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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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.
Modifications to the protocol would be necessary if latent variables for D and L replaced the dummy codes for the observed measures of timed decoding and receptive vocabulary used here and if control variables such as socio-economic status (SES), gender, and race/ethnicity are added to the model. Alternatives to plotting the results in pie charts can also be considered, such as using Venn diagrams. Pie charts were used here so that percentages of unexplained variance as well as unique and common variances could be displayed.
There are limitations to the application of the method as shown in this study. To simplify the protocol, we selected one observable measure each for D, L, and R instead of using the latent variable modeling approach we usually take to control measurement error19. We eliminated control variables such as SES, gender, and race/ethnicity and used cross-sectional data with a planned missing data design rather than complete longitudinal data. We focused on decomposing variance at the individual student level rather than clustering students within classrooms and schools. Finally, the method shown in the protocol for decomposing variance into percentages of unique and common effects of L and D in predicting R yields descriptive results. There is no easy way to obtain a formal statistical test of the significance of the common variance.
This technique for decomposing the variance in R into the unique and common effects due to L and D has significant advantages over existing methods of looking solely at unique effects. Most importantly, the technique illustrates how individual difference characteristics covary and how one unique effect may pale in comparison to the effect shared with another characteristic. The analyses resulting from the current protocol showed that substantial amounts of variance in reading comprehension were due to the common effects of D and L (ranging from 19% in grade 1 to 13% in grade 10) that appeared to come at the expense of the unique contribution in D over the grades. In other words, the regression results showed a decline in the proportion of variance accounted for by D from 43% in grade 1 to 25% in grade 7 to 19% in grade 10. However, when the variance was decomposed, the unique contribution of D in grade 1 was only 24% and that declined in grades 7 and 10 to 7% and 6%, respectively. This finding has important educational implications because the emphasis on decoding in interventions in the elementary grades comes from the unique effect of D in regression results in spite of the weak effects of decoding interventions in the upper elementary and secondary grades in a meta-analysis25. The amount of common variance that D and L together explain in predicting reading comprehension, especially in the elementary grades, suggests that more instructional emphasis should be placed on the integration of linguistic knowledge at the word-level26,27.
Regression results for L showed a fairly constant picture of L contributing substantial proportions of variance to reading comprehension across the grades, 36% in grade 1 to 54% in grade 10. However, when the method of decomposing the variance was used, the unique contribution of L over the grades showed a dramatic increase from 17% in grade 1 to 28% in grade 7, to 42% in grade 10. The finding that L accounts for so much variance in R in the secondary grades is even more apparent in the SVR studies conducted from a latent variable modeling approach16,17,19 and suggests the value of instruction on the linguistic elements that make text cohesive26,28.

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 border="0" cellpadding="0" cellspacing="0" width="573">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">IBM SPSS Statistics Software</td>
			<td style="width:71px;">IBM</td>
			<td style="width:119px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Microsoft Office Excel</td>
			<td style="width:71px;">Microsoft</td>
			<td style="width:119px;"></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 of L will increase. Second, the unique contribution of L and the shared contributions of D and L will significantly account for the majority of variance beyond the primary grades.
Participants were 372 students in grade 1, 299 students in grade 7, and 122 students in grade 10 in general education classrooms from 18 schools in two large urban districts in Florida (one in northern Florida and the other in central Florida). The study followed guidelines for human subjects and parental consent was obtained. The ethnicity breakdown across grades for the study was: Approximately 30% Black; 30% Hispanic; 30% White; 5% Asian, 3% multicultural; 2% Other. The range of participation in the federal lunch program at the 18 participating schools was from 21.5% to 100%, with a median of 59%.
Single, observable measures for D, L, and R were selected for the regression analyses. The measure of decoding was time-limited (45 s) sight word decoding from the Test of Word Reading Efficiency-222. L was measured by a receptive vocabulary test, the Peabody Picture Vocabulary Test (PPVT-4)23, widely used in the participating schools. In this measure, students see&#160;four pictures and point to the one that depicts the word the examiner says. R was assessed with a nationally-normed reading comprehension test, the Gates-MacGinitie Reading Test-4 (GMAT-4)24. The GMAT-4 is administered in small groups of 10 students in grade 1. Students read parts of a passage and indicate the picture that corresponds to the passage. The GMAT-4 is group-administered in grades 7 and 10. Passages consist&#160;of both literary and informational text and questions are both literal and inferential and appear in a multiple-choice format. Students can&#160;look back at the passage. For all three measures, coefficients for reliability were above 0.90. A planned missing data design with three forms was used to reduce testing time. The D and L measures were administered in one session and the reading comprehension test in another session.
The regression analysis for grade 1 accounted for 60% of the total variance in reading comprehension. The individual variance models showed that the proportion of variance in reading comprehension due to D was 43% and that separately, the proportion of variance in reading comprehension due to L was 36%. These variance estimates are the squared correlation from separate statistical models of each predictor and outcome, which is why their sum from separate models (43 + 36 = 79) was greater than the total amount of variance explained (60%). When the total variance in grade 1 was decomposed into unique and common effects, D uniquely explained 24% of the variance in R and L uniquely explained 17% (see Figure 1). The common variance of D and L was 19%.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58557/58557fig1.jpg" /> 
Figure 1. Total percent of variance explained in grade 1 reading comprehension decomposed into unique and common effects of language and decoding and unexplained variance. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58557/58557fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In grade 7, the regression analysis accounted for 53% of the total variance in reading comprehension. The individual variance models showed that the proportion of variance in reading comprehension due to D was 25% and the proportion of variance in reading comprehension due to L was 46%. Figure 2 shows that D uniquely explained 7% of the variance in R and that L explained 28%. The common variance of D and L in explaining variance in R was 18%.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58557/58557fig2.jpg" /> 
Figure 2. Total percent of variance explained in grade 7 reading comprehension decomposed into unique and common effects of language and decoding and unexplained variance. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58557/58557fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In grade 10, the regression analysis accounted for 61% of the total variance in reading comprehension. The individual variance models showed that the proportion of variance in reading comprehension due to D was 19% and the proportion of variance in reading comprehension due to L was 54%. Figure 3 shows that&#160;D uniquely accounted for 6% of the variance, whereas L uniquely accounted for 42% of the variance. The common variance of D and L in explaining variance in R was 13%.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58557/58557fig3.jpg" /> 
Figure 3. Total percent of variance explained in grade 10 reading comprehension decomposed into unique and common effects of language and decoding and unexplained variance. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58557/58557fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding at JoVE.com

