Representational drawing ability is associated with the syntactic language comprehension phenotype in autistic individuals

Figurative Drawing Abilities Map onto Distinct Cognitive Mechanism from Non-Figurative Abilities in 77,000 Participants with Neurodevelopmental Disorders | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Figurative Drawing Abilities Map onto Distinct Cognitive Mechanism from Non-Figurative Abilities in 77,000 Participants with Neurodevelopmental Disorders Andrey Vyshedskiy , Rohan Venkatesh , Edward Khokhlovich doi: https://doi.org/10.1101/2024.07.26.24310995 Andrey Vyshedskiy 1 Boston University, Metropolitan College , Boston, Massachusetts, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: vysha{at}bu.edu Rohan Venkatesh 2 University of Missouri-Kansas City , Kansas City, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Edward Khokhlovich 3 Independent Researcher , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Non-figurative motifs (parallel lines, hand stencils) constitute the earliest known human art, dating to ∼400,000 years ago. In contrast, figurative art—depicting recognizable animals and humans—emerges abruptly ∼45,000 ya. We hypothesized that this temporal dissociation reflects the sequential evolution of distinct neurocognitive mechanisms underlying language and visual manipulation. Building on our prior taxonomic framework that identified distinct expressive and receptive language mechanisms, we examined their co-occurrence with drawing abilities in individuals with neurodevelopmental disorders. Results revealed specific dissociations: figurative drawing emerged in parallel with the Syntactic Mechanism (responsible for syntactic comprehension). In contrast, non-figurative art correlated with the Modifier Mechanism (mediating the integration of nouns with adjectives). These dissociations suggest that the cognitive architecture required for figurative depiction is tightly linked to the same prefrontal-parieto-temporal system that supports the Syntactic Mechanism. Conversely, earlier-emerging non-figurative artistic traditions appear to rely primarily on a more basic integrative capacity for the Modifier Mechanism. We therefore propose that the appearance of non-figurative art in the Middle Pleistocene likely coincided with the evolutionary emergence of the Modifier Mechanism, whereas the appearance of figurative art reflects the later acquisition of the Syntactic Mechanism and its associated capacity for comprehension of syntactic, relational sentence structure. Introduction Drawing has long been used as a window into symbolic abilities, both for monitoring child development and for diagnosing dementia 1 – 3 . The study of human evolution is no exception, with symbolic capacities frequently inferred from evidence of drawing. Discoveries of artistic artifacts in Eurasia that predate the migration of Homo sapiens have sparked significant excitement in the scientific community. Pre– Homo sapiens artistic artifacts include use of pigments 4 , perforated shells 4 – 8 , line marks on stones and shells 9 , 10 , geometrical figures 11 , and hand stencils painted on cave walls 12 – 18 . While these artifacts may be symbolic, they are non-figurative—they do not represent clear images of humans, animals, or other recognizable objects. Figurative art appears in Eurasia only after the migration of Homo sapience out-of-Africa, around 70,000 years ago. The earliest examples of figurative art include a hunting scene depicting part-human, part-animal figures from the limestone cave of Leang Bulu’ Sipong 4 (Sulawesi, Indonesia), dated to 44,000 years ago 19 ; a painting of an animal at a limestone cave in Indonesian Borneo, dated to 40,000 years ago 20 ; a drawing of a “deer-pig" from the Indonesian island of Sulawesi, dated to 40,000 years ago 21 ; an engraving of an extinct wild cow (aurochs) from the Dordogne region of France, dated 38,000 years ago 22 ; and the lion-man sculpture excavated from the caves of Lone valley in Germany, dated to 39,000 years ago 23 . Does the transition from non-figurative to figurative traditions reflect a fundamental shift in symbolic cognition? One approach to examining the relationship between artistic capacities and cognition is the study of contemporary individuals with neurodevelopmental disorders. Many such individuals harbor deleterious mutations in genes implicated in cognitive and language evolution, including FOXP2 24 – 26 or SRGAP2A 27 , and may thus model features of ancestral neurocognitive architectures. The motivation for this study was two-fold. First, we had access to the largest database of over 100,000 individuals exhibiting a range of symbolic abilities, with parent-reported figurative and non-figurative drawing skills. Second, previous work has demonstrated distinct clustering of receptive and expressive language abilities 28 – 31 . Access to this data enabled the present investigation of associations between drawing skills and language phenotypes. Specifically, we examine how drawing abilities co-occur with different