The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through a subaward to Florida State University from Grant R305F100005 to the Educational Testing Service as part of the Reading for Understanding Initiative. The opinions expressed are those of the authors and do not represent views of the Institute, the U.S. Department of Education, the Educational Testing Service, or Florida State University.

Note: The steps below describe decomposing total variance in a dependent variable (Y) into unique variance, common variance, and unexplained variance components based on two selected independent variables (called <img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" /> and <img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" /> for this example) using software with a graphical user interface and data management software (see Table of Materials).
1. Reading Data into Software with a Graphical User Interface
<ol>
	<li>Click on File.
	<ol>
		<li>Hover the mouse over Open.</li>
		<li>Click on Data.</li>
	</ol>
	</li>
	<li>Locate the relevant data file on the computer.
	<ol>
		<li>If the file type is not consistent with the software with a graphical user interface, click on Files of Type and select the appropriate file format.</li>
	</ol>
	</li>
	<li>Click on Open.</li>
</ol>
2. Estimate the Variance Explained in the Dependent Variable (Y)
<ol>
	<li>Total Variance Explained based on Two Independent Variables &#8212; Total R2. 
	Note: An R2 value is known as the coefficient of determination and represents the proportion of variance for a dependent variable that is explained by a set of independent variables.
	<ol>
		<li>Click on Analyze and hover the mouse over Regression and select Linear.</li>
		<li>Click on the dependent variable in the variable list. Then click on the arrow next to Dependent.</li>
		<li>Click on the two independent variables (X1 and X2) in the variable list. Then click on the arrow next to Independent(s).</li>
		<li>Click OK.</li>
		<li>Click on the viewer window of the software.
		<ol>
			<li>Use the mouse to scroll to the section called Model Summary. Record the value under the column R Square and label this value Total R2.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Total Variance Explained based on <img alt="Equation 3" src="/files/ftp_upload/58557/58557eq3.jpg" />
	<ol>
		<li>Repeat steps 2.1.1 through 2.1.4 using only <img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" /> in the independent variable list.</li>
		<li>Click on the viewer window of the software.
		<ol>
			<li>Use the mouse to scroll to the section called Model Summary. Record the value under the column R Square and label this value <img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" />R2.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Total Variance Explained based on <img alt="Equation 4" src="/files/ftp_upload/58557/58557eq4.jpg" />
	<ol>
		<li>Repeat steps 2.1.1 through 2.1.4 using only <img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" /> in the independent variable list.</li>
		<li>Click on the viewer window of the software.
		<ol>
			<li>Use the mouse to scroll to the section called Model Summary. Record the value under the column R Square and label this value <img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" />R2.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Computing the Unique, Common, and Unexplained Variance Components
<ol>
	<li>Open the data management software.</li>
	<li>Enter the labels Total R2, <img alt="Equation 3" src="/files/ftp_upload/58557/58557eq3.jpg" />R2, and <img alt="Equation 4" src="/files/ftp_upload/58557/58557eq4.jpg" />R2 in cells A1, B1, and C1, respectively.</li>
	<li>Enter the Total R2 value from step 2.1.5.1 in cell A2.</li>
	<li>Enter the <img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" />R2 value from step 2.2.2.1 in cell B2.</li>
	<li>Enter the <img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" />R2 value from step 2.3.2.1 in cell C2.</li>
	<li>Calculate the Unique Variance of Variable 1 (U<img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" />R2) in the data management software.
	<ol>
		<li>In Cell D2 type: &#8220;=A2-C2&#8221; (i.e., Total R2 minus <img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" />R2). In Cell D1 label this value U<img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" />R2.</li>
	</ol>
	</li>
	<li>Calculate the Unique Variance of Variable 2 (U<img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" />R2) in the data management software.
	<ol>
		<li>In Cell E2 type: &#8220;=A2-B2&#8221; (i.e., Total R2 minus <img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" />R2). In Cell E1 label this value U<img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" />R2.</li>
	</ol>
	</li>
	<li>Calculate the Common Variance between Variables 1 and 2 (C<img alt="Equation 5" src="/files/ftp_upload/58557/58557eq5.jpg" />R2) in the data management software.
	<ol>
		<li>In Cell F2 type: &#8220;=A2-D2-E2&#8221; (i.e., Total R2 minus U<img alt="Equation 1" src="/files/ftp_upload/58557/58557eq1.jpg" />R2 minus U<img alt="Equation 2" src="/files/ftp_upload/58557/58557eq2.jpg" />R2). In Cell F1 label this value C<img alt="Equation 5" src="/files/ftp_upload/58557/58557eq5.jpg" />R2.</li>
	</ol>
	</li>
	<li>Calculate the Unexplained Variance (e) in the data management software.
	<ol>
		<li>In Cell G2 type: &#8220;=1-A2&#8221; (i.e., 1-Total R2). In Cell G1 label this value e.</li>
	</ol>
	</li>
</ol>
4. Plot the UX1R2, UX2R2, CX1X2R2, and e values
Note: Values in cells D2, E2, F2, and G2 are plotted.
<ol>
	<li>Click and drag the mouse over Cells D2, E2, F2, and G2 to highlight the data.</li>
	<li>Click on Insert on the ribbon of the data management software.</li>
	<li>Click on Charts | Pie Chart | 2-D Pie Chart.</li>
</ol>

