Name: using cholesky decomposition to explore individual differences in longitudinal relations between reading skills
Uploaded: 2019-09-17T13:00:00.000+00:00
Duration: 6 min 52 s
Description: Watch this Scientific Journal Video about Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills at JoVE.com

This research was supported in part by a grant from the National Institute of Child Health and Human Development (P50 HD052120). Views expressed herein are those of the authors and have neither been reviewed nor approved by the granting agencies.

The steps below describe the process of estimating individual differences underlying longitudinal associations between elementary and middle school reading skills into (A) genetic, (C) shared environmental, and (E) non-shared environmental factors using a statistical modeling program, word processor, and software with a graphical user interface (GUI). This study has been approved by the Institutional Review Board at Florida State University.
1. Preparing data for the statistical modeling program
<ol>
	<li>Prepare the data in a format that can be read by the statistical modeling program of choice. Popular statistical modeling programs include Mx, OpenMx in the platform R, and MPlus9. Mx can read data files in .vl or .dat data formats, OpenMx in any data format, and Mplus in a .dat data format. The example demonstrated here is executed in the program MPlus9. 
	NOTE: A sample data file in a .dat format for six randomly chosen participants is available in supplemental files. Variables used in a sample data file reflect variables used in the input coding file.</li>
</ol>
2. Reading data into the statistical modeling program, running the script, and estimating the effects
<ol>
	<li>Open the statistical modeling program.</li>
	<li>Locate the relevant data file to be read into the statistical modeling program by typing &#8220;File is [insert location of your data file on your computer]&#8221;.</li>
	<li>Click on the icon RUN on the ribbon of the statistical modeling program to obtain estimates for genetic, shared environmental, and non-shared environmental influences from the multivariate Cholesky decomposition method. The annotated input script for the multivariate Cholesky decomposition model for four time points as well as its output using MPlus can be found in supplemental coding files.</li>
	<li>Once the statistical modeling program generates estimates for genetic, shared environmental, and non-shared environmental influences, locate the estimates in the output file under stx11 for path a11, stx21 for path a21, &#8230;, sty11 for path c11, sty21 for path c21, &#8230;, stz11 for path e11, stz21 for path e21, etc.</li>
</ol>
3. Creating a table with generated estimates
<ol>
	<li>Open the word processor.</li>
	<li>Copy the generated estimates into a table in a word processor. The table can be created in a format as indicated in Figure 3. For example, in this case, the estimates for the paths a11, a21, a31, and a41 have values of 0.60, 0.24, 0.63, and 0.18, respectively.</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60061/60061fig3.jpg" /> 
Figure 3: Multivariate Cholesky decomposition modeling standardized path estimates of genetic and environmental influences. <a href="https://www.jove.com/files/ftp_upload/60061/60061fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
4. Plotting genetic, shared environmental, and non-shared environmental influences
<ol>
	<li>Open the software with a GUI.</li>
	<li>Enter the estimates from the created table into cells F3-F16, G4-G16, H5-H16, and I6-I16. A screenshot from the software with a GUI is depicted in Figure 4.</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60061/60061fig4.jpg" /> 
Figure 4: Entering of estimates into the software with a GUI. <a href="https://www.jove.com/files/ftp_upload/60061/60061fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="3">
