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Abstract

Visuospatial skill, the ability to visualize and mentally manipulate information, is a process impacted by different factors like demographics, visual intelligence, and expertise in domains relying on these skills, such as visual arts or engineering. Here, we tested the unique contribution of drawing skills in predicting visuospatial performance while accounting for other factors, such as age and gender. Using a remotely and self-administered protocol, we tested visuospatial and drawing skills in 236 artists, visual and literary, aged 20 to 95 and located across the United States. Drawing accuracy and a measure of visual intelligence emerged as the strongest predictors of visuospatial skill. In addition, we provided further evidence for age-related differences that were driven by response times, along with gender differences in favor of men that were driven by accuracy. Visual arts expertise did not have a moderating effect on age or gender. Overall, the findings highlight the unique contribution of drawing skills to mental transformation performance.

Keywords

mental rotation transfer visual perception aging cognition peak performance

Public Significance Statement

The connection between the visual arts and visuospatial cognition is increasingly recognized. We found that drawing skills predicted individuals’ visuospatial representation and transformation better than age or gender, even though visual IQ played a significant role as well. The findings highlight the connection between drawing ability, which can be improved with targeted training, and visuospatial skill.

Introduction

Visuospatial skills involve mental representation and transformation of objects and their spatial relationships. Visuospatial skills integrate sensory input and prior knowledge, facilitating mental inferences and visual predictions and many everyday activities, like parallel parking (Tversky, 2005). Mental transformation tasks—such as mental cutting, folding, and rotation—are often used to assess visuospatial skills. For instance, mental cutting involves visualizing a 2D cross-section of a 3D shape. Visuospatial skills typically decline with age, which is often more pronounced in speed than accuracy (Techentin et al., 2014). Furthermore, men typically outperform women in tasks that capture visuospatial skills, particularly mental rotation (Voyer et al., 1995). Expertise in relevant activities can also affect performance. For example, drawing skills and visual arts expertise have been linked to enhanced visuospatial skills, along with attention, and memory (Chamberlain, 2018; Drake, Riccio, et al., 2021; Fan et al., 2023; Kozbelt & Ostrofsky, 2018; Martinčević & Vranić, 2023; Perdreau & Cavanagh, 2013). Yet, the field is emerging and limited in scope (Benear et al., 2024). Most studies are underpowered, rely on student-based samples, and frequently do not consider other sources of variance, such as demographic differences and other individual differences like fluid reasoning (visual IQ). Here, we recruited a large sample of practicing visual artists across the lifespan using an online approach to assess the predictive power of visual arts expertise on visuospatial skills, and we also contrasted their performance with practicing literary artists. Conceptualizing visuospatial skills as the intrinsic and dynamic classification of visual-spatial abilities by Uttal and colleagues (2013), we assessed performance by a latent variable consisting of three representative mental transformation tasks: mental cutting, folding, and rotation, and considering both, response time and accuracy.

Visuospatial Skills and Age

Age-related slowing in visuospatial skills and mental transformation has been demonstrated, primarily in mental rotation (Hertzog & Rypma, 1991; Puglisi & Morrell, 1986; Techentin et al., 2014; Zhao et al., 2020). Some of these differences can be explained by older adults prioritizing accuracy at the expense of speed, especially in tasks of low cognitive load (Hertzog et al., 1993; Strayer & Kramer, 1994). Increased difficulty leads to increased response times for both younger and older adults, but older adults show more pronounced increases. This has been attributed to younger adults’ efficiency in simplifying the representations during rotation (Hertzog & Rypma, 1991; Zhao et al., 2020). The slowing of mental transformation may result from the degradation of visuospatial skills, more general declines in domains like working memory and processing speed, or other reasons such as speed and accuracy tradeoff (Brockmole & Logie, 2013; Hartshorne & Germine, 2015; Salthouse, 1996, 2014; Strayer & Kramer, 1994). Understanding how the visuospatial domain changes with age is important given that many activities of daily living rely on visuospatial cognition (Dawson et al., 2010; Tinella et al., 2020).

Effects of age on accuracy are less clear and declines generally are not as prominent as for time (Band & Kok, 2000; Hertzog & Rypma, 1991; Salthouse, 2014). One early study found that at 0°, older adults showed the same mental rotation accuracy as younger but slower response times (Hertzog & Rypma, 1991). As the rotation angle increased, older adults’ performance decreased more than the younger adults’ in both outcomes. Despite overall slower speed, older adults have performed better at keeping accuracy stable at different response times, whereas younger adults are more prone to speed and accuracy tradeoffs (Hertzog et al., 1993; Jansen & Heil, 2009; Sharps & Gollin, 1987). Overall, performance is impacted by a decline in processing speed in combination with degradation of the mental representation quality with age; however, meta-analytic findings suggest that response times on visuospatial tasks are more strongly affected by age than accuracy (Techentin et al., 2014).

Gender Differences in Visuospatial Skills Across Adulthood

Studies have traditionally found a men’s advantage in visuospatial tasks, including mental rotation and folding (Halpern & Collaer, 2005; Hegarty, 2018; Li, 2014; Maeda & Yoon, 2013; Voyer et al., 1995). While the advantage is commonly observed in accuracy, response time differences are context dependent, such as exacerbated by time restrictions (Boone & Hegarty, 2017; Maeda & Yoon, 2013; Prinzel & Freeman, 1995; Tapley & Bryden, 1977). As difficulty increases, both men and women experience performance declines, but changes tend to be more pronounced for women (Jansen & Heil, 2009; Tapley & Bryden, 1977). The meta-analytic evidence is limited by a lack of data on older adults and by the fact that authors often do not separate analyses on accuracy and speed (Linn & Petersen, 1985; Maeda & Yoon, 2013; Voyer et al., 1995). Further, direct comparisons are complicated by the findings that men and women prioritize accuracy and response times differently (Lohman, 1986). More recent work has failed to observe gender-related differences in mental rotation and suggests investigating moderating effects of variables such as education and age (Jost & Jansen, 2024).

Declines in visuospatial skills can accelerate with age, with men showing steeper declines (McCarrey et al., 2016). Borella and colleagues found that men outperform women in mental rotation across a group of 454 adults aged 20–91, but their performance showed steeper age-related differences so that both groups perform similarly in older adulthood (Borella et al., 2014). Further, Jansen & Heil (2009) found that men generally outperform women in mental rotation accuracy in a sample of adults aged 20–70, although the difference between groups got smaller with age. In another study, men and women older adults showed similar slopes of speed and accuracy tradeoff on mental rotation, but women were performing slower on non-rotated items than men (Jansen & Kaltner, 2014). This trend aligns with findings that older adults, especially women, face more pronounces challenges in visuospatial tasks compared to younger adults (Hertzog & Rypma, 1991). Performance factors have been attributed to task and participant-related variables, with the former including the instructions and task administration and the latter including individual differences like reluctance to guess and speed of processing (Contreras et al., 2007). On timed tasks, for example, meta-analyses show that the gap between men and women increases (Voyer, 2011), suggesting differential speed-accuracy trade-offs. Another meta-analysis found that other factors, including processing speed and education level, may moderate performance differences more than gender (Techentin et al., 2014).

