# data[].attributes.body
Check out my marble

# data[].attributes.body
Capture your #workspace  What industries / #business can take advantage of #roomplan ? And often with devices they already have! Here I’m using brilliant

@Polycam3D

Low entry tech - high impact (ceiling)  Reach out for training if you need it

@Apple

@Usingtechbetter

#enterprise

# data[].attributes.attachment_link.title
How to Capture Your Space Like a Pro with RoomPlan

# data[].attributes.attachment_link.title
Commonwealth Games Augmented Reality Special Tutorial

# data[].attributes.body
Using Scaniverse App, capture the outside world in 3D and analyse with accurate time measurements.

# data[].attributes.body
Perfect for Matariki and Indigenous Astronomy.

# data[].attributes.attachment_link.title
Creating AR family star clusters with Keynote and Reality Composer

# data[].attributes.attachment_link.title
Learn How to Create AR User Interactions for Your Business

# data[].attributes.attachment_link.title
How to Create an AR Scene using Reality Composer

# data[].attributes.attachment_link.title
How to Scan Objects for Augmented Reality

# data[].attributes.body
USDZ file attached

# data[].attributes.headline
Rakugaki AR for early learners: PDF print out shapes

# data[].attributes.subject
Rakugaki AR for early learners: PDF print out shapes

# data[].attributes.attachment_caption
Angry Bird style game in Reality Composer

# data[].attributes.headline
Reality Composer: Angry Birds Style Game

# data[].attributes.subject
Reality Composer: Angry Birds Style Game

# data[].attributes.attachment_link.title
Angry Bird style game in Reality Composer

# data[].attributes.body
Great for portals

# data[].attributes.body
Tutorial for teachers - with Narration - masking objects and adding photos from the camera roll.

# data[].attributes.body
Capture your space using the Measure App and then design your flooring. This is a Keynote template with place holders.

# data[].attributes.headline
Below are Interactive AR Scenes and 3D files based on the Lorax

# data[].attributes.subject
Below are Interactive AR Scenes and 3D files based on the Lorax

# data[].attributes.body
Download the Truffula 3D tree as USDZ below.

# data[].attributes.body
Ensure you export as obj before opening in Procreate.

# data[].attributes.attachment_link.readable_text
Overview

The aim of this app is to let you interact with a range of parametric shape generation algorithms, using the adjacent sliders to adjust parameters and see their effects in real-time. As a starting point, use the examples menu as a basis and the 'Randomize' button () to explore different shapes.

The shape parameter sliders are specific to each algorithm whereas the shell parameters allow you to stretch and offset the generated surface into shell-like forms. It should be fast enough on most modern computers and tablets, but you can turn off real-time updating if you need to.

Background

This is another HTML5 version of one of my Java applets. This one grew out of some experiments using spherical harmonics as a way of doing real-time diffuse lighting and shading simulations. As I tend to only really understand stuff when I can see it, I did a quick viewer in Processing a while ago to help make sure I was getting all the calculations correct.

Some of the visualisations and shapes started to look reasonably interesting, so I polished it up a bit and then did this WebGL version. As the basic infrastructure was already there, it seemed logical to add in some other shape types as well.

Spherical Harmonics are really interesting as they can be used to reduce what is usually an inordinately expensive integration of the diffuse lighting environment into a series of much faster dot products. This is done by approximating the diffuse lighting environment, given as a complex luminous distribution over an intergrating sphere, with a matrix of Spherical Harmonics coefficients.

Using this instead of a standard diffuse surface reflection model can make very detailed and realistic diffuse lighting effects possible in close to real-time. Robin Green explains it better and in much more detail in Spherical Harmonic Lighting: The Gritty Details.

Development

This viewer was probably most inspired by Paul Bourke's page on Spherical Harmonics, as well as the many other supershapes viewers written by others. As a lot of my visualisations of building performance involve overlaying analysis data on coloured grids within a 3D model, I figured it was worth spending some time on the generation and animation of them as a way of developing and testing my own grid libraries.

Interesting Features

I guess the most interesting features of this app are as follows:

Animating the shape transitions entirely on the GPU using interpolating shaders,

Developing SVG-based data sliders that scale and interact reasonably well across desktop and mobile,

Doing a fair bit of work with the parameters of each shape so that the random generation is a bit smarter,

Getting my silhouette calculations to work fast enough in Javascript, and

Implementing an app-wide Undo/Redo system.

© Dr. Andrew J. Marsh, 2014.
View Manipulation

You can interactively adjust the 3D view of the model using a mouse, pen or stylus, or by touch on a tablet or phone. You can also use the items in the 3D View Settings popup.

NOTE: You can use the Shift and Ctrl/Meta keys to adjust the increment of each scroll event or key press.

Keyboard Modifiers

The Shift and Ctrl/Meta keys are used pretty extensively to modify interactive data entry. This applies to all increment buttons, scroll wheel motion, slider controls and input elements.

Shift Key:

Increases input to larger values, usually by ten times or significantly larger increments such as 1 month for dates and 1 hour for times.

Ctrl or Meta Key:

Decreases input to smaller values, usually by one tenth or the smallest reasonable increment such as 1 day for dates or 1 minute for times.

NOTE: You can use the scroll wheel to edit a data value when hovering over any slider, numeric input or even table rows that indicate their editibility.

# data[].attributes.attachment_link.description
Overview The aim of this app is to let you interact with a range of parametric shape generation algorithms, using the adjacent sliders to adjust parameters and see their effects in real-time. As a starting point, use the examples menu as a basis and the 'Randomize' button () to explore different shapes.

# data[].attributes.body
Move around the bin to face in direction a den then tap push pull to collect containers.

# data[].attributes.body
Don't forget to activate animation in Reality Composer.

# data[].attributes.body
Watch these quick how to videos

# data[].attributes.attachment_link.description
Basic tutorials for teachers and students to become content creators with Reality Composer. Augmented Reality App for iPad and Mac.

# data[].attributes.headline
Instructions for embed usdz into websites

# data[].attributes.subject
Instructions for embed usdz into websites

# data[].attributes.body
Editable Keynote slide for students to place their ideas as part of the idea phase.

# data[].attributes.body
Place the Truffula trees in Augmented Reality. Tap on the trees to knock them down.

# data[].attributes.headline
Sketchfab - resource for downloading 3D models

# data[].attributes.subject
Sketchfab - resource for downloading 3D models

# data[].attributes.body
Usdz file - tap to create another 1m ruler on top. To be used for informal and formal measurements.

# data[].attributes.body
Single Glass Dome

# data[].attributes.headline
Creating a shark simulation with Sketchfab and RC

# data[].attributes.subject
Creating a shark simulation with Sketchfab and RC

# data[].attributes.body
Made a quick tutorial on how to create custom animations from @Sketchfab models into #RealityComposer Watch it here: https://youtu.be/B97NegTm5VI

73A - Great Hammerhead Shark" (https://skfb.ly/6VoZC) DigitalLife3D is licensed under CCA @AppleEDU @Usingtechbetter #AugmentedReality

# data[].attributes.attachment_link.title
Creating animations and simulations with Reality Composer and Sketchfab

# data[].attributes.headline
Creating 3d models using Super Shape Generator

# data[].attributes.subject
Creating 3d models using Super Shape Generator

# data[].attributes.body
Take a look at this tutorials for iPad using FREE web based tool, procreate and Reality Composer.

# data[].attributes.attachment_link.title
Shape Generator and creating AR sculptures

# data[].attributes.headline
Evidence for a unitary structure of spatial cognition beyond general intelligence

# data[].attributes.subject
Evidence for a unitary structure of spatial cognition beyond general intelligence

# data[].attributes.attachment_link.readable_text
Introduction

Spatial skills are fundamental for everyday life as they make it possible for us to understand and operate in the physical world around us. Studies in primates and other animals have highlighted the importance of spatial ability for evolution and survival. Food-hoarding birds rely on spatial memory to retrieve their caches, which is crucial to their subsistence, and climate harshness has been found to positively drive the evolution of spatial memory skills in black-capped chickadee, another bird species1. Spatial skills are also important in modern technologically oriented societies2,3 as individual differences in spatial skills are associated with positive developmental, educational and life outcomes. Spatial ability reliably predicts scholastic and professional success and career choices, particularly in Science, Technology, Engineering and Mathematics (STEM) and related fields, even after controlling for general cognitive ability. In spite of the increasingly fundamental role that spatial ability has for individuals and contemporary societies3, numerous questions remain regarding the nature of spatial ability as well as its origins and structure7.

What constitutes good spatial skills? Since its earliest conceptualization8, spatial ability has been considered a multifaceted construct comprising several related, yet separable, skills9. One of the most widely adopted definitions of spatial ability describes it as the ability to generate, retain, retrieve, and transform well-structured visual images10. Contrary to this very broad characterization of spatial ability, however, extant research has largely focused on measuring only specific aspects of object-based spatial ability. Among the most widely studied spatial skills are individuals' abilities to mentally rotate shapes11, to visualize objects from different perspectives, and to find figures embedded within other shapes12. A much smaller body of research has considered larger-scale, practical, everyday spatial orientation abilities, such as navigation, map reading, and wayfinding.

Until recent years, studies of spatial orientation skills had been hindered by the difficulty in measuring navigation and wayfinding abilities in real-life settings utilizing rigorous approaches that are standardized across participants. In addition, assessing navigation in the real environment can be highly costly and time consuming and thus unlikely to be scalable to large samples nation wide or world wide. Technological advances in the field of virtual reality (VR) provide a novel powerful tool to study individual differences in spatial orientation skills in realistic settings that can be fully controlled and standardized across participants13,14. Studies assessing the validity of measuring navigation skills using VR have observed strong correlations (~0.60) with performance in real world navigation skills13,15. The reliability of assessing spatial abilities in VR is likely to continue increasing as accelerating technological developments provide progressively immersive and realistic tools.

