We wish to thank everyone who has participated in research conducted in the Language Research Lab at the University of Illinois at Chicago. We also thank Frances Daniel, who was instrumental in helping to develop the programs used to collect the data presented here.

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	<li>Ashby, J., Rayner, K., 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=Eye+movements+of+highly+skilled+and+average+readers:+Differential+effects+of+frequency+and+predictability.">Eye movements of highly skilled and average readers: Differential effects of frequency and predictability.</a> Q. J. Exp. Psychol.. 58A, 1065-1086 (2005).</li><li>Binder, K. S., 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=An+eye-movement+analysis+of+ambiguity+resolution:+Beyond+meaning+access.">An eye-movement analysis of ambiguity resolution: Beyond meaning access.</a> Discourse Processes. 48, 305-330 (2011).</li><li>Bowdle, B., Gentner, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+career+of+metaphor.">The career of metaphor.</a> Psychol. Rev. 112, 193-216 (2005).</li><li>Campbell, S. J., Raney, G. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Life+Is+A+Pencil:+Using+Eye+Tracking+to+Explore+Metaphor+Processing.">Life Is A Pencil: Using Eye Tracking to Explore Metaphor Processing.</a> , (2011).</li><li>Dr&#232;ze, X., Hussherr, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Internet+advertising:+Is+anybody+watching.">Internet advertising: Is anybody watching.</a> J. Interact. Marketing. 17, 8-23 (2003).</li><li>Glucksberg, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+psycholinguistics+of+metaphor.">The psycholinguistics of metaphor.</a> TRENDS Cogn. Sci. 7, 92-96 (2003).</li><li>Juhasz, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+processing+of+compound+words+in+English:+Effects+of+word+length+on+eye+movements+during+reading.">The processing of compound words in English: Effects of word length on eye movements during reading.</a> Lang. Cogn. Process. 23, 1057-1088 (2008).</li><li>Lohse, G. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Consumer+Eye+movement+patterns+on+yellow+pages+advertising.">Consumer Eye movement patterns on yellow pages advertising.</a> J. Advert. 26, 61-73 (1997).</li><li>Kaakinen, J. 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E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eye+movement+control+in+reading+and+visual+search:+Effects+of+word+frequency.">Eye movement control in reading and visual search: Effects of word frequency.</a> Psychonomic Bull. Rev. 3, 245-248 (1996).</li><li>Rayner, K., Raney, G. E., Pollatsek, A., Lorch, R. F., O'Brien, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Ch. 1. Eye movements and discourse processing. , 9-36 (1995).</li><li>Rayner, K., Pollatsek, A., 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=.">.</a> Ch. 4. Psychology of Reading.. , 91-134 (2012).</li><li>Rayner, K., Reichle, E. D., Stroud, M. J., Williams, C. C., Pollatsek, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effect+of+word+frequency,+word+predictability,+and+font+difficulty+on+the+eye+movements+of+young+and+older+readers.">The effect of word frequency, word predictability, and font difficulty on the eye movements of young and older readers.</a> Psychol. Aging. 21, 448-465 (2006).</li><li>Yang, G. Z., Dempere-Marco, L., Hu, X. P., Rowe, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+search:+Psychophysical+models+and+practical+applications.">Visual search: Psychophysical models and practical applications.</a> Image Vis. Comput. 20, 291-305 (2002).</li></ol>

No conflicts of interest exist. The authors have no financial interests in the manufacturers of the equipment described here.

Advances in technology have led to the availability of highly accurate, reliable, and easy-to-use eye tracking systems. In the field of language research, monitoring eye movements allows researchers to determine how readers evaluate a text. Fixation patterns can be used to determine what parts of a text are most difficult to process or are easiest to process, what parts of a text can be understood with a single fixation and what parts require multiple fixations or regressions, and the sequence in which readers process the text. Together, these measures support conclusions about the cognitive processes involved in text comprehension.
Comprehension is based on an interaction between the information contained in a text and the cognitive skills and knowledge applied by the reader; therefore, a complete understanding of text comprehension can only be obtained by using a measure of processing that is sensitive to properties of the text and characteristics of the reader. As noted previously, eye movements vary based on linguistic features, such as word frequency, word length, and sentence complexity1,2,7,10,11, and reader characteristics, such as reading ability and topic knowledge1,9. As such, eye movements provide an ideal measure of text comprehension.
Because eye movements vary based on many linguistic features, precise control of stimuli is essential when studying the cognitive processes involved in text comprehension. Researchers often expend as much effort to develop controlled stimuli as is needed to conduct the actual experiment. Indeed, the research is only as good as the stimuli.
Eye tracking methodologies can provide valuable data for any field of research in which participants are shown visual stimuli and are required to evaluate the stimuli. For example, in the field of advertising, one could determine what parts of a visual ad attract the most attention by measuring what parts of the ad people look at the most5,8. In medical research, one could determine whether interns and experienced physicians evaluate an X-Ray or MRI image in the same manner by looking at the eye movement scan path and how much time is spent evaluating critical physical structures15. In these examples the pattern of eye movements indicates what parts of the image attract the attention of the person viewing the image.

