# Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?

arXiv: https://arxiv.org/abs/2204.12639 · HTML: https://mlsanity.com/paper/2204.12639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Learning to Recombine and Resample Data for Compositional Generalization](https://mlsanity.com/paper/2010.03706/) (2020)
- [Generalized Hindsight for Reinforcement Learning](https://mlsanity.com/paper/2002.11708/) (2020)
- [Long-range and hierarchical language predictions in brains and algorithms](https://mlsanity.com/paper/2111.14232/) (2021)

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

- [Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning](https://mlsanity.com/paper/2212.11353/) (2022)

## Abstract (arXiv)

We develop the few-shot continual learning task from first principles and hypothesize an evolutionary motivation and mechanism of action for executive function as a contrastive value policy which resamples and relabels perception data via hindsight summarization to minimize attended prediction error, similar to an online prompt engineering problem. This is made feasible by the use of a memory policy and a pretrained network with inductive biases for a grammar of learning and is trained to maximize evolutionary survival. We show how this model of executive function can be used to implement hypothesis testing as a stream of consciousness and may explain observations of human few-shot learning and neuroanatomy.