Many important questions about children’s early abilities and learning mechanisms remain unanswered not because of their inherent scientific difficulty but because of practical challenges: recruiting an adequate number of children, reaching special populations, or scheduling repeated sessions. Additionally, small participant pools create barriers to replication while differing laboratory environments make it difficult to share protocols with precision, limiting the reproducibility of developmental research. Here we introduce a new platform, “Lookit,” that addresses these constraints by allowing families to participate in behavioral studies online via webcam. We show that this platform can be used to test infants (11–18 months), toddlers (24–36 months), and preschoolers (36–60 months) and reliably code looking time, preferential looking, and verbal responses, respectively; empirical results of these studies are presented in Scott, Chu, and Schulz ( 2017 ). In contrast to most laboratory-based studies, participants were roughly representative of the American population with regards to income, race, and parental education. We discuss broad technical and methodological aspects of the platform, its strengths and limitations, recommendations for researchers interested in conducting developmental studies online, and issues that remain before online testing can fulfill its promise.

We propose that developmental cognitive science should invest in an online CRADLE, a Collaboration for Reproducible and Distributed Large-Scale Experiments that crowdsources data from families participating on the internet. Here, we discuss how the field can work together to further expand and unify current prototypes for the benefit of researchers, science, and society.

To help address the participant bottleneck in developmental research, we developed a new platform called “Lookit,” introduced in an accompanying article (Scott & Schulz, 2017 ), that allows families to participate in behavioral studies online via webcam. To evaluate the viability of the platform, we administered online versions of three previously published studies involving different age groups, methods, and research questions: an infant ( M = 14.0 months, N = 49) study of novel event probabilities using violation of expectation, a study of two-year-olds’ ( M = 29.2 months, N = 67) syntactic bootstrapping using preferential looking, and a study of preschoolers’ ( M = 48.6 months, N = 148) sensitivity to the accuracy of informants using verbal responses. Our goal was to evaluate the overall feasibility of moving developmental methods online, including our ability to host the research protocols, securely collect data, and reliably code the dependent measures, and parents’ ability to self-administer the studies. Due to procedural differences, these experiments should be regarded as user case studies rather than true replications. Encouragingly, however, all studies with all age groups suggested the feasibility of collecting developmental data online and the results of two of three studies were directly comparable to laboratory results.

The mouse γ-aminobutyric acid (GABA) transporter mGAT1 was expressed in neuroblastoma 2a cells. 19 mGAT1 designs incorporating fluorescent proteins were functionally characterized by [3H]GABA uptake in assays that responded to several experimental variables, including the mutations and pharmacological manipulation of the cytoskeleton. Oligomerization and subsequent trafficking of mGAT1 were studied in several subcellular regions of live cells using localized fluorescence, acceptor photobleach Förster resonance energy transfer (FRET), and pixel-by-pixel analysis of normalized FRET (NFRET) images. Nine constructs were functionally indistinguishable from wild-type mGAT1 and provided information about normal mGAT1 assembly and trafficking. The remainder had compromised [3H]GABA uptake due to observable oligomerization and/or trafficking deficits; the data help to determine regions of mGAT1 sequence involved in these processes. Acceptor photobleach FRET detected mGAT1 oligomerization, but richer information was obtained from analyzing the distribution of all-pixel NFRET amplitudes. We also analyzed such distributions restricted to cellular subregions. Distributions were fit to either two or three Gaussian components. Two of the components, present for all mGAT1 constructs that oligomerized, may represent dimers and high-order oligomers (probably tetramers), respectively. Only wild-type functioning constructs displayed three components; the additional component apparently had the highest mean NFRET amplitude. Near the cell periphery, wild-type functioning constructs displayed the highest NFRET. In this subregion, the highest NFRET component represented ∼30% of all pixels, similar to the percentage of mGAT1 from the acutely recycling pool resident in the plasma membrane in the basal state. Blocking the mGAT1 C terminus postsynaptic density 95/discs large/zona occludens 1 (PDZ)-interacting domain abolished the highest amplitude component from the NFRET distributions. Disrupting the actin cytoskeleton in cells expressing wild-type functioning transporters moved the highest amplitude component from the cell periphery to perinuclear regions. Thus, pixel-by-pixel NFRET analysis resolved three distinct forms of GAT1: dimers, high-order oligomers, and transporters associated via PDZ-mediated interactions with the actin cytoskeleton and/or with the exocyst.

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Device miniaturization technologies have led to significant advances in sensors for extracellular measurements of electrical activity in the brain. Multisite, silicon-based probes containing implantable electrode arrays afford greater coverage of neuronal activity than single electrodes and therefore potentially offer a more complete view of how neuronal ensembles encode information. However, scaling up the number of sites is not sufficient to ensure capture of multiple neurons, as action potential signals from extracellular electrodes may vary due to numerous factors. In order to understand the large-scale recording capabilities and potential limitations of multisite probes, it is important to quantify this variability, and to determine whether certain key device parameters influence the recordings. Here we investigate the effect of four parameters, namely, electrode surface, width of the structural support shafts, shaft number, and position of the recording site relative to the shaft tip. This study employs acutely implanted silicon probes containing up to 64 recording sites, whose performance is evaluated by the metrics of noise, spike amplitude, and spike detection probability. On average, we find no significant effect of device geometry on spike amplitude and detection probability but we find significant differences among individual experiments, with the likelihood of detecting spikes varying by a factor of approximately three across trials.

As more researchers make their datasets openly available, the potential of secondary data analysis to address new questions increases. However, the distinction between primary and secondary data analysis is unnecessarily confounded with the distinction between confirmatory and exploratory research. We propose a framework, akin to library book checkout records, for logging access to datasets in order to support confirmatory analysis where appropriate. This would support a standard form of preregistration for secondary data analysis, allowing authors to demonstrate that their plans were registered prior to data access. We discuss the critical elements of such a system, its strengths and limitations, and potential extensions. DATA CHECKOUT FOR CONFIRMATORY SECONDARY ANALYSIS3 Enabling Confirmatory Secondary Data Analysis by Logging Data 'Checkout'Many scientific practices are based on norms rather than strict rules from our institutions, professional societies, or funders. These norms evolve over time -for example, methods sections of psychology papers have changed greatly since the 1950s, thanks to advances in graphing methods, the reduced role of page limits in paper journals, and changes in the scientific community's beliefs about what information is appropriate or necessary to share. In the last five years, norms around how we replicate and share data have been changing rapidly, to promote both increased transparency in our processes and an increased focus on ensuring -and testing -

Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits, because the behaviors of interest, such as gaze duration and direction, even when collected online, still have to be extracted from video through a laborious process of manual annotation. Recent advances in computer vision raise the possibility of automated annotation of video data. In this paper, we built on a system for automatic gaze annotation in human infants, iCatcher (Erel et al., 2022), by engineering improvements, and then training and testing the system (hereafter, iCatcher+) on two datasets with substantial video and participant variability (214 videos collected in lab and mobile testing centers, and 265 videos collected via webcams in homes; infants and children aged 4 months to 3.5 years). We found that when trained on each of these video datasets, iCatcher+ performed with near human-level accuracy on held out videos on distinguishing “LEFT” and “RIGHT” looking behavior, and “ON” versus “OFF” looking behavior, across both datasets. This high performance was achieved at the level of individual frames, experimental trials, and study videos, held across participant demographics (e.g., age, race/ethnicity) and video characteristics (e.g., resolution, luminance), and generalized to a third, entirely held-out dataset. We close by discussing next steps required to fully automate the lifecycle of online infant and child behavioral studies, representing a key step towards enabling rapid, high-powered developmental research.