When readers read a text, they move their eyes from word to word through an alternating pattern of fixations (points at which the eyes are stationary and focused on a word) and saccades (points at which the eye are moving between words). Fixations following saccades that move the reader forward through a text are called forward fixations and fixations following saccades that move the reader to prior points in a text are called regressive fixations. The basic assumption of eye tracking methods is that increased processing demands are associated with increased processing time or changes in the pattern of fixations. Increased processing time may be reflected by longer duration fixations or a larger number of fixations (forward and regressive).
Eye movements provide several important advantages as a measure of reading behavior relative to measuring reading times for an entire passage or sentence-by-sentence reading times. First, monitoring eye movements produces a continuous, online record of reading performance. This provides the ability to examine text processing demands at a global level (across an entire text), the sentence level (individual sentences), or the local level (individual words or phrases). For example, changes in global difficulty lead to changes in several measures of performance, such as total reading time, number of forward fixations, and the number of regressions. Changes in local level difficulty also affects several measures, such as reading times for individual words, the probability of fixating words, and likelihood of making regressions to specific words. Overall reading times or sentence-by-sentence reading times do not provide such detailed measures of reading performance. Second, eye movements are a natural part of reading; therefore, no additional task demands are placed on a reader. Third, multiple aspects of eye movements may be analyzed (e.g.&#160;fixation duration, saccade length, and regression frequency), providing a window into different elements of the reading process. Fourth, eye movements directly reflect processing demands associated with features of the text being read. For example, eye movements vary as a function of word frequency10,11, word length7, lexical ambiguity2, contextual constraint1, and repetition10,13. Fifth, eye movements reflect individual differences in readers. For example, eye movements vary based on reading ability1, prior knowledge about a topic9, and age of the reader14. Rayner, Pollatsek, Ashby, and Clifton13 provide a thorough review of eye movements during reading. Taken together, these advantages make eye movements an ideal measure of reading behavior.
The research described here used an eye movement methodology to study the cognitive processes involved in text comprehension. Specifically, the experiment was designed to explore how familiar and unfamiliar metaphors are processed4. In this study, participants read short texts presented on a computer monitor while their eye movements were monitored. Each text contained four sentences. The first two sentences provided a context that was consistent with the intended meaning of the metaphor. The metaphors were presented in the third sentence. The fourth sentence served as a neutral conclusion. Examples of texts containing familiar (1) and unfamiliar (2) metaphors are presented below with the metaphors underlined for ease of identification.
<ol>
	<li>Familiar metaphor passage. Peter had never seen such a beautiful girl before. He really hoped that something special would grow between the two of them. He thought to himself that love is a flower. Peter called the girl later that night.</li>
	<li>Unfamiliar metaphor passage. Graduating from college is a very important milestone for many people. It takes a lot of hard work to reach this goal. For many people a degree is a doorway. Graduating from college is something to be very proud of.</li>
</ol>
Past research based on a variety of methods has shown that familiar metaphors are easier to understand (processed faster) than unfamiliar metaphors3,6. The power of the eye tracking method is that the source of processing difficulty can be isolated to specific words. For example, researchers can determine if the extra time needed to comprehend unfamiliar metaphors is obtained by slowing down when reading each word in the metaphors, or slowing down on the last words of the metaphor (when it is clear the prior phrase is a metaphor). Furthermore, patterns of eye movements support inferences about the cognitive processes involved in comprehending the metaphors. For example, when reading novel or unfamiliar metaphors, readers would need to further process the metaphors to extract the figurative meanings. This might be reflected in the eye movement pattern as regressing to the start of the metaphors and then reading through the metaphors a second time. Readers also might try to compare the meanings of the two key words in the metaphors (e.g.&#160;love and flower), which could lead to a pattern of back and forth eye movements between the key words. Alternatively, when reading familiar metaphors, the readers might extract the figurative meanings immediately upon reading the metaphors; therefore, no regressions would be needed. The key point is that eye movement patterns allow researchers to make inferences about the online processes used to comprehend the metaphors. This supports more descriptive conclusions than simply stating that overall processing time is longer for unfamiliar than familiar metaphors.
The study described here illustrates a common method of contrasting eye movement patterns for two types of written stimuli and provides a concrete situation for describing critical aspects of eye movement methodologies. Importantly, the eye movement method described here can be generalized to study many other issues, such as how readers solve word-based math problems that vary in complexity (e.g.&#160;high versus low complexity), or how word problems are solved by domain experts versus novices. Eye movements could be used to determine which words in the problems attract the most attention (i.e.&#160;longest fixation durations and the largest number of fixations) and whether experts and novices focus on the same information. In each case, monitoring eye movements would provide a record of the moment-by-moment changes in processing demands associated with comprehending the problems being read.

