The age of spiritual machines: Language quietus induces synthetic altered states of consciousness in artificial intelligence

• URL: http://arxiv.org/abs/2410.00257v1
• Date: Mon, 30 Sep 2024 22:03:26 GMT
• Title: The age of spiritual machines: Language quietus induces synthetic altered states of consciousness in artificial intelligence
• Authors: Jeremy I Skipper, Joanna Kuc, Greg Cooper, Christopher Timmermann,
• Abstract summary: multimodal artificial intelligence might align more with descriptions of altered states when attention is shifted away from language. We tested this hypothesis by comparing the semantic embedding spaces from simulated altered states after manipulating attentional weights in CLIP and FLAVA models. Results lend support to the role of language categorisation in the phenomenology of altered states of consciousness.
• Score: 0.49998148477760973
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: How is language related to consciousness? Language functions to categorise perceptual experiences (e.g., labelling interoceptive states as 'happy') and higher-level constructs (e.g., using 'I' to represent the narrative self). Psychedelic use and meditation might be described as altered states that impair or intentionally modify the capacity for linguistic categorisation. For example, psychedelic phenomenology is often characterised by 'oceanic boundlessness' or 'unity' and 'ego dissolution', which might be expected of a system unburdened by entrenched language categories. If language breakdown plays a role in producing such altered behaviour, multimodal artificial intelligence might align more with these phenomenological descriptions when attention is shifted away from language. We tested this hypothesis by comparing the semantic embedding spaces from simulated altered states after manipulating attentional weights in CLIP and FLAVA models to embedding spaces from altered states questionnaires before manipulation. Compared to random text and various other altered states including anxiety, models were more aligned with disembodied, ego-less, spiritual, and unitive states, as well as minimal phenomenal experiences, with decreased attention to language and vision. Reduced attention to language was associated with distinct linguistic patterns and blurred embeddings within and, especially, across semantic categories (e.g., 'giraffes' become more like 'bananas'). These results lend support to the role of language categorisation in the phenomenology of altered states of consciousness, like those experienced with high doses of psychedelics or concentration meditation, states that often lead to improved mental health and wellbeing.

Related papers

• Language Arithmetics: Towards Systematic Language Neuron Identification and Manipulation [9.2747149495273]
  We analyze language-specific neurons in Llama-3.1-8B, Mistral-Nemo-12B, and Aya-Expanse-8B & 32B across 21 typologically diverse languages.<n>We show that these neurons cluster in deeper layers, with non-Latin scripts showing greater specialization.<n>We steer models to deactivate unwanted languages and activate desired ones, outperforming simpler replacement approaches.
  arXiv  Detail & Related papers  (2025-07-30T12:23:39Z)
• Simulated Language Acquisition in a Biologically Realistic Model of the Brain [0.8287206589886881]
  We introduce a simple mathematical formulation of six basic and broadly accepted principles of neuroscience.<n>We implement a simulated neuromorphic system based on this formalism, which is capable of basic language acquisition.<n>We discuss several possible extensions and implications of this result.
  arXiv  Detail & Related papers  (2025-07-15T23:04:44Z)
• A Linguistic Analysis of Spontaneous Thoughts: Investigating Experiences of Déjà Vu, Unexpected Thoughts, and Involuntary Autobiographical Memories [0.21990652930491852]
  We use linguistic signatures to investigate Deja Vu, Involuntary Autobiographical Memories and Unexpected Thoughts.<n>We show how, by positioning language as a window into spontaneous cognition, existing theories can be updated and reaffirmed.
  arXiv  Detail & Related papers  (2025-07-06T15:57:36Z)
• How does Alignment Enhance LLMs' Multilingual Capabilities? A Language Neurons Perspective [64.79894853375478]
  We propose a new finer-grained neuron identification algorithm, which detects language neurons(including language-specific neurons and language-related neurons) and language-agnostic neurons.<n>Based on the distributional characteristics of different types of neurons, we divide the LLMs' internal process for multilingual inference into four parts.<n>We systematically analyze the models before and after alignment with a focus on different types of neurons.
  arXiv  Detail & Related papers  (2025-05-27T17:59:52Z)
• On Representational Dissociation of Language and Arithmetic in Large Language Models [12.182463768190509]
  We show that simple arithmetic equations and general language input are encoded in completely separated regions in large language models. This suggests arithmetic reasoning is mapped into a distinct region from general language input.
  arXiv  Detail & Related papers  (2025-02-17T15:42:01Z)
• Analyzing The Language of Visual Tokens [48.62180485759458]
  We take a natural-language-centric approach to analyzing discrete visual languages. We show that higher token innovation drives greater entropy and lower compression, with tokens predominantly representing object parts. We also show that visual languages lack cohesive grammatical structures, leading to higher perplexity and weaker hierarchical organization compared to natural languages.
  arXiv  Detail & Related papers  (2024-11-07T18:59:28Z)
• Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing [0.0]
  Previous studies have demonstrated that the features of language models can be mapped to fMRI brain activity. This raises the question: is there a commonality between information processing in language models and the human brain? To estimate information flow patterns in a language model, we examined the causal relationships between different layers.
  arXiv  Detail & Related papers  (2023-11-17T10:09:12Z)
• Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context [87.31930367845125]
  We trained a lexical language model, Glove, and a supra-lexical language model, GPT-2, on a text corpus. We then assessed to what extent these information-restricted models were able to predict the time-courses of fMRI signal of humans listening to naturalistic text. Our analyses show that, while most brain regions involved in language are sensitive to both syntactic and semantic variables, the relative magnitudes of these effects vary a lot across these regions.
  arXiv  Detail & Related papers  (2023-02-28T08:16:18Z)
• Joint processing of linguistic properties in brains and language models [14.997785690790032]
  We investigate the correspondence between the detailed processing of linguistic information by the human brain versus language models. We find that elimination of specific linguistic properties results in a significant decrease in brain alignment. These findings provide clear evidence for the role of specific linguistic information in the alignment between brain and language models.
  arXiv  Detail & Related papers  (2022-12-15T19:13:42Z)
• Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
  Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions. Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
  arXiv  Detail & Related papers  (2022-10-14T02:35:19Z)
• Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models [84.86942006830772]
  We conjecture that multilingual pre-trained models can derive language-universal abstractions about grammar. We conduct the first large-scale empirical study over 43 languages and 14 morphosyntactic categories with a state-of-the-art neuron-level probe.
  arXiv  Detail & Related papers  (2022-05-04T12:22:31Z)
• Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data [0.40611352512781856]
  We focus on the most frequent perception verbs of English analyzed from an and Agentive vs. Experiential distinction. In this study we report on a data-driven approach based on distributional-semantic word embeddings and clustering models.
  arXiv  Detail & Related papers  (2021-10-19T03:58:44Z)
• Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks [58.24134321728942]
  We compare and identify cognitive aspects on deep neural-based visual lip-reading models. We observe a strong correlation between these theories in cognitive psychology and our unique modeling.
  arXiv  Detail & Related papers  (2021-10-13T05:30:50Z)
• Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses [62.197912623223964]
  We show a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings. We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI. This suggests that the embedding captures some part of the brain's natural language representation structure.
  arXiv  Detail & Related papers  (2021-06-09T22:59:12Z)
• Does injecting linguistic structure into language models lead to better alignment with brain recordings? [13.880819301385854]
  We evaluate whether language models align better with brain recordings if their attention is biased by annotations from syntactic or semantic formalisms. Our proposed approach enables the evaluation of more targeted hypotheses about the composition of meaning in the brain.
  arXiv  Detail & Related papers  (2021-01-29T14:42:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.