# Same Cause; Different Effects in the Brain

arXiv: https://arxiv.org/abs/2202.10376 · HTML: https://mlsanity.com/paper/2202.10376/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 69 references that are in the map.
- First posted on arXiv 21 Feb 2022.

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

- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [Causal interpretation rules for encoding and decoding models in neuroimaging](https://mlsanity.com/paper/1511.04780/) (2015)
- [Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging](https://mlsanity.com/paper/1503.01538/) (2015)

## Built on by (most cited first, 10 of 10)

- [Training language models to summarize narratives improves brain alignment](https://mlsanity.com/paper/2212.10898/) (2022)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [Improving Semantic Understanding in Speech Language Models via Brain-tuning](https://mlsanity.com/paper/2410.09230/) (2024)
- [Speech language models lack important brain-relevant semantics](https://mlsanity.com/paper/2311.04664/) (2023)
- [Language models and brains align due to more than next-word prediction and word-level information](https://mlsanity.com/paper/2212.00596/) (2022)
- [Mapping Brains with Language Models: A Survey](https://mlsanity.com/paper/2306.05126/) (2023)
- [Brain-tuning Improves Generalizability and Efficiency of Brain Alignment in Speech Models](https://mlsanity.com/paper/2510.21520/) (2025)
- [CrossPT-EEG: A Benchmark for Cross-Participant and Cross-Time Generalization of EEG-based Visual Decoding](https://mlsanity.com/paper/2406.07151/) (2024)
- [Syntactic Structure Processing in the Brain while Listening](https://mlsanity.com/paper/2302.08589/) (2023)
- [Linguistic properties and model scale in brain encoding: from small to compressed language models](https://mlsanity.com/paper/2602.07547/) (2026)

## Abstract (arXiv)

To study information processing in the brain, neuroscientists manipulate experimental stimuli while recording participant brain activity. They can then use encoding models to find out which brain "zone" (e.g. which region of interest, volume pixel or electrophysiology sensor) is predicted from the stimulus properties. Given the assumptions underlying this setup, when stimulus properties are predictive of the activity in a zone, these properties are understood to cause activity in that zone. In recent years, researchers have used neural networks to construct representations that capture the diverse properties of complex stimuli, such as natural language or natural images. Encoding models built using these high-dimensional representations are often able to significantly predict the activity in large swathes of cortex, suggesting that the activity in all these brain zones is caused by stimulus properties captured in the representation. It is then natural to ask: "Is the activity in these different brain zones caused by the stimulus properties in the same way?" In neuroscientific terms, this corresponds to asking if these different zones process the stimulus properties in the same way. Here, we propose a new framework that enables researchers to ask if the properties of a stimulus affect two brain zones in the same way. We use simulated data and two real fMRI datasets with complex naturalistic stimuli to show that our framework enables us to make such inferences. Our inferences are strikingly consistent between the two datasets, indicating that the proposed framework is a promising new tool for neuroscientists to understand how information is processed in the brain.