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Visual Abilities and Exploration Behaviors as Predictors of Intelligence in Autistic Children from Preschool to School Age

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Title: Visual Abilities and Exploration Behaviors as Predictors of Intelligence in Autistic Children from Preschool to School Age
Language: English
Authors: Girard, Dominique (ORCID 0000-0001-7822-8724), Courchesne, Valérie (ORCID 0000-0001-7768-5448), Cimon-Paquet, Catherine, Jacques, Claudine (ORCID 0000-0001-6987-189X), Soulières, Isabelle (ORCID 0000-0002-0875-4101)
Source: Autism: The International Journal of Research and Practice. 2023 27(8):2446-2464.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 19
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Visual Perception, Preschool Children, Cognitive Ability, Verbal Communication, Communication Skills, Young Children, Correlation, Intelligence Quotient, Verbal Ability, Perceptual Development, Cognitive Development, Autism Spectrum Disorders, Child Behavior, Foreign Countries
Geographic Terms: Canada (Montreal)
Assessment and Survey Identifiers: Wechsler Preschool and Primary Scale of Intelligence, Raven Progressive Matrices, Autism Diagnostic Observation Schedule, Childrens Embedded Figures Test
DOI: 10.1177/13623613231166189
ISSN: 1362-3613
1461-7005
Abstract: The current prospective cohort study investigated whether early perceptual abilities, measured at preschool age, could predict later intellectual abilities at school age in a group of 41 autistic (9 girls, 32 boys) and 57 neurotypical children (29 girls, 28 boys). More than 80% of the autistic children were considered minimally verbal. Participants were assessed at three time points between the age of 2 and 8 years using the Wechsler Preschool and Primary Scales of Intelligence--Fourth Edition as a measure of full-scale IQ and the Raven's Colored Progressive Matrices as a measure of fluid reasoning abilities (Gf). The performance on two perceptual tests (Visual Search and Children Embedded Figures Test) and the frequency of early non-verbal behaviors served as predictors of later intellectual abilities. Early performance on perceptual tests measured at preschool age was positively related to later full-scale IQ in both autistic and neurotypical children. Furthermore, both early non-verbal behaviors and performance on perceptual tests measured at preschool age were associated with later Gf in the autistic group. In contrast, only the performance on Children Embedded Figures Test was associated with later Gf in the neurotypical group. Early perceptual abilities\and non-verbal behaviors may be indicators of general intelligence and Gf abilities.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1396764
Database: ERIC
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  Value: <anid>AN0172987522;f9d01nov.23;2023Oct17.05:42;v2.2.500</anid> <title id="AN0172987522-1">Visual abilities and exploration behaviors as predictors of intelligence in autistic children from preschool to school age </title> <p>The current prospective cohort study investigated whether early perceptual abilities, measured at preschool age, could predict later intellectual abilities at school age in a group of 41 autistic (9 girls, 32 boys) and 57 neurotypical children (29 girls, 28 boys). More than 80% of the autistic children were considered minimally verbal. Participants were assessed at three time points between the age of 2 and 8 years using the Wechsler Preschool and Primary Scales of Intelligence–Fourth Edition as a measure of full-scale IQ and the Raven's Colored Progressive Matrices as a measure of fluid reasoning abilities (Gf). The performance on two perceptual tests (Visual Search and Children Embedded Figures Test) and the frequency of early non-verbal behaviors served as predictors of later intellectual abilities. Early performance on perceptual tests measured at preschool age was positively related to later full-scale IQ in both autistic and neurotypical children. Furthermore, both early non-verbal behaviors and performance on perceptual tests measured at preschool age were associated with later Gf in the autistic group. In contrast, only the performance on Children Embedded Figures Test was associated with later Gf in the neurotypical group. Early perceptual abilities\and non-verbal behaviors may be indicators of general intelligence and Gf abilities. At the time of diagnosis, parents of autistic children frequently wonder what the future holds for their children in terms of intellectual development. It remains however difficult to answer this question at such a young age. Indeed, while early precursors of intelligence are well known for children following a typical development, these precursors remain to be identified for autistic children. Some theoretical models of intelligence suggest that perceptual abilities or behaviors, as seen early in autistic cognitive development, could be early indicators of intelligence. However, research examining the relation between early perceptual predictors and autistic intelligence over time is needed. This article is the first to consider a variety of early perceptual abilities and behaviors as precursors/predictors of intelligence at school age in autistic children. We showed that better performance in perceptual tasks at preschool age predicted better intellectual abilities measured later in autistic children. Importantly, our sample of autistic children represented the whole spectrum, including children with few to no spoken words, who are an important proportion of autistic preschoolers. While early perceptual abilities and behaviors may not substitute for a formal intellectual assessment, our results support that these indices may help estimate later intellectual level in autistic children. Perceptual abilities have the advantage to be easy to observe at preschool age and seem to fit the cognitive style of autistic children. Assessment methods could probably gain from including and focusing more on the perceptual strengths of autistic children.</p> <p>Keywords: autism; childhood; intelligence; fluid reasoning; perception; trajectory</p> <p>At time of diagnosis, one key question that parents of autistic children have is what the future holds in terms of cognitive development([<reflink idref="bib82" id="ref1">82</reflink>]). However, it remains difficult to answer this question given the considerable heterogeneity of cognitive development, particularly during the preschool years ([<reflink idref="bib33" id="ref2">33</reflink>]; [<reflink idref="bib62" id="ref3">62</reflink>], [<reflink idref="bib63" id="ref4">63</reflink>]; [<reflink idref="bib81" id="ref5">81</reflink>]). Previous findings indeed showed a reduction ([<reflink idref="bib7" id="ref6">7</reflink>]; [<reflink idref="bib93" id="ref7">93</reflink>]), no change ([<reflink idref="bib25" id="ref8">25</reflink>]; [<reflink idref="bib28" id="ref9">28</reflink>]; [<reflink idref="bib33" id="ref10">33</reflink>]; [<reflink idref="bib54" id="ref11">54</reflink>]) or increase in IQ from preschool to school age ([<reflink idref="bib33" id="ref12">33</reflink>]; [<reflink idref="bib63" id="ref13">63</reflink>]; [<reflink idref="bib81" id="ref14">81</reflink>]; [<reflink idref="bib98" id="ref15">98</reflink>]; [<reflink idref="bib103" id="ref16">103</reflink>]), with no prevailing pattern. The stability of IQ in autism, especially when assessed during preschool ([<reflink idref="bib28" id="ref17">28</reflink>]), is lower than what is expected in a neurotypical (NT) population ([<reflink idref="bib11" id="ref18">11</reflink>]; [<reflink idref="bib30" id="ref19">30</reflink>]; [<reflink idref="bib81" id="ref20">81</reflink>]).</p> <p>This heterogeneity in autistic preschoolers' IQ has been linked to various factors including compliance with the task, attentional capacities, and disruptive behaviors on the day of assessment, characterizing young children in general ([<reflink idref="bib1" id="ref21">1</reflink>]). Factors inherent to the autistic phenotype also need to be considered. For instance, an important proportion of autistic preschoolers are minimally or non-verbal ([<reflink idref="bib2" id="ref22">2</reflink>]; [<reflink idref="bib65" id="ref23">65</reflink>]; [<reflink idref="bib77" id="ref24">77</reflink>]; [<reflink idref="bib80" id="ref25">80</reflink>]; [<reflink idref="bib88" id="ref26">88</reflink>]; [<reflink idref="bib102" id="ref27">102</reflink>]; [<reflink idref="bib104" id="ref28">104</reflink>]) and will only develop language at school age ([<reflink idref="bib36" id="ref29">36</reflink>]), thus making the use of conventional IQ tests more difficult with this population ([<reflink idref="bib22" id="ref30">22</reflink>]; [<reflink idref="bib39" id="ref31">39</reflink>]). This is also true for non-verbal subscales of conventional IQ tests which require a certain level of receptive language skills to understand the instructions properly. In sum, it is sometimes challenging to assess autistic children, particularly at preschool age. <emph>Consequently, how can one reveal the intellectual potential and predict the cognitive development of these children at the age of diagnosis? Are there other measures than conventional IQ tests that could be used as a proxy of intellectual level in this population?</emph></p> <p>In NT children, the precursors of intelligence are well established and include language abilities, working memory, executive functions, and processing speed ([<reflink idref="bib14" id="ref32">14</reflink>]; [<reflink idref="bib20" id="ref33">20</reflink>]; [<reflink idref="bib24" id="ref34">24</reflink>]; [<reflink idref="bib32" id="ref35">32</reflink>]; [<reflink idref="bib34" id="ref36">34</reflink>]; [<reflink idref="bib38" id="ref37">38</reflink>]; [<reflink idref="bib44" id="ref38">44</reflink>], [<reflink idref="bib45" id="ref39">45</reflink>]; [<reflink idref="bib90" id="ref40">90</reflink>]; [<reflink idref="bib91" id="ref41">91</reflink>]; [<reflink idref="bib95" id="ref42">95</reflink>]; [<reflink idref="bib96" id="ref43">96</reflink>]). However, in autistic children, early intellectual assessment may not necessarily reflect the child's abilities and early precursors of intelligence remain to be clearly identified ([<reflink idref="bib1" id="ref44">1</reflink>]; [<reflink idref="bib27" id="ref45">27</reflink>]; [<reflink idref="bib33" id="ref46">33</reflink>]). In autism in general, it was proposed that perceptual abilities, which are specific abilities within IQ, play(s) a greater role in cognition ([<reflink idref="bib72" id="ref47">72</reflink>]; [<reflink idref="bib73" id="ref48">73</reflink>]). While some studies failed to find reliable group differences between autistic and NT individuals (see meta-analysis by [<reflink idref="bib99" id="ref49">99</reflink>], many reflected an increased performance of autistic individuals on various perceptual tasks (e.g. [<reflink idref="bib55" id="ref50">55</reflink>]; [<reflink idref="bib89" id="ref51">89</reflink>]; [<reflink idref="bib94" id="ref52">94</reflink>]), an ability notable as soon as preschool age ([<reflink idref="bib55" id="ref53">55</reflink>]; [<reflink idref="bib70" id="ref54">70</reflink>]; [<reflink idref="bib79" id="ref55">79</reflink>]). For example, autistic children have a faster response time in visual search tasks ([<reflink idref="bib17" id="ref56">17</reflink>]; [<reflink idref="bib40" id="ref57">40</reflink>]; [<reflink idref="bib55" id="ref58">55</reflink>]), as well as a faster detection time and accuracy in embedded figure tasks ([<reflink idref="bib53" id="ref59">53</reflink>]; [<reflink idref="bib66" id="ref60">66</reflink>]; [<reflink idref="bib79" id="ref61">79</reflink>]). Increased performance has also been documented in some complex non-verbal tasks, using perceptual material but requiring high level processing—assessing abilities to solve novel problems by inferring and integrating rules—such as Raven's Progressive Matrices ([<reflink idref="bib10" id="ref62">10</reflink>]; [<reflink idref="bib16" id="ref63">16</reflink>]; [<reflink idref="bib22" id="ref64">22</reflink>], [<reflink idref="bib21" id="ref65">21</reflink>]; [<reflink idref="bib23" id="ref66">23</reflink>]; [<reflink idref="bib47" id="ref67">47</reflink>]; [<reflink idref="bib76" id="ref68">76</reflink>]; [<reflink idref="bib94" id="ref69">94</reflink>]).