Name: defining the role of language in infants' object categorization with eye-tracking paradigms
Uploaded: 2019-02-08T15:00:00
Description: Watch this Scientific Journal Video about Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms at JoVE.com

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 image...

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W., Bryk, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hierarchical+Linear+Models:+Applications+and+Data+Analysis+Methods.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+little+labeling+goes+a+long+way:+Semi-supervised+learning+in+infancy.">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://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> eyetrackingR: An R library for eyetracking data analysis. , (2015).</li><li>Kalish, C. W., Zhu, X., Rogers, T. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drift+in+children's+categories:+When+experienced+distributions+conflict+with+prior+learning.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+semi-supervised+learning.">Human semi-supervised learning.</a> Topics in Cognitive Science. 5 (1), 132-172 (2013).</li><li>Keates, J., Graham, S. 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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 ...

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.

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	<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>
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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 ex...

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 ...

This familiarization test method reveals how infants form visual object categories and what kinds of conditions most powerfully support their learning. Using only infants eye movements we can measure how successfully they learn a category. Then we can directly compare that learning across a huge range of different learning conditions.Begin by creating the visual stimuli for the experiment. To create a new continuous category, first design a pair of novel digital images. Then, morph the pair of images together 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. Next, select the familiarization exemplars at evenly spaced intervals from across each learned categories continuum. Select an appropriate number of exemplars and commensurate with the difficulty of the category and age of the participants.To create the exemplars for the test phase, select the midpoints of the familiar categories continuum and the novel categories continuum. Then match the color of the novel exemplar to that of the familiar exemplar using an image manipulation program. For both the labeling and non-labeling phrases use previously recorded auditory stimuli produced by a female native English speaker in a sound proof booth.To collect adequate 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. Using the eye tracker software, 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. Next, generate at least two pseudorandom orders of the learning exemplars with the constraint that no more than two exemplars from the same side of the continuum can be shown consecutively. Now use video editing software to create familiarization videos that pair the previously created auditory stimuli with the visual stimuli as appropriate for each condition.For instance, in the fully supervised condition pair each familiarization exemplar with a labeling phrase. In the unsupervised condition, pair each familiarization exemplar with a non-labeling phrase. In the semi-supervised condition, pair only the first two exemplars in each order with labeling phrases, but the rest with non-labeling phrases.For the reversed semi-supervised condition, pair the final two exemplars with labeling phrases but the first four with non-labeling phrases. Upload these videos into the eye tracker software being sure to order the familiarization videos as determined by the pseudorandomized order. Also upload a short attention grabbing animation displayed in the center of the screen after familiarization, to ensure that most infants look at the center of the screen when the test phase begins.Finally, for each learning category, design two test trials each featuring two exemplars displayed side by side. For both, ensure that one exemplar represents the midpoint of the now familiar category, while the other represents the midpoint of the novel category. Ensure that test trials last 5 to 20 seconds to accumulate sufficient looking, and counterbalance the trials so that the left right positioning of the novel exemplar in the test trial is reversed across the videos.Upload the task trials to the eye tracker software, positioning them after the post familiarization attention getter. Counterbalance these trials presentation, so each infant has an equal chance of seeing a left novel or right novel test trial. Before the infant arrives, setup the eye tracker.Randomly assign the infant to a condition and an order, then open the eye tracker software, and select the assigned condition order pair, and enter the participant number for the recording. After obtaining consent for the study, bring the infant and the caregiver to the eye tracking room. Ensure this room is moderately lit without any distracting decorations on the walls.Seat the caregiver at an appropriate distance for the model of eye tracker being used. If the infant does not wish to sit in the caregivers lap, they may sit on their own or they may sit in a car seat. If the infant is sitting on the caregivers lap, instruct the caregiver not to bias infants behavior in any way, but to try to keep the infant centered on the caregivers lap.Then, provide the caregiver with a pair of blacked out sunglasses to wear, so they cannot see the stimuli. Now ask the infant to look at the eye tracker screen. Consider displaying an engaging image or video to attract their attention.The screen should be positioned, so that the infant eyes are within the calibration window. Next, perform the eye trackers calibration procedure using a five point calibration, if possible. Infants often respond better when the calibration image is animation with auditory accompaniment.If the infant passes calibration, then begin the experiment if not, recalibrate until they are successful. Any infants who cannot be calibrated should be excluded from the study. This figure shows mean preference scores across conditions.Infants in the fully supervised and semi-supervised conditions, displayed novelty preferences significantly above chance. While infants in the unsupervised and reversed semi-supervised conditions, performed at chance levels. Here we see the infants looking patterns during testing.In the fully supervised and unsupervised conditions and in the semi-supervised and reversed semi-supervised conditions, infants pattern of looking to the exemplars diverged between 3450 and 3850 milliseconds. The colored shaded regions indicates standard error of the mean. This procedure can be used with infants of various ages and materials that vary in complexity.As a result, each new design must reflect infants learning capacity at that intended age. We can go on to ask, whether and how infants capacity as measured here, correlates with or even predicts infants acquisition of later language milestones. Such as vocabulary size or word order.This technique has allowed developmental scientist to study cognition and perception, in infants as young as two months. It continues to illuminate the ways that language facilitates learning throughout infancy.

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