Comprehensive Exploration (CE)

My name is Liqiang Huang, and I work in the Department of Psychology at The Chinese University of Hong Kong. You can reach me at lqhuang@cuhk.edu.hk.

This website is dedicated to the concept of the PBS (Precision–Breadth–Simplicity) impossible trinity. Additional information about me can be found on my homepage.

So, what exactly does the PBS impossible trinity mean? Specifically, it entails that precise and simple experimental findings are inevitably fragmented, while broad and simple theoretical notions are inevitably ambiguous.

Already familiar with this? Sure—but the key point is that these reflect a fundamental limit. This is not a problem that can be solved through self-disciplined hard work, nor one that will improve over time. Instead, it calls for a method fundamentally different from the traditional theory-driven approach—what we call the Comprehensive Exploration (CE) approach.

In the CE approach, we use experimental benchmarks on a massive scale—such as a single experiment involving 30,000 hours of data—and then build models that account for the data as accurately as possible while remaining as simple as possible. The goal is to develop cognitive models that are both precise and broad, yet only moderately complex. In this way, they can approach the predictive accuracy of neural networks while remaining interpretable. Details of this approach are described in this paper and further explained on this YouTube channel. A list of published and ongoing projects using CE is available here.

I came up with this approach through a long and painful 12-year struggle. If you're interested in the story, you can take a look at this blog or watch a video.

One particularly noteworthy category is large-scale collaborative CE projects, which are much larger than typical single-lab CE projects and have the potential to be more impactful for the field (see further details).