</p> <p>Previous cross-sectional studies suggest that perceptual abilities are positively correlated to general intellectual abilities in both autistic and NT children and adults ([<reflink idref="bib4" id="ref70">4</reflink>]; [<reflink idref="bib21" id="ref71">21</reflink>]; [<reflink idref="bib50" id="ref72">50</reflink>]; [<reflink idref="bib68" id="ref73">68</reflink>]). Interestingly, perceptual and visuospatial abilities appear to be more strongly associated with non-verbal reasoning abilities than general intellectual abilities in autistic individuals. Moreover, the relation between perceptual skills and non-verbal reasoning abilities appears stronger in autistic individuals versus their NT peers ([<reflink idref="bib19" id="ref74">19</reflink>]; [<reflink idref="bib41" id="ref75">41</reflink>]; [<reflink idref="bib68" id="ref76">68</reflink>]). These findings may indicate that the precursors of intelligence (or their importance) are different in autistic versus NT children, with a greater role of perception in autistic cognitive development.</p> <p>Additional arguments for an increased role of perception in autistic cognition is the fact that some repetitive and restricted behaviors and interests (RRBIs), characteristic of the autism diagnosis, are perceptual by nature ([<reflink idref="bib58" id="ref77">58</reflink>]; [<reflink idref="bib73" id="ref78">73</reflink>]; [<reflink idref="bib105" id="ref79">105</reflink>]). For example, these behaviors include lateral glances, close glances, visual exploration of objects with perceptual characteristics (e.g. lights and rotating objects). Certain restricted interests also involve perceptual processes, such as interests in letters and numbers, mathematical algorithms, and calendars. These non-verbal behaviors are present early in the development and are more prevalent in the autistic population relative to NT population ([<reflink idref="bib58" id="ref80">58</reflink>]; [<reflink idref="bib74" id="ref81">74</reflink>]; [<reflink idref="bib78" id="ref82">78</reflink>]). It has been hypothesized that these early non-verbal behaviors may represent early explicit manifestations of perceptual strengths ([<reflink idref="bib4" id="ref83">4</reflink>]). For example, fast lateral gaze to objects and faces could be a way for autistic children to optimally capture information, while managing otherwise excessive amounts of sensory input ([<reflink idref="bib55" id="ref84">55</reflink>]; [<reflink idref="bib74" id="ref85">74</reflink>]). Some studies formulated the hypothesis of an association between these early non-verbal behaviors and non-verbal IQ in autism ([<reflink idref="bib58" id="ref86">58</reflink>]; [<reflink idref="bib73" id="ref87">73</reflink>]; [<reflink idref="bib105" id="ref88">105</reflink>]), but no study did verify this specific association to our knowledge.</p> <p>Little longitudinal work has formally examined the predictive role of perceptual abilities and behaviors on developmental patterns of change in IQ from preschool to school age in autism. Considering the challenges inherent to conventional intellectual assessment, early perceptual predictors may be useful in estimating intellectual potential among young autistic children when traditional tests cannot be used. Indeed, perceptual abilities have the advantage to be relatively easily observed at preschool age and seem to fit the cognitive style of autistic individuals. While perceptual abilities do not constitute a proper measure of intelligence, and thus cannot directly substitute for it, using these abilities to better predict the intellectual potential of autistic children at the age of diagnosis is an avenue worth exploring.</p> <hd id="AN0172987522-2">Objectives</hd> <p>Our main objectives were to explore (a) whether some perceptual abilities, and non-verbal behaviors and interests measured at preschool age could predict level and trajectory in intelligence at school age, and (b) whether these perceptual predictors are specific to autism or shared with the NT group.</p> <hd id="AN0172987522-3">Methods</hd> <p>This study was formally reviewed and approved by the research ethics committee of Rivière-des-Prairies Hospital (Montreal, Canada). Informed written and verbal consent was obtained from parents prior to participation at each time point.</p> <p>This study is part of a larger ongoing longitudinal study ongoing at the Rivière-des-Prairies Hospital which globally aimed at optimizing the assessment of intellectual potential in preschool autistic children by characterizing their cognitive and adaptive profiles. The pool of participants available for the larger research project includes a cohort of children aged 2 to 5 years with an autism spectrum (AS) diagnosis or with typical development. These children were followed longitudinally in three phases, each spaced 1 year apart, during which various cognitive, adaptive, developmental, and perceptual measures were administered (described in [<reflink idref="bib21" id="ref89">21</reflink>]; [<reflink idref="bib39" id="ref90">39</reflink>]; [<reflink idref="bib52" id="ref91">52</reflink>]). This allowed following the correlates and trajectories of intellectual development of these children over time.</p> <p>As this study was part of a doctoral thesis, the main objectives and a global analysis plan were preregistered internally at the university. There was no public preregistration of this study.</p> <hd id="AN0172987522-4">Participants</hd> <p>Families of children aged under 71 months who received an AS diagnosis at the specialized assessment clinic at Rivière-des-Prairies Hospital between January 2014 and February 2020 were invited to participate in this study. Exclusion criteria for this group included having an identified associated genetic disorder or having an important motor delay (equivalent age < 18 months) susceptible to interfere with test administration. AS diagnosis was based on gold standard instruments and expert clinician judgment. Of the 41 autistic children (9 girls, 32 boys), 34 were assessed using Toddler Module or Module 1 of the ADOS-2 (<emph>n</emph> = 29; [<reflink idref="bib61" id="ref92">61</reflink>]) or ADOS-G (<emph>n</emph> = 5; [<reflink idref="bib60" id="ref93">60</reflink>]) and could produce at the most two-word phrases. Two children were assessed using Module 2 of ADOS-2 and used phrased speech at time of their diagnosis. Five children received an AS diagnosis based on clinical judgment.</p> <p>NT participants were recruited in daycare centers of the same geographic area. The NT group included children without any developmental or neurological condition and with no AS diagnosis in siblings. It was not possible to match them on IQ level with the autistic group, as we wanted to include autistic children representative of the whole spectrum (including autistic children with lower measured IQ and language delay). The NT group allowed verifying how the different abilities measured by perceptual tests and conventional tests are interrelated in a typically developing sample, and contrasting these results with those of the autistic group. A total of 57 NT children were included in this study (29 girls, 28 boys). Participants' characteristics are presented in Table 1.</p> <p>Graph</p> <p>Table 1. Children and families sociodemographic characteristics (n = 98: 38 girls, 60 boys).</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Characteristics</th><th align="left" colspan="2"><italic>n</italic></th><th align="left" colspan="2">%</th></tr></thead><tbody><tr><td colspan="5">Diagnostic group</td></tr><tr><td> Autistic</td><td colspan="2">41</td><td colspan="2">42</td></tr><tr><td> NT</td><td colspan="2">57</td><td colspan="2">58</td></tr><tr><td>Annual income (US$)</td><td /><td /><td /><td /></tr><tr><td> 0–29,999</td><td colspan="2">17</td><td colspan="2">17</td></tr><tr><td> 30,000–49,999</td><td colspan="2">14</td><td colspan="2">14</td></tr><tr><td> 50,000–69,999</td><td colspan="2">10</td><td colspan="2">10</td></tr><tr><td> 70,000–89,999</td><td colspan="2">13</td><td colspan="2">13</td></tr><tr><td> 90,000–119,999</td><td colspan="2">8</td><td colspan="2">8</td></tr><tr><td> 120,000+</td><td colspan="2">23</td><td colspan="2">24</td></tr><tr><td> Missing</td><td colspan="2">13</td><td colspan="2">13</td></tr><tr><th /><th align="left" colspan="2">Mothers</th><th align="left" colspan="2">Fathers</th></tr><tr><th /><th align="left"><italic>n</italic></th><th align="left">%</th><th align="left"><italic>n</italic></th><th align="left">%</th></tr><tr><td colspan="5">Parental level of education</td></tr><tr><td> High school not completed</td><td>4</td><td>4</td><td>2</td><td>2</td></tr><tr><td> High school</td><td>11</td><td>11</td><td>13</td><td>13</td></tr><tr><td> College<xref ref-type="table-fn" rid="tfn2">a</xref></td><td>17</td><td>17</td><td>18</td><td>18</td></tr><tr><td> Undergraduate studies</td><td>28</td><td>29</td><td>32</td><td>33</td></tr><tr><td> Graduate studies</td><td>19</td><td>17</td><td>13</td><td>13</td></tr><tr><td> Postdoctoral fellowship</td><td>3</td><td>3</td><td>2</td><td>2</td></tr><tr><td> Missing</td><td>16</td><td>16</td><td>18</td><td>18</td></tr><tr><td colspan="5">Parental ethnicity</td></tr><tr><td> Asian</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td> Black</td><td>11</td><td>11</td><td>11</td><td>11</td></tr><tr><td> Latina</td><td>4</td><td>4</td><td>3</td><td>3</td></tr><tr><td> Middle Eastern countries</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td> White</td><td>51</td><td>53</td><td>50</td><td>51</td></tr><tr><td> Missing</td><td>13</td><td>13</td><td>15</td><td>15</td></tr><tr><th /><th align="left" colspan="2">Autistic</th><th align="left" colspan="2">NT</th></tr><tr><th /><th align="left" colspan="2"><italic>n</italic></th><th align="left" colspan="2"><italic>n</italic></th></tr><tr><td colspan="5">No. of children assessed by age<xref ref-type="table-fn" rid="tfn3">b</xref></td></tr><tr><td> 2</td><td colspan="2">1</td><td colspan="2">1</td></tr><tr><td> 3</td><td colspan="2">9</td><td colspan="2">14</td></tr><tr><td> 4</td><td colspan="2">21</td><td colspan="2">41</td></tr><tr><td> 5</td><td colspan="2">30</td><td colspan="2">34</td></tr><tr><td> 6</td><td colspan="2">14</td><td colspan="2">23</td></tr><tr><td> 7</td><td colspan="2">10</td><td colspan="2">8</td></tr><tr><td> 8</td><td colspan="2">2</td><td colspan="2">2</td></tr></tbody></table> </ephtml> </p> <p>1 NT: neurotypical.