language phenotypes. If drawing abilities were independent of language phenotypes—like unrelated traits such as hyperactivity— they would be expected to occur with equal frequency across language levels and form a cluster separate from the receptive and expressive language clusters. Conversely, a strong association between a drawing ability and a specific language phenotype would indicate a close maturational link between symbolic drawing skills and language proficiency. Methods Study Participants Participants were children and adolescents using a language therapy app that was made freely available at all major app stores in September 2015 32 – 36 . The app provides various structured language comprehension therapy exercises and is primarily used by caregivers of children with language impairments. Most of the caregivers are presumed to be parents. Once the app was downloaded, caregivers were asked to register and to provide demographic details, including the child’s diagnosis and age. Caregivers completed a 133-item questionnaire (77-item Autism Treatment Evaluation Checklist (ATEC) 37 (Supplementary Tables 1-4); 20-item Mental Synthesis Evaluation Checklist (MSEC) 38 (Supplementary Table 5); 10-item screen time checklist 39 ; 25-item diet checklist 40 ; and 1-item parent education survey) approximately every three months. All 26 language-related items (15 receptive, Table 1 , and 11 expressive, Table 2 ) available in the 133-item questionnaire were included in the cluster analysis as in previously published articles 28 – 31 . Response options were: very true (0 points), somewhat true (1 point), and not true (2 points). View this table: View inline View popup Download powerpoint Table 1. Three language comprehension mechanisms—Syntactic, Modifier, and Command—have been identified in previous studies 28 – 30 . When one mechanism is acquired, the entire range of associated comprehension abilities is also gained. The Command-level abilities (Items 1 to 4) are acquired first. The Modifier-level abilities (Items 5 to 8) are attained next. The Syntactic-level abilities (Items 9 to 15) are acquired last. The language comprehension items are presented exactly as surveyed with parents in both this and earlier studies. Response options were: very true, somewhat true, and not true. Items 1 to 3 were assessed as part of the Expressive Language ATEC 37 subscale 1; the rest of items were a part of the MSEC subscale 38 . View this table: View inline View popup Download powerpoint Table 2. Eleven expressive language items as they were posed to parents. Response options were: very true (0 points), somewhat true (1 point), and not true (2 points). All items were a part of the expressive language ATEC subscale 1. Single-Word, Single-Sentence, and Multi-Sentence clusters have been identified in previous work 31 . The inclusion criteria for this study remained consistent with those of previous studies 28 – 31 : absence of seizures (which commonly result in intermittent, unstable language deficits 41 ), absence of serious and moderate sleep problems (which are also associated with intermittent, unstable language deficits 42 ), age range of 4 to 22 years (the lower age cutoff was chosen to ensure that participants were exposed to complete set of items listed in Table 1 43 , whereas the upper age threshold was constrained by the scarcity of older participants). Table 3 reports participants’ diagnoses as communicated by caregivers. Autism level (mild/Level 1, moderate/Level 2, or severe/Level 3) was reported by caregivers. Pervasive Developmental Disorder and Asperger Syndrome were combined with mild autism for analysis as recommended by DSM-5 44 . A good reliability of such parent-reported diagnosis has been previously demonstrated 45 . View this table: View inline View popup Download powerpoint Table 3. Participants’ diagnoses. When caregivers completed several evaluations, the last evaluation was used for analysis. Thus, the study included a total of 77,616 participants, the average age was 6.6±2.7 years (range of 4 to 22 years), 76.3% participants were males ( Table 3 ). The education level of participants’ parents was the following: 90.8% with at least a high school diploma, 68.7% with at least college education, 35.9% with at least a master’s, and 5.7% with a doctorate. The study was conducted in accordance with the Declaration of Helsinki 46 . Informed consent was obtained from the caregivers of all participants. The study protocol was approved by the Biomedical Research Alliance of New York (BRANY) LLC Institutional Review Board. Drawing assessments The evaluation included three questions about participants’ drawing abilities. One item assessed non-figurative drawing: “[My child] does drawing, coloring, art;” and two items assessed figurative drawing ability: “[My child] draws a VARIETY of RECOGNIZABLE images (objects, people, animals, etc.);” and “[My child] can draw a NOVEL image following YOUR description (e.g. a three-headed horse).” Response options were: “very true,” “somewhat true,” and “not true.” Statistics Unsupervised