# Factor structure and psychometric properties of the French Online Version of the Spontaneous Use of Imagery Scale (SUIS-F)

Alice Napias, Sandra Aka, Stéphanie Mathey, Willy Mayo, Christelle Robert

► To cite this version:

Alice Napias, Sandra Aka, Stéphanie Mathey, Willy Mayo, Christelle Robert. Factor structure and psychometric properties of the French Online Version of the Spontaneous Use of Imagery Scale (SUIS-F). Pratiques Psychologiques, 2025, 10.1016/j.prps.2025.06.001. hal-05400360

HAL Id: hal-05400360

https://hal.science/hal-05400360v1

Submitted on 5 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ELSEVIER

Disponible en ligne sur www.sciencedirect.com

ScienceDirect

et également disponible sur www.em-consulte.com

<figure><img src="image_3.png" /></figure>

Cognitive psychology

# Factor structure and psychometric properties of the French Online Version of the Spontaneous Use of Imagery Scale (SUIS-F)

## *Structure factorielle et qualités psychométriques de la version en ligne française de la Spontaneous Use of Imagery Scale (SUIS-F)*

Alice Napias<sup>a,b</sup>, Sandra Aka<sup>a</sup>, Stéphanie Mathey<sup>a</sup>, Willy Mayo<sup>b</sup>, Christelle Robert<sup>a,*</sup>

<sup>a</sup> University of Bordeaux, LabPsy-UR 4139, Bordeaux, France

<sup>b</sup> University of Bordeaux, INCIA-CNRS UMR 5287, Bordeaux, France

## ARTICLE INFO

**Article history:**

Received 18 February 2025

Accepted 17 June 2025

Available online xxx

**Keywords:**

Mental imagery

Spontaneous Use of Imagery Scale

SUIS-F

Psychometric properties

Online administration

## ABSTRACT

The aim of this study was to examine the factor structure and psychometric properties of the French version of the Spontaneous Use of Imagery Scale (SUIS-F), a self-report 12-item questionnaire evaluating the propensity to engage in mental imagery in daily life. Although the SUIS-F is already available and currently used, its psychometric properties have not yet been documented, and this study was designed to address this gap. The questionnaire was adapted for an online administration. The script for its implementation and scoring is available upon request. Two samples of French participants completed the SUIS-F, two additional tests of mental imagery vividness (i.e., VVIQ, PSIQ-F) and a vocabulary test (i.e., Lextale-FR). Test-retest reliability was evaluated after one month. Sample 1 (n=545) was used to explore the factorial structure, revealing a two-factor six-item structure with a low to acceptable internal consistency and a satisfactory construct validity. This structure was

E-mail address: christelle.robert@u.bordeaux.fr (C. Robert).

https://doi.org/10.1016/j.prps.2025.06.001

1269-1763/© 2025 Société Française de Psychologie. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote>* Corresponding author. University of Bordeaux, Laboratoire de Psychologie UR 4139, 3, place de la Victoire, 33076 Bordeaux, France.</footnote>
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

confirmed in Sample 2 (n = 129). The SUIS-F scores were described
according to age, gender and level of education for both samples.
Recommendations are made to improve the use of the French ver-
sion of the SUIS.

© 2025 Société Française de Psychologie. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

RÉSUMÉ

L'objectif de cette étude était d'examiner la structure factorielle et les qualités psychométriques de la version française du Spontaneous Use of Imagery Scale (SUIS-F), un questionnaire auto-rapporté de 12 items qui évalue la propension à utiliser l'imagerie mentale dans la vie quotidienne. Bien que la SUIS-F soit déjà disponible et utilisée, ses propriétés psychométriques n'ont pas encore été rapportées. Cette étude vise à combler cette lacune. Le questionnaire a été adapté pour une administration en ligne. Le script permettant sa mise en œuvre et sa cotation est disponible sur demande. Deux échantillons de participants français ont complété le SUIS-F, deux tests supplémentaires mesurant la vivacité de l'imagerie mentale (i.e., VVIQ-F, PSIQ-F), et un test de vocabulaire (i.e., Lextale-FR). La fidélité test-retest a été évaluée à un mois d'intervalle. L'échantillon 1 (n = 545) a permis d'examiner la structure factorielle de façon exploratoire, et a indiqué une structure à deux facteurs incluant six items avec une consistance interne de faible à acceptable et une validité de construit satisfaisante. Cette structure a été confirmée auprès de l'échantillon 2 (n = 129). Les scores à la SUIS-F ont été décrits en fonction de l'âge, du genre et du niveau d'éducation pour les deux échantillons. Des recommandations sont formulées pour améliorer l'utilisation de la version française de la SUIS.

© 2025 Société Française de Psychologie. Published by Elsevier Masson SAS. Cet article est publié en Open Access sous licence CC BY (http://creativecommons.org/licenses/by/4.0/).

Mots clés :
Imagerie mentale
Spontaneous use of mental imagery
SUIS-F
Propriétés psychométriques
Adaptation en ligne

Mental imagery is commonly defined as the ability to develop a simulated sensory perception without any exterior stimulation, or “seeing with the mind’s eye” (Kosslyn et al., 2001, p. 635). It has been found to play an important role in everyday life and has received increasing interest from both researchers and clinicians. It is thought to be involved in remembering past memories and planning future events (J. Pearson et al., 2015), in mental navigation (Ghaem et al., 1997), emotion regulation (for a review, see Holmes & Mathews, 2010) and in motor skill learning, both in musicians and athletes (Lotze & Halsband, 2006; Suica et al., 2022). Mental imagery is also known to be linked to psychiatric disorders such as mood and anxiety disorders, self-harm and suicidality, and addiction (Ji et al., 2019). It has become a therapeutic tool used in treatments such as cognitive behavior therapy (Blackwell, 2021; Saulsman et al., 2019). Measuring individual mental imagery abilities with a valid and reliable tool is therefore crucial for both researchers and clinicians. The aim of the present paper was to evaluate the factor structure and psychometric properties of an already available mental imagery questionnaire (SUIS; Kosslyn et al., 1998; Reisberg et al., 2003), that has been translated in French (Ceschi & Pictet, 2008) and is currently used both in clinical and research settings.

Mental imagery is thought to be multidimensional and comprise several aspects such as the ease,
speed, duration, control and vividness of imagery (see Suica et al., 2022). Assessment of mental imagery
abilities is a complex issue as several tools exist (mostly self-report questionnaires), each focusing on
one specific dimension of mental imagery (D. G. Pearson et al., 2013; Suica et al., 2022). Vividness has
been the main focus of these assessments. It is usually evaluated by asking participants to generate
an image 'on command', such as in the Vividness of Mental Imagery Questionnaire (VVIQ; Marks,
1973), which was translated and validated in French (Santarpia et al., 2008). While the VVIQ focuses
on the visual sensory modality, the Plymouth Sensory Imagery Questionnaire (PSIQ, Andrade et al.,
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

2014) extends the assessment of mental imagery to seven sensorial modalities (i.e., visual, auditory, cutaneous, kinesthetic, gustatory, olfactory and organic). These two questionnaires have proven especially useful in the field of psychopathology and clinical research to define individual differences in mental imagery abilities in relation with mental health (D. G. Pearson et al., 2013). The relationship between the vividness of visual mental imagery and mental disorders such as schizophrenia, bipolarity or posttraumatic stress disorder has been extensively documented over the last decades, showing the importance of assessing mental imagery in clinical settings to better understand how it can be used as a therapeutic tool (for reviews, see Blackwell, 2021; Brewin et al., 2010; J. Pearson et al., 2015; Petit et al., 2021).

The daily spontaneous use of mental imagery as a stable characteristic, or trait, is another component of these abilities (Suica et al., 2022). Vividness does not entirely predict the frequency at which an individual uses imagery or is affected by intrusive images (Pearson et al., 2015). Several self-reported questionnaires have been proposed to assess the use of imagery abilities in daily life. For example, Blazhenkova and Kozhevnikov (2009) developed the Object-Spatial Imagery and Verbal Cognitive styles Questionnaire (OSIVQ), a 45-item scale designed to assess individual preferences in processing visual (i.e., both object and spatial imagery) versus verbal information. The OSIVQ was recently adapted and validated in French (Bled & Bouvet, 2021). Notably, the OSIVQ object imagery scores were found to correlate with the VVIQ measure of object imagery. The Spontaneous Use of Mental Imagery Scale (SUIS; Kosslyn et al., 1998; Reisberg et al., 2003) is another self-report questionnaire developed to assess the propensity to engage in mental imagery in daily life. Compared to the OSIVQ, the SUIS is shorter (12 items) and focuses on the object dimension of visual mental imagery. It can be used in both research and clinical settings to describe an often overlooked aspect of interindividual differences in mental imagery use (e.g., Holmes et al., 2011; Keogh & Pearson, 2017; Renner et al., 2019). For example, Holmes et al. (2011) found that patients with bipolar disorder scored higher on the SUIS than healthy controls. Their greater propensity to use mental imagery may contribute to mood instability, thus making it relevant in cognitive behavior therapy.

