Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence

• URL: http://arxiv.org/abs/2512.20929v1
• Date: Wed, 24 Dec 2025 04:19:20 GMT
• Title: Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence
• Authors: Sean C. Borneman, Julia Krebs, Ronnie B. Wilbur, Evie A. Malaia,
• Abstract summary: We present a machine learning framework for decoding neural responses to visual language stimuli in Deaf signers.<n>Our results reveal distributed left-hemispheric and low-frequency coherence as key features in language comprehension.<n>This work demonstrates a novel approach for probing experience-driven generative models of perception in the brain.
• Score: 2.208251557767776
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human language processing relies on the brain's capacity for predictive inference. We present a machine learning framework for decoding neural (EEG) responses to dynamic visual language stimuli in Deaf signers. Using coherence between neural signals and optical flow-derived motion features, we construct spatiotemporal representations of predictive neural dynamics. Through entropy-based feature selection, we identify frequency-specific neural signatures that differentiate interpretable linguistic input from linguistically disrupted (time-reversed) stimuli. Our results reveal distributed left-hemispheric and frontal low-frequency coherence as key features in language comprehension, with experience-dependent neural signatures correlating with age. This work demonstrates a novel multimodal approach for probing experience-driven generative models of perception in the brain.

Related papers

• On the Analogy between Human Brain and LLMs: Spotting Key Neurons in Grammar Perception [0.14337588659482517]
  We identify the most important neurons associated with the prediction of words belonging to different part-of-speech tags.<n>Using the achieved knowledge, we train a classifier on a dataset, which shows that the activation patterns of these key neurons can reliably predict part-of-speech tags on fresh data.
  arXiv  Detail & Related papers  (2025-11-09T20:08:21Z)
• sEEG-based Encoding for Sentence Retrieval: A Contrastive Learning Approach to Brain-Language Alignment [8.466223794246261]
  We present SSENSE, a contrastive learning framework that projects single-subject stereo-electroencephalography (sEEG) signals into the sentence embedding space of a frozen CLIP model.<n>We evaluate our method on time-aligned sEEG and spoken transcripts from a naturalistic movie-watching dataset.
  arXiv  Detail & Related papers  (2025-04-20T03:01:42Z)
• Discovering Chunks in Neural Embeddings for Interpretability [53.80157905839065]
  We propose leveraging the principle of chunking to interpret artificial neural population activities.<n>We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities.<n>We identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts.
  arXiv  Detail & Related papers  (2025-02-03T20:30:46Z)
• Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decoding [25.555303640695577]
  Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools.<n>We developed a diffusion model-based framework to decode visual speech intentions from speech-related non-invasive brain signals.<n>We successfully reconstructed coherent lip movements, effectively bridging the gap between brain signals and dynamic visual interfaces.
  arXiv  Detail & Related papers  (2025-01-09T04:47:27Z)
• Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning [69.8008228833895]
  We propose a small-sized generative neural network equipped with a continual learning mechanism.<n>Our model prioritizes interpretability and demonstrates the advantages of online learning.
  arXiv  Detail & Related papers  (2024-12-23T10:23:47Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• BrainLLM: Generative Language Decoding from Brain Recordings [77.66707255697706]
  We propose a generative language BCI that utilizes the capacity of a large language model and a semantic brain decoder.<n>The proposed model can generate coherent language sequences aligned with the semantic content of visual or auditory language stimuli.<n>Our findings demonstrate the potential and feasibility of employing BCIs in direct language generation.
  arXiv  Detail & Related papers  (2023-11-16T13:37:21Z)
• Tuning In to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language [31.636016502455693]
  We generate supervised representations on eight Natural Language Understanding (NLU) tasks using prompt-tuning. We demonstrate that prompt-tuning yields representations that better predict neural responses to Chinese stimuli than traditional fine-tuning.
  arXiv  Detail & Related papers  (2023-10-05T06:31:01Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• BrainBERT: Self-supervised representation learning for intracranial recordings [18.52962864519609]
  We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition, this Transformer enables classifying complex concepts, with higher accuracy and with much less data. In the future, far more concepts will be decodable from neural recordings by using representation learning, potentially unlocking the brain like language models unlocked language.
  arXiv  Detail & Related papers  (2023-02-28T07:40:37Z)
• Dependency-based Mixture Language Models [53.152011258252315]
  We introduce the Dependency-based Mixture Language Models. In detail, we first train neural language models with a novel dependency modeling objective. We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention.
  arXiv  Detail & Related papers  (2022-03-19T06:28:30Z)
• CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals [60.921888445317705]
  We propose a CogAlign approach to integrate cognitive language processing signals into natural language processing models. We show that CogAlign achieves significant improvements with multiple cognitive features over state-of-the-art models on public datasets.
  arXiv  Detail & Related papers  (2021-06-10T07:10:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.