</p> <ulist> <item>2 Colleges are general and vocational educational institutions that grant 2- or 3-year postsecondary degrees preparing students for university-level education in Quebec.</item> <item>3 Age in years, notwithstanding the time point of the project at which the assessment was done.</item> </ulist> <hd id="AN0172987522-5">Measures</hd> <p></p> <hd id="AN0172987522-6">Full-scale IQ (FSIQ)</hd> <p>The Wechsler Preschool and Primary Scales of Intelligence–Fourth Edition (WPPSI-IV: [<reflink idref="bib101" id="ref94">101</reflink>]) is a widely used measure of general intelligence in both clinical and research settings. It is normed for children aged 2 years 7 months to 7 years 7 months, with a version designed for children under 4 and one for children of 4 years and older. These two versions include, respectively, 5 (Receptive Vocabulary, Information, Block Design, Object Assembly, Picture Memory) and 6 (Information, Similarities, Block Design, Matrix Reasoning, Picture Memory, Bug Search) core subtests allowing the computation of a FSIQ score in percentiles.</p> <hd id="AN0172987522-7">Fluid reasoning abilities (Gf)</hd> <p>The Board Form of the Raven's Colored Progressive Matrices (RCPM: [<reflink idref="bib83" id="ref95">83</reflink>]) was used to measure Gf. Raven's Matrices are among the most commonly used cognitive assessments in research studies ([<reflink idref="bib56" id="ref96">56</reflink>]) as this test uses non-verbal material and is relatively independent of culture. The RCPM includes three sets of 12 items (A, Ab, B) of increasing difficulty and complexity within and across sets. Each item presents a pattern or a 2 × 2 matrix that the child must complete by choosing which of the six movable pieces best completes the matrix. The Netherlands norms, from 3 years and 9 months to 10 years and 2 months, were used to derive percentiles from raw scores obtained by participants.</p> <hd id="AN0172987522-8">Perceptual abilities</hd> <p>Perceptual abilities were assessed using two different tests requiring selective visual attention: the Visual Search (VS) Task and the Children Embedded Figures Test (CEFT). The two tasks also recruit distinct cognitive processes, as VS involves processing speed abilities more specifically whereas CEFT involves disembedding abilities ([<reflink idref="bib69" id="ref97">69</reflink>]).</p> <hd id="AN0172987522-9">VS task</hd> <p>The VS task was the same as the one in [<reflink idref="bib21" id="ref98">21</reflink>]. Children were asked to find a target letter among sets of 5, 15, 25, 50 or 75 distracters. There were two conditions: (a) the feature condition, in which the target letter differed from distracters in shape (e.g. a red T hidden among red Xs and green Ss), and (b) the conjunction condition in which the target had either the color or the shape in common with the distracters, and thus, only the conjunction of attributes defined the target (e.g. a red X hidden among red Ts and green Xs). Each combination of number of distracters (<reflink idref="bib5" id="ref99">5</reflink>) and condition (<reflink idref="bib2" id="ref100">2</reflink>) was presented six times for a total of 60 trials. Each stimulus (i.e. target among distracters) was printed out on 28 × 21.5 cm plasticized card. Three different target letters were used in the task, and each was printed on thick plasticized cardboard (3 × 2.4 cm), so the children could manipulate it and answer by placing it over the corresponding target letter on the stimulus. The time (in seconds) required to find the target was used as a measure of performance. The number of correct answers was not used as there was a ceiling effect on this test.</p> <hd id="AN0172987522-10">Children Embedded Figures Test</hd> <p>The CEFT ([<reflink idref="bib57" id="ref101">57</reflink>]) involves finding a target shape camouflaged within a larger design with semantic meaning. The CEFT is made up of 14 practice trials and 25 test trials. To minimize verbal instructions, as it was done in previous studies ([<reflink idref="bib22" id="ref102">22</reflink>], [<reflink idref="bib21" id="ref103">21</reflink>]), we removed the instruction not to rotate the target shape, which is normally part of the test instructions. We used the number of correct answers on the test, but not response time as it was only recorded for successful items.</p> <hd id="AN0172987522-11">Perceptual repetitive behaviors and interests</hd> <p>Perceptual repetitive behaviors and interests were measured using the Montreal Stimulating Play Situation—revised version ([<reflink idref="bib52" id="ref104">52</reflink>]). This standardized play situation is videotaped and lasts approximately 30 min. About 40 toys specifically chosen for their perceptual properties (e.g. toys with lights, musical toys, rotating toys) were displayed in a playroom or presented to the child by an experimenter. Undergraduate students were trained over multiple sessions to code repetitive behaviors (e.g. lining up objects) using Observer XT 11 (Noldus Information Technology Inc.) until they reached a percentage of agreement of 90%. Each repetitive behavior was defined in a repertoire, so that each instance could be easily coded. In the context of this study, only the early non-verbal behaviors described below were considered in the analysis.</p> <hd id="AN0172987522-12">Early non-verbal behaviors</hd> <p>Early non-verbal behaviors were defined as repetitive behaviors that were atypical by their nature (e.g. lateral glances at objects) or by their intensity (e.g. lining up objects) and had a perceptual component. A score was calculated for each participant by doing the sum of the frequency of the following: grouping objects based on their perceptual characteristics, lining up objects, writing with plastic letters on board, close gaze at objects, lateral glances at objects, and obstructed gaze at object. Scores were then divided by the total duration of the Montreal Stimulating Play Situation and multiplied by 3600 s. The resulting score therefore represented the number of times the child did early non-verbal behaviors per hour.</p> <hd id="AN0172987522-13">Covariates</hd> <p>In addition to the child's age at T1, sex, and group, family socioeconomic status (SES) was computed. Standardized scores (<emph>Z</emph>-scores) of maternal and paternal years of education, and family income were averaged to create a family SES index.</p> <hd id="AN0172987522-14">Community involvement statement</hd> <p>Parents of autistic children and practitioners from Rivière-des-Prairies Hospital were involved in discussions for priority-setting surrounding the broader longitudinal research project. There was however no community involvement in the specific analyses reported here.</p> <hd id="AN0172987522-15">Procedure</hd> <p>This longitudinal study included three time points. The first time point was at the age of diagnosis during preschool (T1; <emph>M</emph> = 53.38 months, <emph>SD</emph> = 9.53, Range = 26.67–70.00). The second and third time points took place approximately 1 year, (T2; <emph>M</emph> = 67.86 months, <emph>SD</emph> = 10.72, Range = 41.00–98.00) and 2 years later (T3; <emph>M</emph> = 79.60 months, <emph>SD</emph> = 10.28, Range = 57.50–107.00). During the first time point, participants were exposed to the Montreal Stimulating Play Situation designed to elicit restricted and repetitive behaviors in preschool children. Across all time points, children also had to complete a variety of tasks measuring FSIQ and Gf levels as well as perceptual skills.</p> <p>Among our sample of 98 children, 89 completed the FSIQ assessment at Time 1 (T1), 64 at Time 2 (T2), and 41 at Time 3 (T3). Also, 78 children completed the Gf assessment at T1, 65 at T2, and 45 at T3. In all, the 98 children of our sample had available data on at least one of the FSIQ or Gf assessment points (i.e. T1, T2 or T3; see Table S1 for information on missing data).</p> <hd id="AN0172987522-16">Preliminary analyses</hd> <p>Attrition analyses suggested that the number of missing data was not associated with family SES, group (i.e. autistic or NT) or performance on perceptual predictors (VS time, CEFT score and early non-verbal behaviors), all <emph>p</emph>s > 0.05. However, child's age at T1 was significantly associated with the number of missing data, <emph>r</emph> =.23, <emph>p</emph> = 0.02, such that children who were older at T1 had more missing data. Missing data are considered missing at random when other observed variables are associated with the probability of missingness ([<reflink idref="bib29" id="ref105">29</reflink>]), as it is the case in our study. Consequently, missing data were handled using the robust full-information maximum likelihood (MLR) estimator, as per current best practices, which allows the estimation of model parameters using all available data and increases statistical power ([<reflink idref="bib51" id="ref106">51</reflink>]; [<reflink idref="bib86" id="ref107">86</reflink>]).</p> <hd id="AN0172987522-17">Analytic strategy</hd> <p>To describe intraindividual trajectories of children's FSIQ and Gf levels over time, multilevel growth curves analyses were conducted using Mplus ([<reflink idref="bib75" id="ref108">75</reflink>]). As opposed to structural equation modeling framework, multilevel modeling (MLM) framework can easily handle partially missing data, unequally spaced time points, and data collected across a range of ages within a particular measure point ([<reflink idref="bib13" id="ref109">13</reflink>]; [<reflink idref="bib51" id="ref110">51</reflink>]; [<reflink idref="bib92" id="ref111">92</reflink>]). Simulation studies confirm that MLM is robust for models with missing data, and for inclusion of participants with incomplete data in final models ([<reflink idref="bib18" id="ref112">18</reflink>]; [<reflink idref="bib29" id="ref113">29</reflink>]; [<reflink idref="bib42" id="ref114">42</reflink>]). Using MLM also allows for the exploration of intraindividual change over time (level 1; within-subject) as well as inter-individual differences in intercept and slopes (level 2; between-subjects: [<reflink idref="bib48" id="ref115">48</reflink>]). Furthermore, it allows examining the links between variables of interests and between-subjects' differences in both intercept and slope. Using MLM, adequate statistical power is achieved with as few as 30–50 level 2 units (i.e. 30–50 children: [<reflink idref="bib13" id="ref116">13</reflink>]). All these attributes make MLM particularly well suited to the methodological design of our study.