The Malleability of Visuospatial Skills

Visuospatial skills have shown to be associated with life experiences, such as spatially demanding careers (Bailey & Dewar, 2013; Meneghetti et al., 2018; Nemoto et al., 2020; Salthouse et al., 1990; Uttal et al., 2013). For example, a group of recently retired older adults who had careers in STEM (science, technology, engineering, and mathematics) outperformed their non-STEM peers in a set of visuospatial tasks (Bailey & Dewar, 2013; Neigel et al., 2017). In another study, recently retired men architects performed more accurately in mental transformation tasks than their peers from different careers, but their performance still did not match that of younger adults (Salthouse et al., 1990). Further, the recently retired architects had longer response times than the unselected older adults, suggesting a differential speed and accuracy tradeoff as a function of expertise. Older adults’ visuospatial skills can be improved through targeted training, highlighting the domain’s malleability (Basak et al., 2008; Cassavaugh & Kramer, 2009; Green & Bavelier, 2008; Meneghetti et al., 2018; Nemoto et al., 2020).

Just as age effects can be attenuated by experience, gender differences in spatial skills may also be moderated by experience, such as STEM or architecture careers (Baenninger & Newcombe, 1989; Cassavaugh & Kramer, 2009; Charlesworth et al., 2005; Feng et al., 2007; Kozhevnikov et al., 2010; Levine et al., 2005; Quaiser‐Pohl & Lehmann, 2002; Stieff et al., 2014). Meta-analytic evidence suggests similar improvements in visuospatial skills for men and women after training (Baenninger & Newcombe, 1989; Kass et al., 1998; Neubauer et al., 2010; Uttal et al., 2013). While experience can modulate performance to help individuals with lower skills catch up, individuals at all skill levels can continue improving. One bourgeoning field is visual arts engagement, which aims to understand the cognitive and perceptual benefits of visual arts. To the best of our knowledge, no studies to date have critically analyzed whether specific aspects of visual art, such drawing skills, moderate the described biological individual differences in visuospatial skill.

Visual Art Engagement: A Case for Visuospatial Skill Building

Arts engagement, particularly through music and theater, has shown a positive relation to cognitive domains including memory, attention, and processing speed (Alves, 2013; Banducci et al., 2017; Noice et al., 2014; Noice & Noice, 2008; Rouse et al., 2021; Slevc et al., 2016). Yet, the impacts in the field of visual art engagement on cognition have been less explored (Bolwerk et al., 2014; Noice et al., 2014). A major limitation in this research has been a lack of a clear framework linking visual art practices to expected cognitive outcomes. Observational drawing, which is a type of visual art that involves recreating what one is observing, allows researchers to more closely look at the effects of expertise (Kozbelt & Ostrofsky, 2018; Kozbelt & Seeley, 2007). Drawing is guided by strategies—such as isolating shapes and breaking down forms—and skills in this domain have been associated with enhanced visuospatial skills (Cavanagh, 2005; Chamberlain et al., 2019; Gombrich, 2000; Kozbelt, 2001; Perdreau & Cavanagh, 2013, 2014; Vodyanyk & Jaeggi, 2023). Drawing skills are often a stronger predictor of cognitive and perceptual differences than group-level distinctions, and longitudinal training is related to improvements in high-level perception (Chamberlain et al., 2013, 2019, 2021). Despite these insights, the existing research and its quality are limited, including issues such as small sample sizes, heterogenous college-aged art student populations, low correlations between tasks within and between constructs, reliability concerns, and the potential for a self-selection bias where individuals with higher spatial skills naturally gravitate towards the arts (Benear et al., 2024).

The Present Study

The current study includes a large online sample of practicing artists using a cross-sectional design to assess visuospatial performance across a set of tasks. We first aimed to replicate past findings on age-related and gender-related differences in visuospatial skills in a large sample of practicing artists across the United States. Participants self-administered the study, which has advantages like collecting data from large and more diverse samples. Studies show that online self-administration leads to similar engagement and data quality as compared with in-lab or supervised studies (Atkins et al., 2022; Backx et al., 2020; Collins et al., 2022; Feenstra et al., 2018; Launes et al., 2023). Going beyond assessment of accuracy and response time separately, we used a measure that accounts for both, minimizing speed and accuracy tradeoffs—the Balanced Integration Score (BIS; Liesefeld & Janczyk, 2019). This is particularly important when comparing across developmental and individual differences (Draheim et al., 2019; Starns & Ratcliff, 2010). For instance, both older and younger adults, as well as men and women may prioritize accuracy or speed to varying degrees (Hertzog et al., 1993; Lohman, 1986). The BIS is calculated by taking the difference between standardized accuracy and response time, and it has been previously used to examine individual differences in mental rotation tasks (Liesefeld et al., 2015). Given that older adults may be more likely than younger adults to focus on accuracy at the expense of response times, a joint measure is better suited to compare across individuals. We hypothesized a (i) negative relationship between age and visuospatial skills. Further, we hypothesized that (ii) gender would be a significant predictor of visuospatial skills (with men showing higher performance), and that (iii) gender differences would be attenuated with age.

Importantly, we were interested in the independent role of visual arts expertise in predicting visuospatial skills in contrast with these individual differences, and whether there may be a moderating effect. Specifically, we tested the degree to which drawing skills and group affiliation (visual artist or not) predict visuospatial skills. We expected that (iv) drawing skills will predict visuospatial skills irrespective of group, and that (v) group would explain its own unique variance as well. Lastly, we predicted that (vi) expertise may have a moderating effect on age- and gender-related differences.

To answer our research questions, using an online and self-administered approach, we collected data from a sample that included men and women artists ranging from younger to older adulthood. The artists actively practiced either observational drawing or descriptive writing, with a range in training and expertise.

