The authors have no acknowledgments.

The Institutional Review Boards of Kent State University and Stetson University have approved all methods described here.
1. Eligible Participants
NOTE: The purpose of this research is to understand reading processes in skilled adult readers. Thus, certain eligibility requirements must be met. Such controls ensure that results are directly applicable to a population of skilled adult readers with typical cognitive processes.
<ol>
	<li>Recruit participants that meet the following eligibility requirements: must be at least 18 years old, have no reading disabilities (dyslexia, alexia, dysgraphia, etc.), have normal or corrected vision (glasses or contacts are acceptable), and speak English as their native language.</li>
</ol>
2. Experimental Stimuli
NOTE:&#160;Stimuli construction involves the selection of individual target words as well as the creation of sentence contexts in which those target words are embedded.
<ol>
	<li>Prepare stimuli for oculomotor corrective regressions.
	<ol>
		<li>Select a set of at least 30 target words to be embedded into sentences. The target word (wordn) is the word on which word skipping and regressive eye movements will be measured. Select words from an English language corpus, such as CELEX14, which provides word frequencies. Select words that are low frequency, with frequencies lower than 30 counts per million. Choose a three-letter content word to facilitate word skipping.</li>
		<li>Select the pre-target words (wordn-1) so that the reader is likely to fixate it before skipping or fixating wordn. Again, using an English language corpus, select words that are high frequency, with at least 30 counts per million. Choose a five to seven letter content word to increase the likelihood that it will be fixated and not skipped.</li>
		<li>Select the post-target word (wordn+1) so that the reader is likely to fixate it after skipping or fixating wordn. Using the same English language corpus from the previous two steps, select a high frequency preposition that is at least four letters long.</li>
		<li>Using the words selected in the previous three steps, create sentence contexts in which these three words will be embedded. Place wordsn-1, wordn, and wordn+1 in the middle of the sentence. See step 2.2.6 for some example stimuli. 
		&#8203;NOTE: Sentences should be neutral in context without any emotionally charged language. The sentence frames need to be written so that that the overall sentence is grammatical and that wordsn-1, wordn, and wordn+1 fit smoothly into the sentence frame. The target wordn should not be predictable from the context that precedes it (see step 2.2.5 for information about norming to ensure that target words are not predictable).</li>
		<li>After sentence frames are created, norm the stimuli using a cloze task to ensure that the target wordn is not predictable from the sentence context.
		<ol>
			<li>In this task, recruit a separate set of participants.</li>
			<li>Have these participants view each sentence context up until the target word, without including the target, and produce a word that they think best fits the sentence.</li>
			<li>Let the participants complete this task using pen and paper or enter words on a computer using a program or any other software package for behavioral research. 
			NOTE: If the target word is produced by more than 20% of participants, then it is considered predictable from the sentence context and must be replaced with a less predictable word and normed again. In the original experiment11, 100 participants completed the cloze task, and no target word was predicted from the preceding context.</li>
		</ol>
		</li>
		<li>See an example sentence below. In this sentence, green is wordn-1, gem is the three-letter low-frequency unpredictable wordn, and around is wordn+1. In this experiment, the target word was controlled to be low frequency (mean = 10.54 counts per million, standard deviation = 6.97)14. 
		The actress wore a green gem around her neck at the awards show. 
		Another set of 10 experimental stimuli are presented below from the original experiment14. 
		He stared at the large urn across the room. 
		She was afraid of the black fox until it wagged its tail. 
		He tried to grab the small eel from the pond. 
		She wanted to see the great zoo inside of the new theme park. 
		They walked around the whole gym after their workout. 
		The child asked the young elf about working in Santa&#39;s shop. 
		There was always a short cot under the bed at her cousin&#39;s house. 
		There was really heavy fog along the coastline this morning. 
		He placed his right ski above his left one when he performed the trick. 
		She performed her first gig since she wrote some new songs.</li>
	</ol>
	</li>
	<li>Prepare stimuli for comprehension-based regressions.
	<ol>
		<li>Obtain sentence contexts for this experiment by adapting from a previous study that already has normed sentence frames with ambiguous homophones12. Download this article and view Appendix A, which provides neutral sentence frames that end with an ambiguous homophone.</li>
		<li>Using these sentence frames, write the second half of the sentence that disambiguates the homophone to its subordinate meaning. Determine the subordinate meaning of the target word using the University of South Florida homograph norms15. For example, the subordinate meaning of the homophone grade is &#34;hill incline&#34; while the dominant meaning is &#34;evaluation scale.&#34; Thus, for the sentence &#34;Mike didn&#39;t like the grade&#8230;&#34; add &#34;&#8230;of the hill that they were driving down.&#34; This ending makes it so that the ambiguous homophone &#34;grade&#34; is disambiguated to its subordinate meaning by the word &#34;hill.&#34; Each sentence should have two short function words between the ambiguous homophone and the words that disambiguates it. Another set of 10 example sentences are presented below14 
		Barbara examined the corn on her foot after it became painful. 
		Ben finally noticed the bluff when the trail suddenly ended. 
		Betty searched for the file for her nails since they were torn. 
		Debbie liked the case that the lawyer decided to take on. 
		Everyone noticed the crab when he whined about everything. 
		Fred closely examined the table and the chart in the report. 
		Harry didn&#39;t like the suit that was filed against him. 
		He was happy about the press when the wrinkle was removed from his pants. 
		Helen looked at the cardinal in the church on Sunday. 
		It was a very bad sentence that the judge delivered to the defendant. 
		Jen easily saw the ruler and the queen at the palace.</li>
	</ol>
	</li>
</ol>
3. Comprehension Check
<ol>