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K., Kieffer, M. J., Kelley, J. G., Harris, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+academic+vocabulary+instruction+for+linguistically+diverse+adolescents:+Evidence+from+a+randomized+field+trial.">Effects of academic vocabulary instruction for linguistically diverse adolescents: Evidence from a randomized field trial.</a> American Educational Research Journal. 51 (6), 1159-1194 (2014).</li><li>Lawrence, J., Crosson, A., Par&#233;-Blagoev, E., Snow, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Word+generation+randomized+trial:+Discussion+mediates+the+impact+of+program+treatment+on+academic+word+learning.">Word generation randomized trial: Discussion mediates the impact of program treatment on academic word learning.</a> American Educational Research Journal. 52 (4), 750-786 (2015).</li></ol>

The authors declare that they have no competing financial interests.

There are three critical steps in the protocol for decomposing the variance in R into unique and common variance due to L and D. First, subtract the R2 in the L-only model from the full model to obtain the unique R2 for D. Second, subtract the R2 for the D-only model from the full model to obtain the unique R2 for L. Third, to obtain the common variance explained by L and D, subtract the sum of the two unique R2 from the R2 for the full model.
<p class...

The Simple View of Reading1 (SVR) continues as a popular model of reading because of its simplicity-reading (R) is the product of decoding (D) and language (L)-and because SVR tends to explain, on average, approximately 60% of explained variance in reading comprehension2. SVR predicts that correlations between D and R will decline over time and that correlations between L and R will increase over time. Studies generally support this prediction3,4,5. There are disagreements, however, about the functional form of SVR, with additive models (D + L = R) explaining significantly more variance in reading comprehension than product models (D &#215; L = R)6,7,8, and a combination of sum and product [R = D + L + (D &#215; L) explaining the largest amount of variance in reading comprehension3,9.
Recently the SVR model has expanded beyond regressions based on observed variables to latent variable modeling using confirmatory factory analysis and structural equation modeling. D is typically measured with untimed or timed reading of real words and/or nonwords and R is usually measured by a standardized reading test that includes literacy and informational passages followed by multiple-choice questions. L is typically measured by tests of expressive and receptive vocabulary and, especially in the primary grades, by measures of expressive and receptive syntax and listening comprehension. Most longitudinal studies report that L is unidimensional10,11,12,13. However, another longitudinal study14 reports a two-factor structure for L in the primary grades and a unidimensional structure in grades 4 and 8. Recent cross-sectional studies report that a bifactor model best fits the data and predicts R15,16,17,18. For example, Foorman et al.16 compared unidimensional, three-factor, four-factor, and bifactor models of SVR in data from students in grades 4-10 and found that a bifactor model fit best and explained 72% to 99% of the variance in R. A general L factor explained variance in all seven grades and vocabulary and syntax uniquely explained variance only in one grade each. Although the D factor was moderately correlated with L and R in all grades (0.40-0.60 and 0.47-0.74, respectively), it was not uniquely correlated with R in the presence of the general L factor.