</p> <hd id="AN0172987522-18">Modeling trajectories of FSIQ and Gf over time</hd> <p>Intraindividual trajectories of FSIQ and Gf level over time were first modeled at level 1 (within-person change over time) and differences between children were then examined at level 2 (between-person change over time). Two unconditional models were specified to ascertain the best-fitting trajectory models in FSIQ and Gf levels. The Model A (i.e. fixed linear model) included the fixed effect of children exact age in years, coded such that the intercept represented average FSIQ level or Gf level at 5 years (representing school entry in Quebec) and the slope represented the average yearly change in FSIQ or Gf level. The Model B (random linear model) included the random effect of time (i.e. between-subjects variability in individual intercepts and slopes). Using children's exact age enabled us to flexibly handle individually varying time scores and to estimate the trajectory in child FSIQ and Gf levels from 2 to 8 years.</p> <p>The log likelihood (an indicator of deviance) and the Akaike information criterion were used to assess goodness of fit. Lower values indicated better representation of the data by the model ([<reflink idref="bib43" id="ref117">43</reflink>]). The random effects were retained if the model's log likelihood (LL) was significantly lower or remained the same with the addition of the random terms, based on an adjusted chi-square difference test (i.e. adapted to the MLR estimator), or if the model's Akaike information criterion was lowered with the addition of the random terms.</p> <p>Finally, all continuous predictors were centered at the grand mean so that the intercept represents the estimated initial status (baseline level) for individuals with an average value on each predictor.</p> <hd id="AN0172987522-19">Predicting trajectories of FSIQ and Gf levels over time</hd> <p>After modeling both trajectories of FSIQ and Gf, a preliminary condition model was tested, including the effects of the potential covariates (i.e. child's age at T1, family SES, and sex) on FSIQ and Gf trajectories. Only the covariates significantly associated with the slope, the intercept, or with missing data were deemed relevant for our analyses and retained in the final models. Child's age at T1 was included in all final models as it was associated with missing data, as mentioned above. Only these final models were retained to increase parsimony, maximize statistical power, and to reduce the noise that may be caused by the high number of covariates included in the preliminary models ([<reflink idref="bib59" id="ref118">59</reflink>]).</p> <hd id="AN0172987522-20">Determining whether the predictors of trajectories of FSIQ and Gf levels are the same in both...</hd> <p>Group was included in the final models because our second objective was to examine whether the same variables predict the slope and intercept in autistic and NT children.</p> <hd id="AN0172987522-21">Final predictive models</hd> <p>Final predictive models, including the retained covariates, were estimated for each main predictor (i.e. VS time, CEFT score and early non-verbal behaviors).</p> <hd id="AN0172987522-22">Results</hd> <p></p> <hd id="AN0172987522-23">Preliminary analyses</hd> <p>Table 2 displays the descriptive statistics for all continuous variables. All variables were normally distributed (skewness < 2.0; kurtosis < 7.0), except for Gf at T2 in the autistic group and Gf at T3 in the NT group, which showed high skewness and kurtosis. MLR estimation was used, as it is robust to non-normality ([<reflink idref="bib29" id="ref119">29</reflink>]; [<reflink idref="bib51" id="ref120">51</reflink>]).</p> <p>Graph</p> <p>Table 2. Descriptive statistics and p -values for intergroup differences.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" colspan="5">Autistics (9F, 32M)</th><th align="left" colspan="5">NT (29F, 28M)</th><th /></tr><tr><th /><th align="left"><italic>n</italic></th><th align="left"><italic>M</italic> (<italic>SD</italic>)</th><th align="left">Range</th><th align="left">SK</th><th align="left">Kr</th><th align="left"><italic>n</italic></th><th align="left"><italic>M</italic> (<italic>SD</italic>)</th><th align="left">Range</th><th align="left">SK</th><th align="left">Kr</th><th align="left"><italic>p</italic></th></tr></thead><tbody><tr><td>Visual Search<xref ref-type="table-fn" rid="tfn5">a</xref></td><td>38</td><td>9.78 (5.72)</td><td>2.95 –30.65</td><td>1.50</td><td>3.27</td><td>56</td><td>6.08 (2.84)</td><td>2.08–13.90</td><td>0.73</td><td>−0.29</td><td><.001</td></tr><tr><td>CEFT Score<xref ref-type="table-fn" rid="tfn6">b</xref></td><td>36</td><td>12.19 (4.50)</td><td>4.00–20.00</td><td>−0.04</td><td>−0.76</td><td>56</td><td>11.91 (5.29)</td><td>2.00–24.00</td><td>0.44</td><td>−0.37</td><td>.790</td></tr><tr><td>NV behav<xref ref-type="table-fn" rid="tfn7">c</xref></td><td>36</td><td>38.89 (26.91)</td><td>0.00–128.26</td><td>1.75</td><td>3.91</td><td>44</td><td>32.94 (19.68)</td><td>7.57–83.45</td><td>1.07</td><td>0.41</td><td>.258</td></tr><tr><td>FSIQ (T1)<xref ref-type="table-fn" rid="tfn8">d</xref></td><td>33</td><td>27.89 (29.37)</td><td>0.05–93.00</td><td>0.98</td><td>−0.30</td><td>56</td><td>73.21 (23.59)</td><td>10.00–99.00</td><td>−0.96</td><td>0.16</td><td><.001</td></tr><tr><td>FSIQ (T2)<xref ref-type="table-fn" rid="tfn8">d</xref></td><td>26</td><td>31.93 (32.35)</td><td>0.10–97.00</td><td>0.64</td><td>−1.19</td><td>38</td><td>70.68 (20.25)</td><td>25.00–99.00</td><td>−0.47</td><td>−0.78</td><td><.001</td></tr><tr><td>FSIQ (T3)<xref ref-type="table-fn" rid="tfn8">d</xref></td><td>15</td><td>24.81 (30.96)</td><td>0.20–88.00</td><td>1.30</td><td>0.41</td><td>26</td><td>76.46 (20.23)</td><td>25.00–98.00</td><td>−1.12</td><td>0.74</td><td><.001</td></tr><tr><td>Gf (T1)<xref ref-type="table-fn" rid="tfn8">d</xref></td><td>31</td><td>82.11 (30.74)</td><td>1.00–100.00</td><td>−1.83</td><td>1.84</td><td>47</td><td>92.22 (8.84)</td><td>66.70–99.50</td><td>−1.52</td><td>1.55</td><td>.084</td></tr><tr><td>Gf (T2)<xref ref-type="table-fn" rid="tfn8">d</xref></td><td>28</td><td>90.75 (14.11)</td><td>25.00–99.00</td><td>−4.11</td><td>18.64</td><td>37</td><td>91.19 (12.73)</td><td>50.00–100.00</td><td>−2.05</td><td>3.59</td><td>.897</td></tr><tr><td>Gf (T3)<xref ref-type="table-fn" rid="tfn8">d</xref></td><td>17</td><td>80.87 (29.19)</td><td>2.50–95.50</td><td>−2.04</td><td>3.00</td><td>28</td><td>93.24 (8.04)</td><td>58.30–99.00</td><td>−3.69</td><td>14.36</td><td>.105</td></tr><tr><td>Age at T1</td><td>41</td><td>54.77 (10.35)</td><td>26.67–70.00</td><td>−0.52</td><td>−0.12</td><td>57</td><td>52.37 (8.85)</td><td>30.00–69.60</td><td>−0.06</td><td>−0.38</td><td>.222</td></tr><tr><td>Family SES</td><td>34</td><td>−0.43 (0.62)</td><td>−1.69 to 0.61</td><td>−0.45</td><td>−0.43</td><td>52</td><td>0.24 (0.73)</td><td>−1.33 to 2.15</td><td>0.01</td><td>0.33</td><td><.001</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 SK: skewness; Kr: kurtosis; NT: neurotypical; SD: standard deviation; CEFT: Children Embedded Figures Test; NV behav: Early non-verbal behaviors; FSIQ: full-scale IQ; Gf: fluid reasoning abilities; SES: socioeconomic status.</item> <item>5 Time (s).</item> <item>6 Raw scores.</item> <item>7 Early non-verbal behaviors (number of behaviors/hour).</item> <item>8 Percentiles.</item> </ulist> <p>Zero-order correlations among covariates (i.e. child's age at T1, family SES, sex, and group) and main variables (i.e. VS time, CEFT score, and early non-verbal behaviors) are shown in Table S2.</p> <hd id="AN0172987522-24">Main analyses</hd> <p></p> <hd id="AN0172987522-25">Trajectory models of FSIQ level</hd> <p>An adjusted chi-square difference test using the model's log likelihood revealed that a random linear model (Model B) was not significantly different from a fixed linear model (Model A: see Table 3), χ<sups>2</sups>(<reflink idref="bib2" id="ref121">2</reflink>) = 0.37, <emph>p</emph> = 0.831. As described in the analytic strategy, Model B was retained as the fit was not significantly worse than model A. Children started with an average percentile score of 53.75 at 5 years (γ<subs>00</subs>), and it remained relatively stable over time as children's FSIQ level had a small non-significant decrease of 1.93 percentiles per year (γ<subs>10</subs>). The covariance between the slope and intercept was not significant, which indicates that children who had a higher FSIQ level at 5 years did not show a faster or slower decrease between 5 and 8 years than those who had lower FSIQ level at baseline.</p> <p>Graph</p> <p>Table 3. Trajectory models of FSIQ level.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" colspan="3">FSIQ level (ICC = 0.80)</th></tr><tr><th /><th align="left">Par</th><th align="left">Model A</th><th align="left">Model B</th></tr></thead><tbody><tr><td>Intercept-initial status (5 years)</td><td>γ<sub>00</sub></td><td>53.64 (3.26)<xref ref-type="table-fn" rid="tfn10">***</xref></td><td>53.75 (3.29)<xref ref-type="table-fn" rid="tfn10">***</xref></td></tr><tr><td>Linear slope (yearly change)</td><td>γ<sub>10</sub></td><td>−1.95 (1.26)</td><td>−1.93 (1.32)</td></tr><tr><td>Within-person variance (residual)</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mi>E</mi><mn>2</mn></msubsup></mrow></math></p></td><td>226.66 (47.14)<xref ref-type="table-fn" rid="tfn10">***</xref></td><td>209.43 (49.31)<xref ref-type="table-fn" rid="tfn10">***</xref></td></tr><tr><td>Variance in initial status</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>0</mn><mn>2</mn></msubsup></mrow></math></p></td><td>891.22 (101.82)<xref ref-type="table-fn" rid="tfn10">***</xref></td><td>891.02 (109.81)<xref ref-type="table-fn" rid="tfn10">***</xref></td></tr><tr><td>Variance in rate of change</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>1</mn><mn>2</mn></msubsup></mrow></math></p></td><td>−</td><td>12.47 (35.44)</td></tr><tr><td>Slope intercept covariance</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msub><mi>σ</mi><mrow><mn>01</mn></mrow></msub></mrow></math></p></td><td>−</td><td>5.34 (25.52)</td></tr><tr><td>Goodness-of-fit</td><td>LL</td><td>−899.40</td><td>−899.22</td></tr><tr><td /><td>AIC</td><td>1806.79</td><td>1810.43</td></tr></tbody></table> </ephtml> </p> <ulist> <item>9 FSIQ: full-scale IQ; ICC: intraclass correlation; Par: Parameters; LL: log likelihood; AIC: Akaike information criterion.</item> <item>10 Standard errors are within parentheses. Model A: fixed linear model; Model B: random linear model *** <emph>p</emph> <.001.</item> </ulist> <hd id="AN0172987522-26">VS time 1 , CEFT score, 2 and early non-verbal behaviors as predictors of FSIQ level</hd> <p>A preliminary conditional model assessed the links between potential covariates (i.e. child's age at T1, family SES, sex, and group) and FSIQ level trajectory parameters (i.e. between-subjects variability in the intercept and slope). This model revealed that family SES (γ<subs>02</subs> = 12.21, <emph>p</emph> < 0.001) and group (γ<subs>03</subs> = 53.62, <emph>p</emph> < 0.001) were significantly related to the intercept. The final model included the relevant covariates (i.e. child's age at T1, family SES, and group), each of the perceptual predictors (i.e. VS time, CEFT score, and early non-verbal behaviors) and the interaction terms between the group and the selected predictor (see Table 4).</p> <p>Graph</p> <p>Table 4. Final model FSIQ trajectory with predictors.