	<li>Calculate the variance of genetic, shared environmental, and non-shared environmental influences by squaring the estimates in cells F3-F16, G4-G16, H5-H16, and I6-I16. Type the squared values in cells J3-J16, K4-K16, L5-L16, and M6-M16.</li>
	<li>Calculate the percentage variance by multiplying values in cells J3-J16, K4-K16, L5-L16, and M6-M16 by 100. Type the percentage values in cells N3-N16, O4-O16, P5-P16, and Q6-Q16. Steps 4.3 and 4.4 are depicted in Figure 5.</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60061/60061fig5.jpg" /> 
Figure 5: Illustration of steps 4.3 and 4.4. <a href="https://www.jove.com/files/ftp_upload/60061/60061fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="5">
	<li>Calculate the extent to which genetic influences carry over (overlap) from elementary to middle school.
	<ol>
		<li>In cell R3, type &#8220;0&#8221;.</li>
		<li>In cell R4, type &#8220;=N4&#8221;. This is the extent to which genetic influences from the first time point carry over to the second time point. In this case, it indicates genetic influences from letter naming fluency in kindergarten carrying over to phoneme segmentation fluency in kindergarten.</li>
		<li>In cell R5, type &#8220;= N5+O5&#8221;. This is the degree to which genetic influences from the first two time points carry over to the third time point. In this case, it indicates genetic influences from letter naming fluency in kindergarten and phoneme segmentation fluency in kindergarten carrying over to word-level reading skills in grade 1.</li>
		<li>In cell R6, type &#8220;= N6+O6+P6&#8221;. This is the extent to which genetic influences from the first three time points carry over to the fourth time point. In this case, it indicates genetic influences from letter naming fluency in kindergarten, phoneme segmentation fluency in kindergarten, and word-level reading skills in grade 1 carrying over to reading comprehension in grade 7.</li>
	</ol>
	</li>
	<li>Calculate the extent to which shared environmental and non-shared environmental influences carry over (overlap) from elementary to middle school much the way as in step 4.5.</li>
	<li>Calculate the extent to which unique genetic, shared environmental, and non-shared environmental factors come online at each particular time point (i.e., grade).
	<ol>
		<li>Copy the percentages from cells N3, O4, P5, and Q6 into cells S3, S4, S5, and S6, respectively, to obtain the extent to which unique genetic factors come online at each grade.</li>
		<li>Copy the percentages from cells N8, O9, P10, and Q11 into cells U3, U4, U5, and U6, respectively, to obtain the extent to which unique shared environmental factors come online at each grade.</li>
		<li>Copy the percentages from cells N13, O14, P15, and Q16 into cells W3, W4, W5, and W6, respectively, to obtain the extent to which unique non-shared environmental factors come online at each grade.</li>
	</ol>
	</li>
	<li>To ensure all calculations are correct, the values in cells R3-W3, R4-W4, R5-W5, and R6-W6 should each add up to 100. Steps 4.5&#8211;4.7 are depicted in Figure 6.</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60061/60061fig6.jpg" /> 
Figure 6: Illustration of steps 4.5-4.8. <a href="https://www.jove.com/files/ftp_upload/60061/60061fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="9">
	<li>Plot genetic overlapping as well as genetic unique influences by clicking and dragging the mouse over cells R2&#8211;R6 and S2&#8211;S6 to highlight the data.</li>
	<li>Click on the Insert menu.</li>
	<li>Click on Charts &#62; Stacked Column.</li>
	<li>Repeat steps 4.9&#8211;4.11 for shared environmental and non-shared environmental overlapping as well as unique influences. Choose cells T2&#8211;T6 and U2&#8211;U6 to plot shared environmental influences, and choose cells V2&#8211;V6 and W2&#8211;W6 for non-shared environmental influences.</li>
</ol>