Hierarchical Cluster Analysis (UHCA) is a robust method for detecting patterns of co-occurring abilities within noisy data. It is fully data-driven, requiring no a priori assumptions about the number or structure of clusters, and thus enables the identification of naturally emerging groupings based solely on the statistical relationships among variables. UHCA was performed using Ward’s agglomeration method with a Euclidean distance metric. An additional statistical approach, Principal Component Analysis (PCA), was used to confirm the UHCA findings. PCA employs a fundamentally different mathematical framework for dimensionality reduction, transforming high-dimensional, noisy measures into a compact set of orthogonal components that capture the dominant sources of variance in the data. This data-driven projection spatially organizes abilities that co-occur more frequently closer together in a two-dimensional component space, providing an independent convergence test for the clustering structure. Analysis was performed in R, freely available language for statistical computing 47 . Code and data can be downloaded from https://doi.org/10.17605/OSF.IO/2QK5B ???. Results Clustering analysis of language comprehension abilities Fifteen language comprehension abilities were assessed by caregivers ( Table 1 ). To examine patterns of co-occurrence among these abilities, we applied unsupervised hierarchical cluster analysis (UHCA)—a data-driven method that produces tree-like diagrams, called dendrograms, which visually represent the hierarchical relationships between clusters of items. Abilities that frequently co-occur are automatically positioned closer together, while those that co-occur less often, appear farther apart. The UHCA-derived dendrogram ( Figure 1A ) replicated the hierarchical cluster structure reported in our prior work 28 – 30 . The first cluster included “ [My child] knows his/her name ”, “ [My child] responds to ‘No’ or ‘Stop’,” “[My child] responds to praise,” and “ [My child] follows some commands ” (items 1 to 4 in Table 1 ) and was previously termed the Command Mechanism. Download figure Open in new tab Figure 1. Clustering analysis of 15 language comprehension items. (A) The dendrogram representing the hierarchical clustering of language comprehension abilities. (B) Principal component analysis of language comprehension abilities shows clear separation between Command, Modifier, and Syntactic items. Principal component 1 accounts for 46.1% of the variance in the data. Principal component 2 accounts for 11.2% of the variance in the data. The second cluster included “ [My child] understands color and size modifiers,” “ [My child] understands several modifiers in a sentence,” “ [My child] understands size superlatives,” and “ [My child] understands numbers” (items 5 to 8 in Table 1 ) and was designated as the Modifier Mechanism in our prior work. The third cluster included “ [My child] understands spatial prepositions,” “ [My child] understands verb tenses,” “ [My child] understands flexible syntax,” “ [My child] understands possessive pronouns,” “ [My child] understands explanations about people and situations,” “ [My child] understands simple stories,” and “ [My child] understands elaborate fairytales” (items 9 to 15 in Table 1 ) and was previously termed the Syntactic Mechanism. Principal component analysis (PCA) ( Figure 1B ) also showed a clear separation between the Command, Modifier, and Syntactic clusters. Each cluster occupied a unique region in the principal component space, with no overlap observed. This separation suggests that the underlying structure of the data effectively captures the functional differences among the clusters, providing strong evidence for their distinct classification. Our previous analysis using both UHCA and PCA demonstrated that the Command, Modifier, and Syntactic clusters were highly stable across multiple evaluation methods (Ward, Average, Complete, McQuitty) 28 , 29 , age groups (4 to 6 years, 6 to 12 years, 12 to 22 years) 28 , 29 , time points (first vs. last evaluation) 28 , 29 , genders 28 , spoken-languages (English, Spanish, Portuguese, Italian, Russian, Chinese, French, German, or Korean) 30 , diagnostic categories 28 , 30 , and levels of parental education 28 . The reasons for the emergence of three distinct clusters of co-occurring abilities become clear when viewed through the lens of developmental dynamics. Some individuals with language impairments plateau at the Command level, others at the Modifier level, while only a subset progress to the Syntactic level 48 . Individuals who plateau at the Command level typically acquire the full set of Command abilities; those who plateau at the Modifier level acquire both Command and Modifier abilities; and only individuals who reach the Syntactic level go on to acquire syntactic abilities. This developmental stratification explains the observed clustering pattern: the four Command abilities (items 1–4 in Table 1 ) reliably co-occur and are grouped by UHCA and PCA into the Command cluster; the four