Method

Participants

Visual and literary artists were recruited in 2021–22 through public contact information from online artists’ directories such as Poets & Writers and Daily Paintworks (DailyPaintworks; Poets & Writers) and public Facebook business pages. We focused our searches for directories using nation-wide, state-wide, or city-wide search criteria, resulting in collecting participant from all over the United States. Participants’ geographical locations varied from urban, suburban, and rural, with participants from different regions of the United States including South, Midwest, Northeast, and West Coast. Individuals under 18, living outside the United States, or practicing both visual art and writing were not eligible. There were professionals and amateurs in both groups, resulting in a wide range of drawing levels measured using an expertise task (more below). Participants had a 1-in-10 chance of winning a $100 Amazon Gift Card. We emailed or privately messaged individuals who appeared eligible to participate, from which 245 visual artists and 277 writers expressed interest. Of those that expressed interest, 78 visual artists and 112 writers did not enroll in the study, and 64 visual artists and 61 writers enrolled in the study but did not complete the survey. This resulted in a 65% completion rate for both visual artists and writers. Twenty-five participants (14 visual artists, and 11 writers) were missing a measure of drawing accuracy due to technical issues. We excluded participants who marked their gender as non-binary (artists = 2, writers = 1). Three writers and two visual artists were excluded for falling outside of 4 SD of the mean in mental transformation response times within their respective groups (Two outliers in mental cutting, two in mental folding, one in mental rotation). Our final analytical sample consisted of 236 participants: 120 were visual artists (65 = women) and 116 were literary artists (61 = women).

Demographic descriptive data are provided in Table 1. There were no significant differences in age and gender distributions across the two groups. Both visual artists and writers reported the same range of years of education (12–20 years), but visual artists averaged closer to 16 years of education and writers averaged closer to 18 years of education, mapping onto bachelor’s and master’s degree attainments, respectively. The visual artists predominantly reported working as illustrators (n = 19), artists (n = 15), designers (n = 12), with other jobs including education (n = 8), service (n = 8), and the medical field (n = 4). Writers predominantly reported working in writing (n = 55), education (n = 24), administration (n = 10), and journalism (n = 9). Note that all writers reported their profession, however, 33 visual artists did not provide this information. Writers also indicated significantly higher perceived social standing in the country (writers’ range = 3–10, visual artists’ range = 2–10), and community (writers’ range = 3–10, visual artists’ range = 2–10). The age range for visual artists was 20–86, and 22–95 for writers, respectively. Given the focus on lifespan differences, note that over 1/3 of the sample was aged 65+. There were no significant racial/ethnic differences between the two groups, although the sample was predominantly white. These statistics may be reflective of the demographics of the available directories, although we recruited through diverse organizations as well.

Table 1.

Demographic Data as a Function of Group and Group Comparisons

	Visual artists (n = 120)		Writers (n = 116)		p	d
Age (mean, SD)	55.23	17.16	55.69	16.00	.83	−0.03
Gender (women n, %)	65	54.2%	61	52.6%	.81	0.06
Years of school (mean, SD)	16.33	2.08	17.95	1.89	<.001	−0.81
Subjective social standing (mean, SD)
In the country	6.20	1.83	6.70	1.53	.02	−0.29
In your community	6.23	1.84	6.83	1.73	.01	−0.33
Race/ethnicity (n, %)					.14	8.45
Asian	7	5.8%	4	3.4%
Black	2	1.7%	6	5.2%
Hispanic	8	6.7%	3	2.6%
White	98	81.6%	100	86.2%
Multiracial	2	1.7%	3	2.6%
Other	3	2.5%	0	0%

Note. Significant comparisons are indicated in bold font. d = Cohen’s d for age, years of school, and subjective social standing; d = X² for gender and race/ethnicity.

General Procedure

Participants completed an online assessment in Qualtrics taking around two hours which included a battery of visual tasks, a timed drawing task, and demographic/expertise questionnaires. Prior to beginning the study, participants read and confirmed a study information sheet through which we obtained their consent. The order and content of the design aimed to reduce motivational and fatigue-related issues by interleaving more cognitively demanding tasks with less demanding questionnaires. The average time taken to complete the full study did not significantly differ between the groups.

Measures

Visuospatial Skill

Mental Transformation

We used three tasks to measure mental transformation, a subdomain of visuospatial skills (Vodyanyk & Jaeggi, 2023). For this report, we focused exclusively on 3D visualization given that drawing from observation typically involves learning to translate the three-dimensional environment onto a two-dimensional surface. Each trial was presented on a separate page, and the scored trials were preceded by a guide with examples. Participants were instructed to move as quickly as possible without compromising accuracy, but there was no time limit. The three tasks are described in more detail below. Selected measures are considered canonical variations of 3D mental transformation tasks (mental cutting; mental folding; mental rotation). Note that the reliabilities reported for each task are from the current dataset. Power analyses are described in the statistical procedures below.

3D Mental Rotation (Ganis & Kievit, 2015; Shepard & Metzler, 1971)

Participants were presented with two three-dimensional shapes side by side. The shapes were either mirror-reversed and rotated or only rotated. Participants completed 30 scored trials. Accuracy was measured by the proportion correct (Cronbach’s α = 0.91) and response time by average time per trial in seconds (Cronbach’s α = 0.94).

Mental Cutting - Santa Barbara Solids Test (SBST (Cohen & Hegarty, 2012))

Participants were presented with a three-dimensional shape with a cross section and asked to choose one of four 2D shapes which accurately represents the cross-section from overhead. Participants completed 30 scored trials. Accuracy was measured by the proportion correct (Cronbach’s α = 0.91) and response time by the average time per trial (Cronbach’s α = 0.92).

Mental Folding – Spatial Relations: Differential Aptitudes Test (SR (Ekstrom & Harman, 1976))

Participants were presented with a three-dimensional shape folded out in two dimensions and asked to imagine its assembled form. They were instructed to choose from four options of three-dimensional shapes for each question. Participants completed 19 scored trials. Accuracy was measured by the proportion correct (Cronbach’s α = 0.89) and response time by the average time spent per trial (Cronbach’s α = 0.94).

Speed and Accuracy Composites

To combine accuracy and response time for each measure, we first standardized all the outcomes for each task (i.e., accuracy and response time). Next, we computed the Balanced Integration Score (BIS) for each task, which was calculated by subtracting the response time z-score from the accuracy z-score (Liesefeld & Janczyk, 2019). For interpretation, a higher BIS value indicates higher performance. The internal consistency between the three BIS scores was high (Cronbach’s α = 0.80).

Drawing Skill

Drawing Task

The drawing task was used to measure representational drawing skills. Participants were given seven minutes to draw a still-life presented on the screen, which was a reference photo from Carson (2012; Bravo - Direct view). They saw the following prompt: “You have 7 min to draw the following still-life. First, focus on capturing the shapes of each object. Once every object has been drawn, then focus on shading.” We quantified drawing accuracy using two scoring systems. Protocols were outlined to standardize each measurement process, and each drawing was scored by two raters for each type of scoring system. Raters received training in the scoring protocols and established interrater reliability before proceeding to score all the drawings. Different raters were used for each of the scoring methods. After checking for inter-rater reliability by making sure that the agreement was within a certain threshold, both raters’ scores were averaged for each scoring system.