The SUIS was first developed in English by Kosslyn et al. (1998) and validated by Reisberg et al. (2003). Each item describes a real-life situation (e.g., "When I think about a series of errands I must do, I visualize the stores I will visit") for which participants are asked to answer on a 5-point scale whether the description accurately reflects their tendency to react, from 1 ("never appropriate") to 5 ("always completely appropriate"). Summing the scores for all the items provides a total score ranging from 12 to 60, with higher scores indicating a stronger propensity to use mental imagery in daily life. This questionnaire has been translated and adapted in several languages, such as Dutch (Nelis et al., 2014), German (Görgen et al., 2016), Japanese (Tanaka et al., 2018) and more recently Persian (Rajeh et al., 2023) and Polish (Zaleskiewicz et al., 2023). Its psychometric quality is usually described as good for student populations (for a review, see Suica et al., 2022). Internal consistency (i.e., the extent to which items measure the same construct) is generally satisfactory, ranging from α =.66 for the German version (Görgen et al., 2016) to .98 for the English one (i.e., correlations of each item with the rest of the scale, Reisberg et al., 2003). Although a relationship between SUIS and VVIQ scores is commonly reported, it is generally moderate (e.g., Nelis et al., 2014; Reisberg et al., 2003) suggesting that the constructs underlying both imagery questionnaires are not identical. The factorial structure of the SUIS has been explored in various languages, revealing a one-factor structure preferred to a two-factor one in Dutch (Nelis et al., 2014), a one-factor structure in Japanese (Tanaka et al., 2018) and German (Görgen et al., 2016), and a four-factor structure in Persian (Rajeh et al., 2023). A French adaptation of the SUIS is already available for French-speaking clinicians and researchers (SUIS-F, Ceschi & Pictet, 2018). This version has been used in published researches (e.g., Pillaud et al., 2024), but to our knowledge, its factor structure and psychometric properties have not yet been studied in the French population.

The present study was designed to provide an evaluation of the factor structure and psychometric
properties of the French version of the SUIS (Ceschi & Pictet, 2018). Athough the SUIS-F is already
available and currently used in both research and clinical contexts, its psychometric properties have
not yet been documented. By addressing this gap, we aim to provide data that may support clinicians
and researchers interested in using this instrument in their respective settings. The questionnaire was
adapted for an online administration (see also Nelis et al., 2014 for an online adaptation of the SUIS
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

in Dutch). Developing an online version can be useful in both clinical and research contexts, since online studies have become prevalent (Kim et al., 2019) and online screening can be used to save time during clinical interviews, as scoring can be automatized. The evaluation of the factor structure and psychometric properties (i.e., internal consistency, test-retest reliability, and construct validity) of the online version of the SUIS-F was conducted in two independent French samples: (1) one composed of young adult students to explore the factorial structure of the data, and (2) another composed of the general population. As stated by Suica et al. (2022), most mental imagery questionnaires have been evaluated in student samples, so it remains unclear whether conclusions can be extended to the general population and/or wider age ranges. Construct validity was assessed using tools that, according to the literature, are theoretically and empirically related to the tendency to use visual mental imagery in daily life (i.e., the VVIQ and the PSIQ-F; for the same procedure see e.g., Clark & Maguire, 2020; Nelis et al., 2014; Reisberg et al., 2003). Moreover, it is worth noting that the VVIQ has already been validated in French, while the PSIQ-F, although not yet formally validated, is available in French and frequently used in combination with the SUIS. In this study, we prioritized VVIQ scores due to its established validation. Nonetheless, given the PSIQ-F's availability and its relevance in both clinical and research contexts, we considered it important to examine its correlations with the SUIS-F. We also planned to provide a shorter version of the SUIS-F, as did Nelis et al. (2014), if the data drawn from the factorial analyses suggested it was more relevant. As mental imagery is evaluated through verbal descriptions, we assessed individual language proficiency (using the Lextale-FR test, Brysbaert, 2013) to examine whether mental imagery scores are related to the level of vocabulary of the individuals. Finally, we described the distribution of scores in both samples, considering classical individual variables (i.e., age, gender, socio-demographic status). The present study (including the main objectives, method and statistical analyses) was preregistered on Open Science Framework (OSF, https://osf.io/35f49/?view_only=SUIS-F).

# 1. Method

## 1.1. Participants

Two samples drawn from the French population were included, i.e., one with only university students ranging from 18 to 25 years old and another from the general population with a wider age range and educational backgrounds. Both included only participants aged 18 or more, who confirmed having no diagnosed psychological or neurological disorder at the time of the study. Written consent was necessary and participants did not receive any financial compensation.

Sample 1 was composed of college students from the University of Bordeaux who were recruited via email through university mailing lists. Out of 1,068 participants who started the online survey, a total of 545 individuals completed the study<fnref n="1" /> (417 females, 4 non-binary<fnref n="2" />, mean age = 19.85, SD age = 1.7, ranging from 18 to 25 years). The level of education in Sample 1 was of 13.5 years on average (SD education = 1.3) and the mean vocabulary score on the LexTale test was 87.27% (SD vocabulary = 7.74). Participants were informed of the main purpose of the study and the possibility to take the retest by giving their approval. Out of 400 participants who agreed to be contacted for the second part of the study, a total of 125 individuals completed the retest one month later (101 females, 1 non-binary, mean age = 19.78, SD age = 1.68).

Sample 2 was composed of 129 participants who completed the survey (89 females, mean age = 41.83, SD age = 16.6, between 18 and 78 years old) out of the 199 participants who started it. Participants were recruited through social media, a national recruitment mailing list for scientific research, and personal contacts of the researchers. Their average level of education was 15.5 years (SD education = 2.4) and their mean vocabulary score on the LexTale test was 91.71% (SD vocabulary = 7.11).

<footnote n="1">¹ A sample of 545 participants exceeds widely accepted guidelines for running EFA, which suggest at least 5 to 10 participants per item (see Costello & Osborne, 2005; DeVellis, 2017). According to Comrey and Lee's (1992) scale, a sample size over 500 is considered 'very good' to 'excellent' for factor analysis.</footnote>

<footnote n="2">² As the number of non-binary participants was very low, we did not report a specific mean and standard deviation for this gender identity later in the paper.</footnote>
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

They were also informed that the second part of the study would take place one month later if they were interested, and out of the 117 participants who accepted to participate in the second part of the study, 68 participants (50 females, mean age = 44.1, SD age = 16.75) completed the retest procedure.

## 1.2. Materials

### 1.2.1. Spontaneous Use of Imagery Scale-French (SUIS-F)

The SUIS, originally developed by Kosslyn et al. (1998) and later translated into French (SUIS-F) by Ceschi and Pictet (2018), is already accessible and currently used in both research and clinical contexts (e.g., Di Simplicio et al., 2016; Pillaud et al., 2024). It comprises 12 items that evaluate self-reported daily use of mental imagery. Throughout the text, we refer to the items by numbering them following the order in which they appear in the original (Kosslyn et al., 1998) and translated versions (e.g., Ceschi & Pictet, 2018). Each item describes a situation involving the generation of a mental image. Participants are asked to rate the degree to which the description is accurate for themselves on a 5-point scale ranging from 1 ('never appropriate') to 5 ('always completely appropriate'). An example of item is "When I think about visiting a relative, I almost always have a clear mental picture of him or her" (item 5). The total score corresponds to the sum of responses to the 12 items and ranges from 12 to 60.