Modifier skills (items 5–8) co-occur and form the Modifier cluster; and the seven Syntactic abilities (items 9–15) co-occur and constitute the Syntactic cluster. As a control we calculated UHCA and PCA of the 15 language comprehension abilities along with the hyperactivity (Supplementary Figure 1) items. Since this item is not related to language, it would be expected to form a separate cluster. As anticipated, both UHCA and PCA grouped hyperactivity into its own cluster at a significant distance from the three comprehension clusters, indicating random co-occurrence with the language abilities, and, thus, validating the effectiveness of both clustering techniques. Clustering analysis of expressive language abilities Eleven expressive language abilities were assessed by caregivers ( Table 2 ). The dendrogram generated by the UHCA of these abilities ( Figure 2A ) replicated the hierarchical cluster structure reported in our prior work 31 . Three distinct clusters emerged. The first cluster, previously termed Single-Word, comprised the abilities “ [My child] can use one word at a time ” and “ [My child] knows 10 or more words ” ( Table 2 , items 1 and 2). Download figure Open in new tab Figure 2. Clustering analysis of 11 expressive language items. (A) The dendrogram representing the unsupervised hierarchical clustering analysis of expressive language abilities. (B) Principal component analysis of expressive language abilities shows a clear separation between Single-Word, Single-Sentence, and Multi-Sentence items. Principal component 1 accounts for 51.2% of the variance in the data. Principal component 2 accounts for 10.9% of the variance in the data. The second cluster, termed Single-Sentence, included the abilities “ [My child] can use 2 words at a time ”, “ [My child] can use three words at a time ”, “ [My child] can use sentences with 4 or more words ”, “ [My child] explains what they want ”, and “ [My child] speech tends to be meaningful/relevant ” (items 3 to 7). The third cluster, termed the Multi-Sentence, encompasses the abilities “ [My child] carries on fairly good conversation ”, “ [My child] has normal ability to communicate for their age ” , “[My child] asks meaningful questions ”, and “ [My child] often uses several successive sentences” (items 8 to 11). PCA ( Figure 2B ) also showed a clear separation between the Command, Modifier, and Syntactic clusters. Previous analysis explored the stability of the expressive language clusters 31 . The reasons for the emergence of the three expressive language clusters mirror those underlying the three comprehension clusters. Specifically, some children plateau at the Single-Word level, others at the Single-Sentence level, while the majority progress to the Multi-Sentence level (manuscript in preparations). Consequently, clustering analysis of large cohorts of children with language impairments consistently identify these developmental plateaus as sets of co-occurring abilities, reflecting the natural grouping of linguistic competencies. As a control we calculated UHCA and PCA of the 11 expressive language abilities along with the hyperactivity (Supplementary Figure 2) item. This item is not related to speech and was therefore expected to form a separate cluster. As anticipated, both UHCA and PCA grouped hyperactivity into its own cluster at a significant distance from the three expressive clusters, validating the effectiveness of both clustering techniques. Clustering analysis of language comprehension abilities together with expressive language abilities Figure 3A depicts the UHCA dendrogram of the 15 language comprehension items ( Table 1 ) together with the 11 expressive language items ( Table 2 ). As in the previous study 31 , both the UHCA ( Figure 3A ) and the PCA ( Figure 3B ) show six clusters of items. These clusters are identical to those detected by separate clustering of receptive ( Figure 1 ) and expressive ( Figure 2 ) items: three language comprehension clusters (Command, Modifier, and Syntactic) and three expressive language clusters (Single-Word, Single-Sentence, and Multi-Sentence). Download figure Open in new tab Figure 3. Clustering analysis of 15 receptive language and 11 expressive language items. (A) The dendrogram represents the UHCA of 26 items. (B) PCA of the 26 items shows a clear separation between the six clusters: three language comprehension clusters on the top (Command, Modifier, and Syntactic) and three expressive clusters on the bottom (Single-Word, Single-Sentence, and Multi-Sentence). Principal component 1 accounts for 37.3% of the variance in the data. Principal component 2 accounts for 11.0% of the variance in the data. As a control we calculated UHCA and PCA of the 15 language comprehension abilities together with the 11 expressive language items along with the hyperactivity item, which is not expected to be related to any particular language ability and therefore should cluster into its own group. As expected, both UHCA and PCA clustered the hyperactivity item into its own group at a significant distance from the six