Angle-Based Drawing Scoring

We measured the angles at 10 points of intersection on the original photo and compared each drawing’s angles to the reference. The drawings were scored on a website (Online Protractor|Angle Measuring Tool) by two raters. To ensure inter-rater reliability, each angle on the drawing was re-scored by the two raters until they measured within 5° on each of the 10 angles in each drawing (ICC₁ = 0.99). For the final measure of angle-based drawing accuracy we took the average of each rater’s scores for a given angle, subtracted the participant’s angles from the reference photo’s angles to get an angle of angle error, and calculated an average angle error for each drawing (Cronbach’s α = 0.70).

Rubric-Based Drawing Scoring

Raters were presented with a rubric where they assigned points for features within objects in the composition (local features) and relationships between the objects in the composition (global features). The rubric included three local and global features for each of the seven objects in the still life composition. In addition to local and global features, each object was rated on their general representational quality (whether the object looked three-dimensional) and the quality of their linework (e.g., using squiggly, unconfident lines or a singular bold line). For each category, raters assigned a score from 0 to 2. Two raters scored each drawing; each drawing was re-scored by each rater until their scores were within five points of each other (ICC₁ = 0.98). For the final measure of rubric-based scores, we calculated the average of the two raters’ scores (Cronbach’s α = 0.80).

Other Measures

UCMRT

The University of California Matrix Reasoning Test (UCMRT) assesses visual fluid intelligence (Pahor et al., 2019), serving as a proxy for general mental ability. The task is a validated digital and modified version of the Raven’s Progressive Matrices, where participants saw a grid of 3 × 3 filled with eight shapes that follow a pattern. Participants had to choose one of eight options for the correct shapes that complete the pattern. Participants went through a tutorial prior to completing as many problems as possible (out of 23) in 10 minutes, and we used the proportion correct as dependent variable (Cronbach’s a = 0.79). In line with previous work (Chamberlain et al., 2019; Drake, Riccio, et al., 2021), we used this measure to control for visual IQ in our analyses.

Demographic Information

We collected information on gender, age, and years of schooling. Although it is possible that some participants are transgender, the proportion is likely small and unlikely to change the pattern of results, as less than 1% of the US population identifies as transgender (Crissman et al., 2017).

MacArthur Scale of Subjective Social Status (Adler et al., 2000)

To measure self-reported socioeconomic status (SES), also known as Subjective Social Status (SSS), participants viewed a graphic of a ladder with 10 rungs and read that the ladder represents where people stand in society, where the people at the top of the ladder are best off (10), and those at the bottom are worst off (1). Participants selected 1–10 depending on the rung that best represents where they stand on the ladder. Participants were asked this question twice – about their standing in their community and their standing in the country.

Statistical Procedures

Analyses to answer the main Research Questions were conducted with R version 4.1.2 (R Core Team, 2021) and JASP 0.18 was used for the descriptive analyses (JASP Team, 2023). All outcomes and predictors were standardized using z scores. Gender and group were coded using dummy variables where 1 = women and artists, and 0 = men and writers. Univariate analyses were conducted within-groups on each variable to replace outlier values ±4 SD with missing values, which affected 1% of the total data. A conservative approach was taken (i.e., ±4 SD), given the wide age range of our sample to avoid removing extreme values which could reflect developmental differences. No other data cleaning procedure was conducted.

Power Analysis

Power analysis was conducted through Monte Carlo simulations’ method following the procedures proposed by Beaujean (2014) and Baranger and colleagues (2023) for regression models and interaction effects, respectively. The R code to estimate the power can be found in the public OSF folder (Vodyanyk, 2024).

Our first research question aimed to replicate whether age, gender, and their interaction predict visuospatial skills. Assuming a moderate relationship between age and visuospatial skills (r = −0.45) (Techentin et al., 2014), the power to find the predicted effects was equal to 1. Assuming (i) a moderate relationship between gender and visuospatial skills with men outperforming women (r = 0.40) (Voyer, 2011), and (ii) a correlation between the interaction effect age * gender and visuospatial skills (r = 0.20) based on the results reported by (Geiser et al., 2008), with a sample of 225 individuals, the power to detect the interaction effect age * gender was 0.986. The second and main research question of the present study aimed to examine whether drawing skills and visual arts experience (i.e., drawers versus writers) predicted visuospatial skills. We assumed a strong correlation between drawing skills and visual arts experience (r = 0.70), and we predicted a correlation between drawing skills and mental transformation (r = 0.17), and a correlation between visual arts experience and mental transformation (r = 0.25) based on past results (Chamberlain et al., 2019, p. 19; Martinčević & Vranić, 2023). The power to detect these effects was 0.98 and 1, respectively. Additionally, exploratory analyses aimed to examine the interactions between age, gender and drawing skills, and between age, gender and visual arts experience. As we did not find past literature testing interactions between age, gender and both variables, we therefore assumed a lower value than the main effects (r = 0.15), as recommended by Baranger and colleagues (2023). With our sample size, the power to detect the following interaction effects age * drawing, gender * drawing, age * group, gender * group was above 0.80 provided these effects indeed exist.

Analytic Approach

First, preliminary analyses were conducted to examine group differences in the main outcome variables of the study through t-tests. Further, Pearson bivariate correlations were computed to assess the relationships between the variables included in the regression models. Second, confirmatory factor analysis (CFA) was employed to fit measurement models of visuospatial reasoning and drawing skills, respectively. We used a Bayesian structural equation modeling (SEM) approach and the R package blavaan (Merkle & Rosseel, 2018). This was a necessary step to evaluate whether robust constructs can be estimated from the tasks used. To evaluate the measurement models, we considered (i) the fit index Bayesian root-mean-square error of approximation (BRMSEA), where values below 0.05 indicate good fit to the data (Hu & Bentler, 1999), and (ii) the factor loadings of the latent constructs, which not only must have values above 0.30 and be significantly different from zero, but also no task should show a dominant loading (Raykov, 2001).

Third, Bayesian SEM models were employed to test each of the hypotheses of the present study. Effect sizes were quantified in terms of increased explained variance (R²). Bayesian SEM models fit was evaluated through the BRMSEA, whereas for model comparisons, we used the WAIC (Watanabe-Akaike Information Criterion; Watanabe, 2010). For both Bayesian CFA and Bayesian SEM models, the parameters were computed using four Markov Chain Monte Carlo (MCMC) chains, each comprising 4,000 iterations and 1,000 warm-up samples.

