A Scientific Framework for Spatial Computing

Strive for Zero Learning Curve

We recently unveiled Guideline #1: Think Spatial in our blog series. In this post, we wanted to share and explain the underlying scientific framework for spatial interfaces and our guidelines in order to help you see where we're coming from. We highly recommend downloading all nine guidelines (see the blue box entitled "Get All of Meta's AR Design Guidelines") so that you can get a better understanding of how they all tie together with the ultimate goal of enhancing our abilities to create, communicate, and collaborate.

 

A Scientific Framework for Spatial Computing: Strive for Zero Learning Curve

Meta’s philosophy begins with a single, powerful idea: minimizing the time and effort necessary to understand an interface and take effective action. Ultimately, every guideline in this document is founded on this goal, with its logical conclusion being the eventual arrival of a true, zero learning curve experience.

 

The gatekeeper here is the brain, of course. That’s why in the world of spatial computing, the foundation of interface design is neuroscience.

 

The Bayesian Brain and the Neural Path of Least Resistance
Our approach to the brain begins with a probabilistic model called Bayesian statistics, which provides a versatile way to predict a user’s response to a new interface. When the user needs to perform a task, their first instinct will be to look for an element that represents the tool associated with that task. The user will do so based on their mental model of the world, called the prior, and compare it to the new interface, called the input.

 

By designing interfaces that match the expected priors of most users, the learning curve will be reduced and the user will experience faster, more accurate results. Therefore, the optimal interface is the one which leverages the user’s existing priors—their mental model of the world—as much as possible, thus reducing the time and effort to accomplish a task. In practice, the best priors tend to be those that mimic, or at least closely allude to, corresponding objects and tools in the real world.

This goal of minimizing effort, or cognitive burden, is what we call The Neural Path of Least Resistance.

Illustration-of-Priors-aka-Mental-Models-that-Help-Us-Understand-What-Our-Task-Should-Be-Like.png

Different tools we use for our tasks have different fit to our priors, or mental models of what the tool for our task should be like.

 

If the design of the interface does not rely on priors from the real world, but is instead based on something arbitrary like abstract icons, memorized gestures, or keyboard shortcuts, then the user must undergo the mentally taxing process of redefining their mental model and memorizing new rules, which requires the help of the entire brain. This may be an appropriate trade-off in narrow use-cases for specific “power-user” domains, but as a general rule it violates the Neural Path of Least Resistance and is not recommended for general audience applications.


In short, reduce the learning curve by leveraging the user’s priors and experience in the physical world. The remaining guidelines provide techniques for applying this concept to Spatial Interface design.

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