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			<td height="61" style="height:61px;width:165px;">Eye Tracker</td>
			<td style="width:165px;">SR Research Ltd.</td>
			<td style="width:165px;">EyeLink 1000 Remote Desktop model</td>
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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.

Multiple aspects of eye movements may be analyzed, and these are often categorized as global and local measures. Global measures reflect eye movement behavior across an entire trial, such as overall reading time, the average fixation duration for all words, and the total number of fixations (both forward and regressive). Local measures reflect eye movement behavior for a specific target word or set of target words (such as words in the metaphors) and are referred to as regions of interest. Local measures include fixation times for target words, the probability of fixating target words, the number of fixations on target words, and the number of regressions to target words, to name a few. In addition, local measures are often discussed in terms of first run, second run, and total time. First run (also called first pass) refers to fixations made on a target word before moving to another word. This can be thought of as the first encounter with the target word. Second run (also called second pass) refers to fixations made on a target word after having left the target word initially. These are generally regressions to the target words. Total time includes all fixations made on the target words (all runs combined). More complex measures are also used to evaluate processing time and eye movement patterns, such as regression path duration, which is defined as the total time from initially encountering a word to moving to the subsequent word. For example, if a reader (1) fixated the last word in a metaphor, (2) returned to fixate the first word in the metaphor, (3) fixated at the last word again, and then (4) fixated the first word in the next sentence, the regression path duration would include the first three fixations in this example.
Eye movements from a sample trial are presented in Figure 2. The circles represent fixation locations and the yellow lines represent saccades, which show how the reader moved from word to word. The extra processing difficulty associated with the metaphor can be seen by the density of fixations on the metaphor. The fixations can be grouped by regions of interest (e.g.&#160;words in the metaphors) to determine how much time was spent on each word and the number of fixations made on each word for familiar and unfamiliar metaphors. The results shown in Figure 3 demonstrate that more time was spent processing the two key content words in unfamiliar metaphors than in familiar metaphors.
The advantages of recording eye movements as opposed to reading times for an entire passage or sentence-by-sentence reading times can be seen in Figures 2 and 3. For example, there are five fixations on the metaphor region (Figure 2), three forward fixations and two regressive fixations, which reflect the reader reading through the metaphor to &#34;doorway&#34; and then returning (regressing) to &#34;degree.&#34; In essence, the metaphor was read twice. This outcome would go unnoticed if only sentence reading times or overall reading times were measured. As a second example, Figure 3 shows that more time was spent reading the last word of the metaphor than the other three words in the metaphor, and that reading time was faster for familiar than unfamiliar metaphors for three of the four words in the metaphors. Measuring sentence-by-sentence reading times would indicate longer reading times for sentences containing familiar metaphors than unfamiliar metaphors, but it would be impossible to know if the extra reading time was distributed across all words in the metaphor or was confined to specific words, and how much time was spent on each word would be unknown. These two examples show the benefit of recording continuous, online reading behavior.
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/50780/50780fig1highres.jpg" src="/files/ftp_upload/50780/50780fig1.jpg" /> 
Figure 1. The left picture shows a participant positioned on the forehead/chin rest while looking at a computer display. The infrared light source and video camera are located beneath the display. The right picture shows the experimenter&#39;s display. The large image in the top frame shows the participant&#39;s face around the right eye (the eye being tracked) and the small image shows a close up of the right eye. The blue areas are areas of high infrared light reflection from the participant&#39;s hair (large image) and pupil (small image). The cross hairs over the eye identify the center of the pupil and the corneal reflection near the bottom of the pupil. <a href="https://www.jove.com/files/ftp_upload/50780/50780fig1highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 3" fo:content-width="5in" fo:src="/files/ftp_upload/50780/50780fig3highres.jpg" src="/files/ftp_upload/50780/50780fig3.jpg" /> 
Figure 2. Eye movements from a sample passage containing an unfamiliar metaphor (a degree is a doorway). The circles indicate fixation locations and the yellow lines indicate saccade paths. Larger circles represent longer duration fixations. The small numbers next to the circles indicate fixation duration in milliseconds (msec). Gaps (no saccade line, such as between the words many people) indicate points where a track loss occurred due to an artifact such as subjects momentarily closing their eyes. The figure shows a regression from doorway to degree within the metaphor.
<img alt="Figure 2" fo:content-width="5in" fo:src="/files/ftp_upload/50780/50780fig2highres.jpg" src="/files/ftp_upload/50780/50780fig2.jpg" /> 
Figure 3. Total fixation duration (msec) on words in familiar and unfamiliar metaphors. The words in the horizontal axis correspond to the sample familiar (F) and unfamiliar (U) metaphors. The data represent an average across 10 familiar and 10 unfamiliar metaphors.

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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. 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.

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26.7K Views.  University of Illinois at Chicago. 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.