Online data collection methods are expanding the ease and access of developmental research for researchers and participants alike. While its popularity among developmental scientists has soared during the COVID-19 pandemic, its potential goes beyond just a means for safe, socially distanced data collection. In particular, advances in video conferencing software has enabled researchers to engage in face-to-face interactions with participants from nearly any location at any time. Due to the novelty of these methods, however, many researchers still remain uncertain about (...) the differences in available approaches as well as the validity of online methods more broadly. In this article, we aim to address both issues with a focus on moderated data collected using video-conferencing software. First, we review existing approaches for designing and executing moderated online studies with young children. We also present concrete examples of studies that implemented choice and verbal measures and looking time across both in-person and online moderated data collection methods. Direct comparison of the two methods within each study as well as a meta-analysis of all studies suggest that the results from the two methods are comparable, providing empirical support for the validity of moderated online data collection. Finally, we discuss current limitations of online data collection and possible solutions, as well as its potential to increase the accessibility, diversity, and replicability of developmental science. (shrink)

We investigated people's ability to infer others’ mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents’ desires throughout. When the agent observed, but did not cause the outcome, participants’ ability to recover the agent's beliefs depended on the evidence they got. When the agent caused the event, participants’ judgments also depended on the probability of the action ; when actions were improbable given the mental states, people failed to recover the agent's (...) beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout, consistent with the proposal that people rationally integrate information about others’ actions and emotional reactions to infer their unobservable mental states. (shrink)

We study the relationship between corporate social performance and financial performance by comparing the portfolio returns of firms with changes in corporate social responsibility (CSR) intensity. Using an extensive US sample from the MSCI ESG database, we find that improvement in the overall CSR is generally value enhancing. The relationship varies with CSR dimensions. More importantly, the relationship shifts differently for various CSR dimensions during the crisis period when trust in the society is low and financial resource is limited. Improvement (...) in environment, human rights, and product characteristics shows higher financial returns during the financial crisis period, whereas the value enhancement of improvement in employee relations is more pronounced during the non-crisis period. (shrink)

Children posit unobserved causes when events appear to occur spontaneously. What about when events appear to occur probabilistically? Here toddlers saw arbitrary causal relationships in a fixed, alternating order. The relationships were then changed in one of two ways. In the Deterministic condition, the event order changed ; in the Probabilistic condition, the causal relationships changed. As intended, toddlers looked equally long at both changes. We then introduced a previously unseen candidate cause. Toddlers looked longer at the appearance of a (...) hand and novel agent in the Deterministic than the Probabilistic conditions, but looked equally long at novel non-agents, suggesting that by 2 years of age, toddlers connect probabilistic events with unobserved agents. (shrink)

Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system. This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of (...) data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory network and recurrent neural network, Bi-LSTM has the advantages of stronger recognition ability. Finally, precision, recall, accuracy, and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances. (shrink)