	<li>Verify that all participants are reading for comprehension throughout the experiment. Thus, randomly present a series of comprehension questions throughout the experiment. Use simple statements that are followed by a yes or no question to ensure that the participant was comprehending each sentence. Present the comprehension questions separately from the sentence. Eye movement patterns may be different when a participant is not paying close attention to the text. Remove data from any participant who scores less than 80% accuracy on the comprehension questions from data analysis. There should be comprehension questions on at least 10% of trials. Comprehension questions should follow filler sentences, not experimental sentences. An example of four comprehension questions are presented below: 
	Sentence:&#160; &#160; &#160; Christina went to California with her parents last year. 
	Question:&#160; &#160; &#160; &#160;Did Christina go to Vermont last year? 
	Sentence:&#160; &#160; &#160; After Mark graduated from college he found a job immediately. 
	Question:&#160; &#160; &#160; &#160;Did Mark fail to find a job? 
	Sentence:&#160; &#160; &#160; The telephone rang just as Samantha crawled into bed. 
	Question:&#160; &#160; &#160; &#160;Did someone call Samantha last night? 
	Sentence:&#160; &#160; &#160; The tourists regretted not being able to stay longer in Florida. 
	Question:&#160; &#160; &#160; &#160;Did the tourists want to stay longer?</li>
</ol>
4. Eye Tracking Procedure
<ol>
	<li>Recruit at least 50 participants for each experiment (oculomotor control and comprehension-based regressions) to ensure sufficient data for analyses; drop trials without regressions. Prepare at least 40 experimental stimuli for each experiment, with at least 10 comprehension questions, and at least 20 filler sentences for each experiment. Each experiment should take approximately 30 minutes to complete.</li>
	<li>Use an eye-tracker with a sampling rate of 1,000 Hz, which means that eye positions are measured 1,000 times per second. Measure the eye movements from the right eye. 
	NOTE: This sampling rate is important as eye movement behavior is extremely fast, and a sample rate of less than 1,000 Hz might not be sensitive enough to capture all cognitive processing.</li>
	<li>Have the participants seated about 60 cm (24 in.) away from a computer screen. Use a 21 in. screen or other screens for the original experiment14.
	<ol>
		<li>To stabilize head movements, have participants rest their chin on a chin rest. Allow participants to adjust the height of the chin rest and chair until they are comfortable.</li>
		<li>Instruct the participants to rest their fingers on a &#34;yes&#34; button and a &#34;no&#34; button that they will use to answer comprehension questions.</li>
	</ol>
	</li>
	<li>To calibrate participants&#39; eye positions, use a nine-dot calibration. Instruct the participants to gaze at a white circle in the center of a black screen. Participants will move their eyes to the white circle as it moves around to eight other positions on the screen. Have the participants keep their eyes on the circle until it moves to a new position.
	<ol>
		<li>After calibration, validate eye positions. If participants&#39; eye positions are corrected within one degree of visual angle, then ask the participants move on to the reading portion of the study.</li>
	</ol>
	</li>
	<li>Have each sentence appear one at a time in random order in the center of the screen. Use any monospaced font such as Courier so that all characters take up the same amount of space on the participant&#39;s retina. Intermix filler sentences and comprehension sentences (followed by questions) with experimental sentences.</li>
	<li>Prior to each sentence, have the participant gaze at a white circle on the left side of the screen that corresponds to the position of the first letter of the first word. When participants are looking at this position, have the experimenter press a button that controls the onset for reading. 
	NOTE: This experiment uses self-paced reading, so the participant will press the &#34;yes&#34; button when they have finished reading the sentence. After reading each sentence, the white circle will appear on the screen again and the process will repeat with the next sentence.</li>
	<li>Perform a drift correct prior to each sentence to ensure that calibration remains accurate. If the eye position is off by more than one degree of visual angle, recalibrate the participant&#39;s eye positions.</li>
</ol>
5. Quantitative Analysis of Data
<ol>
	<li>After all data have been collected, open the program DataViewer, and load in all data files in .edf format if using EyeLink software. Delete fixation durations that are fewer than 100 ms or longer than 1,000 ms as these do not reflect linguistic processing.</li>
	<li>Next, run an Interest Area Report to gather the desired eye movement behavior measures from each word of interest: wordn, and wordn+1. Set interest areas around these items using a delimiter and the default settings for interest area size in Experiment Builder. Select several variables in order to determine the type of regressive eye movement and the presence or absence of an IOR effect: 
	IA_SKIP: This variable indicates whether wordn was skipped. This is important to determine whether the regression to wordn is a return or non-return regression. 
	IA_REGRESSION_IN: This variable indicates whether a regression was made into wordn. Only analyze trials in which this value is 1, otherwise it is not relevant for IOR analyses because no return eye movement was made to this target. 
	IA_REGRESSION_OUT: This variable indicates whether a regression was made out of wordn+1. Only analyze cases in which this variable is 1, which indicates that a return eye movement was made. 
	IA_FIRST_RUN_DWELL_TIME: This variable is a measure of how long the reader spent on wordn+1 on the first pass through the sentence. This duration indicates the amount of processing it took before a regressive eye movement was made. This duration will include any potential inhibition time before a regressive eye movement.</li>
	<li>Now that each trial has been categorized, have the researcher inspect each trial individually to determine whether it counts as an oculomotor- or comprehension-based regressive eye movement. Only inspect trials where IA_REGRESSION OUT of wordn+1 is 1 and where IA_REGRESSION_IN to wordn is 1.
	<ol>
		<li>In DataViewer, select the participant&#39;s data, and open each trial. Click through each fixation to inspect whether the regression out of wordn+1 was made directly into wordn after it has been fixated or skipped. If so, mark this trial to be included in analyses in the data file. Do not include trials where a regression was made to an earlier part of the sentence or when a regression was made out of wordn+1 after processing other parts of the sentence.</li>
	</ol>
	</li>
	<li>After all trials have been marked, analyze data using linear mixed effects models with the languageR and lme4 packages in R Studio16. Specify random slopes and intercepts for both participants and items. Fixed effects should include skipping of the target word and regressions into the target word.</li>
</ol>

