The research reported here was supported by the National Institute of Child Health and Human Development of the National Institutes of Health under award number R01HD083310 and a National Science Foundation Graduate Research Fellowship under grant no. DGE&#8208;1324585. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.

All methods described here have been approved by the Northwestern University Institutional Review Board.
1. Stimuli Creation
NOTE: The visual stimuli (see Figure 1) used in the representative design reported below were originally developed in Havy and Waxman (2016)18 and are available for download at https://osf.io/n6uy8/.
<ol>
	<li>To create a new continuous category, first design a pair of novel digital images. Next, morph the pair of images together, using software (see, e.g., Table of Materials) to form a continuum of exemplars between the two original images. Create at least two categories in this way so that one can serve as the category to be learned while the other provides the novel category exemplar for the test trial.</li>
	<li>Select the familiarization exemplars at evenly spaced intervals from across each learned category&#8217;s continuum (e.g., the 0%, 20%, 40%, 60%, 80%, and 100% exemplars). Select an appropriate number of exemplars (e.g., six). Commensurate with the difficulty of the category and age of the participants.</li>
	<li>To create the exemplars for the test phase, select the midpoints of the familiar category&#8217;s continuum and the novel category&#8217;s continuum (i.e., the 50% exemplar). Then match the color of the novel exemplar to that of the familiar exemplar using an image manipulation program (see, e.g., Table of Materials).</li>
	<li>Record auditory stimuli produced by a female native English speaker in a soundproof booth. If possible, use the same speaker for both labeling phrases (i.e., &#8220;Look at the modi&#8221;) and non-labeling phrases (i.e., &#8220;Look at that!&#8221;).
	<ol>
		<li>Instruct the speaker to produce all utterances in infant- or child-directed speech.</li>
		<li>Select utterances which are approximately the same length across conditions, likely around 1,500 ms per phrase.</li>
	</ol>
	</li>
</ol>
2. Apparatus
<ol>
	<li>Use an appropriate eye-tracker. To collect adequate eye-tracking data for a familiarization-test measure, most widely available eye-trackers will suffice: the objects occupy large portions of the screen, and the data analysis investigates performance over a long window, rather than individual, rapidly occurring eye movements such as saccades.</li>
	<li>Because this task requires eye-tracking infants, ensure that the system conforms to several requirements.
	<ol>
		<li>First, use an &#173;eye-tracker with a remote tracking mode, which does not require infants to place their heads on a chin-rest. Ensure that the eye-tracker can tolerate relatively large head movements or readjustments.</li>
		<li>Second, use a relatively large screen to display the images to infants, (e.g., 57 x 45 cm).</li>
		<li>Third, use an extendable arm mount for the eye-tracker to facilitate data collection by allowing the researcher to adjust the height of the eye-tracker to each infant.</li>
		<li>Fourth,&#160;make the eye-tracking equipment unobtrusive, focusing infants&#8217; attention solely on the display screen. For instance, some systems integrate the eye-tracking equipment with the display monitor or mount the equipment directly below the monitor.</li>
	</ol>
	</li>
	<li>Note that this task can also be completed by hand-coding high-quality video data of the infants&#8217; looking behavior. While hand-coding techniques may pose some challenges for using the more fine-grained time-course analyses, hand-coded data are entirely sufficient&#160;for the aggregate looking analyses.</li>
</ol>
3. Task Design
<ol>
	<li>In the eye-tracker&#8217;s associated software (see, e.g., Table of Materials), create four different conditions: Fully Supervised, Unsupervised, Semi-supervised, and Reversed Semi-supervised. Ensure these conditions are separate, so that each infant will see only one condition.</li>
	<li>Generate at least two pseudo-random orders of the learning exemplars, with the constraint that no more than two exemplars from the same side of the continuum (0-40% or 60-100%) can be shown consecutively.</li>
	<li>Create familiarization videos that pair the auditory stimuli with the visual stimuli as appropriate for each condition.
	<ol>
		<li>Combine the visual and auditory stimuli in video editing software (see, e.g., Table of Materials). Present all images on the same background. Set the onset of the auditory stimulus to an appropriate range, between 500 ms and 1,500 ms after the onset of the visual stimulus. Use this short delay to ease infants&#8217; processing load 19.</li>
		<li>For instance, in the Fully Supervised condition, pair each familiarization exemplar with a labeling phrase.</li>
		<li>In the Unsupervised condition, pair each familiarization exemplar with a non-labeling phrase.</li>
		<li>In the Semi-supervised condition, pair only the first two exemplars in each order with labeling phrases but the rest with non-labeling phrases.</li>
		<li>For the Reversed Semi-supervised condition, pair the final two exemplars with labeling phrases but the first four with non-labeling phrases (see Figure 1).</li>
		<li>Upload these videos into the eye-tracker software, ordering the familiarization videos as determined by the pseudo-randomized order.</li>
	</ol>
	</li>
	<li>Upload a short (10 s or less) attention-grabbing animation displayed in the center of the screen after familiarization: this will ensure that most infants are looking to the center of the screen when the test phase begins.</li>