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" rowspan="3">Par</th><th align="left" colspan="3">FSIQ level</th></tr><tr><th /><th align="left">Pred 1: VS time</th><th align="left">Pred 2: CEFT score</th><th align="left">Pred 3: NV behav</th></tr><tr><th /><th align="left"><italic>B</italic> (<italic>SE</italic>)</th><th align="left"><italic>B</italic> (<italic>SE</italic>)</th><th align="left"><italic>B</italic> (<italic>SE</italic>)</th></tr></thead><tbody><tr><td colspan="5">Initial status, π<sub>oi</sub></td></tr><tr><td> Intercept (5 years)</td><td>γ<sub>00</sub></td><td>54.68 (2.49)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>53.93 (2.95)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>55.51 (2.95)<xref ref-type="table-fn" rid="tfn13">***</xref></td></tr><tr><td> Age at T1</td><td>γ<sub>01</sub></td><td>−10.24 (2.40)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>−6.70 (2.96)<xref ref-type="table-fn" rid="tfn13">*</xref></td><td>−2.07 (2.90)</td></tr><tr><td> SES</td><td>γ<sub>02</sub></td><td>6.64 (2.68)<xref ref-type="table-fn" rid="tfn13">*</xref></td><td>4.84 (2.98)</td><td>4.53 (3.23)</td></tr><tr><td> Group</td><td>γ<sub>03</sub></td><td>23.67 (6.92)<xref ref-type="table-fn" rid="tfn13">**</xref></td><td>38.41 (7.10)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>38.09 (7.13)<xref ref-type="table-fn" rid="tfn13">***</xref></td></tr><tr><td> Predictor</td><td>γ<sub>04</sub></td><td>−14.61 (2.91)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>5.56 (2.83)<xref ref-type="table-fn" rid="tfn13">*</xref></td><td>−2.66 (2.98)</td></tr><tr><td> Interaction (Group × Pred)</td><td>γ<sub>05</sub></td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td colspan="5">Rate of change</td></tr><tr><td> Child age</td><td>γ<sub>10</sub></td><td>−1.82 (1.50)</td><td>−1.46 (1.62)</td><td>−1.62 (1.65)</td></tr><tr><td> Age at T1</td><td>γ<sub>11</sub></td><td>−1.98 (1.89)</td><td>−0.92 (2.28)</td><td>−2.81 (2.13)</td></tr><tr><td> SES</td><td>γ<sub>12</sub></td><td>−1.72 (1.57)</td><td>0.20 (1.55)</td><td>0.44 (1.53)</td></tr><tr><td> Group</td><td>γ<sub>13</sub></td><td>3.55 (3.84)</td><td>0.77 (3.47)</td><td>−1.01 (3.52)</td></tr><tr><td> Predictor</td><td>γ<sub>14</sub></td><td>−0.12 (1.73)</td><td>−2.07 (1.59)</td><td>−2.08 (1.07)</td></tr><tr><td> Interaction (Group × Pred)</td><td>γ<sub>15</sub></td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>Within-person variance</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mi>E</mi><mn>2</mn></msubsup></mrow></math></p></td><td>219.38 (55.23)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>202.90 (55.77)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>213.84 (59.87)<xref ref-type="table-fn" rid="tfn13">***</xref></td></tr><tr><td>Variance in initial status</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>0</mn><mn>2</mn></msubsup></mrow></math></p></td><td>236.13 (62.54)<xref ref-type="table-fn" rid="tfn13">*</xref></td><td>362.39 (84.42)<xref ref-type="table-fn" rid="tfn13">***</xref></td><td>384.50 (91.95)<xref ref-type="table-fn" rid="tfn13">***</xref></td></tr><tr><td>Variance in rate of change</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>1</mn><mn>2</mn></msubsup></mrow></math></p></td><td>9.26 (29.28)</td><td>9.83 (30.24)</td><td>11.55 (32.87)</td></tr><tr><td>Slope intercept covariance</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msub><mi>σ</mi><mrow><mn>01</mn></mrow></msub></mrow></math></p></td><td>−12.42 (24.45)</td><td>−2.66 (31.99)</td><td>−11.38 (35.97)</td></tr><tr><td>Goodness-of-fit</td><td>LL</td><td>−743.66</td><td>−730.49</td><td>−679.79</td></tr><tr><td /><td>AIC</td><td>1515.31</td><td>1488.99</td><td>1387.58</td></tr></tbody></table> </ephtml> </p> <ulist> <item>11 FSIQ: Full-Scale IQ; AIC: Akaike information criterion; CEFT: Children Embedded Figure Test; LL: log likelihood; Par: parameters; NV behave: early non-verbal behaviors; Pred: predictor; SE: standard errors; SES: socioeconomic status; VS: Visual Search.</item> <item>12 All predictors are centered at their grand mean.</item> <item>13 <emph>p</emph> < 0.05; **<emph>p</emph> < 0.01; ***<emph>p</emph> < 0.001.</item> </ulist> <p>Across all models, it was found that NT children had generally better FSIQ performance compared to autistic children (all <emph>p</emph>s < 0.01).</p> <hd id="AN0172987522-27">VS time</hd> <p>The interaction term (group × VS time) was not significantly associated with the FSIQ intercept or slope, and was therefore removed from the final model. The VS time, measured between 2 and 5 years, was not related to the slope, but it was significantly and negatively associated with the intercept (i.e. FSIQ at 5 years), above and beyond the child age at T1, family SES, and group. These results show that, in both autistic and NT groups, children who found the targets more quickly on VS had a higher FSIQ level at 5 years and that they consistently had a higher score than their peers over time (see Figure 1).</p> <p>Graph: Figure 1. Yearly change in FSIQ level (percentiles) according to response time (s) on Visual Search in the whole sample.</p> <hd id="AN0172987522-28">CEFT score</hd> <p>The interaction term (group × CEFT score) was not associated with the FSIQ intercept or slope; therefore, it was removed from the final model. In both groups, the raw score on CEFT, measured between 2 and 5 years, was not related to the slope. However, it was significantly and positively associated with the intercept (i.e. FSIQ at 5 years), above and beyond the child's age at T1, family SES, and group. These results suggest that in both groups, children having a higher CEFT score at baseline had a higher FSIQ level at 5 years and that they consistently had a higher score than their peers over time (see Figure 2).</p> <p>Graph: Figure 2. Yearly change in FSIQ level (percentiles) according to performance on CEFT in the whole sample.</p> <hd id="AN0172987522-29">Early non-verbal behaviors</hd> <p>The interaction term (group × early non-verbal behaviors) was not significantly associated to the intercept (i.e. FSIQ at 5 years) or slope, and was therefore removed from the final model. The frequency of early non-verbal behaviors, measured between 2 and 5 years, was not related to the FSIQ level at 5 years or to the slope. This result indicates that, in both groups, children who manifested more frequent early non-verbal behaviors did not demonstrate a higher or lower FSIQ level at 5 years.</p> <hd id="AN0172987522-30">Trajectory models of Gf level</hd> <p>An adjusted chi-square difference test using the model's log likelihood revealed that a random linear model (Model B) was not significantly different from a fixed linear model (Model A), χ<sups>2</sups>(<reflink idref="bib2" id="ref122">2</reflink>) = 4.55, <emph>p</emph> = 0.103 (see Table 5). Model B was retained as it was not significantly worse than Model A. On average, children's Gf level showed a non-significant decrease of 0.57 percentiles per year (γ<subs>10</subs>), starting with an average percentile score of 89.30 at 5 years (γ<subs>00</subs>). The covariance between the slope and intercept was not significant, which indicates that children who had a better Gf level at 5 years did not show a faster or slower decrease between 5 and 8 years than those who had a lower Gf level at T1.</p> <p>Graph</p> <p>Table 5. Trajectory models of Gf level.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" colspan="3">Gf level (ICC = 0.70)</th></tr><tr><th /><th align="left">Par</th><th align="left">Model A</th><th align="left">Model B</th></tr></thead><tbody><tr><td>Intercept-initial status (5 years)</td><td>γ<sub>00</sub></td><td>88.56 (1.99)<xref ref-type="table-fn" rid="tfn15">***</xref></td><td>89.30 (1.93)<xref ref-type="table-fn" rid="tfn15">***</xref></td></tr><tr><td>Linear slope (yearly change)</td><td>γ<sub>10</sub></td><td>−0.98 (1.15)</td><td>−0.57 (1.26)</td></tr><tr><td>Within-person variance (residual)</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mi>E</mi><mn>2</mn></msubsup></mrow></math></p></td><td>114.88 (51.46)<xref ref-type="table-fn" rid="tfn15">*</xref></td><td>40.60 (28.75)</td></tr><tr><td>Variance in initial status</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>0</mn><mn>2</mn></msubsup></mrow></math></p></td><td>273.21 (133.33)<xref ref-type="table-fn" rid="tfn15">*</xref></td><td>285.05 (123.79)<xref ref-type="table-fn" rid="tfn15">*</xref></td></tr><tr><td>Variance in rate of change</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>1</mn><mn>2</mn></msubsup></mrow></math></p></td><td>−</td><td>72.21 (47.04)</td></tr><tr><td>Slope intercept covariance</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msub><mi>σ</mi><mrow><mn>01</mn></mrow></msub></mrow></math></p></td><td>−</td><td>−55.68 (62.41)</td></tr><tr><td>Goodness-of-fit</td><td>LL</td><td>−793.69</td><td>−781.62</td></tr><tr><td /><td>AIC</td><td>1595.38</td><td>1575.24</td></tr></tbody></table> </ephtml> </p> <ulist> <item>14 Gf: fluid reasoning abilities; ICC: intraclass correlation; Par: parameters; LL: log likelihood; AIC: Akaike information criterion.</item> <item>15 Standard errors are within parentheses. Model A: fixed linear model; Model B: random linear model *<emph>p</emph> <.05. ***<emph>p</emph> <.001.</item> </ulist> <hd id="AN0172987522-31">VS time 3 , CEFT score, 4 and early non-verbal behaviors as predictors of Gf level</hd> <p>A preliminary conditional model assessed the effects of the potential covariates (i.e. child's age at T1, family SES, sex, and group) on Gf level trajectory parameters. This model revealed that none of the covariates were significantly related to the intercept; therefore, only the child's age was retained as it was significantly associated with missing data. The final model included the relevant covariates (i.e. child's age at T1 and group), each of the perceptual predictors (i.e. VS time, CEFT score, and early non-verbal behaviors) and the interaction terms between the group and the selected predictor (see Table 6).</p> <p>Graph</p> <p>Table 6. Final model Gf level trajectories with predictors.