<ol>
	<li>Rayner, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eye+movements+in+reading+and+information+processing:+20+years+of+research.">Eye movements in reading and information processing: 20 years of research.</a> Psychological Bulletin. 124 (3), 372-422 (1998).</li><li>Posner, M. I., Cohen, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Components+of+visual+orienting.">Components of visual orienting.</a> Attention and Performance X: Control Language Processes. (32), 531-556 (1984).</li><li>Klein, R. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibition+of+return.">Inhibition of return.</a> Trends in Cognitive Sciences. 4 (4), 138-147 (2000).</li><li>Henderson, J. M., Luke, S. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oculomotor+inhibition+of+return+in+normal+and+mindless+reading.">Oculomotor inhibition of return in normal and mindless reading.</a> Psychonomic Bulletin and Review. 12 (9), 414 (2012).</li><li>Rayner, K., Juhasz, B., Ashby, J., Clifton, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibition+of+saccade+return+in+reading.">Inhibition of saccade return in reading.</a> Vision Research. 43 (9), 1027-1034 (2003).</li><li>Henderson, J. M., Luke, S. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oculomotor+inhibition+of+return+in+normal+and+mindless+reading.">Oculomotor inhibition of return in normal and mindless reading.</a> Psychonomic Bulletin &amp; Review. 19, 1101-1107 (2012).</li><li>Folk, J. R., Morris, R. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+lexical+codes+in+reading:+Evidence+from+eye+movements,+naming+time,+and+oral+reading.">Multiple lexical codes in reading: Evidence from eye movements, naming time, and oral reading.</a> Journal of Experimental Psychology: Learning, Memory. 21 (6), 1412-1429 (1995).</li><li>Frazier, L., Rayner, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Making+and+correcting+errors+during+sentence+comprehension:+Eye+movements+in+the+analysis+of+structurally+ambiguous+sentences.">Making and correcting errors during sentence comprehension: Eye movements in the analysis of structurally ambiguous sentences.</a> Cognitive Psychology. 14 (2), 178-210 (1982).</li><li>Rayner, K., Chace, K. H., Slattery, T. J., Ashby, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eye+movements+as+reflections+of+comprehension+processing+in+reading.">Eye movements as reflections of comprehension processing in reading.</a> Scientific Studies of Reading. 10 (3), 241-255 (2009).</li><li>Schotter, E. R., Tran, R., Rayner, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Don&#8217;t+believe+what+you+read+(only+once)+comprehension+is+supported+by+regressions+during+reading.">Don&#8217;t believe what you read (only once) comprehension is supported by regressions during reading.</a> Psychological Science. 26 (6), 1218-1226 (2014).</li><li>Eskenazi, M. A., Folk, J. 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D., Warren, T., McConnell, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+EZ+reader+to+model+the+effects+of+higher+level+language+processing+on+eye+movements+during+reading.">Using EZ reader to model the effects of higher level language processing on eye movements during reading.</a> Psychonomic Bulletin and Review. 16 (1), 1-21 (2009).</li><li>Eskenazi, M. A., Folk, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regressions+during+reading:+The+cost+depends+on+the+cause.">Regressions during reading: The cost depends on the cause.</a> Psychonomic Bulletin and Review. 24 (4), 1211-1216 (2017).</li><li>Nelson, D. L., McEvoy, C. L., Walling, J. R., Wheeler, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+University+of+South+Florida+homograph+norms.">The University of South Florida homograph norms.</a> Behavior Research Methods. 12 (1), 16-37 (1980).</li><li>Baayen, R. H., Piepenbrock, R., Gulikers, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> CELEX2 LDC96L14. Web Download. 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The authors have nothing to disclose.

