Meet-the-Expert Lectures
Outstanding Research Award by National Science and Technology Council
Bo-Cheng Kuo, Ph.D.
Professor Kuo’s research expertise lies in cognitive and perceptual psychology, with a focus on exploring the role of selective attention in perception, working memory, long-term memory, and flexible response selection. Professor Kuo has received the Ta-You Wu Memorial Award from the National Science and Technology Council and has led multiple projects. He currently serves as an associate professor at the National Taiwan University.
Maintaining with the Benefit of Expectation
10/26（六）
Working memory (WM) and attention often work together in a mutually supportive manner to guide flexible and adaptive behaviours. Because WM is highly limited in capacity, attention plays an important role in anticipating and gating information that is most relevant to behavioural expectations. In turn, WM controls attention by maintaining task goals, allowing attention to be directed towards items that match these goals. In this talk, I will present recent work from my lab on the interaction between WM and attention. Using EEG, MEG, and fMRI, we focus on the neural mechanisms underlying this interaction, and how it guides human behaviours. In the first section, I will present evidence showing how electrophysiological activity tracks content-specific WM capacity during the retention interval of WM. In the second section, I will demonstrate how temporal expectation based on duration variability can modulate the neural dynamics of alpha oscillations that precede the onset of the memory test. In the final section, I will introduce a MEG-fMRI fusion approach and explain how we apply this method to investigate spatiotemporal neural dynamics for top-down modulation of category-specific information. Together, these studies provide novel evidence for the anticipatory, adaptive nature, and flexibility of our mind and brain.
Ta-You Wu Memorial Award by National Science and Technology Council
Po-Hsien Huang, Ph.D.
Professor Huang’s research expertise includes psychometrics, statistical modeling, and machine learning, and his research interest is in developing quantitative methods from both psychometric and machine learning perspectives. Professor Huang has led multiple projects of the National Science and Technology Council, and currently serves as an associate professor at the National Chengchi University.
Statistical Inference of Feature Importance in Machine Learning
10/26（六）
In recent years, machine learning (ML) has had a revolutionary impact on scientific fields such as computer vision and natural language processing. Although researchers in psychology still prefer using linear models to test psychological theories, ML offers empirical researchers another way to interpret data. The operation of typical ML algorithms is often considered a black box, making them difficult to interpret and infer. However, recent developments in explainable ML have allowed researchers to peek inside the black box, and the statistical inference procedures for examining feature importance (FI) have made it possible to test substantive theories using ML algorithms. This talk aims to compare several statistical inference procedures for testing FI, including the residual permutation test (RPT), conditional predictive impact (CPI), leave-one-covariate-out (LOCO), and plug-in estimation (PIE). We will explain the construction principles of these methods and the inference objects they attempt to target, and evaluate their empirical performance through simulation studies, hoping to provide some practical guidance for ML users in the field of psychology.