Biology subjects

Tikochinski, R.

Publications and source records attributed to Tikochinski, R..

6 recordsLinked to original sources

bioRxiv2026

Speech Tracking in fMRI Reveals a Distinct Response to Sentence Boundaries During Comprehension

Psycholinguistic research and linguistic theory suggest that comprehending continuous speech involves segmenting it into sentences. Yet, direct neuroimaging evidence for this process remains scarce, partly because sentence boundaries coincide with acoustic, prosodic, lexical, and syntactic changes. Here, we used whole-brain fMRI to test whether sentence boundaries elicit a neural response beyond these accompanying speech properties during spontaneous speech comprehension. We modelled BOLD responses to sentence-final words alongside continuous regressors characterizing accompanying speech properties. This approach revealed sensitivity to continuously varying features of speech across multiple brain regions. Notably, the acoustic envelope predicted BOLD responses in bilateral primary auditory cortices. Responses associated with prosodic breaks and emphasis were found in right temporal cortex, syntactic bottom-up node count in the left superior temporal sulcus, and word concreteness in the parahippocampal gyri. Critically, sentence-final words were associated with an increased activation in right temporal regions, after accounting for acoustic, prosodic, lexical, semantic and syntactic properties. These findings provide evidence for a neural response to sentence boundaries consistent with active sentence segmentation. More generally, this study demonstrates the value of continuous-regressor modelling for investigating speech comprehension with fMRI.

neuroscience↗

bioRxiv2026

Dementia Language Models: a generalizable and controllable representation of cognitive impairment

We introduce Dementia Language Models (DLMs)--a generalizable and controllable representation of cognitive impairment through language--alongside an evaluation framework for establishing their validity and clinical grounding. DLMs created by large language models fine-tuned on a small clinical corpus successfully generated patient-like narratives across unseen tasks, received predicted MMSE scores in the impaired range, and produced narratives that neurologists identified with accuracy comparable to real transcripts. The models' internal representations themselves, as well as their non-linguistic decision-making, supported cognitive-state detection in unseen cohorts. The effect was controllable: moving from Healthy toward Dementia in weight space progressively worsened language and predicted MMSE scores while increasing dementia probability. DLMs could support clinician training, hypothesis generation, and scalable experimentation, reserving patient involvement for where it is truly needed.

neuroscience↗

bioRxiv2026

Content-Sensitive Linguistic Representations in the Human Multiple-Demand Network

Neuroimaging dissociates specialized language regions from the domain-general multiple-demand (MD) network, yet the functional contribution of MD regions to language processing remains unresolved. Because MD recruitment during linguistic tasks is conventionally attributed to domain-general cognitive load, prior research has largely prioritized activation magnitude over representational content. Consequently, it remains unclear whether MD cortices function merely as nonspecific amplifiers of effort or actively encode fine-grained linguistic structures. Here we show that the MD network reliably encodes content-sensitive linguistic representations during naturalistic comprehension independently of peak cognitive demand. Using a voxelwise encoding framework with architecturally identical language models trained on distinct semantic domains (BERT and SciBERT) across three naturalistic fMRI datasets, we find that multiple-demand regions track linguistic structure during both high-demand scientific lectures and low-demand narratives. Furthermore, comparative analyses reveal that these representations are sensitive to domain-specific semantic regularities rather than surface-level form alone. Finally, by evaluating relative model alignment alongside the language network, we demonstrate that the representational balance between these two systems shifts dynamically as a function of task context and domain relevance, revealing a complementary division of labor. Together, these findings reframe the multiple-demand network from a purely extrinsic control mechanism into an active, content-sensitive component of a distributed semantic architecture.

neuroscience↗

bioRxiv2026

The brain prioritizes information that strengthens the structure of knowledge

Humans learn surprisingly well from remarkably little. One way by which such feat may be accomplished is strategic information processing. In particular, the brain may prioritize new inputs that are likely to support efficient mental models. Here we show that people prefer semantic information that strengthens the small-world configuration of a knowledge system, a topology previously associated with creativity and comprehension. Such information is prioritized by the brains language network and elicits enhanced activation in the ventromedial prefrontal cortex, which is central to the brains valuation system. These findings (replicated across studies) reveal a principle of information prioritization: the brain assigns value and resources to knowledge according to its capacity to improve the structure of what we know.

neuroscience↗

bioRxiv2024

An Incremental Large Language Model for long text processing in the Brain

Accumulated evidence suggests that Large Language Models (LLMs) are beneficial in predicting neural signals related to narrative processing. The way LLMs integrate context over large timescales, however, is fundamentally different from the way the brain does it. In this study, we show that unlike LLMs that apply parallel processing of large contextual windows, the incoming context to the brain is limited to short windows of a few tens of words. We hypothesize that whereas lower-level brain areas process short contextual windows, higher-order areas in the default-mode network (DMN) engage in an online incremental mechanism where the incoming short context is summarized and integrated with information accumulated across long timescales. Consequently, we introduce a novel LLM that instead of processing the entire context at once, it incrementally generates a concise summary of previous information. As predicted, we found that neural activities at the DMN were better predicted by the incremental model, and conversely, lower-level areas were better predicted with short-context-window LLM.

neuroscience↗

bioRxiv2021

Fine-tuning of deep language models as a computational framework of modeling listeners' perspective during language comprehension

Computational Deep Language Models (DLMs) have been shown to be effective in predicting neural responses during natural language processing. This study introduces a novel computational framework, based on the concept of fine-tuning (Hinton, 2007), for modeling differences in interpretation of narratives based on the listeners perspective (i.e. their prior knowledge, thoughts, and beliefs). We draw on an fMRI experiment conducted by Yeshurun et al. (2017), in which two groups of listeners were listening to the same narrative but with two different perspectives (cheating versus paranoia). We collected a dedicated dataset of ~3000 stories, and used it to create two modified (fine-tuned) versions of a pre-trained DLM, each representing the perspective of a different group of listeners. Information extracted from each of the two fine-tuned models was better fitted with neural responses of the corresponding group of listeners. Furthermore, we show that the degree of difference between the listeners interpretation of the story - as measured both neurally and behaviorally - can be approximated using the distances between the representations of the story extracted from these two fine-tuned models. These models-brain associations were expressed in many language-related brain areas, as well as in several higher-order areas related to the default-mode and the mentalizing networks, therefore implying that computational fine-tuning reliably captures relevant aspects of human language comprehension across different levels of cognitive processing.

neuroscience↗