### 1.2.2. Vividness of Visual Imagery Questionnaire-French (VVIQ-F)

The VVIQ was developed by Marks (1973) and validated in French (VVIQ-F) by Santarpia et al. (2008). It evaluates the vividness of visual mental imagery by rating four scenes that participants are asked to generate, i.e., images of a relative, a rising sun, a shop, and a landscape. For each scene, they answer four questions examining various aspects of the scenes on a 5-point scale (from 1 'no image at all' to 5 'perfectly clear and as vivid as normal vision'). They perform these mental visualizations twice, first with their eyes open and then with them shut. They answer each question twice. The total score corresponds to the sum of responses for the 32 items and ranges from 32 to 160.

### 1.2.3. Plymouth Sensory Imagery Questionnaire-French (PSIQ-F)

The PSIQ was designed by Andrade et al. (2014) and translated in French (PSIQ-F) by Ceschi and Pictet (2018). It contains seven sets of five items, each linked to a sensorial modality, i.e., visual, auditory, cutaneous, gustatory, olfactory, organic, kinesthetic. For each item, participants are asked to evaluate the vividness of their mental representation on a 10-point scale, ranging from 0 ('no image at all') to 10 ('as vivid as in real life'). The final score is the mean of responses to all items. To our knowledge, there is still no published psychometric evaluation of the French version of this questionnaire (Ceschi & Pictet, 2018). The assessment of the French adaptation of the PSIQ is underway in our laboratory.

### 1.2.4. LexTale-Fr

The LexTale questionnaire was designed by Lemböfer and Broersma (2012) and was adapted in French (LexTale-Fr) by Brysbaert (2013). It evaluates the participants' knowledge of French vocabulary. It consists of 84 sequences of letters, i.e., 56 real French words and 28 pseudowords for which participants have to decide whether they are correctly spelt. The scoring procedure (Dujardin et al., 2022) defines the percentage of correct responses, taking into account both the correct selection of real words (W) and the correct deselection of pseudowords (PW) using the following formula: $(W + 2 \times PW)/112$. Norms developed from a sample of native French speakers aged 18-26 years (Dujardin et al., 2022) indicate a mean score of 88.40 (SD = 4.29).

## 1.3. Procedure

Ethical approval was obtained from the Ethics Committee of the Psychology Laboratory at Bordeaux University (LabPsy UR 4139, N 2021.10.CLE.011). The procedure was identical for both samples. The survey was programmed and run online with the Psytoolkit software (Stoet, 2010, 2017). The script is available upon request. After receiving key information about the research, participants had to provide their written consent. The various questionnaires were presented on different survey pages
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

in the survey. The SUIS-F and the PSIQ-F, both central to the assessment of convergent validity and measuring related aspects of mental imagery, were presented in a randomized order to minimize potential order effects. This was followed by the VVIQ-F and the LexTale-Fr, which were administered in a fixed sequence to ensure consistency across participants. The 12 items of the SUIS-F were presented in one table with five columns on the right, corresponding to the 5-point response scale. The PSIQ-F was presented on one page. We followed the presentation of the original and translated versions. The VVIQ-F was delivered on two pages: one featured the 16 items that participants had to perform with their eyes open, and the second consisted of all the items that participants had to perform with their eyes shut. For the LexTale-Fr, the 84 sequences of letters were presented on the same survey page. Participants had to check the corresponding box when they decided that the series of letters was a real word. They could change their answer if necessary. After taking these four questionnaires, participants were asked demographic questions such as their age, gender, highest diploma obtained, educational major (for Sample 1 only) or professional category (for Sample 2 only). They were then asked if they wished to take part in the second session, in which case an anonymous code was created to be able to link the two surveys anonymously. One month later, participants were invited to participate in a second online session. Although the test-retest intervals in previous adaptations of the SUIS varied significantly across studies (i.e., ranging from 1 week in Rajeh et al., 2023 to five months in Nelis et al., 2014), a one-month delay seemed to align with guidelines suggesting that the interval between test and retest should be long enough for participants to forget their initial responses, but not so long that their views on the topic might change (see DeVon et al., 2007). Given that the use of mental imagery is expected to remain stable over time, as suggested by Kosslyn et al. (1998) and observed by Suica et al. (2022), a one-month delay was deemed appropriate. In the second survey, participants only had to respond to the SUIS-F and the PSIQ-F presented in a random order, so that the test-retest reliability of both questionnaires could be evaluated.

## 1.4. Statistical analyses

Analyses were conducted using R 4.1.2 (R Core Team, 2023). The main ones conducted on both samples were pre-registered on OSF (https://osf.io/35f49/?view_only=SUIS-F). Descriptive analyses of the summed SUIS-F scores classified by age, gender and level of education were conducted to take the socio-demographic variables into account. Since the French version of the SUIS has not yet been validated and the adaptation of the SUIS in other languages has not always shown a one-factor structure (e.g., Rajeh et al., 2023), we first conducted an EFA, followed by a CFA, to assess the structure of the SUIS-F (for the same procedure, see Nelis et al., 2014).

We first performed an exploratory factor analysis (EFA) on Sample 1 to examine the factorial structure of the data collected from the university students. We first checked the possibility of factoring the variables using a correlation matrix, Kaiser-Meyer-Olkin (KMO) index and Bartlett's test. A parallel analysis was conducted to determine the number of recommended extracted factors. The exploratory factor analysis was performed on all 12 items and only saturations on factors above .32 were considered sufficient to associate an item to a factor (Costello & Osborne, 2005). In the event of a multi-factorial structure, an oblique rotation was applied as the questionnaire measures only one factor, and there are no theoretical arguments for the emergence of orthogonal factors (Costello & Osborne, 2005; Fabrigar et al., 1999; Matsunaga, 2010; Watkins, 2018). We assessed the internal consistency using Cronbach's alpha and McDonald's omega indices (Béland et al., 2018). We also considered correlations between scores on the SUIS-F and on two other questionnaires of mental imagery (i.e., PSIQ-F and VVIQ-F) to account for construct validity. To evaluate test-retest reliability, we examined the auto-correlations of items between the first and second tests.

A confirmatory factor analysis (CFA) was performed on Sample 2 to test the structure that emerged from Sample 1, using the lavaan package (Rosseel, 2012). Goodness-of-fit was assessed based on several fit indices. Parsimonious fit was considered acceptable for $\chi^2/df$ values < 3, incremental fit was estimated by the Tucker-Lewi index (TLI) and comparative fit index (CFI), and was considered acceptable when values were > .90. Absolute fit was estimated using root mean square error of approximation (RMSEA) and standardized root mean residual (SRMR): indices were judged acceptable when < .08 (Bentler & Bonett, 1980; Browne & Cudeck, 1992; Marsh et al., 2004; Matsunaga, 2010). We used the
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

<table><caption>**Table 1**
Descriptive statistics of total scores on SUIS-F for Sample 1 (n = 545).</caption><thead><tr><th rowspan="2">Demographic variable</th><th rowspan="2">Status</th><th rowspan="2">n</th><th colspan="2">SUIS-F score</th></tr><tr><th>M</th><th>SD</th></tr></thead><tbody><tr><td rowspan="4">Age</td><td>18 years old</td><td>133</td><td>44.27</td><td>6.36</td></tr><tr><td>19 years old</td><td>126</td><td>42.91</td><td>7.39</td></tr><tr><td>20 years old</td><td>122</td><td>43.80</td><td>7.43</td></tr><tr><td>Between 21-25 years old</td><td>152</td><td>41.98</td><td>8.27</td></tr><tr><td rowspan="2">Gender</td><td>Female</td><td>406</td><td>43.95</td><td>7.01</td></tr><tr><td>Male</td><td>123</td><td>40.75</td><td>8.42</td></tr><tr><td rowspan="3">Level of education</td><td>12 years</td><td>137</td><td>44.29</td><td>6.07</td></tr><tr><td>12 to 15 years</td><td>354</td><td>42.99</td><td>7.82</td></tr><tr><td>17 to 20 years</td><td>42</td><td>41.29</td><td>8.03</td></tr><tr><td rowspan="5">Domain of studies</td><td>Law</td><td>200</td><td>43.87</td><td>6.98</td></tr><tr><td>Technology and sciences</td><td>139</td><td>42.11</td><td>7.92</td></tr><tr><td>Humanities</td><td>91</td><td>42.31</td><td>8.47</td></tr><tr><td>Biology and health</td><td>103</td><td>44.11</td><td>6.55</td></tr></tbody></table>

