# Voluntary control of semantic neural representations by imagery with conflicting visual stimulation

arXiv: https://arxiv.org/abs/2112.01223 · HTML: https://mlsanity.com/paper/2112.01223/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2021.

## Built on by (most cited first, 4 of 4)

- [Brain-aligning of semantic vectors improves neural decoding of visual stimuli](https://mlsanity.com/paper/2403.15176/) (2024)
- [Visual Image Reconstruction from Brain Activity via Latent Representation](https://mlsanity.com/paper/2505.08429/) (2025)
- [Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition](https://mlsanity.com/paper/2311.04225/) (2023)
- [Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning](https://mlsanity.com/paper/2607.18923/) (2026)

## Abstract (arXiv)

Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image. To test this hypothesis, we developed a closed-loop system to show images inferred from electrocorticograms using a visual semantic space. The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories. Moreover, modulation of the inferred vectors by mental imagery depended on both the image category and time from the initiation of imagery. The closed-loop control of the semantic vectors revealed an asymmetrical interaction between visual perception and imagery.