Even though latent variable modeling has expanded SVR by shedding light on the dimensionality of L and the unique role that L plays in predicting R beyond the primary grades, no studies of SVR except one by Foorman et al.19 have partitioned the variance in reading comprehension into what is due uniquely to D and L and what is shared in common. This is a big omission in the literature. Conceptually it makes sense that D and L would share variance in predicting written language because word recognition entails the linguistic skills of phonology, semantics, and discourse at the sentence and text levels20. Similarly, linguistic comprehension must be connected to orthographic representations of phonemes, morphemes, words, sentences, and discourse if text is to be understood21. Multiplying D by L does not yield the knowledge shared by these components. Only decomposition of the variance into what is unique and what is shared by D and L in predicting R will reveal the integrated knowledge crucial to the success of educational interventions.
The one study by Foorman et al.19 that decomposed the variance of reading comprehension into what is unique and what is shared in common by D and L employed a latent variable modeling approach. The following protocol demonstrates the technique with data from students in grades 1, 7, and 10 based on single observed variables for D (timed decoding), L (receptive vocabulary), and R (standardized reading comprehension test) to make the decomposition process easy to understand. The data represent a subset of the data from Foorman et al.19.

<table><tbody><tr><td>IBM SPSS Statistics Software</td><td>IBM</td><td></td><td></td></tr><tr><td>Microsoft Office Excel</td><td>Microsoft</td><td></td><td></td></tr></tbody></table>

decomposing the variance in reading comprehension to reveal the unique and common effects of language and decoding

The Simple View of Reading is a popular model of reading that claims that reading is the product of decoding and language, with each component uniquely predicting reading comprehension. Although researchers have argued whether the sum rather than the product of the components is the better predictor, no researchers have partitioned the variance explained to examine the extent to which the components share variance in predicting reading. To decompose the variance, we subtract the R2 for the language-only model from the full model to obtain the unique R2 for decoding. Second, we subtract the R2 for the decoding-only model from the full model to obtain the unique R2 for language. Third, to obtain the common variance explained by language and decoding, we subtract the sum of the two unique R2 from the R2 for the full model. The method is demonstrated in a regression approach with data from students in grades 1 (n = 372), 6 (n = 309), and 10 (n = 122) using an observed measure of language (receptive vocabulary), decoding (timed word reading), and reading comprehension (standardized test). Results reveal a relatively large amount of variance in reading comprehension explained in grade 1 by the common variance in decoding and language. By grade 10, however, it is the unique effect of language and the common effect of language and decoding that explained the majority of variance in reading comprehension. Results are discussed in the context of an expanded version of the Simple View of Reading that considers unique and shared effects of language and decoding in predicting reading comprehension.

The objective of this study was to investigate the contributions of unique and common variance of language (L) and decoding (D) to predicting reading comprehension (R) in grades 1, 7, and 10 in Florida, a state whose demographics are representative of the nation as a whole. There were two hypotheses regarding predictions of the variance explained in reading comprehension. First, after the primary grades, the unique contribution of D will significantly decrease, and the unique contribution...

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Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Here we present a protocol for decomposing the variance in reading comprehension into the unique and common effects of language and decoding.

The Simple View of Reading is a popular model of reading that claims that reading is the product of decoding and language, with each component uniquely ...