Likely due, at least in part, to such difficulties in assessing multiple spatial orientation skills reliably in large, representative samples, few studies have examined the structure of spatial orientation ability and its association with other spatial skills. More broadly, evidence concerning the nature and factor structure of spatial ability remains mixed, with most studies focusing on differentiating between relatively few measures rather than examining the communalities across a broad range of spatial skills7,10,16,17. In our previous work18, we have shown that a general factor of spatial ability captures a substantial proportion of variance across numerous tests of spatial skills, and that communalities across tests are largely explained by shared genetic variance18. However, one major limitation characterized our previous study: although we considered ten object-based spatial abilities, including tests of rotation, visualization and scanning abilities, we did not include measures of spatial orientation, such as navigation, map reading, and wayfinding.

The omission of spatial orientation measures has special theoretical relevance because evolutionary and cognitive theories have pointed to a distinction between the ability to mentally manipulate objects on a small scale (object-based spatial skills) and the ability to orient in large-scale environments (spatial orientation ability). This proposition is partly supported by psychological studies suggesting that the two abilities are influenced by separate cognitive processes and brain structures. For example, in a study of the association between performance in object-based psychometric spatial tests and large-scale spatial learning, partial support was found for a differentiation between these skills. Individual differences in measures of spatial learning (measuring skills such as placing landmarks on a map, intra-route distance estimates and route reversal) were unrelated to variation in object-based spatial tests. However, the ability to learn maze and maze reversal, was found to be related to both object-based tests and spatial learning22. Other studies in the field of cognitive psychology have found evidence for a partial dissociation between object-based tests and large-scale spatial orientation skills.

Neuroimaging studies have also provided preliminary converging evidence for the distinction between object-based abilities and spatial orientation skills, suggesting that the two are supported by separate brain networks. Object-based spatial skills, and particularly mental rotation ability, were found to be primarily associated with activation of the parietal lobes26. Conversely, variation in ﻿learning and remembering the layout of large-scale spaces has been found to be related to processing in the hippocampus and the medial temporal lobes27.

Other theoretical accounts and studies, however, have suggested that object-based and spatial orientation skills might be closely related. For example, theories concerning the evolution of sex differences have argued that individual variation in object-based spatial skills, such as mental rotation, are the product of different selection pressures for large-scale spatial orientation abilities between males and females over evolutionary history28,29. Therefore, these theories suggest that spatial orientation and object-based spatial skills largely reflect a common set of abilities. Empirical evidence also supports the idea of a largely unitary set of abilities. A study of the association between object-based spatial abilities, measured with a limited battery of three psychometric tests, and large-scale spatial orientation skills, measured both in realistic settings and a virtual environment, found a substantial correlation ( r = 0.60) between the two15.

The proposition of a unitary set of cognitive processes underlying object-based and spatial orientation skills is consistent with the idea that these are aspects of a more general set of cognitive abilities. It is plausible that at the heart of individual differences in all spatial skills is general cognitive ability, or general intelligence ( g). G is a psychometric construct that emerged at the beginning of the twentieth century from observations that almost all cognitive tests correlate moderately and positively30. Individuals performing highly on one cognitive test are also likely to show good performance on other tests of cognitive abilities, and g indexes this covariance observed between cognitive measures. Therefore, g is thought to represent individual differences in the domain-general abilities to plan, learn, think abstractly, and solve problems that are necessary for successfully completing cognitive tests31.

In our previous work on the factor structure of object-based spatial tests, we have shown that individual differences in spatial abilities cluster into a unitary factor, at both the observed and genetic levels, even after accounting for g 18. Along the same lines, another study found that the association between object-based and spatial orientation abilities was largely independent of verbal ability15. These studies suggest that the coherence of spatial abilities is not simply due to their being part of g, but rather inherent in the spatial domain itself. However, neuropsychological evidence contradicts this view. Case studies of patients with neuropsychological impairments suggest that damage to navigation-related structures in humans typically leads to broad memory deficits that are not limited to the spatial domain10.

Extant literature is therefore characterized by contrasting theories and evidence with respect to the factor structure and associations between object-based spatial abilities, assessed mostly through psychometric tests, and large-scale spatial orientation skills, assessed both in real settings and VR. The lack of a cohesive account is likely due to a paucity of studies that have investigated the association between object-based and large-scale spatial orientation skills with a sufficiently diverse battery of tests. In addition, to our knowledge, no study to date has investigated their links within a genetically informative framework, testing the hypothesis that a common genetic network, independent of g, supports performance in all spatial skills.

The current study addresses these limitations by investigating the structure of spatial ability using two comprehensive online batteries of object-based and spatial orientation skills, administered to a large genetically informative sample of twins aged 19-22. Importantly, we assessed spatial orientation abilities with an innovative gamified battery of six tests measuring navigation, map reading, wayfinding and large-scale scanning and perspective-taking skills set in a virtual environment. The current work has three main aims: first, we examined the factor structure and origins of spatial orientation skills; second, we investigated the structure and genetic and environmental origins of spatial ability across sixteen tests of object-based and spatial orientation skills; third, we explored the role that g has in unifying individual differences in performance across tests of spatial abilities.

Results

Individual differences in spatial orientation can be measured reliably in a virtual environment and are moderately heritable

We first assessed whether our newly developed gamified battery set in a virtual environment could effectively capture individual differences in spatial orientation skills in our large sample of twins. Beyond showing good test-retest reliability (average r = 0.74, ranging from 0.60 to 0.89; see Method for information on the reliability of each tests), the six tests-scanning, perspective taking, navigation based on landmarks, navigation following directions, route memorizing and map reading-showed normal distributions, with acceptable values for skewness and kurtosis (<+/-2; Fig. 1 and Supplementary Table 1). Therefore, our gamified battery was able to discriminate and reliably capture variation in spatial orientation abilities.

All variables were residualized for age and sex, and standardized in one randomly selected half of the sample (only one twin within each pair was randomly selected for descriptive and phenotypic analyses in order to account for the non-independence of observations); full descriptive statistics for both randomly selected halves of the sample are presented in Supplementary Table 1. The black dots represent means and the error bars standard deviations. SC scanning, PT perspective taking, NL navigation landmarks, ND navigation directions (cardinal points), RM route memory, MR map reading.

Because sex differences are often found for spatial abilities (though not always in the same direction)32,33, we examined whether performance differed between males and females. We found significant differences in performance between males and females across all tests (Supplementary Table 2). Males outperformed females in all tests, effect sizes ranged between small and moderate. The biggest effect size was observed for map reading ( R ² = 0.17) and the smallest effect size was observed for scanning ( R ²  = 0.03).

We applied the twin method (see Method section) to calculate heritability estimates for the six measures of spatial orientation; these are presented in Fig. 2. Heritability estimates, the extent to which variation in a trait is accounted for by genetic differences34, ranged from 14 to 57%. The remaining variance in all tests was accounted for by nonshared environmental factors, environmental factors that do not contribute to similarities between siblings34, with the only exception being the test of orientation ability using landmarks, which showed a significant proportion of shared environmental variance (15%). These substantial nonshared environmental estimates in part reflect measurement error.

A additive genetic; C shared environmental; E nonshared environmental components of variance. Error bars show 95% confidence intervals. SC scanning, PT perspective taking, NL navigation landmarks, ND navigation directions (cardinal points), RM route memory, MR map reading.

Given the significant sex differences observed at the phenotypic level, we conducted univariate full sex-limitation model fiting (see Method section) to examine whether these estimates of heritability differed between males and females. We found significant quantitative sex differences for navigation ability and several of the subtests (Supplementary Table 3), that is, differences in the magnitude of genetic and environmental influences were observed for males and females. However, the effect sizes of these differences were small to moderate. For example, for an overall composite measure of navigation ability, heritability was 52% (95% CI: 0.31; 0.70) for males and 54% for females (95% CI: 0.29; 0.62). For these reasons, and to increase power, the full sample was used in subsequent analyses, combining males and females, and same- and opposite-sex twin pairs.

A single 'navigation' factor captured the variance common across all tests of spatial orientation

We applied factor analysis (see Method section) to examine the covariance structure across the six tests in the spatial orientation battery. The results showed that the six tests correlated substantially and clustered into one common factor, capturing between 32 and 57% of the variance in each individual test, which we named ' Navigation', as it indexed abilities that are generally described in the literature as spatial navigation skills (see Supplementary Table 4-factor structure, and Fig. 4-intercorrelations between tests). This unifactorial model provided a good fit for the data ( χ ² = 269.937 (148), p  = <0.001; comparative fit index (CFI) = 0.968; Tucker-Lewis index (TLI) = 0.971; root mean square error of approximation (RMSEA) = 0.030; standardized root mean square residual (SRMR) = 0.049).

We used the Common Pathway model (see Method section) to examine the extent to which genetic (A), shared environmental (C) and nonshared environmental (E) effects were common or specific across the six tests (Fig. 3). We found that the heritability of the common navigation factor was 64% (95% CIs = 0.41-0.91); shared environmental and nonshared environmental factors accounted for smaller proportions of variance, 8% (95% CIs 0.00-0.43) and 28% (95% CIs 0.21-0.36), respectively. The largest part of the genetic variance in navigation ability was shared across all tests; between 66 and 100% of the heritability of each test was captured by the common factor of navigation. Consequently, test-specific genetic effects were found to account for between 0 and 34% of the genetic variance in each test of spatial orientation (Supplementary Table 5).