The current research provides a method for distinguishing between two different types of regressive eye movements during reading - those that are based on comprehension difficulty and those that are based on oculomotor error. The data provide evidence that a low-level attentional process, IOR, may depend on the type of regression. It was found that IOR only occurs for oculomotor-based regressions, but not for comprehension based regressions14. Thus, it is important to distinguish between different types of regressive eye movements when drawing conclusions about cognitive processing during reading.
While this protocol uses a commonly accepted and straightforward eye-tracking methodology, the critical steps are in creating the stimuli. It is important to control as many aspects of the stimuli as possible so that the researcher can accurately discriminate the two different types of regressive eye movements. It appears as though using biased ambiguous homophones that lead the reader to select the contextually incorrect meaning of the word results in comprehension-based regressive eye movements. In contrast, using short, low frequency, content words results in oculomotor corrective regressive eye movements. One modification that can be made to this design is using better control of the word from which the regression is launched. For example, in the comprehension-based regression stimuli, we were unable to control the length or frequency of the word from which the regression was launched in the original study14. In the analyses, we included the length and frequency of that word as covariates; however, it would be best to control the words as tightly as possible so that any variability can be attributed to IOR and not to other linguistic processing.
Given the design of the experiment, researchers are only able to analyze a small subset of the data. In the original experiment, only 15% of trials involved a regression that was made to the target word for oculomotor error and only 24% of trials involved a regression that was made to the target word for comprehension difficulty14. All other trials were discarded from analyses, as they were not relevant to the research question. Thus, a limitation of this design is that it requires large sample sizes of participants and a large number of items in the experiment. A typical sample size used in reading studies may not provide enough power to detect differences when they exist.
Future research could replicate and expand on these findings by investigating whether low skill and high skill readers vary in the degree to which they make different types of regressions. It is already known that beginning readers make more regressive eye movements than more skilled readers17, and that low-skill readers make more regressive eye movements than high skill readers18. However, it is not yet known whether there are differences between low skill and high skill readers on the IOR latency and whether they differ on the likelihood of making the two types of regressions. It is reasonable to expect that low skill readers would be more likely to make comprehension-based regressions than high skill readers would. However, there may be no differences in their probability of making oculomotor-based regressions as these rely less on the linguistic processing skills that distinguish high skill and low skill readers.