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 ...

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Collecting reliable behavioral data from toddlers and preschoolers is challenging. As a result, there are significant gaps in our understanding of human auditory development for these age groups. This paper describes an observer-based procedure for measuring hearing sensitivity with a two-interval, two-alternative forced-choice paradigm. Young children are trained to perform a play-based, motor response (e.g., putting a block in a bucket) whenever they hear a target signal. An experimenter observes the child&#39;s behavior and makes a judgment about whether the signal was presented during the first or second observation interval; the experimenter is blinded to the true signal interval, so this judgment is based solely on the child&#39;s behavior. These procedures were used to test 2 to 4 year-olds (n = 33) with no known hearing problems. The signal was a 1,000 Hz warble tone presented in quiet, and the signal level was adjusted to estimate a threshold corresponding to 71%-correct detection. A valid threshold was obtained for 82% of children. These results indicate that the two-interval procedure is both feasible and reliable for use with toddlers and preschoolers. The two-interval, observer-based procedure described in this paper is a powerful tool for evaluating hearing in young children because it guards against response bias on the part of the experimenter.

This paper describes a procedure for measuring hearing sensitivity in 2 to 4 year-old children. Children are trained to perform play-based responses when they hear a target signal. Thresholds are then estimated in a two-interval, two-alternative forced-choice paradigm, based on observations of the child&#39;s behaviors.

Collecting reliable behavioral data from toddlers and preschoolers is challenging. As a result, there are significant gaps in our understanding of human ...

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, the traditional ways to monitor behavioral changes in dogs are complicated and not efficient. In the current clinical evaluation, a new, simple monitoring system was used to assess the effectiveness of a specific diet in positively modulating the intense and restless activity of 24 dogs of different ages and breeds. This protocol describes how to easily and rapidly evaluate improvement in a set of symptoms related to generalized anxiety by using a specific sensor, a mobile phone app, a wireless router, and a computer. The results showed that dogs treated with specific diets showed significant improvement in the times spent active and at rest after 10 days (p &lt; 0.01 and p &lt; 0.05, respectively). These dogs also showed an overall significant improvement in clinical and behavioral symptoms. A specific sensor, along with its related hardware, was demonstrated to successfully monitor behavioral changes relating to movement in dogs.

We report a simple approach to evaluate the effectiveness of a specific diet in positively modulating the daily activity and clinical and behavioral symptoms of dogs with evident behavioral disturbances.

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, ...