We applied the common pathway model to parse the genetic (A), shared environmental (C) and nonshared environmental (E) variance that is shared across all the tests (represented by the A, C and E paths leaving from the common Navigation factor) from the genetic and environmental variance that is specific to each test (indexed by the individual A, C, and E latent factors from each rectangle). Each individual test loaded substantially onto a common factor, which we named Navigation factor (loadings ranging from λ = 0.54 for scanning ability to λ  = 0.75 for navigation based on landmarks). All A, C, and E paths are standardized and squared.

Environmental factors were largely specific to each test, as indicated by the considerable size of the specific E paths (bottom of Fig. 3); between 64 and 90% of the nonshared environmental variance was found to be specific to each test. The common navigation factor only captured between 10 and 36% of nonshared environmental variance in each test of spatial orientation (Supplementary Table 5).

Substantial associations between measures of spatial orientation and object-based spatial tests

We investigated the structure of spatial ability across a greater diversity of spatial tests. To this end, we extended our analyses beyond the six tests of spatial orientation to incorporate 10 additional tests of object-based spatial skills18. This additional battery of spatial tasks included measures that very closely align with traditional psychometric tests of spatial ability, including mental rotation, visualization, 2D and 3D drawing ability, and mechanical reasoning. Figure 4 presents phenotypic correlations between the 16 spatial tests included in the two batteries (spatial orientation and object-based) and their correlations with g.

Starting from the bottom left of the matrix, the first six tests are part of the spatial orientation battery. ND navigation according to directions, NL navigation according to landmarks, MR map reading, RM route memory, PT perspective taking, SC scanning. The following 10 tests were part of the other battery assessing object-based spatial skills: obj CS cross-section, obj 2d 2d drawing, obj PA pattern assembly, obj EM Elithorn Maze, obj MecR mechanical reasoning, obj PF paper folding, obj 3d 3d drawing, obj Rot mental rotation, obj PT perspective taking, obj Maz mazes, g general cognitive ability. All correlations were significant at p  < 0.001; variables were residualized for age and sex and standardized prior to analyses.

Correlations between spatial tests were positive and ranged from modest (between 0.10 and 0.30) to strong (>0.5) with r ranging between 0.17 and 0.56. Stronger links were observed between certain tests within each battery. For example, the four tests assessing navigation and map reading skills in the spatial orientation battery clustered more strongly together ( r ranging from 0.44 to 0.56). The same was observed for measures of 2D and 3D drawing, pattern assembly, paper folding, and mental rotation in the object-based battery ( r ranging from 0.34 to 0.54).

We conducted a series of confirmatory factor analyses to formally evaluate the covariance structure between the 16 spatial tests. We tested different theoretical models about the structure of spatial skills, starting from the simplest model and progressing to increasingly complex representations of the structure of spatial skills. The first model we tested was a one-factor model (Fig. 5a), positing that variation in spatial orientation and object-based skills could be largely considered a unitary ability. Although all tests loaded substantially onto a single factor (Fig. 5a), model fit indices ( χ ² = 692.730 (104), p  < 0.001, CFI = 0.890, TLI = 0.873, RMSEA = 0.061, SMRS = 0.059) suggested that this structure did not provide a good fit for the data.

a Unifactorial model of spatial ability; b Three-factor model of spatial ability. Obj CS cross-section, obj 2D 2D drawing, obj PA pattern assembly, obj SR shapes rotation, obj MecR mechanical reasoning, obj PF paper folding, obj 3D 3D drawing, obj PT perspective taking, obj EM Elithorn Maze, obj Maz Mazes, SC scanning, PT perspective taking, NL navigation according to landmarks, ND navigation according to directions, RM route memory, MR map reading.

Secondly, we tested whether including two factors of spatial ability (one for each battery, Supplementary Fig. 1) would provide a more accurate description of the structure of spatial skills. This model provided a good fit ( χ ² = 316.000 (103), p  < 0.001, CFI = 0.958, TLI = 0.951, RMSEA = 0.037, SMRS = 0.040). However, it also presented one major limitation: due to the substantial difference in test administration and properties of the two batteries, we could not exclude the possibility that the two separate factors emerging from this analysis were a product of differences between the two batteries, rather than underlying a real set of separate, although substantially correlated, abilities. In addition, the two batteries included some cases of parallel measures, so that specific skills were tested in both batteries using different methods (e.g., scanning and perspective taking).

In order to overcome this limitation, we tested another two-factor model, but this time we constructed the two factors based on theoretically driven differences between the constructs. The first factor included all those tests that are described in the literature as tapping spatial orientation abilities (navigation, wayfinding, and map reading) available across the two batteries. This resulted in six tests loading onto a first factor of ' Spatial Orientation': navigation according to directions, navigation according to landmarks, map reading, route memory and two tests originally part of the object-based battery, Elithorne maze and mazes. The second factor of ' Object Manipulation' included the eight remaining tests included in the object-based battery along with the scanning and perspective-taking measures included in the spatial orientation battery (Supplementary Fig. 2). However, this model did not provide a good fit for the data (Supplementary Table 6a).

We then tested whether a bifactor model would present a more accurate reflection of the structure of spatial ability. The bifactor model allowed us to examine how each test of spatial skills loaded onto a general factor of spatial ability, after removing the variance specific to each battery. The two specific factors are likely to include a mixture of true battery-specific underlying abilities and methodological artefacts common to every test within each battery. This bifactor model, presented in Supplementary Fig. 3, provided an adequate yet not excellent fit for the data (Supplementary Table 6a).

The last model we examined was based on the structure of the correlations observed between the 16 spatial tests (Fig. 4), which clustered into three main components. Consequently, this fourth model included three factors representing individual differences in: (1) Object Manipulation, (2) Navigation and (3) Visualization abilities (Fig. 5b). This model provided a good fit for the data ( χ ² = 351.870 (101), p < 0.001, CFI = 0.953, TLI = 0.944, RMSEA = 0.041, SMRS = 0.041). However, the three factors were strongly correlated ( r ranging from 0.73 to 0.95). These strong correlations are reflective of an underlying common set of abilities across the three factors. Consequently, we re-specified the model as a hierarchically structured model of spatial skills: The 16 tests of spatial skills clustered onto three separate abilities (object manipulation, navigation and visualization), which in turn loaded onto a common factor of Spatial Ability (Fig. 6). AIC indices and Akaike weights35 indicated that this model provided a better fit as compared with the other one and two-factor models (Supplementary Tables 6a and 6b). This three-factor hierarchical model provided a better fit for the data, when compared with a simpler hierarchical model including two first order factors: Object manipulation and Navigation/Visualization (see Supplementary Fig. 4).

Within each blue rectangle are the ten tests that were included in the object-based spatial battery, while shaded in red are the six tests that are part of the spatial orientation battery set in a naturalistic virtual environment. Obj CS cross-section, obj 2D 2D drawing, obj PA pattern assembly, obj SR shapes rotation, obj MecR mechanical reasoning, obj PF paper folding, obj 3D 3D drawing, obj PT perspective taking, obj EM Elithorn Maze, obj Maz Mazes, SC scanning, PT perspective taking, NL navigation according to landmarks, ND navigation according to directions, RM route memory, MR map reading. Both the phenotypic ( χ ² = 351.870 (101), p < 0.001, CFI = 0.953, TLI = 0.944, RMSEA = 0.041, SMRS = 0.041) and genetic ( χ ² = 1681.128 (1040), p  = < 0.001; CFI = 0.941; TLI = 0.944; RMSEA = 0.026; SRMR = 0.056) model provided good fit for the data.

This hierarchical characterization of spatial skills describes the complexity of the structure of individual differences in spatial abilities, while highlighting the strong interconnection between all abilities at a higher level of analysis. The higher order factor of spatial ability accounted for a large portion of individual differences in the navigation ( R ² = 0.79), object manipulation ( R ² = 0.69) and visualization ( R ²  = 1.00) factors. We adopted this hierarchical characterization of individual differences in spatial skills in subsequent analyses.

A common genetic network underlies performance in all spatial tests

We used the multivariate twin method to analyze the genetic and environmental origins of the hierarchical structure of spatial abilities. First, we found that a model decomposing variation in spatial abilities into additive genetic (A) and nonshared environmental (E) factors provided a good fit for the data ( χ ² = 1681.128 (1040), p  < 0.001, CFI = 0.941, TLI = 0.944, RMSEA = 0.026, SMRS = 0.056). That is, there was no evidence that shared environmental variance, which encompasses those experiences that make children growing up in the same family more similar to one another beyond their genetic similarity, played a meaningful role in accounting for individual differences in spatial skills.

This hierarchical AE model (Fig. 6) showed that spatial skills clustered together largely due to shared genetic variance. The common spatial ability factor was in fact highly heritable (84%) and subsumed 67% of the genetic variance in object manipulation. This is calculated, based on path tracing, as the standardized squared genetic variance in the general factor of spatial ability (0.84) multiplied by twice the path estimate for object manipulation (0.81) divided by the total genetic variance (0.84 × 0.81 ² + 0.27), resulting in (0.84 × 0.81 ²)/(0.84 × 0.81 ² + 0.27). The common factor of spatial ability accounted for 93% of the genetic variance in the navigation factor and for the entirety of the genetic variance in the visualization factor (see Supplementary Table 7 for the full model including 95% confidence intervals). Nonshared environmental variance accounted for a much smaller proportion of individual differences in the common spatial ability factor (16%). Nonshared environmental factors, which at the test-specific level include measurement error, were the major source of test-specific variance.