Results

Descriptive Statistics

Descriptive information and effect sizes by group for the cognitive measures used in our model is presented in Table 2. On average, drawing accuracy as measured by both rubric- and angle-based scoring was over 1 SD higher in the visual artists compared to the writers. Representative examples from a visual artist with high drawing accuracy and a writer with low drawing accuracy are visualized in Figure S1. The accuracy was also higher in visual artists across all three mental transformation tasks, ranging from over 10% for mental cutting and folding, to around 6% for mental rotation, which happened to show performance closest to ceiling level. However, response times were slower in visual artists across the three tasks, ranging from 2–15 s. The largest differences in response time were for mental folding, whereas there was a less than 3 s average difference per trial for the other tasks. Writers scored 4% higher on UCMRT as compared to visual artists, though this difference was not statistically significant.

Table 2.

Descriptive Data

	Visual artists (n = 120)			Writers (n = 116)			d	p
	N	Mean	SD	N	Mean	SD	d	p
Drawing skill
Angle-based	106	8.37	3.48	107	13.82	5.65	1.162	<.001
Rubric-based	108	85.84	11.31	109	70.50	14.39	1.184	<.001
Mental rotation
Accuracy	120	0.87	0.17	116	0.81	0.20	0.301	.022
Response time	120	18.69	8.78	115	16.73	8.01	0.284	.031
Mental folding
Accuracy	120	0.68	0.25	116	0.58	0.27	0.408	.002
Response time	119	75.94	43.52	115	61.02	38.13	0.414	.002
Mental cutting
Accuracy	120	0.68	0.17	116	0.58	0.20	0.551	<.001
Response time	119	34.77	15.72	115	31.26	14.70	0.231	.079
UCMRT	103	0.40	0.17	101	0.44	0.16	−0.269	.056

Note. Accuracy is proportion correct, and response time is seconds. Significant group differences are bolded. d = Cohen’s d. UCMRT = University of California Matrix Reasoning Task, a measure of visual IQ.

Preliminary Analysis: Pearson Bivariate Correlations

The main correlations between the measures in our model are described below and the full table can be found in the supplementary material (Table S1).

Age

Drawing skills as assessed by rubric-based accuracy were weakly negatively correlated with age (r = −0.15, p = .031), but accuracy as assessed by angles remained stable across age. Visuospatial differences were mainly driven by longer response times (r = 0.29 (p < .001) through r = 0.42 (p < .001)), whereas accuracy remained mainly stable (r = −0.07 (p = .294) through r = 0.03 (p = .625)). Age was also negatively correlated with the UCMRT (r = −0.45, p < .001). Age positively correlated with the SES metrics, with weak yet significant correlations for years of education (r = 0.17, p = .010), and stronger correlations for the subjective social standing metrics (r_country = 0.37, p < .001, r_rcommunity = 0.32, p < .001).

Gender

The only gender-related differences were on accuracy for the visuospatial measures, which ranged from r = −0.25 (p < .001) in mental folding to r = −0.19 (p = .003) in mental rotation. There was no significant correlation to response times. Gender was also not correlated with the other measures, including drawing accuracy and the UCMRT.

Drawing

Both measures of drawing accuracy strongly correlated with each other (r = 0.60, p < .001), but only rubric-based accuracy was significantly correlated with the UMCRT (r = 0.22, p = .002). Accuracy on each of the visuospatial measures was positively correlated with drawing accuracy as measured by both angle-based accuracy (r = 0.34−0.38, p < .001) and rubric-based accuracy (r = 0.35−0.39, p < .001). Response times were uncorrelated.

Visuospatial Skill

The three mental transformation tasks were all correlated among each other. Correlations for accuracy ranged from r = 0.60 (p < .001) to r = 0.69 (p < .001) and correlations for response times ranged from r = 0.52 (p < .001) to r = 0.61 (p < .001). The UCMRT was positively correlated with the visuospatial tasks (r = 0.23 (p < .001) through r = 0.40 (p < .001)). Both higher accuracy and faster response times were related to UMCRT performance. Although the correlations were weak, slower response times on the mental cutting task were related to higher perceived standing in the community (r = 0.20, p = .002).

UCMRT

As described above, the UCMRT was negatively correlated with age. UCMRT performance was positively correlated with rubric-based drawing and the three visuospatial measures—both accuracy and response times. Higher perceived standing in the community was negatively correlated with the UCMRT (r = −0.15, p = .030).

Measurement Models for Latent Variables

A Bayesian CFA was used to evaluate the measurement model for visuospatial skills. The latent factor was predicted by three observed tasks: mental cutting, folding, and rotation. The measurement model fit was very good (BRMSEA = 0.000) and further information can be found in the Supplemental Materials S2. Similarly, Bayesian CFA was used to evaluate the measurement model for drawing skills. The latent variable was predicted using two measures of drawing accuracy from a drawing task: angle-based and rubric-based accuracy. The measurement model showed a very good fit to the data (BRMSEA = 0.000), and all factor loadings can be found in the Supplemental Materials S2.

Predictors of Visuospatial Skill

Bayesian SEM was employed to examine predictors of a latent variable capturing visuospatial skills, with a latent variable of drawing skills as a key predictor, alongside other variables of interest, including age (and its quadratic term), gender, group, and performance in the UCMRT as a covariate. We ran a series of models that addressed the research questions, and a model was selected based on comparative fit and variance explained. The final model explained 52.6% of the variance in visuospatial skills (cf. Figure 1). The fit was good (BRMSEA = 0.049; waic = 4496.272). Below, we report the non-standardized beta coefficients and in Figure 1 we report the standardized coefficients. Of note, in Bayesian SEM, each estimate is the posterior mean of the parameter distribution. Uncertainty around parameters is summarized by the credible intervals (CI). For example, the 95% CI spans the range where the parameter most likely falls, with 95% probability given the data and prior distributions. We interpret a parameter as credibly non-zero when its CI does not include 0. If the CI overlaps 0, the evidence is insufficient to claim a non-zero effect at that credibility level, and the sign and magnitude of the effect should be treated as uncertain.

Figure 1.

Structural equation model predicting visuospatial skills

Covariances Among Predictors

Group and drawing skills were significantly covaried (r = 0.25, CI 95% [0.19, 0.32]), though not to the extent where collinearity posed a concern. Drawing skill also positively covaried with the UCMRT (r = 0.11, CI 95% [0.01, 0.23]). In contrast, the UCMRT was not significantly associated with group; in fact, the trend appeared in the opposite direction where writers slightly outperformed visual artists in the UCMRT (r = −0.07, CI 95% [−0.13, −0.01]). Age was negatively related to the UCMRT (r = −.45, CI 95% [−0.60, −0.32]), while gender showed minimal associations with other predictors.