	<li>Finally, for each learning category, design two test trials, each featuring two exemplars displayed side-by-side. Ensure that for both test trials, one exemplar will represent the midpoint of the now-familiar category while the other represents the midpoint of the novel category.
	<ol>
		<li>Counterbalance the trials so that the left/right positioning of the novel exemplar in the test trial is reversed across videos.</li>
		<li>Upload these test trials to the eye-tracker software, positioning them after the post-familiarization attention-getter. Counterbalance these trials&#8217; presentation so each infant has an equal chance of seeing a left-novel or right-novel test trial.</li>
		<li>Ensure that test trials last at least 5 s, and up to 20 s, in order for children initially looking away to accumulate sufficient looking.</li>
	</ol>
	</li>
</ol>
[Place Figure 1 here]
4. Study Procedure
<ol>
	<li>Before the infant arrives, set up the eye-tracker.
	<ol>
		<li>Randomly assign the infant to a condition and an order.</li>
		<li>Open the eye-tracker software and select the assigned condition/order pair.</li>
		<li>Now enter the participant number for this recording.</li>
	</ol>
	</li>
	<li>After performing the consent process, bring the infant and the caregiver to the eye-tracking room. Ensure the room is moderately lit without any distracting decorations on the walls.</li>
	<li>Place a chair in front of the eye-tracker at an appropriate distance for the model of eye-tracker being used. Seat the caregiver in this chair and the infant on the caregiver&#8217;s lap. If the infant does not wish to sit in the caregiver&#8217;s lap, they may sit on their own, or they may sit in a car seat.</li>
	<li>If the infant is sitting on the caregiver&#8217;s lap, instruct the caregiver not to bias infants&#8217; behavior in any way but to try to keep the infant centered on the caregiver&#8217;s lap. Provide caregivers with a pair of blacked-out sunglasses to wear so they cannot see the stimuli.</li>
	<li>Ask the infant to look at the eye-tracker screen; consider displaying an engaging image or video to attract their attention. Position the screen so that infants&#8217; eyes are within the calibration window.</li>
	<li>Perform the eye-tracker&#8217;s calibration procedure. Use a five-point calibration if possible, but less comprehensive calibrations are also likely to be adequate. Infants often respond better when the calibration image is an animation with auditory accompaniment.</li>
	<li>If the infant passes calibration, then begin the experiment. If not, recalibrate until they are successful. Any infants who cannot be calibrated are excluded.</li>
	<li>If multiple experiments are run consecutively, or if a single experiment is quite long, consider re-calibrating after each section.</li>
</ol>
5. Data Analysis
<ol>
	<li>Use data analysis software to perform this analysis (e.g., see Table of Materials).</li>
	<li>Create areas of interest (AOIs) around the exemplar positions on the left and right sides of the screen.</li>
	<li>For familiarization trials, use the appropriate AOI to assess the time infants spent looking to the exemplar displayed on each trial. Exclude any infant who does not show sustained looking for a majority of the exemplars (e.g., require that infants attend to 4 of a possible 6 familiarization exemplars for at least 25% of those trials).</li>
	<li>For the test trial, include only infants&#8217; first 5 s of accumulated looking. For younger infants, from 3 to 12 months of age, consider using a longer window such as 10 seconds of accumulated looking. Consider excluding infants who show insufficient sustained looking at test (e.g., accumulating less than 2.5 s of looking) or who fail to look to both of the exemplars.</li>
	<li>Now create a preference score for each infant&#8217;s test trial by dividing the amount of time spent looking to the novel exemplar by the total amount of time looking to both exemplars. To analyze these proportions, transform them first with an empirical logit or arc-sin square-root to make them suitable for analysis with linear models.</li>
	<li>For a time-course analysis of infants&#8217; looking behavior at the test, separate data into small bins (e.g., between 10 and 100 ms), and calculate a preference score within each bin for each infant.</li>
	<li>Perform an analysis of the time-course data, testing whether infants&#8217; pattern of looking throughout the test trial varies by condition. Note that multiple forms of analysis may answer this question, including a cluster-based permutation analysis20, as demonstrated here, and growth curve modeling.21
	<ol>
		<li>For a cluster-based permutations analysis, select a t-value threshold, corresponding to the desired alpha level (recommended alphas range from .01 to .20; note that this alpha value does not represent the overall test&#8217;s alpha level, merely the level required for individual time-bins to exceed the threshold). Sum the t-statistics for every consecutive time-bin that surpasses the chosen t-threshold; these cumulative t-statistics indicate the size of the divergences between conditions in the data.</li>
		<li>To determine if these divergences are greater than expected by chance, perform at least 1,000 simulations with the condition labels randomly shuffled. Evaluate the unshuffled data&#8217;s divergences against this chance-based distribution. 
		NOTE: It is this comparison of the original divergence against the chance-based distribution that determines the false-positive rate of the analysis, rather than the number of time-bins in which t-tests were conducted or even the t-value threshold selected for those initial t-tests. As a result, this analysis provides a conservative alternative to directly reporting the results from multiple t-tests across pre-specified time-bins (e.g., conducting tests every 500 ms).</li>
	</ol>
	</li>
</ol>

