These results suggest that people build internal causal models that abstract away irrelevant information. Through surprise tests, we gain insights into what these models look like, finding that they shape memory, prediction, and generalization.

w Steven Shin, Chuqi Hu & @paulm-k.bsky.social

We develop a causal abstraction that infers a causal story of how the data was generated, paying more attention to factors that mattered for the prediction task. This model captures participants' generalization judgments better than a feature-based model, despite having many fewer parameters.

This time, we also asked participants to predict what would happen in novel situations. For example, we showed them two familiar cubes on a novel ramp. These generalization trials also featured ramps that were facing the opposite direction from what they had seen before.

In Exp 3, participants either viewed forward-facing ramps, or backward-facing ones. The cubes always ended up on the right side. Again, after having learned to predict which cubes cross the finish line, we surprised and asked them where exactly the cubes would be. Ps made similar errors as in Exp 2.

Exp 2: Participants predict whether a cube on ramp will cross a finish line. Either cube color or ramp color is diagnostic. A surprise question about exactly where the cube will end up reveals systematic errors: they knew on which side of the line the cube would end up, but not the exact location.

Exp 1: Participants learn whether color or shape matter for turning on a machine. In a surprise test, we ask them what they saw last. They frequently misremember (e.g., choosing a differently colored object when only the shape mattered). Only happens with enough evidence to learn the rule!

When people are asked to predict what happens next, do they learn simple feature-outcome mappings, or do they learn causal models that capture the underlying generative process? If they do learn causal models, how can we tell? We ran 3 experiments (N=1080) using two paradigms to find out.

www.youtube.com/watch?v=Ltdp... 🤓 📵

Better paper link: osf.io/preprints/ps...

This project was expertly led by David Rose (davdrose.github.io) in collaboration with @siyingzhg.bsky.social, Sophie Bridgers, Hyo Gweon, and myself.

📄 doi.org/10.31234/osf...
🔗https://github.com/cicl-stanford/counterfactual_development

Getting more precise estimates about when counterfactual thinking develops allows us to better understand how it impacts other cognitive capacities.

We hope that the "dropping things" task will be used and adapted by others to study counterfactual thinking and its development 👍

So what did we find? We tested 480 children and 91 adults online. Participants saw 4 (Exp 1) or 6 different scenarios. We find that children perform above chance when they're around 5 years of age. And we find a marked shift in performance around 7 years of age (where most children seem to get it).

Three experiments rule out simpler explanations:

1️⃣ Different objects; children might answer based on preference.
2️⃣ Same objects; children might anticipate what would happen (hypothetical thinking).
3️⃣ Same objects, outcome revealed later; children need genuine counterfactual thinking.

The "dropping things" task removes language and tests genuine counterfactual thinking.

Granny drops two objects: an 🥚 and a 🏀. Two friends catch them. Granny would like to thank them but only has one sticker. Who should she give it to? Not catching the 🥚 would have been worse, so "Suzy"!

Estimates of when counterfactual thinking develops range from 2-12 years. Two potential reasons: language & reasoning.

💬 A question like: "Where would Peter have been if there hadn’t been a fire?” is difficult to understand!

🤔 Counterfactual and hypothetical thinking are different!

"A signaling theory of self-handicapping"

📢New from: @yangxiang.bsky.social @gershbrain.bsky.social @tobigerstenberg.bsky.social

www.sciencedirect.com/science/arti...