# Learning distant cause and effect using only local and immediate credit assignment

arXiv: https://arxiv.org/abs/1905.11589 · HTML: https://mlsanity.com/paper/1905.11589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

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

- [Active perception and disentangled representations allow continual, episodic zero and few-shot learning](https://mlsanity.com/paper/2602.19355/) (2026)

## Abstract (arXiv)

We present a recurrent neural network memory that uses sparse coding to create a combinatoric encoding of sequential inputs. Using several examples, we show that the network can associate distant causes and effects in a discrete stochastic process, predict partially-observable higher-order sequences, and enable a DQN agent to navigate a maze by giving it memory. The network uses only biologically-plausible, local and immediate credit assignment. Memory requirements are typically one order of magnitude less than existing LSTM, GRU and autoregressive feed-forward sequence learning models. The most significant limitation of the memory is generalization to unseen input sequences. We explore this limitation by measuring next-word prediction perplexity on the Penn Treebank dataset.