<ol><li>Eimas, P. D., Quinn, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Studies+on+the+Formation+of+Perceptually+Based+Basic-Level+Categories+in+Young+Infants%5BTitle%5D)+AND+%22Child+Development%22%5BJournal%5D)">Studies on the Formation of Perceptually Based Basic-Level Categories in Young Infants.</a> Child Development. 65 (3), 903-917 (1994).</li>
<li>Madole, K. L., Oakes, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Making+sense+of+infant+categorization%3A+Stable+processes+and+changing+representations%5BTitle%5D)+AND+%22Developmental+Review%22%5BJournal%5D)">Making sense of infant categorization: Stable processes and changing representations.</a> Developmental Review. 19 (2), 263-296 (1999).</li>
<li>Gelman, S. A., Markman, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Categories+and+induction+in+young+children%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">Categories and induction in young children.</a> Cognition. 23 (3), 183-209 (1986).</li>
<li>Ferry, A. L., Hespos, S. J., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Categorization+in+3-+and+4-month-old+infants%3A+An+advantage+of+words+over+tones%5BTitle%5D)+AND+%22Child+development%22%5BJournal%5D)">Categorization in 3- and 4-month-old infants: An advantage of words over tones.</a> Child development. 81 (2), 472-479 (2010).</li>
<li>Fulkerson, A. L., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Words+%28but+not+Tones%29+Facilitate+Object+Categorization%3A+Evidence+From+6-+and+12-Month-Olds%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">Words (but not Tones) Facilitate Object Categorization: Evidence From 6- and 12-Month-Olds.</a> Cognition. 105 (1), 218-228 (2007).</li>
<li>Balaban, M. T., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Do+words+facilitate+object+categorization+in+9-month-old+infants%3F%5BTitle%5D)+AND+%22Journal+of+Experimental+Child+Psychology%22%5BJournal%5D)">Do words facilitate object categorization in 9-month-old infants?</a> Journal of Experimental Child Psychology. 64 (1), 3-26 (1997).</li>
<li>Waxman, S. R., Braun, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Consistent+%28but+not+variable%29+names+as+invitations+to+form+object+categories%3A+New+evidence+from+12-month-old+infants%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">Consistent (but not variable) names as invitations to form object categories: New evidence from 12-month-old infants.</a> Cognition. 95 (3), B59-B68 (2005).</li>
<li>Balaban, M. T., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+examination+of+the+factors+underlying+the+facilitative+effect+of+word+phrases+on+object+categorization+in+9-month-old+infants%5BTitle%5D)+AND+%22Proceedings+of+the+20th+Boston+University+Conference+on+Language+Development%22%5BJournal%5D)">An examination of the factors underlying the facilitative effect of word phrases on object categorization in 9-month-old infants.</a> Proceedings of the 20th Boston University Conference on Language Development. 1, 483-493 (1996).</li>
<li>Waxman, S. R., Markow, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Words+as+invitations+to+form+categories%3A+evidence+from+12-+to+13-month-old+infants%5BTitle%5D)+AND+%22Cognitive+Psychology%22%5BJournal%5D)">Words as invitations to form categories: evidence from 12- to 13-month-old infants.</a> Cognitive Psychology. 29 (3), 257-302 (1995).</li>
<li>Zhu, X. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aZhu%2C+X+%22Semi-supervised+learning+literature+survey%22">Semi-supervised learning literature survey</a>. , (2005).</li>
<li>Chapelle, O., Scholkopf, B., Zien, A. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aChapelle%2C+O+%22Semi-supervised+learning%3A+Adaptive+computation+and+machine+learning%22">Semi-supervised learning: Adaptive computation and machine learning</a>. , MIT Press. Cambridge, Mass., USA. (2006).</li>
<li>Zhu, X., Goldberg, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Introduction+to+semi-supervised+learning%5BTitle%5D)+AND+%22Synthesis+lectures+on+artificial+intelligence+and+machine+learning%22%5BJournal%5D)">Introduction to semi-supervised learning.</a> Synthesis lectures on artificial intelligence and machine learning. 3 (1), 1-130 (2009).</li>
<li>Ferry, A. L., Hespos, S. J., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nonhuman+primate+vocalizations+support+categorization+in+very+young+human+infants%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Nonhuman primate vocalizations support categorization in very young human infants.</a> Proceedings of the National Academy of Sciences of the United States of America. 110 (38), 15231-15235 (2013).</li>
<li>Hunter, M. A., Ames, E. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+multifactor+model+of+infant+preferences+for+novel+and+familiar+stimuli%5BTitle%5D)+AND+%22Advances+in+infancy+research%22%5BJournal%5D)">A multifactor model of infant preferences for novel and familiar stimuli.</a> Advances in infancy research. , (1988).</li>
<li>Rose, S. A., Feldman, J. F., Jankowski, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Infant+visual+recognition+memory%5BTitle%5D)+AND+%22Developmental+Review%22%5BJournal%5D)">Infant visual recognition memory.</a> Developmental Review. 24 (1), 74-100 (2004).</li>