The method described in this paper was designed to investigate the role of inhibition of return (IOR) in regressive eye movements during reading, focusing on regressions triggered as a result of comprehension difficulty versus those triggered as a result of oculomotor error. Specifically, we investigated whether regressions launched as a result of comprehension difficulty and those launched as a result of oculomotor error are subject to IOR effects.
Regressive eye movements, or regressions, are eye movements that move backwards through the text. Depending on reader and text characteristics, 10-25% of eye movements move backwards1. This has led researchers to investigate whether IOR effects affect regressive eye movements during natural reading. IOR is an oculomotor effect that results in increased latency to return attention to a target that had been previously attended compared to a target that had not been previously attended2. While much of the work done to establish IOR effects has involved non-reading visual attention tasks3, the effect has been extended to reading4,5.
The work examining regressions and IOR in reading has focused on whether an oculomotor effect such as IOR can influence eye movement control in reading. One study5 found evidence of IOR in a reading task. They found that readers spent approximately 30 ms longer on the fixation preceding a regression. This was interpreted as the IOR &#34;cost&#34; - the delay before returning to a previously fixated position. This was supported in regular reading and mindless reading conditions6.
Despite evidence that IOR can be found in regular reading situations, it is clear that regressive eye movements do not all have the same underlying cause. Regressions resulting from comprehension difficulty have been well documented7,8,9,10.&#160;Despite the evidence that eye movements during reading are generally guided by cognitive and linguistic factors1, it is also assumed that sometimes regressions occur in response to low-level oculomotor factors, such as target overshoot1. It is assumed that on some trials, readers mis-program a saccade and land beyond their intended target word (an overshoot). In this case, a short, corrective regressive saccade may occur so that the unintentionally skipped word can be fixated. Given that two underlying mechanisms - linguistic and oculomotor - have been posited for regressive eye movements, it is not clear whether IOR occurs for both. The current method allows for the measurement of IOR effects when regressive eye movements have been launched as a result of comprehension difficulties and as a result of oculomotor overshoot. Thus, the method allows researchers to distinguish the underlying mechanisms of regressive eye movements, allowing for the evaluation of IOR effects.
The current method takes advantage of the two identified mechanisms triggering regressive eye movements. By designing the materials so that re-reading is likely to be triggered by comprehension difficulty or overshoot, researchers are able to examine the circumstances under which IOR might occur in reading. To encourage re-reading as a result of oculomotor error, we embedded short target words that show high skipping rates of about 50% (adapted from previous research11). Skipped words are often followed by a corrective regressive saccade when the skipping is a result of oculomotor error11. The other set of materials was comprised of sentences that contained semantically ambiguous homographic homophones (e.g., grade: school/incline). The sentences were adapted from an ambiguity study12 and contained information as to the intended meaning of the homophone that followed the word. Thus, this would increase the chances that readers would re-read for comprehension. The context was consistent with the less-likely meaning of the homophone, making it likely that readers would have to re-read after having initially selected the more frequent, dominant meaning on their first encounter with the target word. The combination of eye-movement monitoring and materials designed to increase regressions makes this method unique in allowing for the examination of regressive eye movements with differing underlying causes.
Understanding the mechanisms underlying regressive saccades and the role that oculomotor factors such as IOR play in them is important to models of eye movement control as well as for understanding the relationship between oculomotor and cognitive control of eye movements. For example, a recent version of the E-Z Reader model of eye movement control employs a 30 ms cost for all regressive eye movements13. However, our methodology demonstrated that such a cost only applies to regressions resulting from oculomotor error.
Eye movement measures allow researchers to track the moment-to-moment cognitive processing during reading1. Recently, models of eye movement control have begun to try to explain the mechanisms underlying regressive eye movements. Since regressions are often launched in relation to comprehension difficulties, any researcher interested in understanding the comprehension processes during reading should attempt to differentiate regressions resulting from oculomotor error versus comprehension processes. This methodology indicates that a cost for regressions is a result only of oculomotor error, serving as a launching point for differentiating between types of regressions. The combination of eye movement measures (regressions, fixation times before regressions) and carefully controlled materials allow for this differentiation.

