# A computationally and cognitively plausible model of supervised and unsupervised learning

arXiv: https://arxiv.org/abs/2010.14618 · HTML: https://mlsanity.com/paper/2010.14618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [We Need to Talk About Classification Evaluation Metrics in NLP](https://mlsanity.com/paper/2401.03831/) (2024)

## Abstract (arXiv)

Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.