language clusters, validating the effectiveness of both clustering techniques (Figure S3). Clustering analysis of receptive-expressive language abilities together with drawing abilities Next, we calculated UHCA and PCA of the 15 receptive and 11 expressive abilities along with the non-figurative drawing item “[My child] does drawing, coloring, art” ( Figure 4 ). Both UHCA and PCA analysis clustered non-figurative drawing with the Modifier Mechanism indicating their common co-occurrence. Download figure Open in new tab Figure 4. Clustering analysis of 15 receptive language and 11 expressive language items, along with the caregiver-reported item “[My child] does drawing, coloring, art” (labeled Coloring_Art ). (A) Dendrogram generated using UHCA. (B) PCA, with Principal Component 1 accounting for 36.0% of the variance in the data and Principal Component 2 accounting for 10.6%. The Coloring_Art item clusters with the Modifier Mechanism. Conversely, both UHCA and PCA clustered the figurative-drawing item “[My child] draws a VARIETY of RECOGNIZABLE images (objects, people, animals, etc.)” with the Syntactic Mechanism ( Figure 5 ). Download figure Open in new tab Figure 5. Clustering analysis of 15 receptive language and 11 expressive language items along with the caregiver-reported item “[My child] draws a VARIETY of RECOGNIZABLE images (objects, people, animals, etc.)” labeled DrawsFigurative . (A) Dendrogram generated using UHCA. (B) PCA, with Principal Component 1 accounting for 36.9% of the variance in the data and Principal Component 2 accounting for 10.6%. The DrawsFigurative item clusters with the Syntactic Mechanism. Finally, UHCA and PCA grouped the second figurative-drawing item “[My child] can draw a NOVEL image following YOUR description (e.g. a three-headed horse)” also with the Syntactic Mechanism ( Figure 6 ). Download figure Open in new tab Figure 6. Clustering analysis of 15 receptive language and 11 expressive language items along with the caregiver-reported item “[My child] can draw a NOVEL image following YOUR description (e.g. a three-headed horse)” labeled DrawToDescritption . (A) Dendrogram generated using UHCA. (B) PCA, with Principal component 1 accounting for 38.5% of the variance in the data and Principal component 2 accounting for 10.5%. The DrawToDescritption item clusters with the Syntactic Mechanism. Interpreted in a developmental context, these findings suggest that individuals with language impairments who plateau at the Modifier level typically show a propensity for non-figurative drawing but not for figurative drawing. In contrast, individuals who progress to the Syntactic level usually acquire the ability to draw figuratively as well. Stability of drawing abilities associations with language clusters We examined the stability of the association between drawing abilities and language clusters across different age groups (4-6 years, n=41,188; 6-12 years, n=32,074; and 12-22 years, n=4,354), levels of autism severity (severe, n=11,428; moderate, n=19,844; and mild, n=31,072), native language (English, n=26,020 and non-English, n=51,596), and participants’ sex (female, n=18,413 and male, n=57,398) (Supplementary Figures 4-31). Figurative drawing ability, as reported by caregivers in response to the items “[My child] draws a VARIETY of RECOGNIZABLE images (objects, people, animals, etc.)” and “[My child] can draw a NOVEL image following YOUR description (e.g. a three-headed horse),” consistently clustered with the Syntactic Mechanism (Supplementary Figures 14-31). Conversely, the non-figurative drawing ability, as reported by caregivers in response to the item “[My child] does drawing, coloring, art,” never clustered with the Syntactic Mechanism or any of the three expressive mechanisms. PCA consistently positioned this item closer to the Modifier Phenotype (Supplementary Figures 5-13B) and UHCA clustered it with the Modifier Mechanism in most strata (Supplementary Figures 5, 6, 7, 8, 10, 11, and 13A). These results indicate that non-figurative drawing typically emerges in individuals with the Modifier Phenotype. In three subpopulations, UHCA clustered this item with the Command Mechanism (Supplementary Figures 4, 9, 12A), possibly reflecting insufficient resolution of UHCA to detect clustering in smaller subgroups. Discussion When biological abnormalities slow-down or prevent full language attainment, receptive and expressive language development follows a distinct step-wise pattern. Receptive comprehension progresses through the Command → Modifier → Syntactic Mechanisms, while expressive language advances through Nonverbal → Single-Word → Single-Sentence → Multi-Sentence production. When development stalls, it typically does so at a full step rather than at an intermediate or half-step 48 . The results of this study indicate that drawing abilities are strongly associated with specific levels of language comprehension, rather than a stage in expressive language development. The presence of the Syntactic Mechanism is closely linked to figurative drawing: children who do not acquire the