# Behavior measures are predicted by how information is encoded in an individual's brain

arXiv: https://arxiv.org/abs/2112.06048 · HTML: https://mlsanity.com/paper/2112.06048/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 79 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging](https://mlsanity.com/paper/1503.01538/) (2015)
- [A shared neural encoding model for the prediction of subject-specific fMRI response](https://mlsanity.com/paper/2006.15802/) (2020)

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

- [Can Brain Signals Reveal Inner Alignment with Human Languages?](https://mlsanity.com/paper/2208.06348/) (2022)

## Abstract (arXiv)

Similar to how differences in the proficiency of the cardiovascular and musculoskeletal system predict an individual's athletic ability, differences in how the same brain region encodes information across individuals may explain their behavior. However, when studying how the brain encodes information, researchers choose different neuroimaging tasks (e.g., language or motor tasks), which can rely on processing different types of information and can modulate different brain regions. We hypothesize that individual differences in how information is encoded in the brain are task-specific and predict different behavior measures. We propose a framework using encoding-models to identify individual differences in brain encoding and test if these differences can predict behavior. We evaluate our framework using task functional magnetic resonance imaging data. Our results indicate that individual differences revealed by encoding-models are a powerful tool for predicting behavior, and that researchers should optimize their choice of task and encoding-model for their behavior of interest.