Main Predictors of Visuospatial Skills

The UCMRT was the strongest predictor of visuospatial skills (β = 0.40, CI 95% [0.29, 0.52]) followed by drawing skills (β = 0.42, CI 95% [0.23, 0.61]). Gender also significantly predicted visuospatial skills (β = 0.44, CI 95% [0.27, 0.62]), with men outperforming women. Age (β = −0.11, CI 95% [−0.21, 0.00]) negatively, yet weakly, predicted visuospatial skills. Group status was not a significant predictor (β = −0.13, CI 95% [−0.37, 0.11]). The model highlights that visuospatial skills are influenced by individual differences like demographic variables, the UCMRT, and drawing skills (cf. Figure 1). The posterior distributions and priors for all estimates can be found in the supplemental materials.

Alternative Models

In addition to the model presented above, we ran a series of alternative models. We provide a summary of the results of these models here, but the full information can be found in the supplemental materials. The first model tested included just the main predictors of interest – age, gender, drawing skills, and group, explaining 42% of the variance (BRMSEA = 0.120; waic = 2739.99). Drawing skills were the strongest predictor (β = 0.55, CI 95% [0.37, 0.73]), with age and gender also significant, but more moderate, predictors (β_Age = −0.28, CI 95% [−0.38, −0.17], β_Gender = 0.38, CI 95% [0.20, 0.57]). Group showed a weak and non-significant relationship with visuospatial skills (B = −0.19, 95% CI [−0.39, 0.02]). This model highlights the unique contributions of our predictors of interest and the predictive strength of drawing skills compared to group. Adding UCMRT as a predictor increased the fit and explained variance (cf. Figure 1).

In another model, we tested whether visuospatial skills were non-linearly related to age. The fit index was acceptable (BRMSEA = 0.086; waic = 4494.292). The UCMRT and drawing skills were still the strongest predictors (β_UCMRT = 0.39, CI 95% [0.28, 0.51], β_Drawing = 0.40, CI 95% [0.21, 0.60]). Age (β = −0.15, CI 95% [−0.26, 0.00]) and the non-linear term (β = −0.08, CI 95% [−0.18, 0.00]) were at the significance threshold.

Moderating Effects

Additionally, we tested a model that included the interaction of age and gender which was not significant and thus was excluded from a potential candidate model. We also considered whether expertise moderated the effects of age and gender. Group affiliation did not moderate the effect of age but showed interesting results in relation to gender-related differences, which are described in more detail below.

Group and Gender Differences

Adding the interaction of group and gender increased the predictive power of our model by 6% but also resulted in markedly worse fit (BRMSEA = 0.263; waic = 4491.035). Again, the UCMRT and drawing skills emerged as the main predictors (β_UCMRT = 0.38, CI 95% [0.26, 0.49], β_Drawing = 0.44, CI 95% [0.24, 0.64]). The effect of gender became stronger, although less robust (β = 0.62, 95% CI [0.32, 0.92]). Smaller, yet significant, age effects persisted (β = −0.16, 95% CI [−0.27, −0.04]). The interaction of group and gender approached significance (β = −0.14, 95% CI [−0.33, 0.06]) and reduced the robustness of the effect of gender alone. The relationship between gender, group, and visuospatial skills is visualized in Figure 2. The men showed similar performance, and if anything, men writers outperformed the visual artists overall. For the women, however, the trend was that the visual artists outperformed the writers where the women visual artists performed closer to men.

Figure 2.

Visuospatial scores by group and gender

Discussion

Drawing relies on perceptual and attentional mechanisms that underlie the formation and transformation of mental representations. The present study examined the unique contribution of visual arts expertise—specifically, drawing skills and group affiliation—to visuospatial skills in relation to commonly studied individual differences. We first replicated well-established age- and gender-related differences in a mental transformation performance using a latent variable that accounted for both accuracy and response times on three visuospatial tasks: mental rotation, cutting, and folding. Part of the effect of age was nonlinear, with performance differences becoming exponentially more pronounced in later adulthood. Gender also independently predicted performance, with men outperforming women across all ages. Gender was a stronger predictor than age and there was no interaction of age and gender. A portion of the age-related, but not gender-related, differences were attributable to the correlation with the UCMRT, a measure of visual IQ. Regarding expertise, drawing skills but not group affiliation significantly predicted visuospatial skills, even after controlling for visual IQ through the UCMRT. While there were no group-related differences in visuospatial skills using the balanced integration score (BIS), it is notable that visual artists were more accurate, yet slower than writers. These speed-related differences may also explain the numerically higher performance in UCMRT for the writers, which was a timed task. Our results highlight the robust and lifelong association between drawing skill and visuospatial cognition.

Age- and Gender-Related Differences in Visuospatial Skill

We confirmed meta-analytic findings on age-related differences in visuospatial skills as measured by the BIS, which combines response time and accuracy (Techentin et al., 2014); however, these differences were greatly attenuated after accounting for visual IQ, as measured by the UCMRT. When looking at accuracy and speed separately on the three tasks, we saw these differences were mainly driven by response times as consistent with prior work (Techentin et al., 2014). Further, a trend towards a non-linear relationship between age and visuospatial skills was aligned with prior longitudinal findings in visuospatial cognition broadly (Hartshorne & Germine, 2015; McCarrey et al., 2016; Yam et al., 2014). Although the interaction added explained variance and increased the effect of age, it also reduced the model fit. When adding the UCMRT as a predictor, much of the explained variance of age diminished, although some unique variance remained. This highlights that much of the effect of age on visuospatial skills is due to visual fluid reasoning more broadly. Processing speed, as captured by the response time aspect of performance, steadily declines with age, and is likely one of the factors underlying the observed age-related differences. The BIS provides a sensitive and useful metric that captures age-related differences in performance across the adult lifespan, particularly as younger and older adults may adopt different response strategies (Forstmann et al., 2011; Starns & Ratcliff, 2010).

In line with our second hypothesis, we found an overall effect of gender on visuospatial skills, which replicates prior meta-analytic evidence showing a consistent men’s advantage in tasks like mental rotation (Voyer, 2011). Contrary to our third hypothesis, there was no interaction between age and gender on visuospatial skills, suggesting some stability in observed gender differences across the adult lifespan. While larger age-related declines in men are reported for cognitive domains like working memory, visuospatial ability, and motor speed, (Cansino et al., 2013; McCarrey et al., 2016; Pliatsikas et al., 2019), meta-analyses find that this interaction may not extend to visuospatial skills (Techentin et al., 2014). Looking at response time and accuracy separately showed that gender differences were primarily driven by accuracy. We found that under untimed and unsupervised conditions, gender differences in visuospatial accuracy were robust, even though meta-analytic evidence shows that differences in visuospatial task performance between men and women appear to be more pronounced when there is a time limit (Voyer, 2011). Given that we did not see differences between men and women in our timed visuospatial task (UCMRT; cf. Figure 1), and the fact that response times were similar between the two groups in the mental transformation tasks, but their accuracies differed as a function of gender, it may be possible that other participant-related differences than gender might have contributed to our results. For example, gender differences may stem in part from attention and decision-making processes, including the use of specific cognitive strategies (Hegarty, 2018; Scheer et al., 2018; Toth & Campbell, 2019). For example, during mental rotation, men are more likely to use a global, object-to-object strategy, while women may rely more on perspective shifts or part-by-part analysis, which may be more likely to result in transformation errors (Halpern & Collaer, 2005; Hegarty, 2018). Similarly, men tend to adopt allocentric frames of reference, whereas women more often rely on egocentric strategies (Hegarty, 2018; Wolbers & Hegarty, 2010). While our study did not capture strategies that participants were using, the robust effect of gender is in line with much of the past literature, and the trending interaction with group suggests that strategy use and/or expertise-related factors may at least partially account for our findings.