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">Par</th><th align="left" colspan="3">Gf level</th></tr><tr><th /><th /><th align="left">Pred 1: VS time</th><th align="left">Pred 2: CEFT score</th><th align="left">Pred 3: NV behav</th></tr><tr><th /><th /><th align="left"><italic>B</italic> (<italic>SE</italic>)</th><th align="left"><italic>B</italic> (<italic>SE</italic>)</th><th align="left"><italic>B</italic> (<italic>SE</italic>)</th></tr></thead><tbody><tr><td colspan="5">Initial status, π<sub>oi</sub></td></tr><tr><td> Intercept (5 years old)</td><td>γ<sub>00</sub></td><td>91.56 (1.51)<xref ref-type="table-fn" rid="tfn18">***</xref></td><td>89.19 (2.00)<xref ref-type="table-fn" rid="tfn18">***</xref></td><td>88.51 (2.23)<xref ref-type="table-fn" rid="tfn18">***</xref></td></tr><tr><td> Age at T1</td><td /><td>−2.15 (2.36)</td><td>−0.88 (2.08)</td><td>−1.17 (2.19)</td></tr><tr><td> Group</td><td>γ<sub>03</sub></td><td>−0.09 (2.67)</td><td>8.85 (4.71)</td><td>7.23 (4.85)</td></tr><tr><td> Predictor</td><td>γ<sub>04</sub></td><td>−8.03 (2.32)<xref ref-type="table-fn" rid="tfn18">**</xref></td><td>5.58 (2.00)<xref ref-type="table-fn" rid="tfn18">**</xref></td><td>2.91 (1.83)</td></tr><tr><td> Autistic</td><td>γ<sub>04</sub></td><td>−13.33 (2.82)<xref ref-type="table-fn" rid="tfn18">***</xref></td><td>−</td><td>8.10 (3.78)<xref ref-type="table-fn" rid="tfn18">*</xref></td></tr><tr><td> NT</td><td>γ<sub>04</sub></td><td>−4.23 (2.87)</td><td>−</td><td>−0.83 (1.77)</td></tr><tr><td> Interaction (Group × Pred)</td><td>γ<sub>05</sub></td><td>9.10 (3.35)<xref ref-type="table-fn" rid="tfn18">**</xref></td><td><italic>ns</italic></td><td>−8.91 (4.27)<xref ref-type="table-fn" rid="tfn18">*</xref></td></tr><tr><td colspan="5">Rate of change</td></tr><tr><td> Child age</td><td>γ<sub>10</sub></td><td>−1.42 (0.94)</td><td>−0.45 (1.25)</td><td>0.42 (1.84)</td></tr><tr><td> Age at T1</td><td /><td>−3.09 (1.64)</td><td>−2.48 (1.40)</td><td>−1.07 (1.43)</td></tr><tr><td> Group</td><td>γ<sub>13</sub></td><td>1.30 (1.72)</td><td>0.01 (3.23)</td><td>0.94 (4.18)</td></tr><tr><td> Predictor</td><td>γ<sub>14</sub></td><td>−1.13 (2.04)</td><td>0.75 (0.88)</td><td>−1.00 (1.51)</td></tr><tr><td> Autistic</td><td>γ<sub>14</sub></td><td>2.37 (3.18)</td><td>−</td><td>−</td></tr><tr><td> NT</td><td>γ<sub>14</sub></td><td>−3.64 (1.85)<xref ref-type="table-fn" rid="tfn18">*</xref></td><td>−</td><td>−</td></tr><tr><td> Interaction (Group × Pred)</td><td>γ<sub>15</sub></td><td>−6.00 (2.92)<xref ref-type="table-fn" rid="tfn18">*</xref></td><td><italic>ns</italic></td><td><italic>ns</italic></td></tr><tr><td>Within-person variance</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mi>E</mi><mn>2</mn></msubsup></mrow></math></p></td><td>43.29 (32.16)</td><td>43.33 (31.89)</td><td>41.44 (30.67)</td></tr><tr><td>Variance in initial status</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>0</mn><mn>2</mn></msubsup></mrow></math></p></td><td>171.96 (69.37)<xref ref-type="table-fn" rid="tfn18">*</xref></td><td>259.01 (101.76)<xref ref-type="table-fn" rid="tfn18">*</xref></td><td>244.65 (109.03)</td></tr><tr><td>Variance in rate of change</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msubsup><mi>σ</mi><mn>1</mn><mn>2</mn></msubsup></mrow></math></p></td><td>56.39 (39.10)</td><td>63.23 (45.38)</td><td>65.84 (53.84)</td></tr><tr><td>Slope intercept covariance</td><td><p><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><msub><mi>σ</mi><mrow><mn>01</mn></mrow></msub></mrow></math></p></td><td>−25.38 (29.58)</td><td>−52.03 (52.84)</td><td>−56.55 (77.83)</td></tr><tr><td>Goodness-of-fit</td><td>LL</td><td>−735.60</td><td>−735.12</td><td>−667.66</td></tr><tr><td /><td>AIC</td><td>1499.21</td><td>1494.25</td><td>1363.32</td></tr></tbody></table> </ephtml> </p> <ulist> <item>16 Gf: fluid reasoning abilities; AIC: Akaike information criterion; CEFT: Children Embedded Figures Test; LL: log likelihood; NT: neurotypical; Par: parameters; Pred: predictor; NV behav: early non-verbal behaviors; SE: standard errors; SES: socioeconomic status; VS: Visual Search.</item> <item>17 All predictors are centered at their grand mean.</item> <item>18 <emph>p</emph> < 0.05; **<emph>p</emph> < 0.01; ***<emph>p</emph> < 0.001.</item> </ulist> <p>Across all models, it was found that NT and autistic children had generally similar Gf levels (all <emph>p</emph>s > 0.05).</p> <hd id="AN0172987522-32">VS time</hd> <p>The interaction term (group × VS time) significantly predicted both the Gf level intercept (i.e. Gf at 5 years) and the slope, above and beyond the child's age at T1. The inspection of these significant interactions suggests that (a) the simple effect of VS time on Gf level at 5 years is greater in the autistic group and (b) the simple effect of VS time on the slope of Gf is greater in the NT group.</p> <p>Among autistic children, having a shorter VS time (i.e. better performance), measured between 2 and 5 years, was significantly associated with the intercept (i.e. Gf level at 5 years), and this relation remained constant over time as there was no effect of VS response time on the slope in this group (see Figure 3a). In contrast, among NT children, a shorter VS time at 2–5 years was not related to a higher or lower Gf level at 5 years, but it predicted a faster rate of change in Gf level, after accounting for the child's age at T1. For each second faster on VS time, NT children's yearly Gf change was 3.64 percentiles better on average. These results suggest that among NT children, VS time did not predict Gf skills at 5 years, but shorter VS time predicted faster change in Gf between 5 and 8 years (see Figure 3b).</p> <p>Graph: Figure 3. Yearly change in Gf level (percentiles) according to response time (s) on Visual Search in autistic participants (a) and neurotypical participants (b).</p> <hd id="AN0172987522-33">CEFT score</hd> <p>The interaction term (group × CEFT score) was not associated with the Gf intercept nor the slope; therefore, it was removed from the final model. In both groups, the raw score on CEFT, measured between 2 and 5 years, was not related to the slope. However, it was significantly and positively associated with the intercept (i.e. Gf at 5 years), above and beyond the child's age at T1 and group. These results suggest that both autistic and NT children having a higher score on CEFT at baseline had a higher Gf level at 5 years, and that they consistently had a higher score than their peers over time (see Figure 4).</p> <p>Graph: Figure 4. Yearly change in Gf level (percentiles) according to performance on CEFT in the whole sample.</p> <hd id="AN0172987522-34">Early non-verbal behaviors</hd> <p>The interaction term (group × early non-verbal behaviors) was not associated with the slope, but significantly predicted the Gf intercept (i.e. Gf at 5 years), above and beyond the child's age at T1. The inspection of this significant interaction suggests that the simple effect of early non-verbal behaviors on Gf level at 5 years is greater in the autistic than in the NT group. Among autistic children, displaying more frequent early non-verbal behaviors, measured between 2 and 5 years, was significantly associated with a higher Gf level at 5 years, and this relation remained constant over time as there was no effect of early non-verbal behaviors on the slope in this group. Hence, autistic children who manifested more early non-verbal behaviors had consistently higher Gf level over time (see Figure 5a). In contrast, among NT children, displaying more early non-verbal behaviors between 2 and 5 years was not related to their Gf level at 5 years, after accounting for the child's age at T1, and they did not subsequently show faster, nor slower, change from 5 to 8 years. Therefore, NT children displaying more (or less) early non-verbal behaviors had similar Gf level over time (see Figure 5b).</p> <p>Graph: Figure 5. Yearly change in Gf level (percentiles) according to frequency (#/hour) of early non-verbal behaviors in autistic participants (a) and neurotypical participants (b).</p> <hd id="AN0172987522-35">Discussion</hd> <p>This paper set out to (a) examine whether some perceptual abilities or perceptual behaviors and interests measured at preschool age could predict the FSIQ and Gf levels and their trajectories at school age and (b) determine whether the predictors of FSIQ and Gf levels and trajectories were the same in both autistic and NT groups. While we cannot infer a causal link in this study, our results showed that the performance on perceptual tests done at preschool age is associated with a higher FSIQ level at 5 years in both autistic and NT children. Furthermore, our findings suggest that both perceptual behaviors and performance on perceptual tests at preschool age are related to a higher Gf level at 5 years in autistic children, whereas only CEFT score predicts Gf level in NT children.</p> <p>Therefore, our study suggests that the performance on perceptual tests and early perceptual behaviors measured at preschool age could be useful in predicting the intellectual level at school age, especially for autistic children who would not be necessarily able to complete a conventional assessment at a very young age. Indeed, while approximately 78% of autistic children completed the Raven's Progressive Matrices or the WPPSI-IV at the age of diagnosis, over 90% of children completed the perceptual tasks. Thus, when it is difficult to assess preschool autistic children using conventional tests, simple tasks measuring perceptual skills could possibly constitute an interesting avenue for estimating the intellectual potential of these children. Finally, both VS tasks and embedded figure tests require motor skills, executive abilities, and depend on the child's cooperation to adequately complete them. Therefore, there might be challenges in assessing young autistic children even when using a strength-based approach. In these cases, it would be interesting to focus on early non-verbal behaviors as they appeared associated with higher Gf abilities at school age in our study.</p> <p>This study builds on a growing body of cross-sectional work suggesting associations between perceptual abilities and intelligence, particularly when non-verbal instruments are used as a measure of intelligence ([<reflink idref="bib12" id="ref123">12</reflink>]; [<reflink idref="bib21" id="ref124">21</reflink>]; [<reflink idref="bib49" id="ref125">49</reflink>]; [<reflink idref="bib68" id="ref126">68</reflink>]). Regarding early non-verbal behaviors, albeit their frequency was independent of FSIQ, there was a significant positive association with Gf in the autistic group. This latter finding questions the common association of RRBIs with poorer developmental outcomes in autism ([<reflink idref="bib5" id="ref127">5</reflink>]; [<reflink idref="bib35" id="ref128">35</reflink>]; [<reflink idref="bib97" id="ref129">97</reflink>]). This pattern of results tends to appear when studying RRBIs as a whole. However, the studies documenting the association between individual behaviors and developmental outcomes find divergent results: while some behaviors seem to be associated with developmental delays, others appear to be associated with better developmental outcomes ([<reflink idref="bib9" id="ref130">9</reflink>], [<reflink idref="bib8" id="ref131">8</reflink>]; [<reflink idref="bib74" id="ref132">74</reflink>]; [<reflink idref="bib85" id="ref133">85</reflink>]). Therefore, promoting a fine-grained approach when studying RRBIs might help unravel their specific associations with developmental outcomes in autism.</p> <p>Our findings of greater associations between perceptive and cognitive measures in autism are also consistent with the enhanced perceptual functioning model ([<reflink idref="bib72" id="ref134">72</reflink>]; [<reflink idref="bib73" id="ref135">73</reflink>]). According to this theoretical model, there would be a greater role of perception within cognition in autism compared with the NT population. This greater role of perception would first manifest itself through various early non-verbal behaviors such as lateral glances or close glances. The older the child, the more it becomes possible for assessors to administer complex tasks, ranging from visual search tasks and perceptual reasoning tasks, and even conventional tests of general intelligence ([<reflink idref="bib71" id="ref136">71</reflink>]). This chain of manifestation of early non-verbal behaviors and perceptual skills mirrors the results of our longitudinal study, suggesting that early non-verbal behaviors as well as performance on perceptual tasks at preschool age predict more complex perceptual reasoning skills and general intelligence at school age. In situations where autistic children risk being underestimated using conventional assessment (or when it is difficult for the examiner to conduct a conventional assessment), the intellectual potential of preschool autistic children could possibly be estimated through simple observations and perceptual tasks such as the manifestation of early non-verbal behaviors, or the performance on VS tasks and embedded figure tests. Our findings are also coherent with the "p" factor hypothesis, emphasizing that perception is a fundamental component of autistic cognition and intelligence ([<reflink idref="bib68" id="ref137">68</reflink>]). In contrast, the performance of NT individuals on tasks measuring diverse abilities (i.e. language, memory, executive functioning, perceptual skills) would depend more on their general IQ level ([<reflink idref="bib67" id="ref138">67</reflink>]). Although perceptual tasks do not constitute a proper measure of intelligence and cannot directly substitute for it, they appear to be <emph>associated</emph> with intelligence, and particularly so in autistic children. Furthermore, among autistic children, early perceptual skills and behaviors seem related to Gf. Thus, it is possible that these perceptual tasks and behaviors could help predict the intellectual potential of these children.