<li>Wetherford, M. J., Cohen, L. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Developmental+changes+in+infant+visual+preferences+for+novelty+and+familiarity%5BTitle%5D)+AND+%22Child+Development%22%5BJournal%5D)">Developmental changes in infant visual preferences for novelty and familiarity.</a> Child Development. , 416-424 (1973).</li>
<li>Perone, S., Spencer, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Autonomous+visual+exploration+creates+developmental+change+in+familiarity+and+novelty+seeking+behaviors%5BTitle%5D)+AND+%22Frontiers+in+psychology%22%5BJournal%5D)">Autonomous visual exploration creates developmental change in familiarity and novelty seeking behaviors.</a> Frontiers in psychology. 4, 648(2013).</li>
<li>Havy, M., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Naming+influences+9-month-olds%E2%80%99+identification+of+discrete+categories+along+a+perceptual+continuum%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">Naming influences 9-month-olds’ identification of discrete categories along a perceptual continuum.</a> Cognition. 156, 41-51 (2016).</li>
<li>Althaus, N., Plunkett, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Timing+matters%3A+The+impact+of+label+synchrony+on+infant+categorisation%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">Timing matters: The impact of label synchrony on infant categorisation.</a> Cognition. 139, 1-9 (2015).</li>
<li>Maris, E., Oostenveld, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nonparametric+statistical+testing+of+EEG-+and+MEG-data%5BTitle%5D)+AND+%22Journal+of+Neuroscience+Methods%22%5BJournal%5D)">Nonparametric statistical testing of EEG- and MEG-data.</a> Journal of Neuroscience Methods. 164 (1), 177-190 (2007).</li>
<li>Raudenbush, S. W., Bryk, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hierarchical+Linear+Models%3A+Applications+and+Data+Analysis+Methods%5BTitle%5D)+AND+%22SAGE%22%5BJournal%5D)">Hierarchical Linear Models: Applications and Data Analysis Methods.</a> SAGE. , (2002).</li>
<li>LaTourrette, A., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+little+labeling+goes+a+long+way%3A+Semi-supervised+learning+in+infancy%5BTitle%5D)+AND+%22Developmental+Science%22%5BJournal%5D)">A little labeling goes a long way: Semi-supervised learning in infancy.</a> Developmental Science. , e12736(2018).</li>
<li>Dink, J., Ferguson, B. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aDink%2C+J+%22eyetrackingR%3A+An+R+library+for+eyetracking+data+analysis%22">eyetrackingR: An R library for eyetracking data analysis</a>. , (2015).</li>
<li>Kalish, C. W., Zhu, X., Rogers, T. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Drift+in+children%27s+categories%3A+When+experienced+distributions+conflict+with+prior+learning%5BTitle%5D)+AND+%22Developmental+Science%22%5BJournal%5D)">Drift in children's categories: When experienced distributions conflict with prior learning.</a> Developmental Science. 18 (6), 940-956 (2015).</li>
<li>Gibson, B. R., Rogers, T. T., Zhu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Human+semi-supervised+learning%5BTitle%5D)+AND+%22Topics+in+Cognitive+Science%22%5BJournal%5D)">Human semi-supervised learning.</a> Topics in Cognitive Science. 5 (1), 132-172 (2013).</li>
<li>Keates, J., Graham, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Category+Markers+or+Attributes+Why+Do+Labels+Guide+Infants%27+Inductive+Inferences%3F%5BTitle%5D)+AND+%22Psychological+Science%22%5BJournal%5D)">Category Markers or Attributes Why Do Labels Guide Infants' Inductive Inferences?</a> Psychological Science. 19 (12), 1287-1293 (2008).</li>
<li>Booth, A. E., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+horse+of+a+different+color%3A+Specifying+with+precision+infants%E2%80%99+mappings+of+novel+nouns+and+adjectives%5BTitle%5D)+AND+%22Child+development%22%5BJournal%5D)">A horse of a different color: Specifying with precision infants’ mappings of novel nouns and adjectives.</a> Child development. 80 (1), 15-22 (2009).</li>
<li>Perszyk, D. R., Waxman, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Listening+to+the+calls+of+the+wild%3A+The+role+of+experience+in+linking+language+and+cognition+in+young+infants%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">Listening to the calls of the wild: The role of experience in linking language and cognition in young infants.</a> Cognition. 153, 175-181 (2016).</li>
<li>Althaus, N., Mareschal, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Labels+direct+infants%E2%80%99+attention+to+commonalities+during+novel+category+learning%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">Labels direct infants’ attention to commonalities during novel category learning.</a> PLoS ONE. 9 (7), e99670(2014).</li>
<li>Fulkerson, A. L., Haaf, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+influence+of+labels%2C+non-labeling+sounds%2C+and+source+of+auditory+input+on+9-+and+15-month-olds%E2%80%99+object+categorization%5BTitle%5D)+AND+%22Infancy%22%5BJournal%5D)">The influence of labels, non-labeling sounds, and source of auditory input on 9- and 15-month-olds’ object categorization.</a> Infancy. 4 (3), 349-369 (2003).</li>
</ol>

