Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity

Predicting subjective ratings of affect and comprehensibility with text features: a reader response study of narrative poetry

Literary reading is an interactive process between a reader and a text that depends on a balance between cognitive effort and emotional rewards. By studying both the crucial features of the text and of the subjective reader reception, a better understanding of this interactive process can be reached. In the present study, subjects (N=31) read and rated a work of narrative fiction that was written in a poetic style, thereby offering the readers two pathways to cognitive rewards: Aesthetic appreciation and narrative immersion. Using purely text-based quantitative descriptors, we were able to independently and accurately predict the subjective ratings in the dimensions comprehensibility, valence, arousal, and liking across roughly 140 pages of naturalistic text. The specific text features that were most important in predicting each rating dimension are discussed in detail. In addition, the implications of the findings are discussed more generally in the context of existing models of literary processing and future research avenues for empirical literary studies.

Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.