<ol>
	<li>Muter, V., Hulme, C., Snowling, M. J., Stevenson, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phonemes,+rimes,+vocabulary+and+grammatical+skills+as+foundations+of+early+reading+development:+Evidence+from+a+longitudinal+study.">Phonemes, rimes, vocabulary and grammatical skills as foundations of early reading development: Evidence from a longitudinal study.</a> Developmental Psychology. 40 (5), 665-681 (2004).</li><li>Schatschneider, C., Fletscher, J. M., Francis, D. J., Carlson, C. D., Foorman, B. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kindergarten+prediction+of+reading+skills:+A+longitudinal+comparative+analysis.">Kindergarten prediction of reading skills: A longitudinal comparative analysis.</a> Journal of Educational Psychology. 96 (2), 265-282 (2004).</li><li>Byrne, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Longitudinal+twin+study+of+early+literacy+development:+Preschool+and+kindergarten+phases.">Longitudinal twin study of early literacy development: Preschool and kindergarten phases.</a> Scientific Studies of Reading. 9 (3), 219-235 (2005).</li><li>Christopher, M. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+and+environmental+etiologies+of+the+longitudinal+relations+between+prereading+skills+and.">Genetic and environmental etiologies of the longitudinal relations between prereading skills and.</a> Child Development. 86 (2), 342-361 (2015).</li><li>Neale, M. C., Cardon, L. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methodology for Genetic Studies of Twins and Families. , (1992).</li><li>Byrne, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+and+environmental+influences+on+early+literacy.">Genetic and environmental influences on early literacy.</a> Journal of Research in Reading. 29 (1), 33-49 (2006).</li><li>Byrne, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+and+environmental+influences+on+aspects+of+literacy+and+language+in+early+childhood:+Continuity+and+change+from+preschool+to+grade+2.">Genetic and environmental influences on aspects of literacy and language in early childhood: Continuity and change from preschool to grade 2.</a> Journal of Neurolinguistics. 22 (3), 219-236 (2009).</li><li>Erbeli, F., Hart, S. A., Taylor, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Longitudinal+associations+among+reading+related+skills+and+reading+comprehension:+A+twin+study.">Longitudinal associations among reading related skills and reading comprehension: A twin study.</a> Child Development. 89 (6), e480-e493 (2018).</li><li>Muth&#233;n, L. K., Muth&#233;n, B. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Mplus. The comprehensive modeling program for applied researchers: User&#8217;s guide. , (2012).</li><li>Hart, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+how+nature+and+nurture+affect+the+development+of+reading:+An+analysis+of+the+Florida+Twin+Project+on+Reading.">Exploring how nature and nurture affect the development of reading: An analysis of the Florida Twin Project on Reading.</a> Developmental Psychology. 49 (10), 1971-1981 (2013).</li><li>Taylor, J., Roehrig, A. D., Hensler, B. S., Connor, C. M., Schatschneider, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Teacher+quality+moderates+the+genetic+effects+on+early+reading.">Teacher quality moderates the genetic effects on early reading.</a> Science. 328 (5977), 512-514 (2010).</li></ol>