General cognitive ability (g) measured with several tests of verbal and nonverbal ability over development, only partly accounts for the genetic clustering of spatial skills

It is well established that cognitive skills correlate with each other, and that a substantial portion of variation in different abilities can be accounted for by a general factor of cognitive ability ( g), both at the observed and genetic level. We applied a Cholesky decomposition (Method) to examine to what extent the genetic and environmental variance in spatial ability could be captured by g. The Cholesky approach, similar to hierarchical regression, parses the genetic and environmental variation in each trait into that which is accounted for by traits that have previously been entered into the model and the variance which is unique to a newly entered trait. We applied this method to examine the extent to which the clustering of spatial tests into a common factor of spatial ability could be accounted for by the broader g factor. The results presented in Fig. 7 (see Supplementary Fig. 5 for the full model) showed that g accounted for 55% of the genetic variance in the second-order common spatial ability factor. In other words, 45% of the genetic variance in spatial ability was independent of g. As described in greater detail in the Method section, our measure of g was obtained combining multiple tests of verbal and nonverbal cognitive ability administered during development (from age 7 to 16). Tests of spatial ability were not included in our measure of g over development.

For the common spatial ability factor the bar is divided into the genetic and environmental contributions independent of g and those that are accounted for by the genetic and environmental variance in g. Results are from a Cholesky decomposition (see Supplementary Fig. 5 for the full model).

When we accounted for g at different levels in the models (Supplementary Figs. 6- 11), results remained consistent with the existence of a general genetic network of spatial skills that covaries independently of g. Results remained highly consistent when we considered a measure of g constructed from one verbal and one nonverbal test collected when participants were 16-years-old (Supplementary Fig. 12).

Discussion

The current study provides new knowledge on the structure and nature of spatial ability by addressing three outstanding issues in the field of spatial cognition. First, we examined the structure of spatial orientation abilities, measured with a novel gamified battery set in a virtual environment that included a broad range of measures tapping putatively different aspects of spatial orientation ability. Second, we explored the structure of the associations between spatial orientation skills and object-based spatial tests, a topic that remains mostly unexplored in the cognitive psychology literature and is characterized by strong, contrasting theoretical views. Third, we investigated the extent to which an index of the developmentally stable component of g accounted for the shared variance observed across spatial skills. Across these three broad aims, we leveraged the genetically informative quality of our twin sample to address parallel questions related to the genetic and environmental structure of spatial ability and of its association with g. At every level of analysis our results highlighted communalities rather than differences across tests of spatial ability, largely supporting a unitary structure of spatial cognition.

Support for the unitary structure of spatial cognition first emerged from phenotypic analyses of our battery of spatial orientation tasks. This finding of a strong general component of variation was remarkable given the breadth of spatial orientation skills covered by our newly developed battery. In fact, the development of this novel gamified, battery set in a virtual environment was guided by a careful process of literature review aimed at covering all the main domains of spatial orientation described in the existing literature. This resulted in six broad domains that ranged from navigation according to directions and large-scale perspective taking, which, based on Newcombe and Shipley's (2015) taxonomy, could be categorized as extrinsic-dynamic spatial abilities, to route memory and large-scale scanning, which, based on the same taxonomy, could be described as extrinsic-static spatial abilities7. Although extrinsic-static and extrinsic-dynamic abilities have been proposed to be separate skills7, and a meta-analysis of the effects of training spatial ability partly supported this distinction for a few selected tests38, our results contradict this largely theoretical taxonomy.

We found support for a unitary structure of spatial orientation skills not only at an observed (phenotypic) level, but also in terms of the genetic and environmental factors supporting spatial orientation skills. We found that a common factor of 'navigation ability' was 64% heritable and captured between 66 and 100% of the heritability of the six individual tests of spatial orientation, and to a lesser extent their nonshared environmental variance (between 10 and 36%). This suggests that, to the extent that measures of spatial orientation covary, they do so largely due to their shared genetic variance. These results push our knowledge of the nature of spatial orientation skills further, providing support for a unitary structure of spatial orientation skills at the genetic level.

Further support for a unitary structure of spatial cognition emerged when we considered an even greater breadth of spatial tests, including, in addition to our six measures of spatial orientation, ten psychometric tests of object-based spatial skills, administered in the same sample as part of another gamified spatial battery. These sixteen tests of spatial skills were specifically selected to cover all the main areas of spatial cognition identified in extant literature, making the current work, to our knowledge, the most comprehensive investigation of spatial abilities to date. We approached the examination of the structure of associations between such a broad umbrella of spatial measures by moving through increasing levels of complexity.

A simple unitary account of spatial ability, represented by a general factor common to all measures, did not provide an accurate description of the foundations of spatial skills. At first glance, the results could have been interpreted as supporting the existence of three factors of spatial ability. These three factors described individual differences in navigation, object-based abilities and visualization. Existing taxonomies of spatial ability7, differentiate not only between static and dynamic spatial skills, but also between intrinsic and extrinsic abilities. Consistent with this account we observed a partial differentiation between object-based spatial tests, such as mental rotation, that are largely concerned with the intrinsic properties of objects, and visualization tests, such as perspective taking and scanning, which are largely concerned with extrinsic relations among objects7,39. However, the very strong correlations, from 0.73 to 0.95, observed between the object-based, navigation and visualization factors contradicted this putative distinction, and opened the possibility that a coherent, underlying set of abilities held these three factors together.

Factor analytic evidence supported this hierarchical account of spatial cognition: All sixteen tests loaded onto three, substantially correlated, factors (navigation ability, object-based ability and visualization ability), which in turn loaded strongly on a common factor of spatial ability. Particularly striking was the clustering of tests of scanning and perspective taking abilities into a single factor, Visualization. In fact, this visualization factor included measures belonging to both spatial ability batteries. This is significant since the tests were administered in very different formats and, according to proposed accounts of spatial skills, might reflect separate spatial abilities (large vs. small-scale spatial skills). Despite these theoretical and practical differences in task administration, we found that our measures of scanning and perspective taking across the two batteries reflected a common set of visualization abilities.

A hierarchical structure, which highlights both communalities and differences between cognitive tests, has also been found to provide the most accurate characterization in other domains of cognition, most notably executive functions40,41,42,43. Also consistent with what has been observed for individual differences in executive functions, we found that genetic factors were largely shared across all tests of spatial abilities. These results point to the existence of a common genetic network at the basis of individual differences in spatial ability, therefore providing additional support for a unitary account of spatial cognition.

A further line of evidence supporting the existence of a unitary account of spatial cognition was provided by our analyses examining the role of g in the clustering of spatial ability at the genetic and environmental levels. We found that individual differences in g correlated moderately with all individual tests of spatial skills and substantially with the common spatial ability factor. However, nearly half of the substantial genetic variance in spatial ability was found to be independent of the genetic variance in g, measured aggregating multiple cognitive tests over development. Taken together, our results indicate that spatial skills cluster together phenotypically and genetically beyond the simple fact that they are all tests reflecting a general, developmentally stable, capacity for planning, thinking abstractly and solving problems, all skills that are indexed by g 36. It should be noted that, since the genetic and environmental components of cognitive abilities have differential longitudinal stabilities, aggregating across waves might have resulted in 'cancelling out' environmental variance that is specific to each developmental stage, in favour of aggregating stable genetic variance in g over development37.

In summary, our current work provides a threefold line of support for the unitary nature of spatial cognition, partly independent of other measures of cognitive skills. Interestingly, this unitary account of abilities is at odds with individuals' perceptions of their own ability and feelings towards spatial activities. In our previous work examining the structure of spatial and mathematics anxiety, we found evidence for a separation between the anxiety people feel towards spatial navigation and the anxiety towards object-based skills, such as completing difficult jigsaw puzzles and building flat-pack furniture from instructions44. This observed difference in perceptions and feelings towards different spatial activities might contribute to explaining why ideas, theories and taxonomies of spatial cognition have mostly favoured a multifaceted account of spatial skills.

Although our study provides a highly comprehensive investigation of the structure of spatial ability in a large sample and addresses several outstanding research questions concerning spatial cognition, it was limited by the technology available to us at the time. Although we developed a new gamified battery set in a virtual environment to reliably examine individual differences in spatial orientation skills, it is possible that assessment in a computer-simulated environment might not be able to capture individual differences in spatial orientation and navigation as well as does assessment in real-life settings. It has been proposed that spatial orientation in computer-simulated environments might reflect an allocentric (object-to-object) approximation of the abilities involved in egocentric (self-to-object) real-life spatial orientation45. However, studies that have examined the reliability of measuring navigation skills in VR, as compared with real-life settings, have found good concordance between the two13. While we leveraged the newest technological developments to create a realistic virtual environment to host our gamified test, future studies might explore navigation in VR by applying even more immersive tools such as, for example, head-mounted displays (e.g., oculus technology).

Our finding of a unitary structure of spatial cognition across sixteen diverse tests of spatial skills is likely to inform several disciplines beyond cognitive psychology. Investigations on the nature and structure of spatial ability have concerned researchers in a wide range of scientific disciplines, from evolutionary biology to neuroscience, ecology and molecular genetics. Our evidence for a largely unitary phenotypic and genetic network supporting individual differences in spatial cognition can serve as a basis for future research on the nature of spatial ability across all these disciplines and suggests a shift in our consideration of the architecture of human cognitive abilities. These findings are also likely to inform the development of programs aimed at advancing STEM learning through training spatial skills46.