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6.7K Views.  Florida State University. Here we present a protocol for decomposing the variance in reading comprehension into the unique and common effects of language and decoding.

Video: Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

The present article describes how to use eye tracking methodologies to study the cognitive processes involved in text comprehension. Measuring eye movements during reading is one of the most precise methods for measuring moment-by-moment (online) processing demands during text comprehension. Cognitive processing demands are reflected by several aspects of eye movement behavior, such as fixation duration, number of fixations, and number of regressions (returning to prior parts of a text). Important properties of eye tracking equipment that researchers need to consider are described, including how frequently the eye position is measured (sampling rate), accuracy of determining eye position, how much head movement is allowed, and ease of use. Also described are properties of stimuli that influence eye movements that need to be controlled in studies of text comprehension, such as the position, frequency, and length of target words. Procedural recommendations related to preparing the participant, setting up and calibrating the equipment, and running a study are given. Representative results are presented to illustrate how data can be evaluated. Although the methodology is described in terms of reading comprehension, much of the information presented can be applied to any study in which participants read verbal stimuli.

The present article describes how to use eye tracking methodologies to study the cognitive processes involved in text comprehension. Descriptions of eye tracking equipment, how to develop experimental stimuli, and procedural recommendations are included. The information presented can be applied to most any study using verbal stimuli.

The present article describes how to use eye  tracking methodologies to study the cognitive processes involved in text  comprehension. Measuring eye ...

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing and the perception of the visual world. The recording of eye-gaze patterns has provided good evidence for the contribution of both the visual context and linguistic/world knowledge to language comprehension. Initial research assessed object-context effects to test&#160;theories of modularity in language processing. In the introduction, we describe how subsequent investigations have taken the role of the wider visual context in language processing as a research topic in its own right, asking questions such as how our visual perception of events and of speakers contributes to&#160;comprehension informed by&#160;comprehenders&#39; experience. Among the examined aspects of the visual context are actions, events, a speaker&#39;s gaze, and emotional facial expressions, as well as spatial object configurations. Following an overview of the eye-tracking method and its different applications, we list the key steps of the methodology in the protocol, illustrating how to successfully use it to study visually-situated language comprehension. A final section presents three sets of representative results and illustrates the benefits and limitations of eye tracking for investigating the interplay between the perception of the visual world and language comprehension.

The present article reviews an eye-tracking methodology for studies on language comprehension. To obtain reliable data, key steps of the protocol must be followed. Among these are the correct set-up of the eye tracker (e.g., ensuring good quality of the eye and head images) and accurate calibration.

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing ...

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded via an eye tracker as the participant produces or comprehends a spoken language describing the concurrent visual world. This paradigm has high versatility, as it can be used in a wide range of populations, including those who cannot read and/or who cannot overtly give their behavioral responses, such as preliterate children, elderly adults, and patients. More importantly, the paradigm is extremely sensitive to fine grained manipulations of the speech signal, and it can be used to study the online processing of most topics in language comprehension at multiple levels, such as the fine grained acoustic phonetic features, the properties of words, and the linguistic structures. The protocol described in this article illustrates how a typical visual world eye tracking study is conducted, with an example showing how the online processing of some semantically complex statements can be explored with the visual world paradigm.

The visual world paradigm monitors participants' eye movements in the visual workspace as they are listening to or speaking a spoken language. This paradigm can be used to investigate the online processing of a wide range of psycholinguistic questions, including semantically complex statements, such as disjunctive statements.

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded ...

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As such, understanding the causes and mechanisms of regressions plays an important role in understanding eye movement behavior. Inhibition of return (IOR) is an oculomotor effect that results in increased latency to return attention to a previously attended target versus a target that was not previously attended. Thus, IOR may affect regressions. This paper describes how to design materials to distinguish between regressions caused by comprehension-related and oculomotor processes; the latter is subject to IOR. The method allows researchers to identify IOR and control the causes of regressions. While the method requires tightly controlled materials and large numbers of participants and materials, it allows researchers to distinguish and control the types of regressions that occur in their reading studies.