Authors have nothing to disclose.

The objective of this study was to demonstrate how the well-established method within behavioral genetics, the multivariate Cholesky decomposition method, can effectively be used for understanding relations across variables in temporal context. Specifically, this method allows estimation of the extent to which unique genetic and environmental influences arise during particular time points (e.g., school grade), as well as demonstrating the overlap of genetic and environmental influences across many time points.
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Becoming a skilled reader with the ability to fluently read and comprehend text is important for children&#8217;s school outcomes. To prevent the development of reading problems, it is vital to understand the extent to which different reading skills predict reading comprehension. Existing research has shown that pre-reading and word-level reading skills in elementary school longitudinally predict reading comprehension in middle school1,2. Individual differences in these predictions mostly point to underlying genetic (and to some extent, environmental) factors from kindergarten up to grade four3,4. However, there is a need to explore whether these same genetic and environmental factors continue to influence these predictions up to middle school grades.
One method to gain a better understanding of individual differences underlying the associations between elementary and middle school reading skills is using behavioral genetic methodology, specifically the Cholesky decomposition method. The Cholesky decomposition method is considered one of the gold standard analyses in behavioral genetics. This method is easy to program and solve and allows for the decomposition of variance and covariance into (A) genetic, (C) shared environmental, and (E) non-shared environmental influences, usually in a sample of twins. An example of a univariate (one variable) Cholesky decomposition is indicated in Figure 1. The A latent factor refers to genetic effects, which are genetic influences inherited from parents. The C latent factor refers to shared environmental effects, which are aspects of the environment that serve to make twins more similar, such as home and school environments. Lastly, the E latent factor refers to non-shared environmental effects, which are environmental influences that are unique to each twin and contribute to differences between twins, such as each&#8217;s own experience. The E factor also captures measurement error.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60061/60061fig1v2.jpg" /> 
Figure 1: Decomposition into (A) genetic, (C) shared environmental, and (E) non-shared environmental influences. <a href="https://www.jove.com/files/ftp_upload/60061/60061fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The A, C, and E factors in Figure 1 estimate the extent to which genes and environments influence one (reading) variable. Still, to investigate individual differences underlying longitudinal associations between more than one reading skill from elementary to middle school, longitudinal analysis is necessary. To answer longitudinally motivated research questions, a multivariate Cholesky decomposition method is used here5. Conceptually, the multivariate Cholesky decomposition method is similar to hierarchical multiple regression, such that independent contribution of genetic and environmental factors is assessed after the contributions of previous factors have been taken into account.
For instance, in a multivariate Cholesky decomposition with longitudinal data at four time points (see Figure 2), the first set of factors [genetic (A1), shared environmental (C1), and non-shared environmental (E1)] contributes to the variance of all variables, represented as paths a11, a21, a31, a41, c11, c21, ..., e11, etc., from A1, C1, E1 factors to each variable. The second set of factors (A2, C2, E2) contributes to the variance of the second and subsequent variables after controlling for the first time point. The second set of factors is represented as paths a22, a32, a42, c22, c32, ..., e22, etc. Then, influences of the third set of factors (A3, C3, E3) are estimated for the third and fourth variables after controlling for the previous two time points. They are represented as paths a33, a43, c33, c43, e33, e43. Finally, influences of the fourth set of factors (A4, C4, E4) are measured for the final time point after controlling for all previous time points. They are represented as paths a44, c44, e44.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60061/60061fig2.jpg" /> 
Figure 2: Multivariate Cholesky decomposition model for four time points. <a href="https://www.jove.com/files/ftp_upload/60061/60061fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In this longitudinal application of the multivariate Cholesky decomposition method, genetic and environmental influences at each time point are estimated after the effects of previous time points have been controlled for. As such, this method allows determination of the extent to which unique genetic and environmental influences come online at each particular time point, independent of influences from previous time points (these effects are estimated by paths a11, a22, a33, a44, c11, c22, &#8230;, e11, e22, etc.). In addition, the method also enables examination of the degree to which the same (overlapping) genetic and environmental influences are shared between time points. In other words, it can be determined to which extent genetic and environmental influences carry over from one time point to another (i.e., these effects are estimated by paths a21, a31, a41, a32, a42, a43, c21, c31, ..., e21, etc.). It should be noted that paths a11, c11, and e11 represent all possible genetic and environmental influences up to and including the first time point, which can be either unique or overlapping with previous time points. However, time points prior to the first time point are not estimated; hence, it cannot be accurately determined whether they represent unique or overlapping influences. For simplification purposes, they are included as unique influences in the current report.
The order of the measured variables entered into a Cholesky decomposition is arbitrary. However, the order is usually driven by a theoretical perspective. This is also the case in the current study, in which the order was based on the development of reading skills, such that reading skills in elementary school are predictive of reading comprehension in middle school.
There are several reports in the literature investigating genetic and environmental factors underlying longitudinal associations of reading skills utilizing the Cholesky decomposition method. These prior studies mostly focused on investigating relations between reading skills among elementary schoolers6,7. There is only one published study examining individual differences associated with reading from elementary grades into middle school grades using the multivariate Cholesky decomposition method8. This protocol details the multivariate Cholesky decomposition method from that specific report to explore individual differences in longitudinal relations between kindergarten letter knowledge, kindergarten phonological awareness, first grade word-level reading skills, and seventh grade reading comprehension.
The study findings focus on using the multivariate Cholesky decomposition method to distinguish between two types of genetic and environmental influences. First, it is shown how to estimate genetic and environmental influences that carry over (overlap) from elementary to middle school reading (e.g., estimating paths a43, c43, and e43, which are genetic and environmental influences on word-level reading skills from first grade that affect reading comprehension in seventh grade). Second, it is demonstrated how to estimate unique genetic and environmental influences that come online at each particular grade (e.g., estimating paths a33, c33, and e33, which are unique genetic and environmental influences on word-level reading skills that arise in first grade).

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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.

Standardized estimates for genetic, shared environmental, and non-shared environmental influences from the multivariate Cholesky decomposition model are depicted in Figure 7. In general, results revealed that individual differences in kindergarten pre-reading and first grade word-level reading skills accounted for a large proportion of the variance of genetic (40%) as well as shared environmental (39%) influences on seventh grade reading comprehension. In addition, result...

Watch this Scientific Journal Video about Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills at JoVE.com

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

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 ...

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6.2K Views.  Texas A&M University. 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.

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

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

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The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain ...

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 ...