Methods

Sample

Participants were part of the Twins Early Development Study (TEDS), a longitudinal study of twins born in the United Kingdom between 1994 and 1996. The families in TEDS are representative of the British population in their socio-economic distribution, ethnicity and parental occupation. See Rimfeld et al. for additional information on the TEDS sample47,48. The present study focuses on data collected in a sample of 2,660 TEDS twins aged 19-22 ( M = 21.23, SD = 0.53, range = 2.29). All individuals with major medical, genetic or neurodevelopmental disorders were excluded from the dataset. These included twins with ASD, cerebral palsy, Downs syndrome, chromosomal or single-gene disorders, organic brain problems, e.g. hydrocephalus, profound deafness and developmental delay. TEDS twins completed two online batteries assessing multiple aspects of spatial abilities. One was set in a virtual environment and assessed six aspects of large-scale spatial navigation and orientation skills. 2660 twins (178 pairs were MZ males, 169 pairs were MZ females, 325 pairs were DZ males, 260 pairs were DZ females and 398 pairs were opposite sex) took part in this battery (868 complete twin pairs). 74.3% of participants who completed the spatial orientation battery ( N = 1978; 740 complete pairs) also completed an online battery of tests developed to assess ten aspects of object-based spatial abilities. At least five months passed between the administration of the two batteries (median time lag = 265.00 days). The time lag was not associated with spatial measures (Supplementary Table 8). The object-based spatial battery was administered first, starting from May 2015, while the data collection for the spatial orientation battery started in September 2015. The Institute of Psychiatry, Psychology and Neuroscience ethics committee at King's College London approved the study. Informed consent was obtained from all participants prior to data collection.

Measures

Spatial orientation battery

Putatively different facets of spatial orientation skills were assessed through a novel gamified battery called 'Spatial Spy'. Participants were invited to solve a mystery by collecting clues while orienting and navigating around the streets of a virtual environment (Fig. 8). The online battery was developed in Unity ( https://unity3d.com) by ETT Solutions. After a comprehensive literature review, we identified four core aspects of spatial orientation and navigation skills: (1) navigating when reading a map; (2) navigating based on a previously memorized map or route; (3) navigating following directions (e.g., cardinal points), and (4) navigating using reference landmarks. In addition to these four abilities, the spatial orientation battery included two tests based on paradigms that have been frequently used in the object manipulation spatial literature: perspective-taking and scanning. Two research aims motivated the decision to include these two tests in the battery. First, we aimed to explore how perspective taking and scanning measured within a large-scale spatial framework (i.e., within a more naturalistic context approximating a virtual environment) related to the same abilities assessed within a smaller-scale, object manipulation framework (i.e., psychometric tests collected as part of another online battery). Second, due to the innovative and experimental nature of the spatial orientation battery, we included measures of scanning and perspective taking, for which we had corresponding data from more traditional psychometric tests, in order to explore the external validity of assessing spatial skills within this new virtual environment. The measures included in this spatial orientation battery are described in detail below. The statistical properties (distribution characteristics and test-retest reliability) of each measure were assessed through two pilot studies.

a Map Reading; b route memory; c navigation directions; d navigation landmarks; e scanning; f perspective taking.

The final battery started with a training session that helped participants become acquainted with using the cursor or mouse for navigating around the virtual environment, as well as with the requirements and mechanics of each of the six tests. The battery was administered online, with participants taking the tests in web browsers on their own desktop or laptop computers, using a mouse or trackpad to 'look' around the virtual environment, and the keyboard to move. The battery took between 35 and 60 min (median time 43 min) to complete and participants could pause at any time by pressing the key 'P' on their keyboard and could resume the game at any given time. Prior to the testing session, participants were provided with practice trials for every test. A two-minute video providing examples of how each subtest was implemented within the Spatial Spy virtual environment is available at the following link https://www.youtube.com/watch?v=wHj0-19rbiI. Following is a detailed description of each test included in the spatial orientation battery. Test-retest reliability for each measure was calculated as part of a pilot study including a sample of 100 participants who completed the battery twice over the space of two months.

Map Reading (Fig. 8a), assessed individual differences in the ability to efficiently read a map to travel from one location to another. Once a map had appeared on the top-right corner of the screen, a flashing yellow dot on the map indicated participants' starting location (A), while a red pointer designated the end-point location on the map (B). Participants were instructed to get from A to B by finding the fastest route and notified that they had 1 min to complete their mission. If participants could not reach their destination within 60 s, they were 'teleported' back to the initial location and allowed a second opportunity to complete the task. The ability was assessed though five non-consecutive iterations of increasing difficulty. Each iteration was allocated a score of 2 if participants had successfully travelled from A to B through the quickest (most direct) route, a score of 1 if participants had successful completed the mission but had not selected the fastest route, and a score of 0 if participants had failed to complete the mission. This created a final maximum score of 10. The final score was calculated by combining this accuracy score with participants' reaction time (time taken to successfully complete the mission), equally weighted. The test showed good test-retest reliability ( r = 0.69, p < 0.001) and distribution (Fig. 1).

Route Memory (Fig. 8b), assessed individual differences in the ability to travel from one location to another by remembering the content of a map. As for the map reading condition, a map appeared on the top-right corner of the screen, with a flashing yellow dot indicating participant's starting location (A), and a red pointer designating the end-point location (B). However, the route memorizing test asked participants to memorize the content of the map before the map disappeared from the screen. Participants were given 20 s to memorize the map and plan the route before travelling from A to B and were allowed 120 s to reach the target location. The number of increasingly difficult iterations, procedure and scoring were the same as those for the previously described map-reading without memory task. Test-retest reliability was acceptable ( r = 0.60, p < 0.001), and distribution (Fig. 1).

Navigation Directions (Fig. 8c) assessed participants' skills in navigating around a virtual environment following instructions based on directions. At the start of the task, participants were 'teleported' to one location of the virtual environment and given instructions to navigate around the virtual city in terms of compass points (north, south, east and west). The test included five non-consecutive iterations of increasing difficulty and each iteration comprised 4-6 tasks. Each task that was solved correctly was assigned a score of 1. Participants were allowed a maximum of three attempts to respond correctly to each task and consequently proceed to the next set of instructions. After three consecutive failed attempts, the iteration was discontinued and the remaining tasks in that iteration (if any) were assigned a score of 0. Each iteration had a time limit of 180 s, if the time limit expired before participants had completed all the tasks, the remaining tasks for that iteration were discontinued and assigned a score of 0. There was no progress bar or timer on screen to help participants keep track of time; however, "hurry up" prompts appeared on screen as the time limit approached. At the end of each iteration (either successfully completed or discontinued) participants were teleported to another part of the virtual environment to complete the subsequent iteration. For the first two iterations the image of a compass providing cardinal directions was available on the top-left corner of the screen, but the compass was not available for the last three iterations, making them more difficult to complete. Examples of instructions were: ' Now turn east' and ' You are facing southwest. Go north and immediately turn west'. The final score was calculated by combining the accuracy score with participants' reaction time (time taken to successfully complete each iteration), equally weighted. The test showed excellent test-retest reliability ( r = 0.89, p < 0.001) and distribution of the scores (Fig. 1).

Navigation Landmarks (Fig. 8d) measured the ability to navigate following instructions based on the descriptive features of the destination or other nearby landmarks. The test included five non-consecutive iterations each comprising 4 or 5 tasks. Each task lasted for a maximum of 60 s, so participants had 60 s to reach a certain landmark within the virtual environment. If the time limit expired before participants had reached the required landmark, they were discontinued, teleported to the landmark in question, and were able to proceed to the next task. Each task solved correctly, meaning that participants were able to reach the described landmark within the time limit, was assigned a score of 1, while for each trial when participants were not able to reach the location in 60 s, they were assigned a score of 0. Neither a map nor a compass was provided to help participants navigate around the environment. Examples of instructions are: ' Now reach the tall white pyramid skyscraper', and ' The message instructs you to go to the park near the old clock tower'. The target landmark was visible at the start of the session, but it was not always in plain sight as participants were navigating throughout the city to reach the target landmark. The final score was calculated by combining this accuracy score with participants' reaction time (time taken to successfully complete each iteration), equally weighted. The test showed excellent test-retest reliability ( r = 0.80, p < 0.001) and distribution of the scores (Fig. 3a).

Large-scale Scanning (Fig. 8e) measured participants' ability to quickly process visual information and identify a target object, a black briefcase, located somewhere nearby within the virtual city. The target object remained the same across the five non-consecutive iterations of increasing difficulty. When looking for the target, participants' perspective could be rotated freely in any direction, but could not be moved vertically or horizontally around the virtual environment. Participants could identify the target object by clicking on the mouse or trackpad within 60s. Within the time limit, participants were allowed four attempts to correctly spot the target object and, as for all other tasks, they were encouraged to do it as quickly as possible. Feedback was provided after each attempt, and as soon as participants had identified the target object correctly, they were 'teleported' to the next task. It was not possible to pause half-way through the 60-s iteration. The final score was calculated by combining this accuracy score with participants' reaction time (time taken to successfully complete each iteration). The test showed excellent test-retest reliability ( r = 0.80, p < 0.001) and wide distribution of the scores (Fig. 1).

Large-scale Perspective-taking (Fig. 8f) measured participants' ability to identify objects from a different perspective in large-scale 'naturalistic' settings. The test comprised five iterations of increasing difficulty that followed the same test rules. Each iteration started with a CCTV-like image showing an aerial shot of a location within the virtual world, and within this location one target object was depicted flashing on screen for ten seconds. During this initial stimulus presentation, participants could not move within the virtual environment, so all participants were exposed to the same image of the flashing target object. After the ten seconds had elapsed, the CCTV image disappeared and participants were teleported back to the target location within the virtual environment, which shifted their perspective back to ground level; they were then instructed to identify the target object as quickly as possible. When looking for the target object (the one that was flashing when presented from the CCTV perspective), participants' perspective could be freely rotated but could not be moved vertically or horizontally around the virtual environment. Participants could identify the target object by clicking on it with their mouse or trackpad within 60 s. Within the time limit, participants were allowed four attempts to correctly spot the target object and they were encouraged to do it as quickly as possible. A message would appear on the screen after each attempt (either 'Yes' or 'Try again') to provide participants with feedback on their performance, and each iteration terminated either after a successful attempt, or after participants had used up their four attempts, or if they timed out. A 'Hurry up' message was displayed on the screen a few seconds before the time for each iteration elapsed. The test showed good distribution (Fig. 1) and acceptable test-retest reliability ( r = 0.67, p  < 0.001).