The authors have nothing to disclose.

Here, we present a procedure for evaluating the role of labeling in categorization. By presenting 2-year-olds with a realistic mix of labeled and unlabeled exemplars, we demonstrate that very young children are capable of learning in semi-supervised environments, extending work with adults and older children24,25. Thus, this method offers a resolution to the paradox posed above: if even a few labeled exemplars can spark category learning, then labels can be both rare and powerful.
Critical aspects of this paradigm include the use of novel artificial stimuli and short trials, both of which make the task appropriately challenging and engaging for 2-year-olds. In addition, using an eye-tracker, rather than hand-coding infant looking behavior, provides richer and more precise data on participants&#8217; eye gaze; this richness and precision enables the implementation of time-course measures such as the cluster-based permutation analysis.
The central advantages of the familiarization-test paradigm are its straightforward assessment of category learning and its simplicity as a passive looking task. That is, the task directly tests category learning, rather than relying on more complex measures like naming behavior or inductive inferences3,26,27. Moreover, because familiarization-test tasks can be administered across a broad developmental range (e.g., from 3 months to 3 years), they offer an opportunity to identify developmental continuity and change.
Indeed, the familiarization-test paradigm presented here was designed for 2-year-olds, but similar designs have been widely used with infants in their first year of life4,6,7,9,28. For these younger infants, of course, the task must be simplified: longer exposure to the familiarization exemplars, more exemplars, simpler categories, and a longer window of looking at test may all improve the task&#8217;s sensitivity for younger infants. More broadly, the familiarization-test paradigm employed here can be easily extended to evaluate the effect of any auditory signal on infant cognition, including silence, sine-wave tones, nonhuman primate vocalizations, and other non-linguistic sounds5,13,29,30.
Limitations of this task stem primarily from its use of a single outcome variable: infants&#8217; preference at the test. This makes the task unsuitable for questions about, for instance, how each familiarization exemplar changes infants&#8217; category learning or the particular features infants use to learn the category. Time-course analyses, such as the cluster-based permutation analysis, can substantially enrich the insight offered by this paradigm. However, while these analyses enable us to draw stronger conclusions about when two conditions differ in performance, they also raise important questions about what factors drive infants&#8217; attentional patterns throughout the test phase, a promising area for future work.&#160;

