# Which Learning Algorithms Can Generalize Identity-Based Rules to Novel Inputs?

arXiv: https://arxiv.org/abs/1605.04002 · HTML: https://mlsanity.com/paper/1605.04002/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2016.

## Built on by (most cited first, 2 of 2)

- [Invariance, encodings, and generalization: learning identity effects with neural networks](https://mlsanity.com/paper/2101.08386/) (2021)
- [Generalization Limits of Graph Neural Networks in Identity Effects Learning](https://mlsanity.com/paper/2307.00134/) (2023)

## Abstract (arXiv)

We propose a novel framework for the analysis of learning algorithms that allows us to say when such algorithms can and cannot generalize certain patterns from training data to test data. In particular we focus on situations where the rule that must be learned concerns two components of a stimulus being identical. We call such a basis for discrimination an identity-based rule. Identity-based rules have proven to be difficult or impossible for certain types of learning algorithms to acquire from limited datasets. This is in contrast to human behaviour on similar tasks. Here we provide a framework for rigorously establishing which learning algorithms will fail at generalizing identity-based rules to novel stimuli. We use this framework to show that such algorithms are unable to generalize identity-based rules to novel inputs unless trained on virtually all possible inputs. We demonstrate these results computationally with a multilayer feedforward neural network.