Object manipulation

Object manipulation was tested using an online, gamified, battery called 'The King's Challenge'18. This test battery measures the major putative dimensions of spatial ability, and is comprised of 10 tests: (1) a mazes task (searching for a way through a 2D maze in a speeded task); (2) 2D drawing (sketching a 2D layout of a 3D object from a specified viewpoint); (3) Elithorn mazes (joining together as many dots as possible from an array); (4) pattern assembly (visually combining pieces of objects together to make a whole); (5) mechanical reasoning (multiple-choice naïve physics questions); (6) paper folding (visualizing where the holes are situated after a piece of paper is folded and a hole is punched through it); (7) 3D drawing (sketching a 3D drawing from a 2D diagram); (8) mental rotation (mentally rotating objects); (9) perspective-taking (visualizing objects from a different perspective), and (10) cross-sections (visualizing cross-sections of objects). The development of the battery is described in detail elsewhere18. A brief demonstration of the battery can be accessed here: https://www.youtube.com/watch?v=awnfeiAPmQc

General cognitive ability (g) over development

General cognitive ability ( g; intelligence) was assessed in TEDS at ages 7, 9, 10, 12, 14, and 16. For the present analyses we created a longitudinal composite measure of g as a mean of these six assessments. At age 7, ' g' was calculated as a mean of conceptual grouping49, a WISC similarities test50, a WISC vocabulary test50, and a WISC picture completion test50 all collected over telephone testing. At age 9, ' g' was calculated as a mean of a shapes test51, a WISC vocabulary test52, a WISC general knowledge task52, and a puzzle test51; all tests were administered in booklets sent to the twins by post. At age 10, ' g' was calculated as a mean of the Raven's Standard Progressive Matrices53, a WISC vocabulary52, WISC picture completion50, and a WISC general knowledge test52; at age 10 and subsequent assessments, all ' g' data were obtained by internet testing. At age 12, ' g' was calculated as a mean of the Raven's Progressive Matrices53, a WISC picture completion50, a WISC vocabulary52, and a WISC general knowledge test52. At age 14, ' g' was calculated as a mean of the Raven's Progressive Matrices53 and a WISC vocabulary test52. At age 16, ' g' was calculated as a mean of Mill Hill Vocabulary test54 and Raven's Progressive Matrices53.

Analytic strategies

The R package 'psych'55 was used to obtain descriptive statistics and correlations, and the R package 'ggplot2'56 was used for data visualization purposes. For all phenotypic analyses, including all model fitting, one twin was selected randomly from each pair to ensure independence of data. The random selection of one twin out of each pair also provided the opportunity of replicating all phenotypic analyses in the other, randomly selected, half of the sample, although we acknowledge that the replication sample is not independent (due to relatedness) and does not provide a full replication. Similar results were obtained when the analyses were conducted on the second half of the sample (see Supplementary Figs. 13, 14, and 15). Structural Equation modelling (SEM) was conducted in Mplus version 857 and Open Mx version 2.058. Full Information Maximum Likelihood (FIML) was applied to account for missingness in the data.

Confirmatory factor analyses

Confirmatory Factor Analysis (CFA) is a data reduction technique whereby latent factors are constructed from observed (measured) indicators based on a pre-imposed structure which is hypothesized to underlie the data. CFA is, in most instances, theory-driven and allows for testing a hypothesis on the associations between variables and their underlying latent constructs. Alternative theoretical models were compared examining multiple model fit indices. Model fit indices include (a) the chi-square test, which indicates the correspondence between the expected and the observed covariance matrices, a chi-square value close to zero indicates greater correspondence between them; (b) the Akaike information criterion that allows us to compare model fit between competing models, with smaller values indicating better fit; (c) Akaike weights35 which provide an index of the conditional probability for each model, Akaike weights range between 0 and 1 and a larger value indicates better fit; (d) the CFI is an incremental fit index that is based on the non-centrality measure. The CFI ranges from 0 to 1.00, with values closer to 1.00 indicating better fit (acceptable values > 0.90); (e) the RMSEA is related to residual in the model. RMSEA values range from to 1 with a smaller RMSEA value indicating better model fit. Acceptable model fit is indicated by an RMSEA value of 0.08 or less59.

Genetic analyses: univariate and multivariate twin modelling

The twin method allows for the decomposition of individual differences in a trait into genetic and environmental sources of variance by capitalizing on the genetic relatedness between monozygotic twins (MZ), who share 100% of their genetic makeup, and dizygotic twins (DZ), who share on average 50% of the genes that differ between individuals. The method is further grounded in the assumption that both types of twins who are raised in the same family share their rearing environments to approximately the same extent60. By comparing how similar MZ and DZ twins are for a given trait (intraclass correlations), it is possible to estimate the relative contribution of genetic factors and environments to variation in that trait. Heritability, the amount of variance in a trait that can be attributed to genetic variance (A), can be roughly estimated as double the difference between the MZ and DZ twin intraclass correlations61. The ACE model further partitions the variance into shared environment (C), which describes the extent to which twins raised in the same family resemble each other beyond their shared genetic variance, and nonshared environment (E), which describes environmental variance that does not contribute to similarities between twin pairs (and also includes measurement error). It also provides confidence intervals for all estimates.

When data are available from opposite-sex and same-sex DZ twin pairs, the standard univariate ACE model can be extended to a sex-limitation model to test for the differences in the aetiologies of sex differences by comparing five sex and zygosity groups: MZ females, DZ females, MZ males, DZ females, and DZ opposite-sex twin pairs34. This method allows for estimating quantitative and qualitative sex differences (i.e., the same factors affecting males and females to a different extent). Differences in the magnitude of ACE estimates for males and females are referred to as quantitative sex differences; qualitative sex differences indicate whether different genetic or environmental factors influence males and females. The sex limitation model is described in detail elsewhere62. Here we conducted sex-limitation model-fitting by fitting a series of nested models and then testing the relative drop of the fit between the models when the parameters for the sexes are forced to be equal58.

The twin method can also be extended to the exploration of the covariance between two or more traits (multivariate genetic analysis). Multivariate genetic analysis allows for the decomposition of the covariance between multiple traits into genetic and environmental sources of variance, by modelling the cross-twin cross-trait covariances. Cross-twin cross-trait covariances describe the association between two variables, with twin 1's score on variable 1 correlated with twin 2's score on variable 2, which are calculated separately for MZ and DZ twins. The examination of shared variance between traits can be further extended to test the aetiology of the variance that is common between traits and of the residual variance that is specific to individual traits. Here we used the common pathway model which is a multivariate genetic model in which the variance common to all measures included in the analysis can be reduced to a common latent factor, for which the A, C, and E components are estimated. As well as estimating the aetiology of the common latent factor, the model allows for the estimation of the A, C, and E components of the residual variance in each measure that is not captured by the latent construct63. The common pathway model estimates the extent to which the general factor of spatial ability is explained by A, C, and E. The common pathway model is illustrated in Fig. 3. Based on factor analytic evidence, the common pathway model can be extended to include multiple common factors and, consequently to the examination of the genetic and environmental associations between the multiple latent factors. This extension of the common pathway model is presented in Fig. 6.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Acknowledgements

We gratefully acknowledge the ongoing contribution of the participants in the Twins Early Development Study (TEDS) and their families. TEDS is supported by a program grant to R.P. from the UK Medical Research Council (MR/M021475/1 and previously G0901245), with additional support from the US National Institutes of Health (AG046938). The research leading to these results has also received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ grant agreement n. 602768. R.P. is supported by a Medical Research Council Professorship award (G19/2). M.M.'s work is partly supported by the David Wechsler Early Career grant for innovative work in cognition. E.M.T. and M.M. are Faculty Research Associates of the Population Research Center at the University of Texas at Austin, which is supported by a grant, 5-R24-HD042849, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). E.M.T. is also supported by a Jacobs Foundation Research Fellowship and NIH grant R01HD083613. KR is supported by the Sir Henry Wellcome Fellowship.

Author information

Author notes

These authors contributed equally: Margherita Malanchini, Kaili Rimfeld.

Authors and Affiliations

1. Department of Biological and Experimental Psychology, Queen Mary university of London, London, UK

Margherita Malanchini

2. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK

Margherita Malanchini, Kaili Rimfeld, Nicholas G. Shakeshaft, Andrew McMillan, Kerry L. Schofield & Robert Plomin

3. Population Research Center, The University of Texas at Austin, Austin, USA

Margherita Malanchini & Elliot M. Tucker-Drob

4. InLab, Department of Psychology, Goldsmiths University of London, London, UK

Maja Rodic & Yulia Kovas

5. ETT Solutions LTD, London, UK

Valerio Rossi

6. Laboratory for Cognitive Investigations and Behavioural Genetics, Tomsk, Russia

Yulia Kovas

7. Department of Speech and Hearing Sciences, University of New Mexico, Albuquerque, USA

Philip S. Dale

8. Department of Psychology, The University of Texas at Austin, Austin, USA

Elliot M. Tucker-Drob

Contributions

Conceived and designed the experiment: M.M., K.R., N.G.S., K.L.S., M.R., Y.K., V.R., R.P. Analyzed the data: M.M., K.R., N.G.S. Drafted the paper: M.M., K.R., R.P. Revised the work critically: M.M., K.R., N.G.S., K.L.S., Y.K., V.R., P.S.D., E.T.D., R.P. All authors approved the final draft. M.M. and K.R. contributed equally to the work.