The method was designed to investigate the role of inhibition of return (IOR) in regressive eye movements during reading. The focus is on differentiating between regressions triggered as a result of comprehension difficulty versus those triggered from oculomotor error, including the role of IOR in the two types of regressions.

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As ...

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Working memory (WM) plays a central role in the comprehension of complex sentences. Its function in the processing of spoken complex sentences is especially evident because spoken complex sentence processing is memory-intensive. The dual-modal interference paradigm has been used to examine how the WM system is involved in complex syntactic processing. This article presents two exemplar experiments involving auditory processing with either intra- or extrasentential interferences. In the first experiment, auditory stimuli [spoken-Chinese relative clause (RC) sentences with two syntactic types: subject-gapped (SRC) vs. object-gapped (ORC)] are interfered with via a visually presented lexical decision task within a sentence and manipulated using three different interference timepoints. In the second experiment, the same auditory stimuli, presented via an auditory window moving technique, are interfered with via a visually presented digital recall task beyond the sentence and manipulated using three digital memory loads. By assessing how the primary task of comprehending the RC sentences is affected by the secondary task, we can tackle the controversial issue concerning Chinese RC processing asymmetry. Our results reveal different patterns of RC processing compared to those reported in previous studies. Experiment 1 manifests no clear RC processing advantages in either SRC or ORC; however, a preference for ORC is observed at the ends of sentences, and a preference for SRC is found at the main verb site. Likewise, Experiment 2 presents a dynamic pattern. Under a no-digit load, SRCs show processing advantages in the RC marker region. However, under higher-digit-load interference, ORCs show processing advantages in the same region. These results lead to the conjecture that no obvious or intrinsic processing asymmetry exists in the processing of Chinese RCs. Using the approach of assessing specific interference during syntactic processing, these experiments demonstrate future research applications that explore the processing metrics of spoken sentences involving working memory.

Here, we present a protocol employing dual-modal interference tasks to examine online processing of spoken-Chinese relative clause sentences. Two exemplar experiments involving auditory processing with intra- and extrasentential interferences are described. The paradigm provides a methodology for addressing the nature of working memory and its effects on sentence processing.

Working memory (WM) plays a central role in the comprehension of complex sentences. Its function in the processing of spoken complex sentences is ...

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People with Alzheimer&#39;s disease (AD) or Mild Cognitive Impairment (MCI) demonstrate even longer responses in written word recognition than elderly controls. Despite the general tendency towards slower recognition in aging and neurocognitive disorders, certain characteristics of words influence word recognition speed regardless of age or neuropathology (e.g., a word&#8217;s frequency of use). We present here a protocol for examining the influence of lexical characteristics on word recognition response times in a simple lexical decision experiment administered to younger and older adults and people with MCI or AD. In this experiment, participants are asked to decide as quickly and accurately as possible whether a given letter string is an actual word or not. We also describe mixed-effects models and principal components analysis that can be used to detect the influence of different types of lexical variables or individual characteristics of participants on word recognition speed.

This article describes how to implement a simple lexical decision experiment to assess written word recognition in neurologically healthy participants and in individuals with dementia and cognitive decline. We also provide a detailed description of reaction time analysis using principal components analysis (PCA) and mixed-effects modeling.

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People ...

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

The Cholesky decomposition method is the gold standard used in the field of behavioral genetics. The method is popular because it is easy to program and solve. Using this method, researchers can explore individual differences in longitudinal relations of different variables across multiple time points. The method allows investigators to decompose variance into (1) unique&#160;genetic, shared and non-shared environmental effects that arise at specific time points as well as (2) overlapping genetic, shared and non-shared environmental effects that carry over from one time point to another. However, the method does not identify the mechanisms or origins underlying these effects. The current report focuses on application of the Cholesky decomposition method in the field of educational psychology. Specifically, it discusses individual differences in longitudinal relations between kindergarten letter knowledge, kindergarten phonological awareness, first grade word-level reading skills, and seventh grade reading comprehension.

