1. IS THE BRAIN A COMPUTER?  

As the world we inhabit becomes more artificial than ever, the study of cognitive science inevitably finds itself permeated and disturbed by the artificial. Throughout the history of cognitive science, the field has been borrowing vocabularies extensively from technologies (computers, supervised learning algorithms, etc.), and learning from the engineering work about intelligence. The study of natural intelligence has mingled with the study of artificial intelligence; at some point, the natural becomes not so clearly distinguishable from the mechanistic.

Maybe what we should ponder on firstly is “what is computing” and “what does it mean for X to be computable.” (Alexander Galloway)

One stance on this question is to believe that the brain is a computer “so that there is hope to understand it one day.” (Christos H. Papadimitriou)


2. TO WHAT EXTENT CAN THE MIND BE UNDERSTOOD?

In fact, what it means to understand per se isn’t all so clear to me either: Is prediction of some mental behavior understanding? For a long time, meteorologists predict the climate by plotting patterns of previous weather to find trends. Is simulation of intelligence understanding? Modern computers can now simulate real-world weather in high accuracy. When we are confident enough to say we understand the mind, how does the explanations or solutions look like? Are they more akin to the first principle equations in physics, or are they about the teleology of the mind?

The problem of the mind has been compared to that of life, and that of light. Light has been demystified by physic which explains the composition (i.e. photons) and mechanism (i.e. photons travel as wave) of the subject matter. The problem of life, specifically the arguments between vitalists and mechanists, is dissolved as a pseudo-problem, and instead with biology and physiology, we get to study life not as a giant mystery but a still fruitful phenomenon to be understand by cumulative steps.




3. WHAT ARE THE VOCABULARIES FOR SYSTEMS?

Students of the brain are rarely taught about the origin of cognitive sicnece: cybernetics.

Today with a renaissance of cybernetics, we shall once again visit the idea of mechanisticism brought forth by cybernetics as well as its vitalism opponent. We can now ask for an alternative, or a synthesis.

I am interested in feedback system as a level of explanation for the brain, among other levels including the cellular, the synaptic, the circuit, and so on. As a side quest I have been researching about the hardly interpretable artificial neural network, and interestingly, its implication of an more empirical approach to computer science that has an analog to sciences on the hardly interpretable brain.


[Mar, 2022]

2021–2022

Vision Lab ↗
at Barnard Neuroscience
supervised by Prof. White
– How do our eyes decide where and when to look at in a natural reading condition, and when words are transposed?

Programing Languages Lab ↗
at Barnard Computer Science
supervised by Prof. Santolucito
– How can program synthesis interface with game AI programing and with players?

Dynamic Perception and Memory Lab ↗
at Columbia Psychology
supervised by Prof. Baldassano
– How do we segment events in streaming real world data like music and games?

2020


Study of One-Village-One-Policy at Barnard Architecture
supervised by Prof. Smith
– How does OVOP villages relate to each others geo-spatially?