Corresponding author

Correspondence to Margherita Malanchini.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and Permissions

About this article

Cite this article

Malanchini, M., Rimfeld, K., Shakeshaft, N.G. et al. Evidence for a unitary structure of spatial cognition beyond general intelligence. npj Sci. Learn. 5, 9 (2020). https://doi.org/10.1038/s41539-020-0067-8
Download citation

# data[].attributes.attachment_link.title
Evidence for a unitary structure of spatial cognition beyond general intelligence - npj Science of Learning

# data[].attributes.attachment_link.description
Performance in everyday spatial orientation tasks (e.g., map reading and navigation) has been considered functionally separate from performance on more abstract object-based spatial abilities (e.g., mental rotation and visualization). However, few studies have examined the link between spatial orientation and object-based spatial skills, and even fewer have done so including a wide range of spatial tests.

# data[].attributes.body
You will need to login into Sketchfab to download his USDZ animations and models.

# data[].attributes.attachment_caption
Reality 20Composer 20Book 20of 20Spells 20updated 20with 20AR 20and 20Fonts

# data[].attributes.headline
Book of Spells: Updated Version with AR and fonts

# data[].attributes.subject
Book of Spells: Updated Version with AR and fonts

# data[].attributes.body
Usdz file of region

# data[].attributes.body
For layering 2d files

# data[].attributes.body
Double Glass Dome

# data[].attributes.attachment_caption
Augmented Reality - System Capabilities - iOS - Human Interface Guidelines - Apple Developer

# data[].attributes.attachment_link.readable_text
Augmented reality (or AR) lets you deliver immersive, engaging experiences that seamlessly blend virtual objects with the real world.

Using the device's camera to present the physical world onscreen live, your app superimposes three-dimensional virtual objects, creating the illusion that these objects actually exist. Depending on the experiences your app offers, people can reorient the device to explore the objects from different angles, interact with objects using gestures and movement, and even join other people in multiuser AR experiences. For developer guidance, see ARKit.

Offer AR features only on capable devices. If your app's primary purpose is AR, make your app available only to devices that support ARKit. If your app includes features that require specific AR capabilities, or if AR features are optional in your app, don't show people an error if they try to use these features on a device that doesn't support them; instead, simply avoid offering the feature on an unsupported device. For developer guidance, see Verifying device support and user permission.

Creating an engaging, comfortable experience

Let people use the entire display. Devote as much of the screen as possible to displaying the physical world and your app's virtual objects. Avoid cluttering the screen with controls and information that diminish the immersive experience.

Strive for convincing illusions when placing realistic objects. Design detailed 3D assets with lifelike textures to create objects that appear to inhabit the physical environment in which you place them. Using information from ARKit, you can scale objects properly and position them on detected real-world surfaces, reflect environmental lighting conditions and simulate camera grain, cast top-down diffuse object shadows on real-world surfaces, and update visuals as the camera's position changes. To help avoid breaking the illusion you create, make sure your app updates scenes 60 times per second so objects don't appear to jump or flicker.

Consider how virtual objects with reflective surfaces show the environment. Reflections in ARKit are approximations based on the environment captured by the camera. To help maintain the illusion that an AR experience is real, prefer small or coarse reflective surfaces that downplay the effect of these approximations.

Use audio and haptics to enhance the immersive experience. A sound effect or bump sensation is a great way to confirm that a virtual object has made contact with a physical surface or other virtual object. Background music can also help envelop people in the virtual world. For guidance, see Playing audio and Playing haptics.

Minimize text in the environment. Display only the information that people need for your app experience.

If additional information or controls are necessary, consider displaying them in screen space. Content in screen space appears fixed to a consistent location either in the virtual world or, less commonly, on the device screen. It's typically easy for people to find and view content in screen space because it remains stationary while the underlying AR environment moves with the device.

Consider using indirect controls when you need to provide persistent controls. Indirect controls are not part of the virtual environment - instead, they are 2D controls displayed in screen space. If people need access to persistent controls in your app, consider placing the controls so that people don't have to adjust how they're holding the device to reach them. Also, consider using translucency in an indirect control to help avoid blocking the underlying scene. For example, the Measure app uses screen space to display a mix of translucent and opaque controls that people use to measure objects in the real world.

Anticipate that people will use your app in a wide variety of real-world environments. People may open your app in a place where there isn't much room to move around or there aren't any large, flat surfaces. Clearly communicate your app's requirements and expectations to people up front to help them understand how their physical environment can affect their AR experience. You might also consider offering different sets of features for use in different environments.

Be mindful of people's comfort. Holding a device at a certain distance or angle for a prolonged period can be fatiguing. To help avoid causing fatigue, consider placing objects at a distance that reduces the need to move the device closer to the object; in a game, consider keeping levels short and intermixed with brief periods of downtime.

If your app encourages people to move, introduce motion gradually. For example, you might not want to make people dodge a virtual projectile as soon as they enter your AR game. Give people time to adapt to the AR experience in your app and then progressively encourage movement.

Be mindful of people's safety. When people are immersed in an AR experience, they're not necessarily aware of their physical surroundings, so making rapid, sweeping, or expansive motions might be dangerous. Consider ways of making your app safe to operate; for example, a game could avoid encouraging large or sudden movements.

Using coaching to get people started

Before people can enjoy an AR experience in your app, they need to move their device in ways that lets ARKit evaluate the surroundings and detect surfaces. In iOS 13 and later, you can use the built-in coaching view to show people what to do and provide feedback during the initialization process. You can also use the coaching view to help people reinitialize AR - a process known as relocalization - after an AR experience is interrupted by, for example, people switching briefly to a different app. For guidance on relocalization, see Handling interruptions; for developer guidance, see ARCoachingOverlayView.

Hide unnecessary app UI while people are using a coaching view. By default, the coaching view appears automatically when initialization or relocalization starts, so you should be prepared to hide unrelated UI to help people focus on the coaching view's instructions.

If necessary, offer a custom coaching experience. Although you can configure the system-provided coaching view to help people provide specific information - such as the detection of a horizontal or vertical plane - you might need additional information or want to use a different visual style. If you want to design a custom coaching experience, use the system-provided coaching view for reference.

Helping people place objects

Show people when to locate a surface and place an object. You can use the system-provided coaching view to help people find a horizontal or vertical flat surface on which to place an object. After ARKit detects a surface, your app can display a custom visual indicator to show when object placement is possible. You can help people understand how the placed object will look in the environment by aligning your indicator with the plane of the detected surface.

When people place an object, immediately integrate that object into the AR environment. Although surface detection quickly and progressively refines accuracy, it's best to avoid waiting for more accurate data before placing an object. Use the information available to respond instantly when people place an object; then, when surface detection completes, subtly refine the object's position if necessary. For example, if people place an object beyond the bounds of the detected surface, gently nudge the object back onto the surface. For developer guidance on refining an object's position, see ARTrackedRaycast.

Consider guiding people toward offscreen virtual objects. Sometimes, it can be difficult for people to locate an object that's positioned offscreen. When this is the case, you can help people find such objects by offering visual or audible cues. For example, if an object is offscreen to the left, you could display an indicator along the left edge of the screen that guides people to point the camera in that direction.

Avoid trying to precisely align objects with the edges of detected surfaces. In AR, surface boundaries are approximations that may change as people's surroundings are further analyzed.

Incorporate plane classification information to inform object placement. For example, only let people place a virtual piece of furniture on a plane that's classified as "floor," or require a plane to be classified as "table" in order to place a virtual game board.

Designing intuitive, delightful object interactions

Let people use direct manipulation to interact with objects when possible. It's more immersive and intuitive when people can interact with onscreen 3D objects by touching them directly, than by using indirect controls in screen space. However, in situations where people are moving around as they use your app, indirect controls can work better.

Let people directly interact with virtual objects using standard, familiar gestures. For example, consider supporting a single-finger drag gesture for moving objects, and a two-finger rotation gesture for spinning objects. For guidance, see Touchscreen gestures.

In general, keep interactions simple. Touch gestures are inherently two-dimensional, but an AR experience involves the three dimensions of the real world. Consider the following approaches to simplifying user interactions with virtual objects.

Respond to gestures within reasonable proximity of interactive virtual objects. It can be difficult for people to be precise when aiming to touch specific points on objects that are small, thin, or placed at a distance. When your app detects a gesture near an interactive object, it's usually best to assume that people want to affect that object.

Support user-initiated object scaling when it makes sense in your app. For example, if your app lets people explore an imaginary environment, it probably makes sense to support object scaling because your app doesn't need to represent the real world. On the other hand, if your app helps shoppers decide on furniture to buy, letting people scale a chair object doesn't help them visualize how the chair will look in a room.

TIP Regardless of the purpose of your app, don't use scaling as a way to adjust the distance of an object. If you enlarge a distant object in an effort to make it appear closer, the result is a larger object that still looks far away.

Be wary of potentially conflicting gestures. A two-finger pinch gesture, for example, is similar to a two-finger rotation gesture. If you implement two similar gestures like this, be sure to test your app and make sure they're interpreted properly.

Strive for virtual object movement that's consistent with the physics of your app's AR environment. People don't necessarily expect an object to move smoothly over a rough or uneven surface, but they do expect objects to remain visible during movement. Aim to keep moving objects attached to real-world surfaces and avoid causing objects to jump or vanish and reappear as people resize, rotate, or move them.

Explore even more engaging methods of interaction. Gestures aren't the only way for people to interact with virtual objects in AR. Your app can use other factors, like motion and proximity, to bring content to life. A game character, for example, could turn its head to look at a person as they walk toward it.

Designing a great multiuser experience

When multiple people share your app's AR experience, each participant maps the environment independently and ARKit automatically merges the maps. For developer guidance, see isCollaborationEnabled.

