2.93</td><td>1.13</td><td>6.606</td><td>0.011</td></tr><tr><td>Day = Day1-12:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>3.0</td><td>1.09</td><td>2.752</td><td>0.007</td></tr><tr><td>Day = Day1-19:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>2.93</td><td>1.13</td><td>2.251</td><td>0.027</td></tr></tbody></table>

## Appendix E.2

phonological errors

<table><thead><tr><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Day = Day1-5:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>0.667</td><td>1.52</td><td>0.439</td><td>0.662</td></tr><tr><td>Day = Day1-12:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>0.312</td><td>1.47</td><td>0.212</td><td>0.832</td></tr><tr><td>Day = Day1-19:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>-1.667</td><td>1.70</td><td>-0.981</td><td>0.330</td></tr></tbody></table>

## Appendix E.3

no-responses

<table><thead><tr><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Day = Day1-5:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>-1.0</td><td>1.52</td><td>-0.656</td><td>0.514</td></tr><tr><td>Day = Day1-12:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>-1.062</td><td>1.48</td><td>-0.720</td><td>0.474</td></tr><tr><td>Day = Day1-19:</td><td></td><td></td><td></td><td></td></tr><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z.ratio</td><td>p.value</td></tr><tr><td>DSL-iVR</td><td>0.167</td><td>1.70</td><td>0.098</td><td>0.922</td></tr></tbody></table>

## Appendix F

control list

<table><caption>glmer(Accuracy ~ Day*Method + (1|Subject) + (1|Stimuli), family = "binomial")</caption><thead><tr><th></th><th>Chisq</th><th>Df</th><th>Pr(>|Chisq|)</th></tr></thead><tbody><tr><td>DAY</td><td>2098</td><td>1</td><td>0,14</td></tr><tr><td>Method</td><td>0,087</td><td>1</td><td>0,76</td></tr><tr><td>DAY*Method</td><td>2900</td><td>1</td><td>0,08</td></tr></tbody></table>

Note. The experimental design of the second study with people with aphasia did not include a control list on which no training was carried out in order to determine the specificity of the therapy carried out. However, it was possible to train on the second unlearned list between Day 1 and Day 5. During this period, the patients carried out therapy on a first list of frequent words using a first method. On the other hand, the second list, which was retrained in the second week, had not yet been trained during this period. We therefore wanted to see if, after training the first list, there was any improvement between Day 1 and Day 5 on this untrained list. The present model indicates that there are no significant differences between Day 1 and Day 5 for either the iVR or the DSL on this untrained list. This shows that there is no transfer effect from a trained list to an untrained list and demonstrates the specificity of the proposed therapy.

## Data availability

The datasets generated and/or analysed during the current study are available in the Yareta repository: https://doi.org/10.26037/yareta: ndrkd3ubp5eydi32hbhizxmhv4.

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