<table border="0" cellpadding="0" cellspacing="0" style="width:572px;" width="573">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Eyelink 1000 Plus</td>
			<td style="width:71px;">SR Research</td>
			<td style="width:119px;"></td>
			<td style="width:167px;">Video-based eye tracker</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Experiment Builder Software</td>
			<td style="width:71px;">SR Research</td>
			<td style="width:119px;"></td>
			<td>Software to build eye tracking experiments</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Data Viewer</td>
			<td style="width:71px;">SR Research</td>
			<td style="width:119px;"></td>
			<td>Software to retireve eye tracking measures</td>
		</tr>
	</tbody>
</table>

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 results of our previous work using this paradigm14 resulted in a 17% regression rate in the oculomotor error condition and a 29% regression rate in the comprehension difficulty condition14. In the oculomotor error condition, 32% of the regressions were to previously fixated words and 68% of regressions were to previously skipped words. The reverse pattern occurred in the comprehension difficulty condition. Of the 29% of regressions, 61% were to previously fixated words whereas 39% were to previously skipped words (See Table 1).
The important variable of interest in these experiments is reading time on wordn+1, or the word from which the regression was launched. An IOR effect would be indicated by longer reading times on wordn+1 prior to the regression when the target wordn was previously fixated compared to when it was skipped. Figure 1 indicated that participants spent significantly more time on wordn+1 prior to a regression to wordn when it was previously fixated (mean = 296 ms, standard error (SE) = 17 ms) than when it was skipped (mean= 253 ms, SE = 14 ms), consistent with an IOR effect. However, no IOR effect was observed in the comprehension difficulty condition. In this condition, reading times on wordn+1 were no different when a regression was made to wordn when it was previously fixated (mean = 296 ms,SE = 10 ms) than when it was previously skipped (mean = 291 ms, SE = 8 ms).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58442/58442fig1.jpg" /> 
Figure 1: Reading times on wordn+1 prior to making a regression to wordn. This figure represents the reading times on wordn+1 from which the regression was launched. The regression was either made to a word that was previously fixated or previously skipped. Regression to the previously fixated words are expected to be longer if there is an IOR effect, demonstrating the latency to move the eyes back to a previously fixated target. Values are mean &#177; SE (error bars). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58442/58442fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Oculomotor Error</td>
			<td colspan="2">Comprehension Difficulty</td>
		</tr>
		<tr>
			<td colspan="2">17% Regression Rate</td>
			<td colspan="2">29% Regression Rate</td>
		</tr>
		<tr>
			<td>Previously Fixated</td>
			<td>Previously Skipped</td>
			<td>Previously Fixated</td>
			<td>Previously Skipped</td>
		</tr>
		<tr>
			<td>32%</td>
			<td>68%</td>
			<td>61%</td>
			<td>39%</td>
		</tr>
	</tbody>
</table>
Table 1: Probability of making a regression to a previously skipped or fixated target word. This table breaks down the probability of making each type of regression. The overall regression rates for the two types are presented on the top lines. The bottom line represents the percentage of times that the eyes returned to a previously fixated or previously skipped word.

Watch this Scientific Journal Video about Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading at JoVE.com

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

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 ...

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Nanyang Technological University

Research

JoVE Journal

Behavior

6.1K Views.  Kent State University. 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.

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

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, Cortisol, and a Sleep vs. Wake Consolidation Delay: Combining Methods to Uncover an Interactive Effect of Sleep and Cortisol on Memory

Although rises in cortisol can benefit memory consolidation, as can sleep soon after encoding, there is currently a paucity of literature as to how these two factors may interact to influence consolidation. Here we present a protocol to examine the interactive influence of cortisol and sleep on memory consolidation, by combining three methods: eye tracking, salivary cortisol analysis, and behavioral memory testing across sleep and wake delays. To assess resting cortisol levels, participants gave a saliva sample before viewing negative and neutral objects within scenes. To measure overt attention, participants&#8217; eye gaze was tracked during encoding. To manipulate whether sleep occurred during the consolidation window, participants either encoded scenes in the evening, slept overnight, and took a recognition test the next morning, or encoded scenes in the morning and remained awake during a comparably long retention interval. Additional control groups were tested after a 20&#160;min&#160;delay in the morning or evening, to control for time-of-day effects. Together, results showed that there is a direct relation between resting cortisol at encoding and subsequent memory, only following a period of sleep. Through eye tracking, it was further determined that for negative stimuli, this beneficial effect of cortisol on subsequent memory may be due to cortisol strengthening the relation between where participants look during encoding and what they are later able to remember. Overall, results obtained by a combination of these methods uncovered an interactive effect of sleep and cortisol on memory consolidation.