Consider enabling people occlusion. If your app supports placing virtual objects behind people who appear in the device's camera feed, enhance the illusion of reality by letting the people occlude the objects. For developer guidance, see Occluding virtual content with people.

When possible, let new participants enter a multiuser AR experience. Unless your app requires all participants to join before the experience begins, consider using implicit map merging to let new people quickly join an ongoing AR experience. For developer guidance, see isCollaborationEnabled.

Reacting to real-world objects

You can enhance an AR experience by using known images and objects in the real-world environment to trigger the appearance of virtual content. For example, an app that recognizes theater posters for a sci-fi film could cause virtual space ships to emerge from the posters and fly around the environment. Another example is an app for an art museum that presents a virtual tour guide when it recognizes a sculpture. To enable experiences like these, your app provides a set of 2D reference images or 3D reference objects, and ARKit indicates when and where it detects any of these items in the current environment. For developer guidance, see Recognizing images in an AR experience.

When a detected image first disappears, consider delaying the removal of virtual objects that are attached to it. ARKit doesn't track changes to the position or orientation of each detected image. To help prevent virtual objects from flickering, consider waiting up to one second before fading them out or removing them.

Limit the number of reference images in use at one time. Image detection performance works best when ARKit looks for 100 or fewer distinct images in the real-world environment. If you need more than 100 reference images, you can change the set of active reference images based on context. For example, a museum guide app could ask permission to use location services to determine the part of the museum a person is in, and then look only for images displayed in that area.

Limit the number of reference images requiring an accurate position. Updating the position of a reference image requires more resources. Use a tracked image when the image may move in the environment or when an attached animation or virtual object is small compared to the size of the image.

Communicating with people

If you must display instructional text, use approachable terminology. AR is an advanced concept that may be intimidating to some people. To help make it approachable, avoid using technical terms like ARKit, world detection, and tracking. Instead, use friendly, conversational terms that most people will understand.

Unable to find a surface. Try moving to the side or repositioning your phone.

Unable to find a plane. Adjust tracking.

Tap a location to place the [name of object to be placed].

Tap a plane to anchor an object.

Try turning on more lights and moving around.

Insufficient features.

Try moving your phone more slowly.

Excessive motion detected.

In a three-dimensional context, prefer 3D hints. For example, placing a 3D rotation indicator around an object is more intuitive than displaying text-based instructions in a 2D overlay. Avoid displaying textual overlay hints in a 3D context unless people aren't responding to contextual hints.

Make important text readable. Use screen space to display text used for critical labels, annotations, and instructions. If you need to display text in 3D space, make sure the text faces people and that you use the same type size regardless of the distance between the text and the labeled object.

If necessary, provide a way to get more information. Design a visual indicator that fits with your app experience to show people that they can tap for more information.

Handling interruptions

ARKit can't track device position and orientation during an interruption, such as when people briefly switch to another app or accept a phone call. After an interruption ends, previously placed virtual objects are likely to appear in the wrong real-world positions. When you enable relocalization, ARKit attempts to restore those virtual objects to their original real-world positions using new information. For developer guidance, see Managing session lifecycle and tracking quality.

Consider using the system-provided coaching view to help people relocalize. During relocalization, ARKit attempts to reconcile its previous state with new observations of the current environment. To enable these observations, you can use the coaching view to help people return the device to its previous position and orientation.

Consider hiding previously placed virtual objects during relocalization. To avoid flickering or other unpleasant visual effects during relocalization, it can be best to hide virtual objects and redisplay them in their new positions.

Minimize interruptions if your app supports both AR and non-AR experiences. One way to avoid interruptions is by embedding a non-AR experience within an AR experience so that people can handle the task without exiting and re-entering AR. For example, if your app helps people decide on a piece of furniture to purchase by placing the item in a room, you might let them change the upholstery without leaving the AR experience.

Allow people to cancel relocalization. If people don't position and orient their device near where it was before an interruption, relocalization continues indefinitely without success. If coaching people to resume their session isn't successful, consider providing a reset button or other way to restart the AR experience.

Indicate when the front-facing camera is unable to track a face for more than about half a second. Use a visual indicator to indicate that the camera can no longer track the person's face. If you need to provide text instructions in this situation, keep them to a minimum.

Suggesting ways to resolve problems

Let people reset the experience if it doesn't meet their expectations. Don't force people to wait for conditions to improve or struggle with object placement. Give them a way to start over again and see if they have better results.

Suggest possible fixes if problems occur. Analysis of the real-world environment and surface detection can fail or take too long for a variety of reasons - insufficient light, an overly reflective surface, a surface without enough detail, or too much camera motion. If your app is notified of these problems, use straightforward, friendly language to offer suggestions for resolving them.

Insufficient features detected.

Try turning on more lights and moving around.

Excessive motion detected.

Try moving your phone slower.

Surface detection takes too long.

Try moving around, turning on more lights, and making sure your phone is pointed at a sufficiently textured surface.

AR icon

Apps can display an AR icon in controls that launch ARKit-based experiences. You can download this icon in Resources.

Use the AR glyph as intended. The glyph should be used strictly for initiating an ARKit-based experience. Never alter the glyph (other than adjusting its size and color), use it for other purposes, or use it in conjunction with AR experiences not created using ARKit.

Maintain minimum clear space. The minimum amount of clear space required around an AR glyph is 10% of the glyph's height. Don't let other elements infringe on this space or occlude the glyph in any way.

AR badges

Apps that include collections of products or other objects can use badging to identify specific items that can be viewed in AR using ARKit. For example, a department store app might use a badge to mark furniture that people can preview in their home before making a purchase.

Use the AR badges as intended and don't alter them. You can download AR badges, available in collapsed and expanded form, in Resources. Use these images exclusively to identify products or other objects that can be viewed in AR using ARKit. Never alter the badges, change their color, use them for other purposes, or use them in conjunction with AR experiences not created with ARKit.

Prefer the AR badge to the glyph-only badge. In general, use the glyph-only badge for constrained spaces that can't accommodate the AR badge. Both badges work well at their default size.

Use badging only when your app contains a mixture of objects that can be viewed in AR and objects that cannot. If all objects in your app can be viewed in AR, then badging is redundant.

Keep badge placement consistent and clear. A badge looks best when displayed in one corner of an object's photo. Always place it in the same corner and make sure it's large enough to be seen clearly (but not so large that it occludes important detail in the photo).

Maintain minimum clear space. The minimum amount of clear space required around an AR badge is 10% of the badge's height. Other elements shouldn't infringe on this space and occlude the badge in any way.

Resources

Related

Developer documentation

Videos

# data[].attributes.attachment_link.title
Augmented Reality - Technologies - Human Interface Guidelines - Design - Apple Developer

# data[].attributes.attachment_link.description
Augmented reality (or AR) lets you deliver immersive, engaging experiences that seamlessly blend virtual objects with the real world. Using the device's camera to present the physical world onscreen live, your app superimposes three-dimensional virtual objects, creating the illusion that these objects actually exist.

# data[].attributes.body
To view as USDZ or bring into Reality Composer add add multimedia.

# data[].attributes.wish_content.attachment_props.text
iPad not only allows you to view #AR for #business, #enterprise, #training BUT also offers free App #realitycomposer to create AR with no coding skills required +easy deployment. Is your business using iPads to their potential? @Usingtechbetter @apple #highered #healthsafety

# data[].attributes.wish_content.attachment_props.original_text
iPad not only allows you to view #AR for #business, #enterprise, #training BUT also offers free App #realitycomposer to create AR with no coding skills required +easy deployment. Is your business using iPads to their potential? @Usingtechbetter @apple #highered #healthsafety https://t.co/T7spsWqnyd

# data[].attributes.attachment_link.embed_code
iPad not only allows you to view #AR for #business, #enterprise, #training BUT also offers free App #realitycomposer to create AR with no coding skills required +easy deployment. Is your business using iPads to their potential? @Usingtechbetter @apple #highered #healthsafety pic.twitter.com/T7spsWqnyd
— Paul Hamilton (@PaulHamilton8) May 19, 2022

# data[].attributes.attachment_link.metadata.attachment_props_source.data.text
iPad not only allows you to view #AR for #business, #enterprise, #training BUT also offers free App #realitycomposer to create AR with no coding skills required +easy deployment. Is your business using iPads to their potential? @Usingtechbetter @apple #highered #healthsafety https://t.co/T7spsWqnyd

# data[].attributes.attachment_link.metadata.attachment_props.original_text
iPad not only allows you to view #AR for #business, #enterprise, #training BUT also offers free App #realitycomposer to create AR with no coding skills required +easy deployment. Is your business using iPads to their potential? @Usingtechbetter @apple #highered #healthsafety https://t.co/T7spsWqnyd

# data[].attributes.attachment_link.metadata.attachment_props.text
iPad not only allows you to view #AR for #business, #enterprise, #training BUT also offers free App #realitycomposer to create AR with no coding skills required +easy deployment. Is your business using iPads to their potential? @Usingtechbetter @apple #highered #healthsafety

# data[].attributes.headline
Adding Images and Keynote drawings to Reality Composer

# data[].attributes.subject
Adding Images and Keynote drawings to Reality Composer

# data[].attributes.body
Tutorial showing how to add images and Keynote drawings to Reality Composer.

# data[].attributes.body
For teachers and students to monitor skill development.

# data[].attributes.body
Interesting study

# data[].attributes.body
This USDZ file can be imported into Reality Composer and students/teachers can add audio, images or other 3D files. Can also add interactive tap behaviours.

# data[].attributes.body
An amazing glb - usdz converter. Great for converting Minecraft models to Augmented Reality.

# data[].attributes.body
Water Tank with Transparency

# data[].attributes.body
Download the iBook

# data[].attributes.body
Traditional style bubble

# data[].attributes.body
From NASA site - model of Mars