Towards a full-stack functionalist theory of consciousness: Identifying its functional profile

Sushrut Thorat; Paras Chopra

Home/Archive/arXiv

arXiv · 2609.33366

Towards a full-stack functionalist theory of consciousness: Identifying its functional profile

Sushrut Thorat·Paras Chopra

View original source ↗Read source document ↗Export metadata ↓

Abstract

If consciousness is functional, a basic question is what awareness of a content actually changes in how a system operates on it. We ask, for a particular content X and mental or bodily process Y, whether awareness of X changes whether, or how well, X can be used in process Y. Crucially, X-aware and X-unaware conditions must be compared in ways that rule out poorer information about X as a sufficient explanation. Our current literature sweep yields a small but informative functional profile. The clearest current evidence concerns goal-sensitive control, especially regulation of attentional influence, with additional evidence linking awareness to metacognitive evaluation. Meanwhile, one feature-binding paradigm suggests that short-lived feature-location binding and retrieval can remain possible without awareness. This preliminary profile suggests a selective rather than universal immediate functional role for awareness. But such a functional profile is not yet a theory of consciousness. A full-stack functionalist theory must also account for the broader phenomena associated with consciousness - the explanatory profile: the structure and manner of presentation of perceptual and affective experience, subject-world organisation, awareness judgments, and problem reports. Finally, the functional and explanatory profiles constrain theory construction from different directions, but neither determines the process architecture that could connect them. Bridging this gap requires abductive construction: proposing candidate architectures, implementing and intervening on them, and progressively revising them as further constraints accumulate. In sum, we propose a framework for constructing full-stack functionalist theories of consciousness, and take the first step towards detailing the functional profile.

Explore related subjects

q-bio.NC

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Sushrut Thorat, Paras Chopra. 2026-09-27. Towards a full-stack functionalist theory of consciousness: Identifying its functional profile. https://arxiv.org/abs/2609.33366

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Emergence of psychopathological computations in large language models

Can large language models (LLMs) instantiate computations of psychopathology? In this work, we establish a computational-theoretical framework to provide an account of psychopathology applicable to LLMs. Based on the framework, we conduct experiments supporting two key claims: first, that network-theoretic computational structures of psychopathology exist in LLMs; and second, that executing these computational structures results in psychopathological functions. We further observe that as LLM size increases, the computational structure of psychopathology becomes denser and the functions more effective. Taken together, the results suggest that network-theoretic computations of psychopathology may have emerged in LLMs. We discuss alternative explanations, including pattern matching, persona modeling, and semantic coherence, and argue that they are either complementary to our interpretation or less consistent with the data.

q-bio.NC↗

arXiv2026

Toward Robust, Reproducible, and Widely Accessible Intracranial Speech Brain-Computer Interfaces: A Comprehensive Narrative Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions

Intracranial language brain-computer interfaces (BCIs) are a promising route for restoring communication in people with severe motor and speech impairments, but clinical translation remains limited by fragmented evidence and unresolved design trade-offs across neuroscience, hardware, algorithm, evaluation, and clinical deployment. This review synthesizes progress in neural mechanisms of overt, mimed, and imagined speech; decision-oriented hardware comparisons of microelectrode array (MEA), electrocorticography (ECoG), and stereotactic electroencephalography (SEEG) recording modalities; experiment design for cross-subject and multilingual generalization; and neural decoding advances spanning sequence models, transformers, articulatory intermediates, and language-prior-assisted frameworks. We highlight persistent bottlenecks, including weak cross-subject transfer, long-term non-stationarity and recalibration burden, heterogeneous and non-comparable evaluation practices, limited naturalistic expressivity (especially for tonal/logosyllabic languages), and low signal-to-noise ratio (SNR) of neural activity in covert speech decoding. Our contributions are threefold: (1) an end-to-end, decision-oriented synthesis linking neural representations to recording choices, experimental design, decoding model architectures, and translational constraints; (2) a structured framework organized around five coupled design questions, together with a unified evaluation framework and a cross-language/cross-task benchmark template integrating objective, perceptual, expressive, conversational, and longitudinal metrics; and (3) user-centered translational guidance covering agency-preserving shared control, verifiable performance priorities, and scenario-specific minimum viable system (MVP) profiles for reliability-first home communication versus fidelity-first conversational speech restoration.

q-bio.NC↗

arXiv2026

Fixed point compositionality via low-rank gluing rules in inhibition-dominated threshold-linear networks

Brains routinely generate highly flexible and complex behaviors on a relatively stable structure and limited resources. A key mechanism underlying this ability is compositionality, which allows the brain to efficiently decompose complex tasks into simpler, reusable primitives. While network modularity has often been linked to compositionality in biological and artificial networks, a rigorous mathematical characterization of this relationship in nonlinear networks is still lacking. In this work, we formally investigate how structural modularity supports functional compositionality in inhibition-dominated threshold-linear networks (TLNs). We introduce a novel class of modular network assembly called low-rank gluings, where component subnetworks with arbitrary internal connectivity are connected via specific low-rank couplings. We prove that the global fixed points of these networks are constrained to be combinations of the local fixed points of their constituent modules. For a more structured subclass, called rank-1 gluings, we provide a complete characterization that determines which combinations of local fixed points yield global ones. We apply these results to graph-based networks, extending fixed point decomposition rules from combinatorial threshold-linear networks (CTLNs) to the more flexible family of generalized CTLNs (gCTLNs), thereby proving that these structural rules are more robust than initially posited. Finally, we demonstrate that these gluing rules provide a mathematically tractable recipe for engineering compositional dynamics, enabling the construction of networks with a combinatorially large repertoire of predictable attractors that can be understood from simpler component motifs, ranging from compositions of fixed points to compositional limit cycles.

q-bio.NC↗