While most animals have communication systems, few exhibit such high-level of complexity as human languages. One central question of linguistics and cognitive science is to explore what human cognitive underpinnings and learning mechanisms are necessary to master such a complex system. One influential approach is Chomskyan generativism which premises a universal set of linguistic parameters and principles that delimits a range of possible variations (UG) as human inductive biases (Chomsky, 1980). However, the language learnability issue (e.g., poverty of stimuli) is made without a specified theory of learning (Pater, 2019). The recent development on typological work (e.g., Dunn et al., 2011), psycholinguistic studies (e.g., Culbertson and Kirby, 2016), and modern neural networks (e.g., Manning et al., 2020a) start to challenge the proposal of a rich linguistic innate endowment. In this dissertation, I present studies that utilized different experimental paradigms to explore the two questions: 1. How to verify whether a typologically common linguistic pattern reflects a human cognitive bias or results from cognitive-external factors? 2. How to verify whether abstract biases or linguistic knowledge can emerge from the input data or needs to be pre-specified to generalize to unseen data? To address these questions, the current dissertation explores two lines of research. Chapter 2 addresses the first question proposed by empirically testing two proposed universal biases (harmonic head ordering and dependency length minimization) using the artificial language experiment framework. I verify the existence of biases in individual language learning, explore the interaction with other cognitive systems and examine the biases cross-linguistically. Chapter 3 targets the second question by probing the linguistic knowledge in state-of-the-art neural networks. I present studies that probe different types of linguistic knowledge ranging from specific knowledge (telicity and negation scope) to the abstract dependency length minimization principle over different model components through structural analysis and behavioral analysis. Collectively, this dissertation contributes to the understanding of the cognitive biases underlying language learning. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]