Visual Arts Expertise and Mental Transformation Skills

Our hypotheses regarding the effect of visual arts expertise were only partially confirmed. First, we did not find group affiliation (i.e., visual vs. literary artist) to predict mental transformation, which went contrary to our hypotheses. In support of our hypotheses though, we found that individual-level drawing skills emerged as a stronger predictor than demographic differences and were nearly on par with the effects of the UCMRT. Drawing skills were also significantly correlated with the UCMRT, but their unique contribution remained. Several previous studies also controlled for visual IQ when examining the potential visual artists’ advantage in visuospatial cognition. For example, one study with 79 participants found a group-level advantage where art students demonstrated higher accuracy than non-art students on a mental rotation task; however, these differences disappeared after controlling for visual IQ (Chamberlain et al., 2019). Similar to our results, these authors also observed a speed-accuracy tradeoff, with visual art students demonstrating greater accuracy but slower response times. They also found a positive correlation of drawing skills to mental rotation (r = 0.22), though the predictive relationship in our model was stronger. In contrast, Drake and colleagues (2021) found a group effect comparing college visual art student and non-art students on mental rotation accuracy in 81 participants, even after controlling for visual IQ. There were no group differences in response times, and visual art students trended towards faster responses (Drake, Riccio, et al., 2021). They did not find drawing skills to correlate with mental rotation, which contrasts with our findings where drawing skills were the stronger predictor. In line with our findings, another study by Drake and colleagues did not find group-related differences in mental rotation with naturalistic stimuli or visual IQ in a group of 73 art and psychology students (Drake, Simmons, et al., 2021). Although, that study did not assess drawing skills which limits conclusions. Our findings provide evidence for a limited effect of group affiliation on visuospatial skills and highlight the contribution of drawing skills.

Regarding the hypotheses of moderating effects of expertise, we did not find a significant interaction between group and demographic factors. Interestingly, group showed a trend towards moderating gender-related differences, with the difference in performance within writers numerically larger than within visual artists. The model, however, was at the significance threshold and reduced the overall fit. Moderating effects of expertise on demographic influences in visuospatial skills have been observed in other domains. Of note, Pietsch & Jansen (2012) found that music and sports students outperformed education students in mental rotation, with men’s advantages attenuated in the music group. The authors point to potential mechanisms of visuospatial enhancements based on fine motor skills and extensive practice (Pietsch & Jansen, 2012). Further studies should explore whether visual arts engagement can attenuate gender differences in visuospatial skills and whether training-related factors play a role.

Exploring Potential Mechanisms

Drawing is a process involving multiple domains, including visual perception, spatial attention, motor planning and execution, and evaluation. Drawing skills are facilitated by the acquisition of novel visual schemas, which engage perception and attention in ways that more accurately reflect the structure of objects, compared to simplified canonical representations (Seeley & Kozbelt, 2008). This form of visual processing directs attention towards three-dimensional forms and their spatial relationships—all processes underlying mental transformation and visuospatial skills. Tasks such as mental rotation rely on similar kinds of three-dimensional forms to how visual artists break down visual information when they draw.

The best way to understand underlying mechanisms is through implementing experimental and longitudinal training-based studies. Some past research has gone beyond cross-sectional data to investigate whether visual arts training can change visuospatial cognition (Chamberlain et al., 2021; Martinčević & Vranić, 2023; Prieto & Velasco, 2010; Sorby, 1999; Sorby et al., 2003). One pretest-posttest study randomly assigned 78 non-artists to one of three conditions, one of which involved drawing. The drawing group showed the greatest improvements in visuospatial memory. For visuospatial skills, learning to draw showed similar, albeit slightly smaller, effects compared to an origami group. The coloring group demonstrated the least improvement (Martinčević & Vranić, 2023). Using a different approach, one longitudinal study found that in Year 4, compared to Year 1, art students exhibited decreases in mental rotation response times, accompanied by smaller increases in accuracy (Chamberlain et al., 2021). The cognitive outcomes were related to improvements in drawing skills. These findings suggest a training effect, but the absence of a control group limits causal conclusions. In a drawing-based intervention feasibility study with older adults, Vodyanyk and colleagues found a baseline correlation between drawing accuracy and visuospatial skills, but no change in cognition as a result of drawing training in a group of 34 participants randomized into a drawing course or wait-list control condition (Vodyanyk & Jaeggi, 2025). A tangential line of research studies whether and how engineering drafting courses can improve performance in visuospatial tasks like mental folding (Charlesworth et al., 2005; Prieto & Velasco, 2010; Sorby, 1999; Sorby et al., 2003). While engineering drafting courses appear to impact visuospatial skills, these effects are not unique, and other activities may also affect performance. For example, Contreras and colleagues found improvements in visuospatial skills of both architecture students who took a drawing course and mathematics students who took a geometry course (Contreras et al., 2018). Thus, engagement in a variety of activities may relate to visuospatial skills and in our study, drawing skills emerged as one of such activities.

Mechanistic understanding will be supported by considering different components related to visual art expertise. Motor skills, especially those developed through expert training, correlate with visuospatial skills (Voyer & Jansen, 2017). While the role of motor function in drawing is well established, research has not explored the mediating effect of motor expertise on the connection between drawing and visuospatial skills (Brew, 2015; Seeley & Kozbelt, 2008). Additionally, a follow up on the interaction between group and gender is warranted, such as understanding how and which factors of visual arts may attenuate visuospatial differences. Expanding comparison groups beyond descriptive writers to include individuals with no artistic background could clarify group-related effects. Potential comparison groups can include individuals from non-artistic careers that engage their visuospatial skills or those in non-artistic careers that don’t involve visuospatial skills. Incorporating naturalistic and timed spatial tasks may also help isolate group differences by increasing cognitive demands, such as under time constraints. Importantly, intervention studies will be needed to uncover causal effects, that is, whether visual arts engagement, and the development of drawing skills more specifically, might promote visuospatial skills.

