hi! i'm Yiling. i'm a phd student at umass amherst's philosophy, working with Hilary Kornblith. i also pursue a graduate certificate in cognitive science. at a high level, i'm broadly interested in both the human comprehension process and computational modeling of reasoning, the specific descriptions of which i have put in the slider below. as such, i have studied philosophy, formal logic, linguistics, psychology, and machine learning extensively, and work in their intersection. previously i was a graduate student at the university of missouri - st. louis's philosophy, working with Gualtiero Piccinini, where i studied philosophy and machine learning, besides which i also studied linguistics and philosophy at the washington university in st. louis. before that i was an undergraduate student at the city university of macau's humanities and social sciences, where i studied psychology and social work.
i'm always happy to connect with like-minded people, feel free to reach out any time.

  • about the human comprehension process

    this question involves, (1) how do we encode the physical world around us and abstract objects and construct corresponding representations? (2) how do we extract, correlate, and organize these representations efficiently and flexibly to construct a proposition with internal attitude and belief? (3) how do we construct a representation of a proposition this includes how to represent the causal structure of the world, and integrate these representations with our model of the world to determine our beliefs and actions.

  • about computational modeling of reasoning

    this question involves, (1) how can intelligence be computationally based? (2) how to develop a computational model that formally and computationally models information processing and reasoning processes in human minds? (3) how to develop a computational model that deals with information about beliefs and makes decisions like human minds?

  • methods

    the research methods I am currently using include, (1) philosophical analysis of LLM to get a better understanding of human intelligence, i.e., using artificial neural networks to understand representations and reasoning in the human brain. (2) formal logic. but I am also interested in seeing how methods from psychological experiments, computational modeling, and reinforcement learning can be brought to bear on these questions.
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