Cognitive processes in categorical and associative priming: a diffusion model analysis

Cognitive processes and mechanisms underlying different forms of priming were investigated using a diffusion model approach. In a series of 6 experiments, effects of prime-target associations and of a semantic and affective categorical match of prime and target were analyzed for different tasks. Significant associative and categorical priming effects were found in standard analyses of response times (RTs) and error frequencies. Results of diffusion model analyses revealed that priming effects of associated primes were mapped on the drift rate parameter (v), while priming effects of a categorical match on a task-relevant dimension were mapped on the extradecisional parameters (t(0) and d). These results support a spreading activation account of associative priming and an explanation of categorical priming in terms of response competition. Implications for the interpretation of priming effects and the use of priming paradigms in cognitive psychology and social cognition are discussed

Empowering statistical methods for cellular and molecular biologists.

We provide guidelines for using statistical methods to analyze the types of experiments reported in cellular and molecular biology journals such as Molecular Biology of the Cell. Our aim is to help experimentalists use these methods skillfully, avoid mistakes, and extract the maximum amount of information from their laboratory work. We focus on comparing the average values of control and experimental samples. A Supplemental Tutorial provides examples of how to analyze experimental data using R software

False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments

Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose statistical methods that can systematically and accurately identify Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g. mobile device type, country), and determine which factors (e.g. age, gender) of users contribute to the heterogeneity of the treatment effect in an A/B test. By applying these methods on both simulation data and real-world experimentation data, we show how they work robustly with controlled low False Discover Rate (FDR), and at the same time, provides us with useful insights about the heterogeneity of identified user groups. We have deployed a toolkit based on these methods, and have used it to measure the Heterogeneous Treatment Effect of many A/B tests at Snap

Genomics analysis on the responses of E. coli cells to varying environmental conditions

The natural living environments of E. coli cells are diverse, varying from mammalian gastrointestinal tracts and soil. Each environment might require distinct metabolic pathways and transporter systems, and long-term evolution has established elaborate regulatory system for E. coli cells to quickly adapt to the changing conditions. Sensing outside stresses and then adopting a different phenotype enable them to take advantage of any possible nutrients and defend against hostile environment. A lot of regulatory mechanisms have been identified by genetic, biochemical and molecular biology methods, and our study aim to build a systematic view on the response of the whole genome to four different environmental conditions. We used statistical tests including Pearson’s tests and Spearman’s tests and multiple testing adjustments to identify feature genes that are induced or repressed significantly across treatment levels. The feature genes identified were partially supported by previous literatures, and some of the novel genes not found in any previous studies may infer a potential research blind spot. Additionally, we compared the correlation tests to the implementation of machine learning algorithms, and discussed the advantage and drawbacks of each method.Statistic