Limitations and Constraints on Generality

The online, self-paced format allowed for data collection during the COVID-19 pandemic from a geographically diverse sample, but the lack of environmental control introduces variability. A more controlled setting could clarify the moderating effects of expertise, age, and gender. Group differences may also be larger if compared against a group not engaged in descriptive writing, which may also rely on visuospatial information processing. Differences in motivation between groups (both visual artists and writers and men and women) may also explain differences, such as differences in speed accuracy tradeoffs between visual artists and writers. Visual artists had higher accuracy on all the visuospatial tasks, but took longer to complete them, especially on the more complex tasks. It is not uncommon for experts in a domain to take slower on relevant tasks in favor of enhanced accuracy (Clark et al., 2013). Further, given that our tasks were untimed, they may not be as effective at capturing group-related differences as timed tasks (Maeda & Yoon, 2013; Voyer et al., 1995). Within a time constraint, visual artists may be less affected by expertise-related slowing at the expense of accuracy. Studies show that visual artists can form more accurate mental representations of objects under short presentations, suggesting that presentation time may influence outcomes (Perdreau & Cavanagh, 2013, 2014). Increasing difficulty and reducing differences in response times may highlight expertise effects, although the BIS helped mitigate some of this trade off.

While our sample was diverse in terms of age, level of expertise, and geographic location, it still lacked diversity in race, ethnicity, and socioeconomic status (SES). Most participants were white and highly educated, with the average participant reporting at least a bachelor’s degree or higher (>16 years of education), limiting the generalizability of findings. The online format also restricted participation to technologically literate older adults with publicly available information about their artistic practice. Therefore, the strength of our predictors, and lack of moderating effects of expertise on age, may not apply to all populations. Additionally, although our sample size had sufficient power to test our main hypotheses, larger samples will address more complex questions including multiple interacting effects. The cross-sectional nature of the findings also limits causal interpretation where individuals with higher natural visuospatial ability may naturally self-select into the visual arts. Further, many activities that individuals engage with in their daily life rely on spatial cognition and thus, it is difficult to control for all factors. While we found that age, gender, visual IQ, and drawing skills all had impacts on visuospatial skills, the exact role of expertise- and training-related effects need further investigation.

Conclusion

Observational drawing is closely tied to visuospatial processing. We found that drawing skills predicted a construct of visuospatial skills independent of demographic differences and visual IQ as measured by the UCMRT. Although visual IQ accounted for significant variance in visuospatial skills, demographic variables contributed far less than drawing skill. The strength of the connection is particularly noteworthy given our lifespan sample that captured a wide variation in expertise. Extending beyond traditional student-focused studies, we used a remote and self-administered design to recruit a larger lifespan sample of practicing artists, men and women, across the United States. We saw that much of group’s effect on visuospatial skills was driven by drawing skills. Our findings contribute to disentangling the effects of different aspects of visual arts expertise among other predictors of visuospatial reasoning skills. Given the roles of learning and modulation of attentional strategies during the development of drawing skills, training studies are essential for establishing causal links, particularly in individuals who can benefit from enhancing visuospatial function.

Supplemental Material

Supplemental Material - Predictors of Visuospatial Skill and the Role of Observational Drawing

Supplemental Material for Predictors of Visuospatial Skill and the Role of Observational Drawing by Mariya M. Vodyanyk, Marc Yangüez, and Susanne M. Jaeggi in Perceptual and Motor Skills.

Supplemental Material

Supplemental Material - Predictors of Visuospatial Skill and the Role of Observational Drawing

Supplemental Material for Predictors of Visuospatial Skill and the Role of Observational Drawing by Mariya M. Vodyanyk, Marc Yangüez, and Susanne M. Jaeggi in Perceptual and Motor Skills.

Supplemental Material

Supplemental Material - Predictors of Visuospatial Skill and the Role of Observational Drawing

Supplemental Material for Predictors of Visuospatial Skill and the Role of Observational Drawing by Mariya M. Vodyanyk, Marc Yangüez, and Susanne M. Jaeggi in Perceptual and Motor Skills.

Supplemental Material

Supplemental Material - Predictors of Visuospatial Skill and the Role of Observational Drawing

Supplemental Material for Predictors of Visuospatial Skill and the Role of Observational Drawing by Mariya M. Vodyanyk, Marc Yangüez, and Susanne M. Jaeggi in Perceptual and Motor Skills.

Footnotes

Acknowledgements

We would like to acknowledge and thank all the artists who were generous with their time and R.O., H.F., D.O., and I.C. for helping with data processing.

ORCID iDs

Mariya M. Vodyanyk

Marc Yangüez

Susanne M. Jaeggi

Consent to Participate

The study (HS# 2021-6554) qualified for a self-determined level of review by the UCI Institutional Review Board and complied with the APA Ethical Principles. The consent process was digital.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All data and relevant code for this project are publicly available and can be accessed through OSF ().

Statements and Declaration

Vodyanyk and Jaeggi conducted the research at UC Irvine and are currently at Northeastern University. The ideas and data reported on in this manuscript were presented as a poster at the Cognitive Aging Conference in 2022. The current study expands on the dataset analyzed in . The design is pre-registered through the previous study, but the current analytical plan is not pre-registered. The original study assessed drawing skill and use of drawing techniques in relation to cognition. The current study uses more robust statistical methods and addresses individual differences, including sex and age. To answer the developmental question, we recruited additional participants who were all 60+, increasing the sample by 72 participants.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Mariya M. Vodyanyk is a PhD Candidate in Psychology at Northeastern University and an affiliate of the Soundmind Collaboratory, the Brain Game Center for Mental Fitness and Well-being, and the Intitute for Cognitive and Brain Health. She received her MA in Education from the University of California, Irvine. Her research explores how artistic expertise, particularly drawing skill, is related to cognitive enhancements across the lifespan.

Marc Yangüez is a researcher in cognitive psychology affiliated with Northeastern University’s Institute for Cognitive and Brain Health, and the Brain Game Center for Mental Fitness and Well-Being. He received his PhD in Neuroscience from the University of Geneva. His work employs advanced multivariate methods to understand individual differences in executive-function across the lifespan, with a particular interest in how evidence-based interventions can support cognitive development, health and well-being.

Susanne M. Jaeggi is Professor at Northeastern University, where she co-directs the Brain Game Center for Mental Fitness and Well-Being and the SoundMind Collaboratory. She is affiliated with the Institute for Cognitive and Brain Health, and the Departments of Psychology, Applied Psychology, and Music. Previously, she was Professor in Education and Cognitive Science at the University of California, Irvine for over a decade, where she directed the Working Memory and Plasticity Lab. Her research program focuses on understanding individual differences in higher cognitive functions, as well as their malleability across the lifespan.

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