We present a protocol used to discover an interactive effect between sleep and cortisol on memory consolidation, particularly for negative arousing images. Specifically, the experimental design utilizes eye tracking, salivary cortisol analysis, and behavioral memory testing &#8211; methods that can be used with both healthy and clinical participants.

Although rises in cortisol can benefit memory consolidation, as can sleep soon after encoding, there is currently a paucity of literature as to how these ...

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational methods, it is difficult to make a quantitative assessment of a child's visual problems. This limits accurate diagnostics in children under the age of 4 years and in children with intellectual disabilities. Here we describe a quantitative method that overcomes these problems. The method uses a remote eye tracker and a four choice preferential looking paradigm to measure eye movement responses to different visual stimuli. The child sits without head support in front of a monitor with integrated infrared cameras. In one of four monitor quadrants a visual stimulus is presented. Each stimulus has a specific visual modality with respect to the background, e.g., form, motion, contrast or color. From the reflexive eye movement responses to these specific visual modalities, output parameters such as reaction times, fixation accuracy and fixation duration are calculated to quantify a child's viewing behavior. With this approach, the quality of visual information processing can be assessed without the use of communication. By comparing results with reference values obtained in typically developing children from 0-12 years, the method provides a characterization of visual information processing in visually impaired children. The quantitative information provided by this method can be advantageous for the field of clinical visual assessment and rehabilitation in multiple ways. The parameter values provide a good basis to: (i) characterize early visual capacities and consequently to enable early interventions; (ii) compare risk groups and follow visual development over time; and (iii), construct an individual visual profile for each child.

A method is described to quantify the quality of visual information processing based on reflexive eye movements in response to specific visual modalities. Reaction times and fixation output parameters are used to characterize visual performance in children with and without visual impairments from 6 months of age.

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational ...

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with pathological involvement of upper and lower motoneurons, subsequently leading to progressive loss of motor and speech abilities. In addition, cognitive functions are impaired in a subset of patients. To evaluate these potential deficits in severely physically impaired ALS patients, eye-tracking is a promising means to conduct cognitive tests. The present article focuses on how eye movements, an indirect means of communication for physically disabled patients, can be utilized to allow for detailed neuropsychological assessment. The requirements, in terms of oculomotor parameters that have to be met for sufficient eye-tracking in ALS patients are presented. The properties of stimuli, including type of neuropsychological tests and style of presentation, best suited to successfully assess cognitive functioning, are also described. Furthermore, recommendations regarding procedural requirements are provided. Overall, this methodology provides a reliable, easy to administer and fast approach for assessing cognitive deficits in patients who are unable to speak or write such as patients with severe ALS. The only confounding factor might be deficits in voluntary eye movement control in a subset of ALS patients.

Cognitive deficits are common in about one third of patients with amyotrophic lateral sclerosis, a neurological condition leading to progressive impairments in speech and movement abilities. To conduct cognitive tests in patients unable to speak or write a reliable and easy to administer eye-tracking paradigm was developed.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with pathological involvement of upper and lower motoneurons, subsequently leading to ...

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 ...

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact with people around them. Head-mounted eye tracking offers a unique opportunity to capture children's dynamic egocentric views and how they allocate visual attention within those views. This protocol provides guiding principles and practical recommendations for researchers using head-mounted eye trackers in both laboratory and more naturalistic settings. Head-mounted eye tracking complements other experimental methods by enhancing opportunities for data collection in more ecologically valid contexts through increased portability and freedom of head and body movements compared to screen-based eye tracking. This protocol can also be integrated with other technologies, such as motion tracking and heart-rate monitoring, to provide a high-density multimodal dataset for examining natural behavior, learning, and development than previously possible. This paper illustrates the types of data generated from head-mounted eye tracking in a study designed to investigate visual attention in one natural context for toddlers: free-flowing toy play with a parent. Successful use of this protocol will allow researchers to collect data that can be used to answer questions not only about visual attention, but also about a broad range of other perceptual, cognitive, and social skills and their development.

Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact ...

