# Type theory in human-like learning and inference

arXiv: https://arxiv.org/abs/2210.01634 · HTML: https://mlsanity.com/paper/2210.01634/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017)
- [Program Synthesis from Polymorphic Refinement Types](https://mlsanity.com/paper/1510.08419/) (2015)
- [Representational efficiency outweighs action efficiency in human program induction](https://mlsanity.com/paper/1807.07134/) (2018)
- [Computational Higher Type Theory I: Abstract Cubical Realizability](https://mlsanity.com/paper/1604.08873/) (2016)

## Built on by (most cited first, 3 of 3)

- [Is This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility](https://mlsanity.com/paper/2507.12553/) (2025)
- [Shades of Zero: Distinguishing Impossibility from Inconceivability](https://mlsanity.com/paper/2502.20469/) (2025)
- [How are Scientific Concepts Birthed? Typing Rules of Concept Formation in Theoretical Physics Reasoning](https://mlsanity.com/paper/2509.10740/) (2025)

## Abstract (arXiv)

Humans can generate reasonable answers to novel queries (Schulz, 2012): if I asked you what kind of food you want to eat for lunch, you would respond with a food, not a time. The thought that one would respond "After 4pm" to "What would you like to eat" is either a joke or a mistake, and seriously entertaining it as a lunch option would likely never happen in the first place. While understanding how people come up with new ideas, thoughts, explanations, and hypotheses that obey the basic constraints of a novel search space is of central importance to cognitive science, there is no agreed-on formal model for this kind of reasoning. We propose that a core component of any such reasoning system is a type theory: a formal imposition of structure on the kinds of computations an agent can perform, and how they're performed. We motivate this proposal with three empirical observations: adaptive constraints on learning and inference (i.e. generating reasonable hypotheses), how people draw distinctions between improbability and impossibility, and people's ability to reason about things at varying levels of abstraction.