<table><caption>**Table 2**
Descriptive statistics of total score on SUIS-F for Sample 2 (n = 129).</caption><thead><tr><th rowspan="2">Demographic variable</th><th rowspan="2">Status</th><th rowspan="2">n</th><th colspan="2">SUIS-F score</th></tr><tr><th>M</th><th>SD</th></tr></thead><tbody><tr><td rowspan="6">Age</td><td>Between 18-25 years old</td><td>25</td><td>42.80</td><td>8.99</td></tr><tr><td>Between 26-30 years old</td><td>20</td><td>39.65</td><td>9.55</td></tr><tr><td>Between 31-40 years old</td><td>24</td><td>35.75</td><td>10.66</td></tr><tr><td>Between 41-50 years old</td><td>15</td><td>42.07</td><td>7.87</td></tr><tr><td>Between 51-60 years old</td><td>21</td><td>42.86</td><td>7.09</td></tr><tr><td>Between 61-70 years old</td><td>15</td><td>40.33</td><td>7.36</td></tr><tr><td rowspan="2">Gender</td><td>Female</td><td>89</td><td>41.75</td><td>8.15</td></tr><tr><td>Male</td><td>39</td><td>37.51</td><td>9.80</td></tr><tr><td rowspan="5">Level of education</td><td>11 years</td><td>6</td><td>44.00</td><td>4.69</td></tr><tr><td>12 years</td><td>19</td><td>43.26</td><td>7.76</td></tr><tr><td>13 to 15 years</td><td>40</td><td>40.42</td><td>9.74</td></tr><tr><td>17 to 20 years</td><td>64</td><td>39.27</td><td>8.71</td></tr><tr><td>Farmers, craftsmen</td><td>5</td><td>43.20</td><td>6.42</td></tr><tr><td rowspan="5">Socio-professional status</td><td>Other or not applicable</td><td>25</td><td>40.56</td><td>7.61</td></tr><tr><td>Executives and higher intellectual professions</td><td>47</td><td>39.83</td><td>8.51</td></tr><tr><td>Employees</td><td>28</td><td>40.18</td><td>11.43</td></tr><tr><td>Intermediate professions</td><td>16</td><td>39.56</td><td>8.02</td></tr><tr><td>Students</td><td>8</td><td>44.50</td><td>7.69</td></tr></tbody></table>

same indices as in Sample 1 to test the internal consistency and construct validity, and computed test-retest reliability indices.

## 2. Results

### 2.1. Descriptive data

In the young student sample, total SUIS-F scores ranged from 12 to 60. Mean score was 43.18 (SD total = 7.43). Means and standard deviations according to socio-demographic variables are presented in Table 1. Females had higher scores than males, t(176.94) = 3.86, *p* < .001. Total scores were slightly negatively correlated with age (Spearman's *r* Age = -.09, *p* = .02) but not correlated with level of education (Spearman's *r* Education = -.07, *p* = .09).

The French general population sample had a mean summed score of 40.43 on the SUIS-F (SD Total = 8.84, range: 12-56). SUIS-F scores according to age, gender, level of education and socio-professional status are presented in Table 2. Females had higher scores than males, t(62.05) = 2.37,
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

p = .02. The summed score was not significantly correlated with age (Spearman's r$_{Age}$ = -.05, p > .05) and was negatively correlated with level of education (Spearman's r$_{Education}$ = -.18, p = .03). The mean SUIS-F score in the second sample was also significantly lower than that of the first sample, t(173.33) = 3.27, p = .001, d = 0.30.

## 2.2. Factor structure of the questionnaire

### 2.2.1. Exploratory Factor Analysis

To conduct the EFA, we first examined the correlation matrix of the 12 items. KMO (MSA = .77, from .70 to .84) and Bartlett's sphericity test ($\chi^2 = 679.5769, df = 66, p < .001$) indicated data suitability for factor analysis. Table 3 indicates the item-total correlation and correlation matrix for each item.

The distribution of scores for each item was significantly different from normal distribution (Shapiro's W between .70 and .90, p < .001). The parallel analysis recommended the extraction of three factors. Eigenvalues for each extracted factor were higher than theoretical values, i.e., 1.99, .35, .24, compared to .38, .19, .14 for the randomly generated data. A first structure of three factors was therefore computed. In line with the hypothesis that only one factor underlies all 12 items, a one-factor structure was also tested (see also Nelis et al., 2014 for the same rationale).

Results of the EFAs run with three factors and one factor are reported in Table 4. The three-factor model presented the first factor with items 5, 9 and 10 saturating over .32, the second factor with items 3, 4 and 8 saturating, and the third factor with only item 2 saturating. This model explained 26% of the cumulative variance across all 12 items, but included only seven items in the factors (Model characteristics: $\chi^2 = 51.29, df = 33, p = .022$). According to the guidelines currently used for EFAs, one item is not sufficient to define a factor and three items are a minimum (Costello & Osborne, 2005). We therefore decided to consider only the first and second factors and reduced the scale to the six items included in these two factors. By computing scores from the addition of item scores and correlating the two scores, they were positively correlated ($M_{factor 1} = 11.25, SD_{factor 1} = 2.81, M_{factor 2} = 11.9, SD_{factor 2} = 2.81$, Spearman's $r_{factors} = .32, p < .001$). A second EFA was conducted to evaluate to what extent one factor could account for the data pattern. One factor explained 17% of the variance observed with eight items saturating (Model characteristics: $\chi^2 = 139.79, df = 54, p < .001$).

Overall, the exploratory factor analysis conducted on a sample of university students indicated that a one-factor solution was not the best model for the French adaptation of the SUIS. Although a one-factor model with eight items might be more informative than the initial 12-item model, the best model for this sample included only six items. However, since the obtained data might be specific to this student population, we applied the factorial structures that emerged in Sample 1 to a second sample of French people from a broader age range and educational backgrounds.

### 2.2.2. Confirmatory factor analysis

We evaluated the three models that emerged from Sample 1: (1) a two-factor structure with six items, (2) a one-factor structure with eight items, and (3) a model with all 12 items. Since they did not include the same items, we compared them only on the basis of their fit indices.

The two-factor six-item model showed acceptable fit indices: $\chi^2(8) = 10.93, p = .20, \chi^2/df = 1.36, CFI = .98, RMSEA = .05, TLI = .96$, and SRMR = .03. The latent factors predicted item variance to be between $r^2 = .13$ (item 4) and $r^2 = .59$ (item 10). The saturation of each item on its latent factor varied between .36 (item 4, which was the only one under .40) and .76 (item 10). The correlation between the two scores reflecting the two factors was highly positive ($M_{factor 1} = 11.12, SD_{factor 1} = 3.45, M_{factor 2} = 10.65, SD_{factor 2} = 2.96$, Spearman's $r = .52, p < .001$). Fig. 1 shows the factors and loadings for each item.

The one-factor eight-item model showed poorer fit indices, $\chi^2(20) = 39.75, p = .005, \chi^2/df = 1.98, CFI = .91, RMSEA = .08, TLI = .88$, and SRMR = .06. Two indicators were not acceptable in this model (i.e., significant *p* value and TLI value). The latent factor predicted item variance to be between $r^2 = .13$ (item 3) and $r^2 = .57$ (item 10). The saturation of each item on the latent factor varied between .38 (item 3, which was the only one under .40) and .75 (item 10).