Categorization is a fundamental building block of human cognition: infants&#8217; categorization abilities emerge early in infancy and become increasingly sophisticated with age.1,2,3 Research has also revealed a powerful role for&#160;language in infant categorization: from 3 months of age, infants learn categories more successfully when category exemplars are paired with language.4,5,6 Moreover, by the end of the first year, infants are attuned to the role of count noun labels in categorization. Pairing category exemplars with a consistent labeling phrase (&#8220;This is a vep!&#8221;) facilitates infants&#8217; category learning relative to providing either a distinct label for each exemplar (&#8220;This is a vep,&#8221; &#8220;This is a dax,&#8221; etc.) or a non-labeling phrase (&#8220;Look at this.&#8221;).7,8,9
In infants&#8217; everyday experiences, however, the vast majority of objects they encounter will likely remain unlabeled. No caregiver could label every object an infant&#160;sees much less provide the labels which apply to every object (e.g., &#8220;malamute,&#8221; &#8220;dog,&#8221; &#8220;pet,&#8221; &#8220;animal&#8221;). This presents a paradox: how can we reconcile the power of labels in infant categorization with their relative scarcity in infants&#8217; daily lives?
To answer this question, we developed a protocol to assess how infants learn categories in a variety of different learning environments, including when they receive a mixture of labeled and unlabeled exemplars. Specifically, we propose that receiving even a few labeled exemplars at the beginning of learning can facilitate categorization&#8212;by enhancing infants&#8217; ability to learn from subsequent, unlabeled exemplars as well. This strategy of using a small number of labeled exemplars as a foundation for learning from a larger number of unlabeled exemplars has been widely implemented in the field of machine learning, spawning a family of semi-supervised learning (SSL) algorithms10,11,12. Of course, the learning strategies implemented are not identical across different kinds of learners: in machine learning, algorithms typically are exposed to many more exemplars, make explicit guesses about each exemplar, and learn multiple categories simultaneously. Nevertheless, both machine and infant learners may benefit from successfully integrating both labeled and unlabeled exemplars&#160;to learn new categories in sparse labeling environments.
Our design focuses on whether 2-year-old children, in the process of acquiring words for numerous new categories, are capable of this kind of semi-supervised learning. We employ a standard infant categorization measure: a familiarization-test task. In this paradigm, 2-year-olds were exposed to a series of exemplars from a novel category during a familiarization phase. Each exemplar was paired with a different auditory stimulus, depending on the condition (i.e., either a labeling or a non-labeling phrase). Then, at the test, all 2-year-olds saw two new objects presented in silence: one object from the now-familiar category and one from a novel category.
If the 2-year-olds successfully form the category during the familiarization phase, then they should distinguish between the two exemplars&#160;presented at the test. Importantly, because a systematic preference for either the novel or familiar test image reflects an ability to distinguish between them, both familiarity and novelty preferences are interpreted as evidence of successful categorization. Note that on a given task, the nature of this preference is a function of infants&#8217; processing efficiency for the stimulus materials, with familiarity preferences associated with less efficient stimulus processing 4,13,14,15,16,17. Presenting the test phase in silence makes it possible to directly assess infants&#8217; success in object categorization and how this success varies according to the information that accompanied the exemplars during familiarization. Thus, this paradigm provides a compelling test of how different types of linguistic environments affect category learning. If labeling enhances category learning in both semi-supervised and fully supervised environments, then 2-year-olds in these conditions should show stronger test preferences than infants in other environments.

<table><tbody><tr><td>Final Cut Pro X</td><td>Apple</td><td>N/A</td><td>Video editing, composition software</td></tr><tr><td>MorphX</td><td>Norrkross</td><td>N/A</td><td>Image-morphing software</td></tr><tr><td>PhotoShop</td><td>Adobe</td><td>N/A</td><td>Image-editing software</td></tr><tr><td>R</td><td>R Core Team</td><td>N/A</td><td>Statistical analysis software</td></tr><tr><td>T60XL Eyetracker</td><td>Tobii Pro</td><td>Discontinued</td><td>Large, arm-mounted eyetracker suitable for work with infants and children</td></tr><tr><td>Tobii Pro Studio</td><td>Tobii Pro</td><td>N/A</td><td>Software directing eyetracker display, data collection</td></tr></tbody></table>

defining the role of language in infants' object categorization with eye-tracking paradigms

Assessing infant category learning is a challenging but vital aspect of studying infant cognition. By employing a familiarization-test paradigm, we straightforwardly measure infants&#8217; success in learning a novel category while relying only on their looking behavior. Moreover, the paradigm can directly measure the impact of different auditory signals on the infant categorization across a range of ages. For instance, we assessed how 2-year-olds learn categories in a variety of labeling environments: in our task, 2-year-olds successfully learned categories when all exemplars were labeled&#160;or the first two exemplars were labeled, but they failed to categorize when no exemplars were labeled or only the final two exemplars were labeled. To determine infants&#8217; success in such tasks, researchers can examine both the overall preference displayed by infants in each condition and infants&#8217; pattern of looking over the course of the test phase, using an eye-tracker to provide fine-grained time-course data. Thus, we present a powerful paradigm for identifying the role of language, or any auditory signal, in infants&#8217; object category learning.