This paper demonstrates use of the gold standard method in behavioral genetics, the Cholesky decomposition method, to estimate unique, overlapping genetic and environmental influences on different variables to answer longitudinally motivated research questions.

The Cholesky decomposition method is the gold standard used in the field of behavioral genetics. The method is popular because it is easy to program and ...

Assessing Dyslexia at Six Year of Age

In recent years, there has been an increase in the prevalence of dyslexia at early ages in different countries and regions. This increase has serious consequences within school and family settings, due to the poor academic performance that characterizes people with dyslexia and the socio-emotional problems they sometimes display. One of the most frequent problems in the identification of the dyslexia is the lack of a common diagnostic protocol that encompasses specific criteria for the assessment of any child. The Learning Disabilities and Development Disorders research group at the University of Malaga has developed a protocol for the early detection of dyslexia. The protocol is based on the diagnostic and response to intervention models. Consequently, it takes into account diagnostic criteria agreed upon by some associations and committees of experts, as well as certain specific cognitive and language determinants that characterize people who present with dyslexia following adequate instruction, according to recent research. The action protocol is developed over several stages, and we propose the use of structured interviews with parents and teachers alongside standardized tests for the evaluation of intelligence, reading and writing, as well as the risk factors that determine the appearance of the problem. This action protocol provides a model for the detection of dyslexia, which seeks to distinguish it from other comorbid problems and identify its characteristics and determinants, in order to offer effective intervention and/or prevention from an early age.

This research sets out a proposed protocol for the identification of dyslexia. The protocol is based on diagnostic and response to intervention models. The proposal involves using structured interviews and standardized tests for the assessment of reading and writing performance and determinant factors.

In recent years, there has been an increase in the prevalence of dyslexia at early ages in different countries and regions. This increase has serious ...

Computerized Dyslexia Assessment for Spanish-Speaking Children Using SICOLE-R

Advancing Dyslexia Assessment in Children Through Computerized Testing

The acquisition of reading skills is an intricate process that demands the cultivation of various domain-general and domain-specific abilities. Consequently, it is unsurprising that many children grapple with maintaining proficiency at the grade level, particularly when confronted with challenges spanning multiple abilities across both domains, as observed in individuals with reading difficulties. Strikingly, despite reading difficulties being among the most prevalent neurodevelopmental disorders affecting school-aged children, the majority of available diagnostic tools lack a comprehensive framework for assessing the full spectrum of cognitive skills linked to dyslexia, with minimal computerized options. Notably, there are currently limited tools with these features available for Spanish-speaking children. The aim of this study was to delineate the protocol for diagnosing Spanish-speaking children with reading difficulties using the Sicole-R multimedia battery. This tool for elementary grades focuses on evaluating cognitive skills that are associated with dyslexia as prescribed by the scientific literature. Specifically, it concentrates on assessing a range of cognitive abilities that studies have demonstrated to be linked to dyslexia. This focus is based on the observation that individuals with dyslexia typically exhibit deficits in several of the cognitive areas evaluated by this digital tool. The robust internal consistency and multidimensional internal structure of the battery were demonstrated. This multimedia battery has proven to be a fitting tool for diagnosing children with reading difficulties in primary education, offering a comprehensive cognitive profile that is valuable not only for diagnostic purposes but also for tailoring individualized instructional plans.

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

The Multimedia Battery for Assessment of General-Domain and Specific-Domain Skills in Reading is a reliable and valid multimedia battery designed to assess cognitive and basic reading skills. It enables the generation of a comprehensive cognitive and reading performance profile, which is particularly beneficial for children with dyslexia.

The acquisition of reading skills is an intricate process that demands the cultivation of various domain-general and domain-specific abilities. ...

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.