Finally, the model including all 12 items in one factor also showed poorer fit indices than the 6-item model, $\chi^2(44) = 72.25, p = .005, \chi^2/df = 1.64, CFI = .89, RMSEA = .07, TLI = .87$, and SRMR = .06.
<table><caption>**Table 3**
Item-total correlation and correlation matrix for Sample 1 (n = 545).</caption><thead><tr><th>Items</th><th>C IT C</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th></tr></thead><tbody><tr><td>1. Quand je vais dans un nouvel endroit, en plus des noms, je préfère avoir des indications qui incluent des descriptions détaillées de certains points de repère (tels que la taille, la forme et la couleur d'une station-service)</td><td>.18</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2. Si je jette un coup d'œil à une voiture qui est partiellement cachée par des buissons, je vais automatiquement « compléter » la totalité de l'image de la voiture dans ma tête</td><td>.25</td><td>.11*</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3. Si je cherche de nouveaux meubles dans un magasin, je visualise toujours comment ces meubles iraient à différents endroits chez moi</td><td>.37</td><td>.09*</td><td>.20***</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>4. Je préfère lire des romans dans lesquels je peux facilement visualiser où se trouvent les personnages et ce qu'ils sont en train de faire, plutôt que des romans difficiles à visualiser</td><td>.23</td><td>.10*</td><td>0</td><td>.20***</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5. Quand je pense rendre visite à un proche, j'ai presque toujours une image claire de lui (elle) qui me vient à l'esprit</td><td>.42</td><td>0</td><td>.12**</td><td>.22***</td><td>.17***</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6. Quand du matériel technique relativement facile est clairement décrit dans un texte, je trouve que les illustrations sont dérangeantes parce qu'elles interfèrent avec ma capacité à visualiser le matériel</td><td>.24</td><td>.10*</td><td>.18***</td><td>.06</td><td>.05</td><td>.11**</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>7. Si quelqu'un me demandait d'additionner deux nombres à deux chiffres (par exemple, 24 et 31), je les visualiserais afin de les additionner</td><td>.27</td><td>.06</td><td>.07</td><td>.18***</td><td>.11**</td><td>.14**</td><td>.10*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>8. Avant de m'habiller pour sortir, je visualise d'abord à quoi je vais ressembler dans différentes tenues vestimentaires</td><td>.41</td><td>.08</td><td>.07</td><td>.29***</td><td>.28***</td><td>.24***</td><td>.13**</td><td>.18***</td><td></td><td></td><td></td><td></td></tr><tr><td>9. Quand je pense à une série de courses à faire, je visualise les magasins où je vais me rendre</td><td>.36</td><td>.08</td><td>.09*</td><td>.17***</td><td>.07</td><td>.23***</td><td>.08</td><td>.20***</td><td>.29***</td><td></td><td></td><td></td></tr><tr><td>10. Quand j'entends la voix d'un(e) ami(e) avant de le (la) voir, une image visuelle de lui (elle) me vient presque toujours à l'esprit</td><td>.46</td><td>.12**</td><td>.18***</td><td>.18***</td><td>.11*</td><td>.43***</td><td>.18***</td><td>.18***</td><td>.25***</td><td>.29***</td><td></td><td></td></tr><tr><td>11. Quand j'entends un animateur radio ou un DJ que je n'ai jamais vu, je me retrouve souvent à imaginer à quoi il (elle) pourrait ressembler</td><td>.34</td><td>.11**</td><td>.21***</td><td>.14**</td><td>.08</td><td>.20***</td><td>.17***</td><td>.13**</td><td>.13**</td><td>.17***</td><td>.28***</td><td></td></tr><tr><td>12. Si j'avais assisté à un accident de voiture et que plus tard j'essayais de me rappeler des détails, je visualiserais ce qui s'est passé</td><td>.27</td><td>.05</td><td>.12**</td><td>.17***</td><td>.14**</td><td>.18***</td><td>-.01</td><td>.07</td><td>.13**</td><td>.21***</td><td>.17***</td><td>.19***</td></tr></tbody></table>

<footnote>* = p < .05, ** = p < .01, *** = p < .001; C IT C: Corrected item-total correlation.</footnote>
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

<table><caption>**Table 4**
Loading coefficients for Exploratory Factor Analysis (Sample 1, n = 545).</caption><thead><tr><th rowspan="2">Items</th><th colspan="4">Three-factor model</th><th colspan="2">One-factor model</th></tr><tr><th>1</th><th>2</th><th>3</th><th>Uniqueness</th><th>1</th><th>Uniqueness</th></tr></thead><tbody><tr><td>Item 1</td><td></td><td>.10</td><td>.16</td><td>.95</td><td>.19</td><td>.96</td></tr><tr><td>Item 2</td><td></td><td></td><td><strong>.69</strong></td><td>.59</td><td>.27</td><td>.92</td></tr><tr><td>Item 3</td><td>-.17</td><td><strong>.57</strong></td><td>.20</td><td>.68</td><td><strong>.43</strong></td><td>.81</td></tr><tr><td>Item 4</td><td>-.16</td><td><strong>.55</strong></td><td></td><td>.80</td><td>.28</td><td>.92</td></tr><tr><td>Item 5</td><td><strong>.53</strong></td><td>.12</td><td></td><td>.65</td><td><strong>.57</strong></td><td>.67</td></tr><tr><td>Item 6</td><td>.15</td><td></td><td>.21</td><td>.90</td><td>.28</td><td>.92</td></tr><tr><td>Item 7</td><td>.17</td><td>.22</td><td></td><td>.88</td><td><strong>.34</strong></td><td>.88</td></tr><tr><td>Item 8</td><td></td><td><strong>.60</strong></td><td>-.12</td><td>.62</td><td><strong>.51</strong></td><td>.74</td></tr><tr><td>Item 9</td><td><strong>.37</strong></td><td>.18</td><td></td><td>.78</td><td><strong>.47</strong></td><td>.78</td></tr><tr><td>Item 10</td><td><strong>.80</strong></td><td>-.13</td><td></td><td>.49</td><td><strong>.59</strong></td><td>.65</td></tr><tr><td>Item 11</td><td>.30</td><td></td><td>.25</td><td>.80</td><td><strong>.40</strong></td><td>.84</td></tr><tr><td>Item 12</td><td>.15</td><td>.16</td><td></td><td>.89</td><td><strong>.34</strong></td><td>.89</td></tr></tbody></table>

Extraction method: Log Likelihood. Factor loadings greater than .32 are shown in bold.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Two-factor model and loadings for each item by confirmatory factor analysis conducted on Sample 2.</figcaption></figure>

indicators were not acceptable in this model (i.e., significant *p* value, CFI and TLI value). The latent factor predicted item variance to be between *r*² = .01 (item 1) and *r*² = .59 (item 10). The saturation of each item on the latent factor varied between .11 (item 1, with three items under .40) and .77 (item 10).

## 2.3. Psychometric properties

Several approaches were used to evaluate internal consistency and construct validity, since the factorial structure was not as theoretically expected. We described the two scores from the two-factor six-item version obtained from the first EFA, and the score from the one-factor eight-item version obtained from the second EFA. We also reported the psychometric qualities of the questionnaire with the 12 items summed, because the latter is already used in practice. For each version, we reported the
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

<table><caption>**Table 5**

Descriptive statistics and correlation coefficients for complementary questionnaires.</caption><thead><tr><th rowspan="2">Survey</th><th rowspan="2">Internal consistency [95%CI]</th><th colspan="3">Descriptive statistics</th><th colspan="2">Three-factor model</th><th colspan="2">One-factor models</th></tr><tr><th>Mean</th><th>SD</th><th>Range (min - max)</th><th>1st factor</th><th>2nd factor</th><th>8 items</th><th>12 items</th></tr></thead><tbody><tr><td colspan="9"><strong>Sample 1</strong></td></tr><tr><td>VVIQ-F</td><td>.94 [.93, .95]</td><td>123.67</td><td>22.73</td><td>34 - 160</td><td>.35***</td><td>.20***</td><td>.41***</td><td>.40***</td></tr><tr><td>PSI-Q-F</td><td>.94 [.93, .95]</td><td>7.32</td><td>1.47</td><td>.51 - 10</td><td>.37***</td><td>.27***</td><td>.45***</td><td>.41***</td></tr><tr><td>Lextale-Fr</td><td>.68 [.64 - .73]</td><td>87.27</td><td>7.74</td><td>55.4 - 100</td><td>-.09*</td><td>-.13***</td><td>-.12**</td><td>-.11**</td></tr><tr><td colspan="9"><strong>Sample 2</strong></td></tr><tr><td>VVIQ-F</td><td>.95 [.95; .97]</td><td>118.62</td><td>26.4</td><td>34-160</td><td>.26***</td><td>.35***</td><td>.36***</td><td>.37***</td></tr><tr><td>PSI-Q-F</td><td>.95 [.95; .98]</td><td>7.91</td><td>1.8</td><td>.89-10</td><td>.34***</td><td>.25**</td><td>.37***</td><td>.36***</td></tr><tr><td>Lextale-Fr</td><td>.82 [.75; .87]</td><td>91.71</td><td>7.11</td><td>52.7 - 100</td><td>-.02</td><td>-.14</td><td>-.12</td><td>-.10</td></tr></tbody></table>

Internal consistency for VVIQ-F and PSI-Q-F: McDonald's Omega - For Lextale-Fr: split-half Spearman-correlation. * = p < .05, ** = p < .01, *** = p < .001.

correlations with the VVIQ-F and PSI-Q-F for construct validity. Finally, the test-retest reliability index was computed.

