About
6
Publications
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Introduction
Puyuan's research interests largely focus on the cognitive mechanisms underlying human learning. During her Ph.D. period at HKU, Puyuan investigated how input uncertainty regulates the engagement of multiple cognitive processes during visual statistical learning. Then, in WPI, Puyuan further examines the multi-component nature of human learning in a mathematics learning context. She explores how top-down and bottom-up cognitive processes interactively underpin mathematics problem-solving.
Skills and Expertise
Current institution
Publications
Publications (6)
The relation between statistical learning and working memory in children with developmental dyslexia (DD) remains unclear. This study employed a distributional and a conditional statistical learning experiment and a working memory task to examine this relation in 651 Chinese 6‐ to 12‐year‐olds with and without DD (NDD = 199, 101 females; NwoDD = 45...
This study investigated how humans process probabilistic-associated information when encountering varying levels of uncertainty during implicit visual statistical learning. A novel probabilistic cueing validation paradigm was developed to probe the representation of cues with high (75% probability), medium (50%), low (25%), or zero levels of predic...
This study investigated the key mechanisms by which cue predictiveness impacts cue perception under different levels of uncertainty during statistical learning. Although previous studies have demonstrated the impacts of cue predictiveness on cue processing during explicit associative learning tasks, it is still unclear whether the same phenomenon c...
This study examined distributional statistical learning of positional, phonetic, and semantic regularities of an artificial orthography in Chinese children aged 8–10 years: 29 with dyslexia, 29 age‐matched controls, and 30 reading‐level matched controls. Despite having positional regularity learning performance comparable to the controls, the child...
