How do we build true novelty?

We have new experiences all the time (maybe reading a blog post about a scientific article is one, and if it is, welcome!), but studying how we learn about new items and new categories poses significant challenges. I mean, we could spend a lot of time trying to learn what objects each of us has encountered, but that seems like a way to fall down a rabbit hole of getting people to try to articulate their long-term memories, which is a bit of a challenge. What if we could short-circuit this problem entirely by having objects that we knew for a fact were genuinely novel? Well, that’s the goal of the ALICE database, created by Alice Xu, Ji Y. Son, and Catherine M. Sandhofer (pictured below) and recently published in Behavior Research Methods – and by doing so, they’ve hopefully kept the rest of us from falling down the rabbit hole.

Why might we need these objects?

To go back to how we started, genuine novelty is, in fact, pretty difficult to generate – and yet it’s remarkably useful in a wide range of research, from how children learn about objects to how languages work, to how we build our conception of categories. One option, if your goal was to study these sorts of questions (or maybe something entirely different, like why it’s so hard to find something out of place in a familiar environment) would be to go to the trouble of making and validating a set of objects, but the ALICE database means that there’s already a set of 30 genuinely unique objects waiting for you.

How do we know these are unique?

To make a resource like this useful, it’s not enough to just build the digital models – every object in the ALICE database is, in fact, a 3D printable object, not just a digital pattern – we need to know that these are genuinely outside of people’s experience. That’s where the three experiments at the heart of this paper come in. Experiment 1 asked a set of observers to look at the 30 objects in the set individually and then to try to describe them, even to map them on to things they recognize. So, if you look at the leftmost object in the figure above, you might say it has a bit of a rainbow shape, or maybe the rightmost object looks a little bit like a house, but while they have some commonalities, they’re very much not those objects. In fact, participants in Experiment 1 really couldn’t agree on what each object in the database was, which is great – it suggests these assemblies of geometric shapes are genuinely unique and novel. That middle object above certainly doesn’t look like an egg that any creature would lay, even in Wonderland.

They’ve all got something in common, don’t they?

To help make the database as useful as possible to as many different researchers and research disciplines as possible, the team behind the ALICE database also completed two more experiments to help add richness to the information everyone can use. In Experiment 2, they asked participants to report which objects were more or less complicated, and used a multidimensional scaling approach to help potential users explore the objects in the database to find objects that were most useful to them. Building on this, Experiment 3 focused on computationally sorting the thirty objects in the database into clusters, helping reveal which objects might belong together.

So, who is the ALICE database for?

While there may not be a Cheshire cat in the database (and you’re unlikely to be stuck at a tea party with some truly strange guests), the ALICE database opens new doors for a wide range of computer-based and hands-on research in a whole host of cognitive topics. In particular, the ability to use these validated models lets researchers get on with doing research, rather than getting stuck in the morass that is stimulus development and validation. You know, if your students are working towards a deadline – for a conference, or the end of the year, or a thesis – these objects will give you and your students a better chance of not having to run around saying “I’m late, I’m late!”

Featured Psychonomic Society article

 Xu, A., Son, J. Y., & Sandhofer, C. M. (2024). A library for innovative category exemplars (ALICE) database: Streamlining research with printable 3D novel objects. Behavior Research Methods, 56(7), 7849-7871. https://doi.org/10.3758/s13428-024-02458-5