Syntactic Mechanism also typically fail to acquire figurative drawing ability. Likewise, the presence of the Modifier Mechanism is strongly associated with non-figurative drawing, and children who do not attain the Modifier Mechanism (i.e. retain the Command Phenotype) also usually do not acquire even non-figurative drawing skills. These results are consistent with data from neurotypical children. Neurotypical children typically acquire the Command Mechanism around age 2, the Modifier Mechanism around age 3, and the Syntactic Mechanism around age 4 years 49 . Their drawing abilities follow a parallel developmental patterns, with non-figurative scribbling emerging around age 3 and figurative “pre-schematic” drawings appearing by about age 4 50 . However, the associations between comprehension and drawing phenotypes are difficult to study in neurotypical populations because children pass through all language levels rapidly and are often introduced to drawing after they acquire the Syntactic Phenotype. In contrast, children with language impairments often remain at inferior levels for years 48 , providing an ideal population in which to examine the relationship between language mechanisms and drawing abilities. Implications for the evolution of human language Syntactic comprehension does not fossilize, making its evolutionary acquisition difficult to determine. Traditionally, it has been examined through the lens of symbolic thinking, which includes drawing ability. This study’s findings suggest that not all drawing abilities depend equally on the syntactic level: figurative drawing is strongly associated with the Syntactic Phenotype, whereas non-figurative drawing is primarily associated with the Modifier Phenotype. This pattern may help explain the clear temporal sequence observed in the archaeological record. Non-figurative motifs—such as parallel lines, dots, and hand stencils—represent the earliest known forms of human visual art, appearing as early as ∼400,000 years ago. In contrast, figurative art, depicting recognizable animals, humans, or scenes, emerges abruptly around 45,000 years ago. Our results suggest that this striking temporal dissociation may reflect the sequential evolutionary emergence of distinct neurocognitive mechanisms underlying the Modifier and the Syntactic Phenotypes. Limitations The main limitation of this study is its associational nature: a syntactic comprehension phenotype does not directly cause drawing abilities. Some individuals may possess the Syntactic Comprehension Phenotype but fail to exhibit it in figurative drawing due to limited cultural encouragement or fine-motor constraints. Conversely, others may lack the Syntactic Phenotype yet display some figurative drawing ability. Autistic savants are particularly notable in this regard, as their exceptional figurative drawing abilities often contrast sharply with markedly limited syntactic skills 51 , 52 . Importantly, the mechanisms supporting figurative drawing in autistic savants may differ from the mental combinatorial pre-planning typically used by neurotypical individuals. For instance, the contours of certain animals can become automatized through extensive practice. In such cases, drawing an animal resembles ‘writing a signature’—executed with minimal conscious planning, enabling drawing of the full outline of the animal in a single fluid motion. In our parent-report assessments, we attempted to mitigate the potential influence of automated drawing by emphasizing ‘VARIETY of animals’ in the item, “My child draws a VARIETY of RECOGNIZABLE images (objects, people, animals, etc.).” However, we cannot determine how many participants in our cohort may have reported the presence of an automatized drawing skill. Funding This research received no external funding. Data Availability De-identified raw data from this manuscript are available from the corresponding author upon reasonable request. Author contributions AV and EK designed the study. RV, EK, and AV analyzed the data. EM and AV wrote the paper. Competing Interests Authors declare no competing interests. Acknowledgements We wish to thank all participants’ caregivers who found time to complete children’s assessments. The language therapy app used to collect the data presented in this manuscript was made possible by the contributions of Rita Dunn, Alexander Faisman, Jonah Elgart, Lisa Lokshina, and Yulia Dumov. Footnotes New version focuses on the evolution of the figurative drawing ability References 1. ↵ Beyn , E. S. & Knyazeva , G. R . The problem of prosopagnosia . J. Neurol. Neurosurg. Psychiatry 25 , 154 ( 1962 ). 2. Levine , D. N. , Warach , J. & Farah , M . Two visual systems in mental imagery: Dissociation of “what” and “where” in imagery disorders due to bilateral posterior cerebral lesions . Neurology 35 , 1010 – 1010 ( 1985 ). OpenUrl CrossRef PubMed 3. ↵ Thomas , G. V. & Silk , A. M . An Introduction to the Psychology of Children’s Drawings . ( New York University Press , 1990 ). 4. ↵ Zilhão , J. et al. 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