# Testing for context-dependent changes in neural encoding in naturalistic experiments

arXiv: https://arxiv.org/abs/2211.09295 · HTML: https://mlsanity.com/paper/2211.09295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [Partial Distance Correlation with Methods for Dissimilarities](https://mlsanity.com/paper/1310.2926/) (2013)
- [Better-Than-Chance Classification for Signal Detection](https://mlsanity.com/paper/1608.08873/) (2016)
- [hyppo: A Multivariate Hypothesis Testing Python Package](https://mlsanity.com/paper/1907.02088/) (2019)
- [The Chi-Square Test of Distance Correlation](https://mlsanity.com/paper/1912.12150/) (2019)

## Abstract (arXiv)

We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data. The approach is agnostic to how information is encoded in neural activity, and can control for a variety of possible confounding factors present in the data. We demonstrate our approach by determining whether it is possible to decode location encoding from prefrontal cortex in the mouse and, further, testing whether the encoding changes due to task engagement.