### 2.3.1. Internal consistency

In the first sample, the internal consistency of the first model (i.e., two-factor six-item version) was not sufficient on the two factors according to recommendations for conducting EFA. For the first factor (i.e., items 5, 9 and 10), Cronbach's alpha was .58 (95% CI = .52 to .64) and McDonald's omega was .58 (95% CI = .52 to .65). For the second factor (i.e., items 3, 4, and 8), Cronbach's alpha was .52 (95% CI = .45 to .59) and McDonald's omega was .53 (95% CI = .45 to .61). The one-factor eight-item model also had low internal consistency. Cronbach's alpha was .67 (95% CI = .63 to .71) and McDonald's omega was .67 (95% CI = .62 to .72). Finally, internal consistency was low when considering the summed score of all 12 items, with a Cronbach's alpha of .68 (95% CI = .64 to .72) and a McDonald's omega of .68 (95% CI = .62 to .73).

In the second sample from the general population, internal consistency was relatively low for the two-factor version. The values are higher for the first factor (i.e., items 5, 9, 10; α = .76, 95% CI = .69 to .83; Ω = .76, 95% CI = .67 to .85), but remain low for the second factor (i.e., items 3, 4, and 8; α = .49, 95% CI = .34 to .64; Ω = .51, 95% CI = .36 to .67). As in Sample 1, one-factor versions were much more acceptable on internal validity. The one-factor eight-item model also had satisfactory internal consistency. Cronbach's alpha was .79 (95% CI = .74 to .84) and McDonald's omega was .80 (95% CI = .73 to .85). Finally, internal consistency was acceptable when considering the summed score of all 12 items, with a Cronbach's alpha of .77 (95% CI = .72 to .83) and a McDonald's omega of .79 (95% CI = .72 to .84).

### 2.3.2. Construct validity

Descriptive data, internal consistency coefficients, and intercorrelations between the questionnaires for both samples are presented in Table 5. The SUIS-F showed positive correlations with both the VVIQ-F (r = .20) and the PSI-Q-F (r = .45), indicating a moderate relationship with other self-report measures of mental imagery. These correlations suggest that individuals reporting frequent spontaneous use of imagery (as measured by the SUIS-F) also tend to report higher vividness and sensory richness of imagery experiences. In contrast, correlations between the SUIS-F and the Lextale-Fr were low and non-significant, ranging from -.02 to -.13 across samples. This pattern suggests that vocabulary knowledge is largely unrelated to self-reported imagery use.

### 2.3.3. Test-Retest reliability

To evaluate test-retest reliability, we performed pairwise correlations between factors identified in the first and second sessions. During the latter, in our first sample of 545 participants, 125 participants answered the SUIS-F a second time; their mean score was 45.14 (SD Total = 7.22, range: 19 to
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

60). Significant positive test-retest correlations were found with the six-item two-factor model (1st Factor: Spearman's *r* <u>1st Factor</u> = .61, *p* < .001; 2nd Factor: Spearman's *r* <u>2nd Factor</u> = .74, *p* < .001). The eight-item one-factor model score also exhibited significant test-retest correlations (Spearman's *r* <u>Factor</u> = .72, *p* < .001). Finally, the test-retest correlation of the total score including the 12 items was also significantly positive (Spearman's *r* <u>Factor</u> = .72, *p* < .001). Although test-retest reliability was questionable for the first factor of the six-item two-factor model, the overall test-retest reliability of the various models was satisfactory in our first sample.

In the second sample of 129 participants, the second session was completed by 68 participants who obtained a mean total score of 41.09 (SD <u>Total</u> = 9.29, ranging from 14 to 56). Overall, pairwise correlations indicated a satisfactory test-retest reliability over a one-month period in that second sample. In the three-factor model, test-retest Spearman correlation coefficients were .80 (*p* < .001) for the first factor and .75 (*p* < .001) for the second factor. For both the one-factor eight-item and 12-item models, the test-retest correlation was .83 (*p* < .001).

## 3. Discussion

The main finding of this study was that scores obtained with the French online adaptation of the SUIS were better explained by two factors as proposed in the initial English version (see Reisberg et al., 2003). This was evidenced both in university students and in individuals from a wider age range and a broader educational background. Although the construct validity, internal consistency and test-retest reliability of the 12-item version was acceptable, the sum of scores should be interpreted with caution since no structural model emerged for this set of items. A shorter six-item version thus appeared to be more appropriate to assess the spontaneous use of mental imagery.

Overall, the present data provide evidence that SUIS-F scores are not driven by a single factor, as was proposed in the original English version. Reisberg et al. (2003) found a high degree of internal consistency between the 12 items, suggesting that they shared the same construct. In comparison with Reisberg et al. (2003), language, culture and time may be viewed as sources of discrepancy in our data. The samples were also clearly different: in Reisberg et al. (2003), the sample was mainly composed of experienced researchers in the field of mental imagery, perception and/or memory, so it was not comparable to ours. In the Dutch version of the SUIS in which the samples were similar to ours, Nelis et al. (2014) found that a two-factor model had a better fit, although it was not selected by the authors to account for the structure of the questionnaire. Interestingly, however, their model was similar to ours. Our model had a good fit, and the two factors relied partially on the same items as in the Dutch model, despite the fact that there were only six items. The first factor that emerged in our study included items 5, 9 and 10, which were also grouped in one factor in the Dutch version (along with items 2, 6, 7, 11 and 12). The second factor included items 3, 4, and 8, which were also grouped in the second factor in the Dutch version (Nelis et al., 2014). Importantly, Rajeh et al. (2023) also found evidence for a factor that grouped items 3, 4 and 8 in the Persian version of the SUIS. These results suggest that the two-dimensional model from our study is relevant for explaining SUIS scores in French. This model was found both in a sample of university students -commonly used in early psychometric studies for their homogeneity (e.g., Henrich et al., 2010) – and in an independent general population sample. This supports the stability and generalizability of the model across different French-speaking groups. More broadly, the factorial structure of the SUIS appears to vary considerably across different language versions, raising important questions regarding its cross-cultural validity. Such discrepancies highlight the necessity of conducting thorough psychometric evaluations for each translated version of the instrument, as the structure observed in one linguistic or cultural context may not generalize to others.

The internal consistency of this model was low to acceptable in both samples but was lower than expected from previous adaptations (e.g., Nelis et al., 2014; Rajeh et al., 2023). This finding holds particular significance for researchers and clinicians considering the use of the French version of the SUIS, as it may affect the measure's precision and limit its relevance for individual assessment. Convergent validity was comparable to that of the Dutch version (Nelis et al., 2014) and test-retest reliability was good, as in other adaptations (e.g., Nelis et al., 2014; Rajeh et al., 2023). This study focused on assessing
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

convergent validity by comparing the SUIS-F with other measures of mental imagery. Future validation work could extend these findings by including divergent validity assessments to further demonstrate that the SUIS-F specifically and uniquely captures the construct of spontaneous use of mental imagery.

The difference in mean SUIS-F scores observed between the two samples may reflect meaningful variation between a general population sample and a younger, student-based sample. This finding highlights the importance of reporting descriptive statistics across age groups, gender, and socio-demographic categories, to gain a more comprehensive understanding of individual differences in the spontaneous use of mental imagery. However, this difference should be interpreted with caution due to the relatively small size of the second sample and the high variability in SUIS-F scores. While the number of participants in the confirmatory study falls below commonly recommended thresholds (e.g., *n* = 200; Hair et al., 2019), previous research has shown that smaller samples (e.g., *n* = 100-150) may still yield reliable results when the model is simple, factors are well-defined, and communalities are high (MacCallum et al., 1999; Wolf et al., 2013). Despite these limitations, the results point to important directions for future research. Increasing the sample size and ensuring greater representativeness would likely enhance the robustness of the results, improve model fit indices, and ultimately strengthen the generalizability of the findings. Another noteworthy finding is that we found a significant gender difference with females reporting higher scores of mental imagery use compared to men. Such pattern is commonly reported in studies assessing the subjective vividness of mental imagery (e.g., Richardson, 1995) and those assessing object imagery preferences (e.g., Blazhenkova & Kozhevnikov, 2009; Bled & Bouvet, 2021). This gender effect also parallels previous findings reported with the SUIS in Dutch (Nelis et al., 2014) or Polish (Zaleskiewicz et al., 2023), although this difference was not always observed (Rajeh et al., 2023; Tanaka et al., 2018). This difference may indicate a variation in the use of mental imagery between men and women. Alternatively, it may suggest that the structure of mental imagery abilities differs as a function of gender, leading the SUIS-F to measure different constructs in men and in women. Further research is necessary to specifically address this issue.

