Many aspects of infant development are assessed using infant looking times to visual and audiovisual stimuli. In this article, we describe a stand-alone software package that allows simultaneous stimulus presentation to infants and recording of their looking times via a keypress by a human observer. The software was developed to run both on 64-bit Intel-based Macs running Mac OS/X 10.10 (Yosemite) or later and on 64-bit Windows 7 and 10. It can present a variety of visual and/or auditory stimuli; is customizable with respect to how trials are initiated, how trial lengths are defined, and the phases of the experiment; and can be used to record looking times online or after the fact, as well as to assess the reliability of coding. The software is freely available at http://habit.ucdavis.edu.

Infants' ability to perform visual short-term memory (VSTM) tasks develops rapidly between 6 and 8 months. Here we tested the hypothesis that infants' VSTM performance is influenced by their ability to individuate simultaneously presented objects. We used a one-shot change detection task to ask whether 6-month-old infants (N = 47) would detect a change in the color of 1 item in a 2-item array when the stimulus context facilitated individuation of the items. In Experiment 1 the 2 items in the display differed in shape and color and in Experiment 2 the onset and offset times of the 2 items differed. In both experiments, 6-month-old infants detected a change, contrasting with previous results. Thus, young infants' encoding of information about individual items in multiple-item arrays is related to their ability to individuate those items. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Adults' visual attention is guided by the contents of visual short-term memory (VSTM). Here we asked whether 10-month-old infants' (N = 41) visual attention is also guided by the information stored in VSTM. In two experiments, we modified the one-shot change detection task (Oakes, Baumgartner, Barrett, Messenger, & Luck, 2013) to create a simplified cued visual search task to ask how information stored in VSTM influences where infants look. A single sample item (e.g., a colored circle) was presented at fixation for 500 ms, followed by a brief (300 ms) retention interval and then a test array consisting of two items, one on each side of fixation. One item in the test array matched the sample stimulus and the other did not. Infants were more likely to look at the non-matching item than at the matching item, demonstrating that the information stored rapidly in VSTM guided subsequent looking behavior.

Research using eye tracking methods has revealed that when viewing faces, between 6 to 10 months of age, infants begin to shift visual attention from the eye region to the mouth region. Moreover, this shift varies with stimulus characteristics and infants' experience with faces and languages. The current study examined the eye movements of a racially diverse sample of 98 infants between 7.5 and 10.5 months of age as they viewed movies of White and Asian American women reciting a nursery rhyme (the auditory component of the movies was replaced with music to eliminate the influence of the speech on infants' looking behavior). Using an analytic approach inspired by the multiverse analysis approach, several measures from infants' eye gaze were examined to identify patterns that were robust across different analyses. Although in general infants preferred the lower regions of the faces, i.e., the region containing the mouth, this preference depended on the stimulus characteristics and was stronger for infants whose typical experience included faces of more races and for infants who were exposed to multiple languages. These results show how we can leverage the richness of eye tracking data with infants to add to our understanding of the factors that influence infants' visual exploration of faces.

Significant work has documented neuroplasticity in development, demonstrating that developmental pathways are shaped by experience. Plasticity is often discussed in terms of the results of differences in input; differences in brain structures, processes, or responses reflect differences in experience. In this paper, I discuss how developmental plasticity also effectively changes input into the system. That is, structures and processes change in response to input, and those changed structures and processes influence future inputs. For example, plasticity may change the pattern of eye movements to a stimulus, thereby changing which part of the scene becomes the input. Thus, plasticity is not only seen in the structures and processes that result from differences in experience, but also is seen in the changes in the input as those structures and processes adapt. The systematic study of the nature of experience, and how differences in experience shape learning, can contribute to our understanding of neuroplasticity in general.

In this video article, Lisa Federer, Research Informationist at the UCLA Louise M. Darling Biomedical Library, describes her experience as a research informationist for a National Institutes of Health-funded research team at the University of California, Los Angeles.

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Infant research is hard. It is difficult, expensive, and time consuming to identify, recruit and test infants. As a result, ours is a field of small sample sizes. Many studies using infant looking time as a measure have samples of 8 to 12 infants per cell, and studies with more than 24 infants per cell are uncommon. This paper examines the effect of such sample sizes on statistical power and the conclusions drawn from infant looking time research. An examination of the state of the current literature suggests that most published looking time studies have low power, which leads in the long run to an increase in both false positive and false negative results. Three data sets with large samples (>30 infants) were used to simulate experiments with smaller sample sizes; 1000 random subsamples of 8, 12, 16, 20, and 24 infants from the overall samples were selected, making it possible to examine the systematic effect of sample size on the results. This approach revealed that despite clear results with the original large samples, the results with smaller subsamples were highly variable, yielding both false positive and false negative outcomes. Finally, a number of emerging possible solutions are discussed.