</p> <hd id="AN0172987522-36">Explaining the associations between perceptual predictors and later FSIQ and Gf across groups...</hd> <p>Although the tasks used as predictors of intellectual outcomes in this study are perceptual by nature, they also involve different cognitive processes. Indeed, a meta-analysis recently suggested that the tasks typically used to study visual perceptual processing are not necessarily measuring the same constructs and underlying cognitive processes ([<reflink idref="bib99" id="ref139">99</reflink>]). [<reflink idref="bib69" id="ref140">69</reflink>] came to similar conclusions in their study involving NT adolescents and adults. They explored the factorial structure of a multitude of visual perceptual tasks (i.e. Block design, Embedded Figure Test, Hidden patterns Test, VS task, etc.), and identified seven factors. Among these factors, two are of interest for the current study. One factor referred to disembedding abilities (i.e. the ability to disembed and detect a simple stimulus from its surroundings—embedding context), including the Wechsler Block Design subtest and the Embedded Figure Test (adult version of the CEFT). Interestingly, the response time at Visual Search task loaded on a different factor: processing speed abilities. As the disembedding abilities (including CEFT) and processing speed abilities (including VS task) were only weakly correlated, it suggests that these two constructs are distinct.</p> <p>The above-mentioned findings could help disentangle the associations observed in our NT group. Their performance on CEFT was associated to both FSIQ and Gf, but their VS performance was only associated to FSIQ. Indeed, CEFT, Block Design subtest (which contributes to FSIQ) and RCPM are all tasks tapping strongly into disembedding and visuospatial abilities ([<reflink idref="bib69" id="ref141">69</reflink>]; [<reflink idref="bib100" id="ref142">100</reflink>]) (also see Table S22 in the supplementary material). They are thus likely to be associated in an NT sample. However, processing speed abilities have been historically linked to general intelligence (and FSIQ) among neurotypical individuals ([<reflink idref="bib6" id="ref143">6</reflink>]; [<reflink idref="bib37" id="ref144">37</reflink>]; [<reflink idref="bib64" id="ref145">64</reflink>]), but seem more distinct from disembedding abilities ([<reflink idref="bib69" id="ref146">69</reflink>]). This is in accordance with our findings showing a significant association between the VS task and FSIQ, but not between VS abilities and Gf, in our NT group.</p> <p>In contrast, in our autistic group, VS and CEFT performances were associated with both FSIQ and Gf. Early non-verbal behaviors were also associated with Gf in the autistic group. This is coherent with other findings suggesting stronger associations between perceptive abilities and Raven's Matrices among autistic compared with NT individuals ([<reflink idref="bib12" id="ref147">12</reflink>]; [<reflink idref="bib19" id="ref148">19</reflink>]; [<reflink idref="bib41" id="ref149">41</reflink>]; [<reflink idref="bib68" id="ref150">68</reflink>]). This different pattern of results may suggest that the underlying constructs of perceptual tasks interact in a different way among autistic individuals than they do among NT individuals. The factorial structure identified in NT groups may not extrapolate easily to the autistic population. Studies exploring the factorial structure of perceptual tasks among autistic individuals would help verify this interpretation.</p> <hd id="AN0172987522-37">Limitations and contributions</hd> <p>The results must be interpreted considering certain limitations. First, our sample size was relatively modest. With a larger sample, it would be interesting to verify gender differences in cognitive profile. The large boys-to-girls ratio in the autistic group, although expected in this population, could have influenced the results. However, in Table S2, exploratory analyses showed that child's sex was not associated with any of the included variables in this study. Furthermore, a growing body of literature suggests that autistic boys and girls present with similar cognitive abilities at preschool age ([<reflink idref="bib26" id="ref151">26</reflink>]; [<reflink idref="bib46" id="ref152">46</reflink>]; [<reflink idref="bib84" id="ref153">84</reflink>]; [<reflink idref="bib87" id="ref154">87</reflink>]). When gender differences are noted, the effect size is small ([<reflink idref="bib15" id="ref155">15</reflink>]; [<reflink idref="bib31" id="ref156">31</reflink>]). Moreover, this study was limited by the lack of a clinical comparison group. Indeed, it would be interesting to investigate the specificity of the current findings with regards to a non-autistic clinical group with developmental delays (language and/or cognition). Also, future studies should try to isolate the specific role of perceptual abilities as predictors of later FSIQ and Gf, by adding non-perceptual tasks or control tasks assessing similar functions (e.g. processing speed, attention, working memory). Furthermore, we had some attrition across time points, as can be expected in longitudinal designs ([<reflink idref="bib86" id="ref157">86</reflink>]). To minimize the bias that may arise with missing data, we used a full-information maximum likelihood estimator as per current best practices ([<reflink idref="bib29" id="ref158">29</reflink>]). Furthermore, the perceptual predictors were not all measured at the same age across the preschool period. This is because families of autistic children were invited to take part in this study shortly after their diagnosis, which they received at different ages. We controlled for child age at the time of assessment to minimize the impact of this limitation. It must also be noted that the measure of early non-verbal behaviors included a variety of object explorations and behaviors, and it would be interesting for future studies to explore the unique role of each of these exploration behaviors in predicting intellectual level and their associations with intelligence in autistic children. Finally, it should be noted that the use of standardized norms can limit the variation of the scores found within a sample of autistic children, because these children do not perform optimally on conventional tasks. This is maybe why some children had diminishing FSIQ performance (in terms of percentiles) over time. Although we could not verify this in our own sample, others showed that a declining IQ in childhood is usually due to a slower-than-expected gain in skills over time rather than an actual loss of skills ([<reflink idref="bib7" id="ref159">7</reflink>]).</p> <p>Nonetheless, we must keep in mind that our sample was composed of autistic children representing the whole spectrum. In this study, the MLM framework allowed us to include autistic children of all levels of intelligence and language abilities in our analyses and to document their FSIQ and Gf trajectories, although some of them could not complete the intellectual assessments at some time points. Also, we used the same assessment tools to measure FSIQ and Gf levels over time to prevent the impact of the choice of tool on our longitudinal effects. Finally, this study provides interesting insight regarding early perceptual abilities and non-verbal behaviors as potential predictors of intellectual development. To our knowledge, we are the first to show a positive association between early non-verbal behavior and Gf in autistic children. As we know from typical development studies that object exploration can be associated with positive developmental outcomes ([<reflink idref="bib3" id="ref160">3</reflink>]), future studies should investigate the unique role of early non-verbal behaviors in association with IQ more in autism.</p> <hd id="AN0172987522-38">Conclusion</hd> <p>In conclusion, this study brings novel understanding of the role of early perceptual abilities in relation to intellectual development in childhood. Our findings support the importance of visual perception in autistic cognition and suggest that intellectual level at school age could be reflected in early perceptual abilities, such as rapid detection time, the ability to find a hidden figure in a more complex image, or the presence of early non-verbal behaviors. The results suggest that measuring early perceptual abilities may be a valid avenue for estimating FSIQ and Gf in autistic children, particularly at preschool age when a proper assessment can be challenging. Ultimately, our results may help improve assessment and intervention methods, so that they include and focus more on the perceptual strengths of autistic children.</p> <hd id="AN0172987522-39">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-aut-10.1177_13623613231166189 for Visual abilities and exploration behaviors as predictors of intelligence in autistic children from preschool to school age by Dominique Girard, Valérie Courchesne, Catherine Cimon-Paquet, Claudine Jacques and Isabelle Soulières in Autism</p> <ref id="AN0172987522-40"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref21" type="bt">1</bibl> <bibtext> D.G. contributed to the study design, data collection, analysis, interpretation of the results, manuscript writing, and revisions. V.C. contributed to the study design, data collection, interpretation of the results and revisions. C.C.P. contributed to data collection and analysis. C.J. contributed to the study design and manuscript revisions. I.S. contributed to the study design, interpretation of the results and manuscript revisions. All authors read and approved the final manuscript.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref22" type="bt">2</bibl> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref160" type="bt">3</bibl> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by CIHR Project Grant (149036) and FRQS junior career award to I.S., as well as Chaire de Recherche Marcel & Rolande Gosselin en Neurosciences Cognitives et Autisme de l'Université de Montréal.