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Children with autism spectrum disorders (ASD) are known to have sensory-perceptual processing deficits that weaken their abilities to attend and perceive social stimuli in daily living contexts. Since daily social episodes consist of subtle dynamic changes in social information, any failure to attend to or process subtle human nonverbal cues, such as facial expression, postures, and gestures, might lead to inappropriate social interaction. Traditional behavioral rating scales or assessment tools based on static social scenes have limitations in capturing the moment-to-moment changes in social scenarios. An eye-tracking assessment, which can be administered in a video-based mode, is therefore preferred, to augment clinical observation. In this study, using the single-case comparison design, the eye-tracking data of three participants, a child with autism spectrum disorder (ASD), another with comorbid attention deficit-hyperactive disorder (ADHD), and a neurotypical control, are captured while they view a video of social scenarios. The eye-tracking experiment has helped answer the research question: How does social attention differ between the three participants? By predefining areas of interest (AOIs), their visual attention on relevant or irrelevant social stimuli, how fast each participant attends to the first social stimuli appearing in the videos, for how long each participant continues to attend to those stimuli within the AOIs, and the gaze shifts between multiple social stimuli appearing concurrently in the same social scene are captured, compared, and analyzed in a video-based eye-tracking experiment.

This is a qualitative comparative case study analysis of eye-tracking data on the first moments of social video scenes as viewed by three participants: one with autism spectrum disorder, one with comorbid attention deficit-hyperactive disorder, and one neurotypical control.

Children with autism spectrum disorders (ASD) are known to have sensory-perceptual processing deficits that weaken their abilities to attend and perceive ...

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during active behavior. An important tool for quantifying behavior is eye tracking, which is an indispensable tool for studying visual attention. However, eye tracking is challenging to use concurrently with invasive electrophysiology and this approach has consequently been little used. Here, we present a proven experimental protocol to conduct single-neuron recordings with simultaneous eye tracking in humans. We describe how the systems are connected and the optimal settings to record neurons and eye movements. To illustrate the utility of this method, we summarize results that were made possible by this setup. This data shows how using eye tracking in a memory-guided visual search task allowed us to describe a new class of neurons called target neurons, whose response was reflective of top-down attention to the current search target. Lastly, we discuss the significance and solutions to potential problems of this setup. Together, our protocol and results suggest that single-neuron recordings with simultaneous eye tracking in humans are an effective method to study human brain function. It provides a key missing link between animal neurophysiology and human cognitive neuroscience.

We describe a method to conduct single-neuron recordings with simultaneous eye tracking in humans. We demonstrate the utility of this method and illustrate how we used this approach to obtain neurons in the human medial temporal lobe that encode targets of a visual search.

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during ...

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

This experimental protocol was designed to investigate whether visual attention is obligatorily deployed at the endpoint of saccades. To this end, we recorded the eye position of human participants engaged in a saccade task via eye tracking and assessed visual orientation discrimination performance at various locations during saccade preparation. Importantly, instead of using a single saccade target paradigm for which the saccade endpoint typically coincides roughly with the target, this protocol comprised the presentation of two nearby saccade targets, leading to a distinct spatial dissociation between target locations and saccade endpoint on a substantial number of trials. The paradigm allowed us to compare presaccadic visual discrimination performance at the endpoint of accurate saccades (landing at one of the saccade targets) and of averaging saccades (landing at an intermediate location in between the two targets). We observed a selective enhancement of visual sensitivity at the endpoint of accurate saccades but not at the endpoint of averaging saccades. Rather, before the execution of averaging saccades, visual sensitivity was equally enhanced at both targets, suggesting that saccade averaging follows from unresolved attentional selection among the saccade targets. These results argue against a mandatory coupling between visual attention and saccade programming based on a direct measure of presaccadic visual sensitivity rather than saccadic reaction times, which have been used in other protocols to draw similar conclusions. While our protocol provides a useful framework to investigate the relationship between visual attention and saccadic eye movements at the behavioral level, it can also be combined with electrophysiological measures to extend insights at the neuronal level.

This experimental protocol combined eye tracking and the assessment of presaccadic visual sensitivity in a dual task paradigm, consisting of a free choice saccade task and a visual discrimination task, to investigate the deployment of visual spatial attention before both, accurate and averaging saccades.

This experimental protocol was designed to investigate whether visual attention is obligatorily deployed at the endpoint of saccades. To this end, we ...