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors

Diagnostic ultrasound imaging has been a common tool in medical practice for several decades. It provides a safe and effective method for imaging structures internal to the body. There has been a recent increase in the use of ultrasound technology to visualize the shape and movements of the tongue during speech, both in typical speakers and in clinical populations. Ultrasound imaging of speech has greatly expanded our understanding of how sounds articulated with the tongue (lingual sounds) are produced. Such information can be particularly valuable for speech-language pathologists. Among other advantages, ultrasound images can be used during speech therapy to provide (1) illustrative models of typical (i.e.&#160;&#34;correct&#34;) tongue configurations for speech sounds, and (2) a source of insight into the articulatory nature of deviant productions. The images can also be used as an additional source of feedback for clinical populations learning to distinguish their better productions from their incorrect productions, en route to establishing more effective articulatory habits.
Ultrasound feedback is increasingly used by scientists and clinicians as both the expertise of the users increases and as the expense of the equipment declines. In this tutorial, procedures are presented for collecting ultrasound images of the tongue in a clinical context. We illustrate these procedures in an extended example featuring one common error sound, American English /r/. Images of correct and distorted /r/ are used to demonstrate (1) how to interpret ultrasound images, (2) how to assess tongue shape during production of speech sounds, (3), how to categorize tongue shape errors, and (4), how to provide visual feedback to elicit a more appropriate and functional tongue shape. We present a sample protocol for using real-time ultrasound images of the tongue for visual feedback to remediate speech sound errors. Additionally, example data are shown to illustrate outcomes with the procedure.

Ultrasound imaging can be used to display the shape and movements of the tongue in real time during speech. The images can be used to determine the nature of speech sound errors. Visual feedback of the tongue can be used to facilitate improvements in speech sound production in clinical populations.

Diagnostic ultrasound imaging has been a common tool in medical practice for several decades. It provides a safe and effective method for imaging ...

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

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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

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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 ...

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is currently a paucity of research exploring how preschool children with autism understand sentences using different types of cues. The mechanisms underlying sentence comprehension remains largely unclear. The present study presents a protocol to examine the sentence comprehension abilities of preschool children with autism. More specifically, a visual world paradigm of eye-tracking is used to explore the moment-to-moment sentence comprehension in the children. The paradigm has multiple advantages. First, it is sensitive to the time course of sentence comprehension and thus can provide rich information about how sentence comprehension unfolds over time. Second, it requires minimal task and communication demands, so it is ideal for testing children with autism. To further minimize the computational burden of children, the present study measures eye movements that arise as automatic responses to linguistic input rather than measuring eye movements that accompany conscious responses to spoken instructions.

We present a protocol to examine the use of morphological cues during real-time sentence comprehension by children with autism.

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Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

The confounding factors of unexpectedness and semantic integration difficulty naturally residing in anomalous sentences in language studies make it difficult to determine the underlying processing mechanism of ERP components. Unlike the traditional static approach of manipulating expectancy through corpus frequency or cloze probability, this protocol proposes a dynamic method to enhance participants&#39; expectancy for rarely-met anomalous sentences by multiple repetitions while maintaining their semantic integration difficulties. To address the time cost increase resulting from multiple repetitions, this protocol proposes to repeat only the strictly simplified core structure extracted from the anomalous sentence before presenting the semantically enriched, much more informative complete anomalous sentence containing the anomalous core structure to reinitiate the semantic integration processing. The complete anomalous sentence elicited a P600 effect. It suggests that the participants did not give up processing the anomalous information after repetitions and the same semantic integration difficulty was successfully reinitiated. Importantly, the representative experimental results reveal that the greatly attenuated N400 effect caused by multiple repetitions was not recovered by the follow-up reinitiated semantic integration difficulty. It suggests that the attenuated N400 effect should be mainly attributed to the enhancement of expectancy for anomalous information by multiple repetitions. The experimental results show that this method can effectively enhance participants&#39; expectancy for anomalous sentences while retaining the semantic integration difficulty.

We present a protocol to dissociate the intertwining factors of integrative difficulty and unexpectedness in semantically anomalous sentences by applying multiple repetitions to enhance participant&#39;s expectancy for anomalous sentences. The dissociation helps to investigate the major contributor of elicited event-related potentials (ERP) effects such as N400 in language studies.

The confounding factors of unexpectedness and semantic integration difficulty naturally residing in anomalous sentences in language studies make it ...

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world around them. Eye tracking is a safe and non-invasive way to collect gaze data from infants, and the preferential looking paradigm is simple to design and only requires the infant to be attending to the screen. By simultaneously showing two visual stimuli that differ in one dimension, we can assess whether infants show different looking behavior for either stimulus, thus demonstrating sensitivity to that difference. The challenges in such experimental approaches are that experiments must be kept brief (no more than 10 min) and be carefully controlled such that the two stimuli differ in only one way. The interpretation of null results must also be carefully considered. In this paper, we illustrate a successful example of an infant eye tracking study with a preferential looking paradigm to discover that 6-month-olds are sensitive to linguistic cues in a signed language despite having no prior exposure to signed language, suggesting that infants possess intrinsic or innate sensitivities to these cues.

Eye tracking studies using a preferential looking paradigm can be used to study infants&#39;&#160;emerging understanding of, and attention to, their external visual world.

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world ...