Paper List
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Developing the PsyCogMetrics™ AI Lab to Evaluate Large Language Models and Advance Cognitive Science
This paper addresses the critical gap between sophisticated LLM evaluation needs and the lack of accessible, scientifically rigorous platforms that in...
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Equivalence of approximation by networks of single- and multi-spike neurons
This paper resolves the fundamental question of whether single-spike spiking neural networks (SNNs) are inherently less expressive than multi-spike SN...
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The neuroscience of transformers
提出了Transformer架构与皮层柱微环路之间的新颖计算映射，连接了现代AI与神经科学。
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Framing local structural identifiability and observability in terms of parameter-state symmetries
This paper addresses the core challenge of systematically determining which parameters and states in a mechanistic ODE model can be uniquely inferred ...
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Leveraging Phytolith Research using Artificial Intelligence
This paper addresses the critical bottleneck in phytolith research by automating the labor-intensive manual microscopy process through a multimodal AI...
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Neural network-based encoding in free-viewing fMRI with gaze-aware models
This paper addresses the core challenge of building computationally efficient and ecologically valid brain encoding models for naturalistic vision by ...
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Scalable DNA Ternary Full Adder Enabled by a Competitive Blocking Circuit
This paper addresses the core bottleneck of carry information attenuation and limited computational scale in DNA binary adders by introducing a scalab...
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ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics
This paper addresses the critical bottleneck of translating high-dimensional single-cell transcriptomic data into interpretable biological hypotheses ...
Dual-Laws Model for a theory of artificial consciousness
Department of mechano-informatics, The University of Tokyo, Japan
30秒速读
IN SHORT: This paper addresses the core challenge of developing a comprehensive, testable theory of consciousness that bridges biological and artificial systems, moving beyond narrow generative mechanisms to encompass functional aspects and causal efficacy.
核心创新
- Methodology Proposes seven fundamental questions (phenomena, self, causation, state, function, contents, universality) as a minimum necessary framework for evaluating consciousness theories, shifting focus from purely generative mechanisms to functional aspects.
- Theory Introduces the Dual-Laws Model (DLM) that formalizes consciousness through supervenience relationships with independent dynamics at two levels, enabling inter-level causation without relying on neural-specific implementations.
- Methodology Unifies the DLM with dual-process theories by mapping Type 1 processes to continuous feedback control at the base level and Type 2 processes to discrete algorithmic control at the supervenience level.
主要结论
- The DLM provides a formal framework where supervenient functions (X_i = b_i(x_i)) enable independent dynamics at two levels, allowing inter-level causation through negative feedback control mechanisms.
- Conscious systems require two unique capabilities: autonomy in goal construction and cognitive decoupling from external stimuli, distinguishing them from instruction-following machines.
- The theory rejects panpsychism and single-layer dynamical systems, proposing that consciousness emerges from dual-level feedback control where the supervenience level (corresponding to 'I') modifies index sequences that determine error functions.
摘要: Objectively verifying the generative mechanism of consciousness is extremely difficult because of its subjective nature. As long as theories of consciousness focus solely on its generative mechanism, developing a theory remains challenging. We believe that broadening the theoretical scope and enhancing theoretical unification are necessary to establish a theory of consciousness. This study proposes seven questions that theories of consciousness should address: phenomena, self, causation, state, function, contents, and universality. The questions were designed to examine the functional aspects of consciousness and its applicability to system design. Next, we will examine how our proposed Dual-Laws Model (DLM) can address these questions. Based on our theory, we anticipate two unique features of a conscious system: autonomy in constructing its own goals and cognitive decoupling from external stimuli. We contend that systems with these capabilities differ fundamentally from machines that merely follow human instructions. This makes a design theory that enables high moral behavior indispensable.