If the SUIS is assumed to assess the general ability to spontaneously use mental imagery, the two factors that emerged may reflect specific occurrences of this ability. Factor 1 (corresponding to items 5, 9 and 10) may refer to the ability to spontaneously visualize familiar entities, such as relatives or known places, while Factor 2 (corresponding to items 3, 4, and 8) may refer to the ability to spontaneously visualize different options or solutions in a given situation (i.e., different outfits, different places for a piece of furniture, different backgrounds and activities for imaginary characters). The two factors identified in our study are positively correlated and may be interpreted within Kosslyn's theoretical framework, in which mental imagery is considered as a general ability that is subsumed by several processes that are not independent from one another (Kosslyn, 2005; Kosslyn et al., 2001). The unidimensional version of the SUIS was assumed to refer to the domain of experience, content and occurrence of imagery as defined by Pearson et al. (2013). Our findings suggest that the construct underlying the general experience of mental imagery may be more complex and multidimensional. Specifically, the spontaneity or vividness of mental imagery could be influenced by the familiarity of the content being imagined. Based on the present findings, several suggestions could be made to improve the French online adaptation of the SUIS. First, close attention should be paid to the items and their interpretation, and a revision of some items with very high uniqueness (e.g., item 1 and 6) may be necessary to improve the quality of the scale. Second, the French translation proposed by Ceschi and Pictet (2018) may warrant revision. To begin with, it remains unclear whether standard validation procedures, such as back-translation and pre-testing, were applied during the translation process. In addition, some items may benefit from improved phrasing such as item 6, which appeared to be difficult to understand. Furthermore, as suggested by Nelis et al. (2014), some items may no longer be relevant nowadays and should be updated. For instance, item 1 focuses on the description of a new place and how to imagine it before going there. With today's new technologies, images of a new place are easy to find with simple Internet or GPS searches. This situation is therefore not often experienced by people nowadays, which may explain why this item was not linked to a measure of mental imagery use in everyday life. Similarly, item 11 asks participants if they usually try to picture the radio announcer when they hear him or her. However, radio broadcasts can now be filmed and watched in real time, which might interfere with the need to use mental imagery in such a situation.
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

People may not even listen to the radio as much as when the questionnaire was first created in 1998. Altogether, we believe that some items should be revised to ensure that the same construct is captured more accurately. Our findings also suggest the relevance of a shorter version of the SUIS-F including only six of the original items (i.e., items 3, 4, 5, 8, 9, 10). This shorter version appeared to measure the frequency of visualizing of different possibilities in a given situation, and familiar entities in everyday life. It may have benefits both for researchers and clinicians who use it with individuals who have the same characteristics as in the present two samples, since it is shorter to administer and provides more reliable data. Importantly, it is apparently superior to the 12-item version as not all items in the latter correlated or measured the same construct, which could lead to misinterpreting the scores.

In conclusion, this study demonstrates the superiority of the two-factor model of the French online version of the SUIS, unlike the one-factor structure of the original English version. The SUIS-F may be improved in several ways. In the long-term perspective, the items that may be considered as outdated should be revised. In the short-term perspective, the shorter six-item version could be used by researchers and clinicians more easily, as it is quick to administer and score. Furthermore, it could easily be combined with another test of mental imagery, such as the PSIQ-F.

## Funding

This work was funded by a PhD scholarship from the French Ministry of Higher Education and Research granted to Alice Napias.

## Disclosure of interest

The authors declare that they have no competing interest.

## References

* Andrade, J., May, J., Deeprose, C., Baugh, S. J., & Ganis, G. (2014). Assessing vividness of mental imagery: The Plymouth sensory imagery questionnaire. *British Journal of Psychology, 105*(4), 547-563. https://doi.org/10.1111/bjop.12050

* Béland, S., Cousineau, D., & Loye, N. (2018). Utiliser le coefficient oméga de McDonald à la place de l'alpha de Cronbach. *McGill Journal of Education*, 52(3), 791-804. https://doi.org/10.7202/1050915ar

* Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. *Psychological Bulletin*, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588

* Blackwell, S. E. (2021). Mental Imagery in the Science and Practice of Cognitive Behaviour Therapy: Past, present, and future perspectives. *International Journal of Cognitive Therapy*, 14(1), 160-181. https://doi.org/10.1007/s41811-021-00102-0

* Blazhenkova, O., & Kozhevnikov, M. (2009). The new object-spatial-verbal cognitive style model: Theory and measurement. *Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition*, 23(5), 638-663. https://doi.org/10.1002/acp.1473

* Blazhenkova, O., & Kozhevnikov, M. (2009). The new object-spatial-verbal cognitive style model: Theory and measurement. *Applied Cognitive Psychology*, 23(5), 638-663. https://doi.org/10.1002/acp.1473

* Bled, C., & Bouvet, L. (2021). Validation of the French version of the object spatial imagery and verbal questionnaire. *European Review of Applied Psychology*, 71(4), 100687. https://doi.org/10.1371/journal.pone.0255039

* Brewin, C. R., Gregory, J. D., Lipton, M., & Burgess, N. (2010). Intrusive images in psychological disorders: Characteristics, neural mechanisms, and treatment implications. *Psychological Review*, 117(1), 210-232. https://doi.org/10.1037/a0018113

* Browne, M. W., & Cudeck, R. (1992). Alternative Ways of Assessing Model Fit. *Sociological Methods & Research*, 21(2), 230-258. https://doi.org/10.1177/0049124192021002005

* Brysbaert, M. (2013). Lextale.FR: A fast, free, and efficient test to measure language proficiency in French. *Psychologica Belgica*, 53(1), 23-37. https://doi.org/10.5334/pb-53-1-23

* Ceschi, G., & Pictet, A. (2018). *Imagerie mentale et psychothérapie: Un ouvrage sur la psychopathologie cognitive*. Mardaga.

* Clark, I. A., & Maguire, E. A. (2020). Do questionnaires reflect their purported cognitive functions? *Cognition*, 195, 104114. https://doi.org/10.1016/j.cognition.2019.104114

* Comrey, A. L., & Lee, H. B. (1992). *A first course in factor analysis (2nd ed.).* Lawrence Erlbaum Associates, Inc.

* Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. *Practical assessment, research, and evaluation*, 10(1), 7. https://doi.org/10.7275/JYJ1-4868

* DeVellis, R. F. (2017). *Scale development: Theory and applications (4th ed.).* Sage.

* Von, H. A., Block, M. E., Moyle-Wright, P., Ernst, D. M., Hayden, S. J., Lazzara, D. J., & Kostas-Polston, E. (2007). A psychometric toolbox for testing validity and reliability. *Journal of Nursing Scholarship*, 39(2), 155-164. https://doi.org/10.1111/j.1547-5069.2007.00161.x

* Di Simplicio, M., Renner, F., Blackwell, S. E., Mitchell, H., Stratford, H. J., Watson, P., & Holmes, E. A. (2016). An investigation of mental imagery in bipolar disorder: Exploring "the mind's eye". *Bipolar Disorders*, 18(8), 669-683. https://doi.org/10.1111/bdi.12453
Dujardin, E., Jobard, G., Vahine, R., & Mathey, S. (2022). Norms of vocabulary, reading, and spelling tests in French university students. *Behavior Research Methods, 54*(4), 1611-1625. https://doi.org/10.3758/s13428-021-01684-5

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. *Psychological Methods, 4*(3), 272.

Ghaem, O., Mellet, E., Crivello, F., Tzourio, N., Mazoyer, B., Berthoz, A., & Denis, M. (1997). Mental navigation along memorized routes activates the hippocampus, precuneus, and insula. *NeuroReport, 8*(3), 739-744.

Görgen, S. M., Hiller, W., & Witthöft, M. (2016). Die Spontaneous Use of Imagery Scale (SUIS)-Entwicklung und teststatistische Prüfung einer deutschen Adaption. *Diagnostica, 62*(1), 31-43. https://doi.org/10.1026/0012-1924/a000135

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). *Multivariate data analysis* (8th ed.). Cengage Learning.

Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? *Behavioral and Brain Sciences, 33*(2-3), 61-83. https://doi.org/10.1017/S0140525X0999152X

Holmes, E. A., Deeprose, C., Fairburn, C. G., Wallace-Hadrill, S. M. A., Bonsall, M. B., Geddes, J. R., & Goodwin, G. M. (2011). Mood stability versus mood instability in bipolar disorder: A possible role for emotional mental imagery. *Behaviour Research and Therapy, 49*(10), 707-713. https://doi.org/10.1016/j.brat.2011.06.008

Holmes, E. A., & Mathews, A. (2010). Mental imagery in emotion and emotional disorders. *Clinical Psychology Review, 30*(3), 349-362. https://doi.org/10.1016/j.cpr.2010.01.001

Ji, J. L., Kavanagh, D. J., Holmes, E. A., MacLeod, C., & Di Simplicio, M. (2019). Mental imagery in psychiatry: Conceptual & clinical implications. *CNS Spectrums, 24*(1), 114-126. https://doi.org/10.1017/S1092852918001487

Keogh, R., & Pearson, J. (2017). The blind mind: No sensory visual imagery in aphantasia. *Cortex, 105*, 53-60. https://doi.org/10.1016/j.cortex.2017.10.012

Kim, J., Gabriel, U., & Gygax, P. (2019). Testing the effectiveness of the Internet-based instrument PsyToolkit: A comparison between web-based (PsyToolkit) and lab-based (E-Prime 3.0) measurements of response choice and response time in a complex psycholinguistic task. *PloS One, 14*(9), e0221802. https://doi.org/10.1371/journal.pone.0221802

Kosslyn, S. M. (2005). Mental images and the brain. *Cognitive Neuropsychology, 22*(3-4), 333-347. https://doi.org/10.1080/02643290442000130

Kosslyn, S. M., Chabris, C., Shephard, J., & Thompson, W. (1998). Spontaneous Use of Imagery Scale [Database record]. *APA PsycTests, https://doi.org/10.1037/t57899-000

Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. *Nature Reviews Neuroscience, 2*(9), 635-642. https://doi.org/10.1038/35090055

Lemhöfer, K., & Broersma, M. (2012). Introducing LexTALE: A quick and valid Lexical Test for Advanced Learners of English. *Behavior Research Methods, 44*, 325-343. https://doi.org/10.3758/s13428-011-0146-0

Lotze, M., & Halsband, U. (2006). Motor imagery. *Journal of Physiology-Paris, 99*(4-6), 386-395. https://doi.org/10.1016/j.jphysparis.2006.03.012

MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. *Psychological Methods, 4*(1), 84-99. https://doi.org/10.1037/1082-989X.4.1.84

Marks, D. F. (1973). Visual Imagery Differences in the Recall of Pictures. *British Journal of Psychology, 64*(1), 17-24. https://doi.org/10.1111/j.2044-8295.1973.tb01322.x

Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings. *Structural Equation Modeling: A Multidisciplinary Journal, 11*(3), 320-341. https://doi.org/10.1207/s15328007sem1103.2

Matsunaga, M. (2010). How to factor-analyze your data right: Do's, don'ts, and how-to's. *International Journal of Psychological Research, 31*(1), 97-110. https://doi.org/10.21500/20112084.854

Nelis, S., Holmes, E. A., Griffith, J. W., & Raes, F. (2014). Mental imagery during daily life: Psychometric evaluation of the Spontaneous Use of Imagery Scale (SUIS). *Psychologica Belgica, 54*(1), 19. https://doi.org/10.5334/pb.ag

Pearson, D. G., Deeprose, C., Wallace-Hadrill, S. M. A., Heyes, S. B., & Holmes, E. A. (2013). Assessing mental imagery in clinical psychology: A review of imagery measures and a guiding framework. *Clinical Psychology Review, 33*(1), 1-23. https://doi.org/10.1016/j.cpr.2012.09.001

Pearson, J., Naselaris, T., Holmes, E. A., & Kosslyn, S. M. (2015). Mental Imagery: Functional Mechanisms and Clinical Applications. *Trends in Cognitive Sciences, 19*(10), 590-602. https://doi.org/10.1016/j.tics.2015.08.003

Petit, F., Munuera, C., Husky, M. M., & M'Bailar, K. (2021). Mental imagery in bipolar disorder: A PRISMA-compliant systematic review of its characteristics, content and relation to emotion. *Journal of Behavioral and Cognitive Therapy, 31*(3), 267-284. https://doi.org/10.1016/j.jbct.2021.03.003

Pillaud, N., Ballot, C., Robert, C., Mathey, S., & Ric, F. (2024). Is the approach avoidance compatibility effect moderated by word imageability? *Quarterly Journal of Experimental Psychology, 77*(6), 1281-1294. https://doi.org/10.1177/17470218231194499

Rajeh, Y. N., Dolatshahi, B., Pourshahbaz, A., & Zarghami, M. (2023). Psychometric Evaluation of the Persian Version of Spontaneous Use of Imagery Scale in the Iranian Population. *Iranian Journal of Psychiatry and Behavioral Sciences, 17*(1), e123402. https://doi.org/10.5812/ijpbs-123402

Reisberg, D., Pearson, D. G., & Kosslyn, S. M. (2003). Intuitions and introspections about imagery: The role of imagery experience in shaping an investigator's theoretical views. *Applied Cognitive Psychology, 17*(2), 147-160. https://doi.org/10.1002/acp.858

Renner, F., Murphy, F. C., Ji, J. L., Manly, T., & Holmes, E. A. (2019). Mental imagery as a "motivational amplifier" to promote activities. *Behaviour Research and Therapy, 114*, 51-59. https://doi.org/10.1016/j.brat.2019.02.002

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. *Journal of Statistical Software, 48*, 1-36. https://doi.org/10.18637/jss.v048.i02

Santarpia, A., Blanchet, A., Poinsot, R., Mininni, G., & Thizon-Vidal, S. (2008). Évaluer la vivacité des images mentales dans différentes populations françaises [Evaluating the vividness of mental imagery in different French samples]. *Pratiques psychologiques, 14*(3), 421-441. https://doi.org/10.1016/j.prps.2007.11.001

Saulsman, L. M., Ji, J. L., & Mcevoy, P. M. (2019). The essential role of mental imagery in cognitive behaviour therapy: What is old is new again. *Australian Psychologist, 54*(4), 237-244. https://doi.org/10.1111/ap.12406

Stoet, G. (2010). PsyToolkit: A software package for programming psychological experiments using Linux. *Behavior Research Methods, 42*(4), 1096-1104. https://doi.org/10.3758/BRM.42.4.1096
Napias, A. et al

Pratiques psychologiques xxx (xxxx) xxx-xxx

Stoet, G. (2017). PsyToolkit: A Novel Web-Based Method for Running Online Questionnaires and Reaction-Time Experiments. *Teaching of Psychology, 44*(1), 24–31. https://doi.org/10.1177/0098628316677643

Suica, Z., Behrendt, F., Gäumann, S., Gerth, U., Schmidt-Trucksäss, A., Ettlin, T., & Schuster-Amft, C. (2022). Imagery ability assessments: A cross-disciplinary systematic review and quality evaluation of psychometric properties. *BMC Medicine, 20*(1), 1–124. https://doi.org/10.1186/s12916-022-02295-3

Tanaka, Y., Yoshinaga, N., Tsuchiyagaito, A., Sutoh, C., Matsuzawa, D., Hirano, Y., Nakazato, M., & Shimizu, E. (2018). Mental imagery in social anxiety disorder: The development and clinical utility of a Japanese version of the Spontaneous Use of Imagery Scale (SUIS-J). *Asia Pacific Journal of Counselling and Psychotherapy, 9*(2), 171–185. https://doi.org/10.1080/21507686.2018.1487455

Watkins, M. W. (2018). Exploratory Factor Analysis: A Guide to Best Practice. *Journal of Black Psychology, 44*(3), 219–246. https://doi.org/10.1177/0095798418771807

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. *Educational and Psychological Measurement, 73*(6), 913–934. https://doi.org/10.1177/0013164413495237

Zaleskiewicz, T., Sobkow, A., & Traczyk, J. (2023). Polish Validation of the Spontaneous Use of Imagery Scale (SUIS): Psychometric Characteristics and Relationships with Risk Propensity. *Imagination, Cognition and Personality, 43*(1), 58–82. https://doi.org/10.1177/02762366231168360, 027623662311683.