Using the protocol above, we ran two experiments22. Analyses were conducted with the eyetrackingR package23, and the data and code are available at https://github.com/sandylat/ssl-in-infancy. In the first experiment, we contrasted a fully supervised condition (n = 24, Mage = 26.8 mo), featuring only labeled exemplars, with an unsupervised condition (n = 24, Mage = 26.9 mo), featuring only unlabeled exemplars.
Fully Supervised vs. Unsupervised Environments
Infants in the Fully Supervised (M = 13.86 s, SD = 3.00) and Unsupervised (M = 14.94 s, SD = 1.91) conditions showed no difference in their attention to the exemplars during familiarization, t(46) = 1.48, p = .14, d = .43.
At test, 2-year-olds in the Fully Supervised condition (M = .59, SD = .15) displayed a significant preference for the novel category exemplar, t(23) = 3.05, p = .006, d = .62, indicating they had successfully formed the category. In contrast, 2-year-olds in the Unsupervised condition (M = .49, SD = .18) looked roughly equally between the objects at test, t(23) = .39, p = .70, d = .08. Performance differed significantly between these conditions, t(46) = 2.27, p = .028, d = .66 (see Figure 2). Finally, a cluster-based permutation analysis of the time-course of looking patterns at test revealed a significant divergence between the two conditions, p = .038, from 3,450 ms to 3,850 ms (see Figure 3).
Semi-supervised vs. Reversed Semi-supervised Environments
Next, we examined whether 2-year-olds could learn categories in semi-supervised environments by integrating labeled and unlabeled exemplars. We predicted that receiving labeled exemplars at the beginning of familiarization in a Semi-supervised condition (n = 24, Mage = 27.3, 12 female), where the labeled exemplars can provide a foundation for learning from the unlabeled exemplars, would facilitate category learning whereas receiving labeled exemplars at the end of familiarization in a Reversed Semi-supervised condition (n = 24, Mage = 27.2, 13 female) would not. That is, receiving labeled exemplars first should enable 2-year-olds to learn more from the unlabeled exemplars than receiving those labeled exemplars after seeing the unlabeled exemplars. &#160;&#160;
Infants in the Semi-supervised condition (n = 24, M = 13.23 s, SD = 3.35) and Reversed Semi-supervised (n = 24, M = 12.58 s, SD = 2.78) conditions showed similar levels of attention to the exemplars during familiarization, t(46) = .73, p = .47, d = .21.
At the test, however, infants in the Semi-supervised condition (M = .59, SD = .14), displayed a significant novelty preference, t(23) = 3.11, p = .005, d = .63, whereas infants in the Reversed Semi-supervised condition (M = .52, SD = .13) performed at chance levels, t(23) = .76, p = .45, d = .16. Infants&#8217; preferences were marginally different between the two conditions, t(46) = 1.80, p = .08, d = .52 (see Figure 2). Moreover, we also conducted a cluster-based permutation analysis of infants&#8217; looking behavior at the test, revealing that the Semi-supervised condition showed a stronger novelty preference than the Reversed SSL condition between 3450ms and 3850ms, p = .047 (see Figure 3). This is exactly the same period of time during which the Fully Supervised condition diverged from the Unsupervised condition, suggesting infants were just as successful at learning the category in the Semi-supervised condition as in the Fully Supervised condition.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59291/59291fig1.jpg" /> 
Figure 1: Sample task design. The familiarization phase consists of 6 trials, each presenting one category member paired with either a labeling or a non-labeling phrase. The test phase simultaneously presents infants with one exemplar from the now-familiar category and one from a novel category. Conditions represent the four conditions presented in the representative results section. This figure has been modified from LaTourrette, A., Waxman, S.R. A little labeling goes a long way: Semi-supervised learning in infancy. Dev. Sci. e12736 (2018). <a href="https://www.jove.com/files/ftp_upload/59291/59291fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59291/59291fig2.jpg" /> 
Figure 2: Mean preference scores across conditions. Infants in the Fully Supervised and Semi-supervised conditions displayed novelty preferences significantly above chance, p &#60; .05. Infants in the Unsupervised and Reversed SSL conditions performed at chance levels. Error bars represent standard errors of the mean. This figure has been modified from22. <a href="https://www.jove.com/files/ftp_upload/59291/59291fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59291/59291fig3.jpg" /> 
Figure 3: The Infant&#8217;s looking patterns during test. In the Fully Supervised and Unsupervised conditions (at left) and in the Semi-supervised and Reversed Semi-supervised conditions (at right), infants&#8217; pattern of looking to the exemplars diverged between 3,450ms and 3,850ms. The grey shaded bar in each graph denotes this divergent period. The colored shaded regions around each condition indicate standard error of the mean. This figure has been modified from22. <a href="https://www.jove.com/files/ftp_upload/59291/59291fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms at JoVE.com

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Here we present a protocol for familiarization-test paradigms which provide a direct test of infant categorization and help to define the role of language in early category learning.

Assessing infant category learning is a challenging but vital aspect of studying infant cognition. By employing a familiarization-test paradigm, we ...

defining-role-language-infants-object-categorization-with-eye

Universidad Científica del Sur

Research

JoVE Journal

Behavior

6.5K Views.  Northwestern University. Here we present a protocol for familiarization-test paradigms which provide a direct test of infant categorization and help to define the role of language in early category learning.

Video: Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

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 ...

