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Ruben Laukkonen

Publications and source records attributed to Ruben Laukkonen.

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Clear Mind: Meditation and the Brain's Signal-to-Noise Ratio

Meditation is quintessentially associated with a clear mind. This paper proposes that diverse findings in the science of meditation can be mapped onto a single, empirically tractable construct: functional signal-to-noise ratio in the brain, or f-SNR. Signal denotes neural variance that tracks the goal-relevant causes of sensory input, while noise denotes residual activity, including irrelevant endogenous fluctuations. Mechanistically, meditation increases f-SNR through two primary operations: selectively enhancing signal and "decluttering" noise. Deepening practice is further proposed to increase f-SNR by reducing self-referential filtering and shifting global neural activity toward a critical regime, a thermodynamically efficient state that maximizes information transmission and dynamic range. This framework has a strong existing evidence base and is readily falsifiable using metrics such as neural variability quenching, mutual information, and multivariate decoding. Notably, our first test of the theory demonstrates that multiple indicators of neural SNR increase alongside meditation depth. The f-SNR account offers a transdiagnostic explanation for the efficacy of meditation across a range of psychopathologies associated with low-SNR states. The theory also has intriguing implications for emerging technology: meditation may improve brain-computer interfaces, or BCIs, by making brain activity easier to read.

q-bio.NC

Positive Alignment: Artificial Intelligence for Human Flourishing

Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of alignment parallels early psychology's focus on mental illness: necessary but incomplete. What we call Positive Alignment is the development of AI systems that (i) actively support human and ecological flourishing in a pluralistic, polycentric, context-sensitive, and user-authored way while (ii) remaining safe and cooperative. It is a distinct and necessary agenda within AI alignment research. We argue that several existing failures of alignment (e.g., engagement hacking, loss of human autonomy, failures in truth-seeking, low epistemic humility, error correction, lack of diverse viewpoints, and being primarily reactive rather than proactive) may be better addressed through positive alignment, including cultivating virtues and maximizing human flourishing. We highlight a range of challenges, open questions, and technical directions (e.g., data filtering and upsampling, pre- and post-training, evaluations, collaborative value collection) for different phases of the LLM and agents lifecycle. We end with design principles for promoting disagreement and decentralization through contextual grounding, community customization, continual adaptation, and polycentric governance; that is, many legitimate centers of oversight rather than one institutional or moral chokepoint.

cs.AI

Contemplative Artificial Intelligence

As artificial intelligence (AI) improves, traditional alignment strategies may falter in the face of unpredictable self-improvement, hidden subgoals, and the sheer complexity of intelligent systems. Inspired by contemplative wisdom traditions, we show how four axiomatic principles can instil a resilient Wise World Model in AI systems. First, mindfulness enables self-monitoring and recalibration of emergent subgoals. Second, emptiness forestalls dogmatic goal fixation and relaxes rigid priors. Third, non-duality dissolves adversarial self-other boundaries. Fourth, boundless care motivates the universal reduction of suffering. We find that prompting AI to reflect on these principles improves performance on the AILuminate Benchmark (d=.96) and boosts cooperation and joint-reward on the Prisoner's Dilemma task (d=7+). We offer detailed implementation strategies at the level of architectures, constitutions, and reinforcement on chain-of-thought. For future systems, active inference may offer the self-organizing and dynamic coupling capabilities needed to enact Contemplative AI in embodied agents.

cs.AI