Biology subjects

Mischler, G.

Publications and source records attributed to Mischler, G..

4 recordsLinked to original sources

bioRxiv2025

Evaluating scientific theories as predictive models in language neuroscience

Modern data-driven encoding models are highly effective at predicting brain responses to language stimuli. However, these models struggle to explain the underlying phenomena, i.e. what features of the stimulus drive the response? We present Question Answering encoding models, a method for converting qualitative theories of language selectivity into highly accurate, interpretable models of brain responses. QA encoding models annotate a language stimulus by using a large language model to answer yes-no questions corresponding to qualitative theories. A compact QA encoding model that uses only 35 questions outperforms existing baselines at predicting brain responses in both fMRI and ECoG data. The model weights also provide easily interpretable maps of language selectivity across cortex; these maps show quantitative agreement with meta-analyses of the existing literature and selectivity maps identified in a follow-up fMRI experiment. These results demonstrate that LLMs can bridge the widening gap between qualitative scientific theories and data-driven models.

neuroscience↗

bioRxiv2025

Large Language Models Reveal the Neural Tracking of Linguistic Context in Attended and Unattended Multi-Talker Speech

Large language models (LLMs) capture long-range contextual structure in natural language and have recently been shown to align with the human brains contextualized linguistic encoding. This makes them a promising computational probe for studying how context-dependent linguistic information is represented during natural speech perception. Speech perception often occurs in multi-talker environments, where attention must dynamically select among competing streams, yet how contextual information from attended and unattended speech is neurally encoded remains underexplored. Here, we investigate how auditory attention modulates neural tracking of context-dependent linguistic representations using electrocorticography (ECoG) and stereoelectroencephalography (sEEG) recordings from three epilepsy patients engaged in a two-conversation "cocktail party" paradigm. To model neural responses to attended and unattended speech streams, we used contextual word embeddings generated by large language models. We find that LLM-derived features reliably predict brain activity for the attended stream and that contextual information from the unattended stream also contributes to neural prediction. Importantly, these contributions extend beyond low-level acoustic features and shallow syntactic information, and depend on the surrounding linguistic context. Moreover, neural tracking of the unattended stream reflects shorter-range contextual integration than that of the attended stream. Together, these findings indicate that neural responses to speech reflect context-dependent linguistic representations from multiple concurrent speech streams, with attention modulating the depth and timescale of contextual integration. Our results highlight the utility of LLMs for probing higher-level linguistic representations in complex, naturalistic listening environments.

neuroscience↗

bioRxiv2025

Parallel hierarchical encoding of linguistic representations in the human auditory cortex and recurrent automatic speech recognition systems

Transforming continuous acoustic speech signals into discrete linguistic meaning is a remarkable computational feat accomplished by both the human brain and modern artificial intelligence. A key scientific question is whether these biological and artificial systems, despite their different architectures, converge on similar strategies to solve this challenge. While ASR systems now achieve human-level performance, research on their parallels with the brain has been limited by biologically implausible, non-causal models and comparisons that stop at predicting brain activity without detailing the alignment of the underlying representations. Furthermore, studies using text-based models overlook the crucial acoustic stages of speech processing. Here, using high-resolution intracranial recordings and a causal, recurrent ASR model, we bridge these gaps by uncovering a striking correspondence between the brains processing hierarchy and the models internal representations. Specifically, we demonstrate a deep alignment in their algorithmic approach: neural activity in distinct cortical regions maps topographically to corresponding model layers, and critically, the representational content at each stage follows a parallel progression from acoustic to phonetic, lexical, and semantic information. This work thus moves beyond demonstrating simple model-brain alignment to specifying the shared underlying representations at each stage of processing, providing direct evidence that both systems converge on a similar computational strategy for transforming sound into meaning.

neuroscience↗

bioRxiv2024

The impact of musical expertise on disentangled and contextual neural encoding of music revealed by generative music models

Music perception involves the intricate processing of individual notes and their contextual relationships within a piece. However, how the brain encodes and organizes these features, particularly in relation to musical expertise, remains unclear. Using noninvasive and invasive electrophysiological recordings alongside generative music models, we reveal that musicians exhibit neural encoding that is more attuned to the separation and integration of complex musical structures, with a pronounced left-hemispheric bias compared to non-musicians. Invasive recordings further highlight a hierarchical and spatially organized representation of musical features and context across brain regions. These findings advance our understanding of how the brain processes music and the role of musical training in shaping auditory cognition.

neuroscience↗