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6-  to 7- Month-Old Infants' Object Categorization

At birth, infants not only prefer listening to human vocalizations, but also have begun to link these vocalizations to cognition: For infants as young as three months of age, listening to human language supports object categorization, a core cognitive capacity. This precocious link is initially broad: At 3 and 4 months, vocalizations of both humans and nonhuman primates support categorization. But by 6 months, infants have narrowed the link: Only human vocalizations support object categorization. Here we ask what guides infants as they tune their initially broad link to a more precise one, engaged only by the vocalizations of our species. Across three studies, we use a novel exposure paradigm to examine the effects of experience. We document that merely exposing infants to nonhuman primate vocalizations enables infants to preserve the early-established link between this signal and categorization. In contrast, exposing infants to backward speech &#8212; a signal that fails to support categorization at any age &#8212; offers no such advantage. Our findings reveal the power of early experience as infants specify which signals, from an initially broad set, they will continue to link to cognition.

At 3-4 months, listening to human and nonhuman primate vocalizations boosts infants&#39; cognition; by 6 months, only human vocalizations exert this cognitive advantage. We describe an exposure manipulation that reveals the powerful shaping role of experience as infants specify which sounds to link to cognition and which to tune out.

At birth, infants not only prefer listening to human vocalizations, but also have begun to link these vocalizations to cognition: For infants as young as ...

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2.
Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings.
First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints.
Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.
Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms.
Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper.
We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have.
Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.

We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object ...

Neuroscience

Portable Intermodal Preferential Looking (IPL): Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

One of the defining characteristics of autism spectrum disorder (ASD) is difficulty with language and communication.1 Children with ASD's onset of speaking is usually delayed, and many children with ASD consistently produce language less frequently and of lower lexical and grammatical complexity than their typically developing (TD) peers.6,8,12,23 However, children with ASD also exhibit a significant social deficit, and researchers and clinicians continue to debate the extent to which the deficits in social interaction account for or contribute to the deficits in language production.5,14,19,25
Standardized assessments of language in children with ASD usually do include a comprehension component; however, many such comprehension tasks assess just one aspect of language (e.g., vocabulary),5 or include a significant motor component (e.g., pointing, act-out), and/or require children to deliberately choose between a number of alternatives. These last two behaviors are known to also be challenging to children with ASD.7,12,13,16
We present a method which can assess the language comprehension of young typically developing children (9-36 months) and children with autism.2,4,9,11,22 This method, Portable Intermodal Preferential Looking (P-IPL), projects side-by-side video images from a laptop onto a portable screen. The video images are paired first with a 'baseline' (nondirecting) audio, and then presented again paired with a 'test' linguistic audio that matches only one of the video images. Children's eye movements while watching the video are filmed and later coded. Children who understand the linguistic audio will look more quickly to, and longer at, the video that matches the linguistic audio.2,4,11,18,22,26
This paradigm includes a number of components that have recently been miniaturized (projector, camcorder, digitizer) to enable portability and easy setup in children's homes. This is a crucial point for assessing young children with ASD, who are frequently uncomfortable in new (e.g., laboratory) settings. Videos can be created to assess a wide range of specific components of linguistic knowledge, such as Subject-Verb-Object word order, wh-questions, and tense/aspect suffixes on verbs; videos can also assess principles of word learning such as a noun bias, a shape bias, and syntactic bootstrapping.10,14,17,21,24 Videos include characters and speech that are visually and acoustically salient and well tolerated by children with ASD.

Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

A reliable home-based way to assess the language comprehension of very young typically developing children, as well as those with autism, is described. The method analyzes children's eye gaze while viewing side-by-side images but hearing an audio that matches only one image. Stimuli are designed with young participants in mind.

One  of the defining characteristics of autism spectrum disorder (ASD) is difficulty  with language and communication.1  Children with ASD's onset of ...

Medicine

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 ...

A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras

Infants and toddlers view the world, at a basic sensory level, in a fundamentally different way from their parents. This is largely due to biological constraints: infants possess different body proportions than their parents and the ability to control their own head movements is less developed. Such constraints limit the visual input available. This protocol aims to provide guiding principles for researchers using head-mounted cameras to understand the changing visual input experienced by the developing infant. Successful use of this protocol will allow researchers to design and execute studies of the developing child&#39;s visual environment set in the home or laboratory. From this method, researchers can compile an aggregate view of all the possible items in a child&#39;s field of view. This method does not directly measure exactly what the child is looking at. By combining this approach with machine learning, computer vision algorithms, and hand-coding, researchers can produce a high-density dataset to illustrate the changing visual ecology of the developing infant.

Infants and toddlers view the world in a fundamentally different way from their parents. Head-mounted cameras provide a tractable mechanism to understand the infant visual environment. This protocol provides guiding principles for experiments in the home or laboratory to capture the egocentric view of toddlers and infants.

Infants and toddlers view the world, at a basic sensory level, in a fundamentally different way from their parents. This is largely due to biological ...

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.

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is ...

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 ...

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 ...