David Cota’s evaluation of the author’s “participatory explanation” approach to consciousness reveals fundamental conceptual problems that plague contemporary consciousness studies. Cota identifies a critical conflation between reason as a symbolic-operational function, and consciousness as a phenomenal-experiential fi...
Kwan Hong TAN· Zenodo (CERN European Organi...· 0 citations
David Cota’s evaluation of the author’s “participatory explanation” approach to consciousness reveals fundamental conceptual problems that plague contemporary consciousness studies. Cota identifies a critical conflation between reason as a symbolic-operational function, and consciousness as a phenomenal-experiential fi...
Kwan Hong TAN· Zenodo (CERN European Organi...· 0 citations
Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative intent into external outputs such as text, synthesized voice, or avatar control. Recent advances in intracortical and electrocorticographic recording, deep sequence models,...
Moein Khajehnejad, Forough Habibollahi, T. Boccato et al.· 0 citations
People increasingly turn to general-purpose AI chatbots for advice about emotional and mental health problems, but the ability of these systems to recognize and appropriately triage psychiatric emergencies remains under-characterized.
We evaluated psychiatric triage performance in 15 frontier AI chatbots using 112 cl...
Veith Weilnhammer, Lennart Luettgau, Christopher Summerfield et al.· 0 citations
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We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study expressivity and trainability in artificial neural networks (ANNs). Causal pieces partition the input and parameter space of a feedforward SNN with single-spike coding into distinct regi...
Dominik Dold, Philipp Christian Petersen· 0 citations
A common assumption in language model development is that cognitive abilities are organized around a general, domain-free intelligence factor, like fluid intelligence in humans. This assumption is rarely tested directly, and prior attempts have done so only at a much smaller scale. We take a latent variable approach to...
Understanding how the brain parses actions and events from time-varying natural inputs is a central challenge in neuroscience. Recent work has used deep neural network (DNN) models to build stimulus-computable fMRI encoding models that predict single-voxel responses to complex natural videos. However, the majority of v...
Iishaan Inabathini, Margaret M. Henderson· 0 citations
Brain-AI alignment is often interpreted as a sign that model and brain perform similar computations. Whether the aligned units are causally involved in model computation is rarely checked. On an abstract pattern-completion task (AAABAAA $\rightarrow$ B), we compare LLM attention-head representations with human EEG and...
Christopher Pinier, Gustaw Opie{\l}ka, Hannes Rosenbusch et al.· 0 citations
Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learned from large unlabelled corpora should capture individual functional dynamics and generalise across cohorts. However, kernel ridge regression (KRR) fitted on functional...
G. Marraffini, Victoria Shevchenko, Carlo Alberto Barbano et al.· 0 citations
Structural reasoning, the ability to recognize and make inferences over the relational structure between objects and concepts, is a hallmark of human cognition, yet prevailing methods often collapse relational topology into flat embeddings, cannot discover hidden structure and lack interpretability. We introduce Neural...
Conditioned DFA (nDFA), a family that adapts established inverse-moment preconditioning to either side of this update, is studied, establishing practical benefits and important limits of conditioning learning with fixed random feedback.
Houman Safaai, V. Reddy, Bernardo L. Sabatini· arXiv.org· 1 citation
Through a four-speaker attention decoding benchmark, it is shown that combining behavioral and physiological signals improves decoding performance over EEG-only approaches, enabling future advances in multimodal auditory attention decoding.
K. M. Naimul Hassan, Ali Alavi, D. Williamson· 0 citations
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