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref70" type="bt">4</bibl> <bibtext> This study was formally reviewed and approved by the research ethic committee of Rivière-des-Prairies Hospital (Montreal, Canada). Informed written and verbal consent was obtained from parents prior to participation at each time point.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref99" type="bt">5</bibl> <bibtext> Dominique Girard</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-7822-8724 Valérie Courchesne</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-7768-5448 Claudine Jacques</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibl id="bib6" idref="ref143" type="bt"></bibl> <bibtext>https://orcid.org/0000-0001-6987-189X Isabelle Soulières</bibtext> </blist> <blist> <bibl id="bib7" idref="ref6" type="bt"></bibl> <bibtext>Graph https://orcid.org/0000-0002-0875-4101</bibtext> </blist> <blist> <bibtext> The data set of this study is available from the corresponding author on reasonable request, in compliance with the requirements of the institutional ethic review board.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref131" type="bt">8</bibl> <bibtext> Considering the feature and conjunction conditions of the visual task together or separately yielded similar results. The mean response time on both feature (γ<subs>04</subs> = –12.84, <emph>p</emph> < 0.001) and conjunction (γ<subs>04</subs> = –13.41, <emph>p</emph> < 0.001) conditions at preschool age were negatively and significantly associated to <emph>FSIQ</emph> at 5 years in both groups. Therefore, those who found the target more quickly in both VS conditions tended to have a better FSIQ score at 5 years. Both conditions of the visual task were used together for a more parsimonious presentation of results.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref130" type="bt">9</bibl> <bibtext> We tested both CEFT score and CEFT time as predictors of FSIQ. Both measures led to the same conclusions. Therefore, we chose to present only CEFT scores results for parsimony. A faster search time on CEFT measured between 2 and 5 years was associated with a higher FSIQ outcome at 5 years in both autistic and NT children (γ<subs>04</subs> = –5.46, <emph>p</emph> = 0.030). However, it was not related to the slope (γ<subs>04</subs> = 1.73, <emph>p</emph> = 0.292). See https://journals.sagepub.com/doi/suppl/10.1177/13623613231166189.</bibtext> </blist> <blist> <bibtext> Considering the feature and conjunction conditions of the visual task together or separately yielded similar results. The mean response time for both conditions of the visual task was associated differently to the <emph>Gf</emph> at 5 years in autistic and NT children. Among autistic children, those who found the target more quickly in both VS feature (γ<subs>04</subs> = –12.15, <emph>p</emph> = 0.014) and conjunction conditions (γ<subs>04</subs> = –13.11, <emph>p</emph> = 0.003) also had better Gf outcomes at 5 years. In contrast, there was no association between the performance in the feature (γ<subs>04</subs> = –0.74, <emph>p</emph> = 0.740) and conjunction conditions (γ<subs>04</subs> = –0.80, <emph>p</emph> = 0.684) and Gf outcomes at 5 years among NT children. Both feature and conjunction conditions of the visual task were used together for a more parsimonious presentation of results.</bibtext> </blist> <blist> <bibtext> We tested both CEFT score and CEFT time as predictors of Gf. Both measures led to the same conclusions. Therefore, we chose to present only CEFT scores results for parsimony. A faster search time on CEFT measured between 2 and 5 years was associated with a higher Gf outcome at 5 years in both autistic and NT children (γ<subs>04</subs> = –5.71, <emph>p</emph> = 0.008). However, it was not related to the slope (γ<subs>04</subs> = –0.93, <emph>p</emph> = 0.314). See https://journals.sagepub.com/doi/suppl/10.1177/13623613231166189.</bibtext> </blist> <blist> <bibtext> * Dominique Girard is also affiliated to Department of psychology, Université du Québec à Montréal, Canada; Claudine Jacques is also affiliated to Autism Research Group, CIUSSS-NIM, Hôpital en santé mentale Rivière-des-Prairies, Montreal, Canada; Isabelle Soulières is also affiliated to Autism Research Group, CIUSSS-NIM, Hôpital en santé mentale Rivière-des-Prairies, Montreal, Canada</bibtext> </blist> </ref> <ref id="AN0172987522-41"> <title> References </title> <blist> <bibtext> Akshoomoff N. (2006). Use of the Mullen Scales of Early Learning for the assessment of young children with autism spectrum disorders. Child Neuropsychology, 12(4–5), 269–277. https://doi.org/10.1080/09297040500473714</bibtext> </blist> <blist> <bibtext> Anderson D. K., Lord C., Risi S., Shulman C., Welch K., DiLavore P. S., Thurm A., Pickles A. (2007). 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  Data: Visual Abilities and Exploration Behaviors as Predictors of Intelligence in Autistic Children from Preschool to School Age
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Girard%2C+Dominique%22">Girard, Dominique</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7822-8724">0000-0001-7822-8724</externalLink>)<br /><searchLink fieldCode="AR" term="%22Courchesne%2C+Valérie%22">Courchesne, Valérie</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7768-5448">0000-0001-7768-5448</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cimon-Paquet%2C+Catherine%22">Cimon-Paquet, Catherine</searchLink><br /><searchLink fieldCode="AR" term="%22Jacques%2C+Claudine%22">Jacques, Claudine</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6987-189X">0000-0001-6987-189X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Soulières%2C+Isabelle%22">Soulières, Isabelle</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0875-4101">0000-0002-0875-4101</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Autism%3A+The+International+Journal+of+Research+and+Practice%22"><i>Autism: The International Journal of Research and Practice</i></searchLink>. 2023 27(8):2446-2464.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 19
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Visual+Perception%22">Visual Perception</searchLink><br /><searchLink fieldCode="DE" term="%22Preschool+Children%22">Preschool Children</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Verbal+Communication%22">Verbal Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+Skills%22">Communication Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Young+Children%22">Young Children</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence+Quotient%22">Intelligence Quotient</searchLink><br /><searchLink fieldCode="DE" term="%22Verbal+Ability%22">Verbal Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Perceptual+Development%22">Perceptual Development</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Development%22">Cognitive Development</searchLink><br /><searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Behavior%22">Child Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Canada+%28Montreal%29%22">Canada (Montreal)</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Wechsler+Preschool+and+Primary+Scale+of+Intelligence%22">Wechsler Preschool and Primary Scale of Intelligence</searchLink><br /><searchLink fieldCode="SU" term="%22Raven+Progressive+Matrices%22">Raven Progressive Matrices</searchLink><br /><searchLink fieldCode="SU" term="%22Autism+Diagnostic+Observation+Schedule%22">Autism Diagnostic Observation Schedule</searchLink><br /><searchLink fieldCode="SU" term="%22Childrens+Embedded+Figures+Test%22">Childrens Embedded Figures Test</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613231166189
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The current prospective cohort study investigated whether early perceptual abilities, measured at preschool age, could predict later intellectual abilities at school age in a group of 41 autistic (9 girls, 32 boys) and 57 neurotypical children (29 girls, 28 boys). More than 80% of the autistic children were considered minimally verbal. Participants were assessed at three time points between the age of 2 and 8 years using the Wechsler Preschool and Primary Scales of Intelligence--Fourth Edition as a measure of full-scale IQ and the Raven's Colored Progressive Matrices as a measure of fluid reasoning abilities (Gf). The performance on two perceptual tests (Visual Search and Children Embedded Figures Test) and the frequency of early non-verbal behaviors served as predictors of later intellectual abilities. Early performance on perceptual tests measured at preschool age was positively related to later full-scale IQ in both autistic and neurotypical children. Furthermore, both early non-verbal behaviors and performance on perceptual tests measured at preschool age were associated with later Gf in the autistic group. In contrast, only the performance on Children Embedded Figures Test was associated with later Gf in the neurotypical group. Early perceptual abilities\and non-verbal behaviors may be indicators of general intelligence and Gf abilities.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1396764
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1396764
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613231166189
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 2446
    Subjects:
      – SubjectFull: Visual Perception
        Type: general
      – SubjectFull: Preschool Children
        Type: general
      – SubjectFull: Cognitive Ability
        Type: general
      – SubjectFull: Verbal Communication
        Type: general
      – SubjectFull: Communication Skills
        Type: general
      – SubjectFull: Young Children
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Intelligence Quotient
        Type: general
      – SubjectFull: Verbal Ability
        Type: general
      – SubjectFull: Perceptual Development
        Type: general
      – SubjectFull: Cognitive Development
        Type: general
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Child Behavior
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Canada (Montreal)
        Type: general
      – SubjectFull: Wechsler Preschool and Primary Scale of Intelligence
        Type: general
      – SubjectFull: Raven Progressive Matrices
        Type: general
      – SubjectFull: Autism Diagnostic Observation Schedule
        Type: general
      – SubjectFull: Childrens Embedded Figures Test
        Type: general
    Titles:
      – TitleFull: Visual Abilities and Exploration Behaviors as Predictors of Intelligence in Autistic Children from Preschool to School Age
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Girard, Dominique
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            NameFull: Courchesne, Valérie
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            NameFull: Cimon-Paquet, Catherine
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            NameFull: Jacques, Claudine
      – PersonEntity:
          Name:
            NameFull: Soulières, Isabelle
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
            – Type: issn-electronic
              Value: 1461-7005
          Numbering:
            – Type: volume
              Value: 27
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Autism: The International Journal of Research and Practice
              Type: main
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