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Learning mathematics is a complex process that requires the development of multiple domain-general and domain-specific skills. It is therefore not unexpected that many children struggle to stay at grade level, and this becomes especially difficult when several abilities from both domains are impaired, as in the case of mathematical learning disabilities (MLD). Surprisingly, although MLD is one of the most common neurodevelopmental disorders affecting schoolchildren, most of the diagnostic instruments available do not include assessment of domain-general and domain-specific skills. Furthermore, very few are computerized. To the best of our knowledge, there is no tool with these features for Spanish-speaking children. The purpose of this study was to describe the protocol for the diagnosis of Spanish MLD children using the BM-PROMA multimedia battery. BM-PROMA facilitates the evaluation of both skill domains, and the 12&#160;tasks included for this purpose are empirically evidence-based. The strong internal consistency of BM-PROMA and its multidimensional internal structure are demonstrated. BM-PROMA proves to be an appropriate tool for diagnosing children with MLD during primary education. It provides a broad cognitive profile for the child, which will be relevant not only for diagnosis but also for individualized instructional planning.

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

BM-PROMA is a valid and reliable multimedia diagnostic tool that can provide a complete cognitive profile of children with mathematical learning disabilities.

Learning mathematics is a complex process that requires the development of multiple domain-general and domain-specific skills. It is therefore not ...

Investigating Oculomotor Responses in Visual Duration Perception Tasks

Eye Movements in Visual Duration Perception: Disentangling Stimulus from Time in Predecisional Processes

Eye-tracking methods may allow the online monitoring of cognitive processing during visual duration perception tasks, where participants are asked to estimate, discriminate, or compare time intervals defined by visual events like flashing circles. However, and to our knowledge, attempts to validate this possibility have remained inconclusive so far, and results remain focused on offline behavioral decisions made after stimulus appearance. This paper presents an eye-tracking protocol for exploring the cognitive processes preceding behavioral responses in an interval comparison task, where participants viewed two consecutive intervals and had to decide whether it speeded up (first interval longer than second) or slowed down (second interval longer). 
Our main concern was disentangling oculomotor responses to the visual stimulus itself from correlates of duration related to judgments. To achieve this, we defined three consecutive time windows based on critical events: baseline onset, the onset of the first interval, the onset of the second interval, and the end of the stimulus. We then extracted traditional oculomotor measures for each (number of fixations, pupil size) and focused on time-window-related changes to separate the responses to the visual stimulus from those related to interval comparison per se. As we show in the illustrative results, eye-tracking data showed significant differences that were consistent with behavioral results, raising hypotheses on the mechanisms engaged. This protocol is embryonic and will require many improvements, but it represents an important step forward in the current state of art.

Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills

We present a protocol that employs eye tracking to monitor eye movements during an interval comparison (duration perception) task based on visual events. The aim is to provide a preliminary guide to separate oculomotor responses to duration perception tasks (comparison or discrimination of time intervals) from responses to the stimulus itself.

Eye-tracking methods may allow the online monitoring of cognitive processing during visual duration perception tasks, where participants are asked to ...

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. ...

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Kindergarten writing involves acquiring fundamental skills, such as letter formation, phonemic awareness, and the gradual use of written language to express ideas. In this context, tablet-based curriculum assessments present new opportunities for teaching and evaluating these early writing abilities. To the best of our knowledge, there is currently no tool with these features available for Spanish-speaking children. Therefore, the primary objective of this study was to present a tablet-based protocol for screening at-risk young writers. This tablet-based protocol is specifically designed for kindergarten-level education and serves as a curriculum-based assessment tool that focuses on evaluating early writing skills in young learners. This user-friendly application incorporates interactive tasks and exercises, including assessments of phonological awareness, name writing, alphabet letter copying fluency, and oral narrative skills. These comprehensive evaluations cover various aspects of writing. This application is meticulously aligned with kindergarten curriculum objectives, ensuring that assessments adhere to educational standards. By providing educators with a digital platform to assess and enhance students' writing skills, this tool empowers them to make data-driven decisions for effective instruction during the early stages of writing development. Moreover, it functions as a curriculum-based measurement (CBM), offers valuable support for identifying potential writing challenges in young learners and continuously monitoring their progress. This feature enables early intervention and tailored instruction to optimize writing skill development.

This protocol presents the step-by-step method for using a writing-based, valid, and reliable application designed for kindergarten-level education. It operates as a curriculum-based assessment tool, specifically focusing on evaluating early writing skills in young learners.

Kindergarten writing involves acquiring fundamental skills, such as letter formation, phonemic awareness, and the gradual use of written language to ...