This is post 3 of a sequence on my framework for doing and thinking about research. Start here.

Introduction

Spend enough time around researchers, and you'll hear talk of "research taste." It's often presented as a somewhat mystical quality distinguishing the seasoned research from the novice – an almost innate sense for which research ideas will flourish and which will fail. While I believe research taste is very real, incredibly valuable, and a key differentiator I look for, I don't think it's mystical or innate. Talent plays an important role, but taste is largely learned, and with the right mindset you can learn faster.

What is research taste? As I define it, research taste is far broader than just picking the right problem at the outset. Research is full of key decisions that will affect the future of the project, without an obvious way to find the right answer: from choosing the research problem itself, to identifying which anomalies are and are not worth exploring, distinguishing an experiment that will be compelling from one that’ll have inconclusive results, etc. I think of taste as the set of intuitions and good judgment that guide a researcher’s decisions throughout the research process, any time an ambiguous or open-ended decision like this arises. This can just be gut feeling, but also having conceptual frameworks you reason through, having novel ideas spark in your mind, etc.

Where does taste come from? If you're new to research, feeling like you lack "taste" is completely normal and expected. You don't need perfect judgment to start. In fact, trying to force it early on can be counterproductive. Think of training your intuition like training a network. It starts poorly initialized and needs lots of diverse, high-quality training data (i.e., research experience). With time, people often develop fairly deep and sophisticated taste, as they see enough examples of research outcomes, but this generally isn’t something people start with.

How to learn it? In my opinion, research taste is one of the hardest skills to learn for being a good researcher. To see why, let's lean more into this analogy of training a neural network. The core problem is you just don't get that much data. Generally the shorter a feedback loop is the more data you will get. By definition research taste is about things that are not immediately obvious. For designing a good experiment...

Joshua Yang

Joshua Yang