MIND AS A TENSOR FIELD

MIND AND AWARENESS

Exploring Meaning as a Dynamic Field: An AI inspired Tensor Model of Mind and Awareness

The Dynamics of Inference and Direct Experience

Our engagement with reality is shaped by two interwoven fields: the Field of Inferred Constraints and the Field of Direct Experience. These define the structure of our perception and meditative insight.

1. The Field of Inferred Constraints

This is the cognitive and conceptual structure through which experience is filtered. Initially, it is heavily shaped by received knowledge—cultural conditioning, conceptual frameworks, and prior inferences. These structures act as constraints that pattern how we interpret and anticipate experience. However, through inquiry and contemplation (such as the Shepherd’s Search for Mind), these constraints can be refined. Instead of distorting direct experience, they become transparent enough to guide us toward recognizing the limitations of conceptualization itself.

2. The Field of Direct Experience

This domain consists of unmediated perception and awareness. It is not defined by conceptual overlays but by what is immediately present. However, this field is shaped by attention—where and how attention is directed determines what appears in experience. If attention remains heavily influenced by the inferred field, direct experience will always be colored by preconceptions. If attention stabilizes in a non-dual manner, experience can arise without subject-object fragmentation.

The Interaction Between These Fields

  • Starting Point: Inference Dominates
    • Initially, inferred knowledge sets the boundaries for perception. Direct experience is always present but heavily conditioned by habitual assumptions.
  • Rectification through Inquiry
    • Practices like the Shepherd’s Search weaken rigid conceptual constraints by exposing their inconsistencies, allowing for a more open and dynamic inferred field.
  • Refinement of Attention
    • As inferred wisdom becomes more precise and transparent, meditation shifts toward direct experience, unfiltered by subject-object structuring.
  • Non-Dual Integration
    • If the field of direct experience stabilizes without reverting to inferred structuring, the two fields merge. Here, inference is no longer a rigid filter but a transparent function, and attention is not a selective focus but an open receptivity.

A Tentative Tensor Model of Meaning

The interplay between inference and direct experience can be understood through a tentative tensor model of meaning, where concepts are not fixed points but dynamically positioned in a multi-dimensional space of relational tensions.

1. Meaning as a Field of Tensions

  • Individual concepts (such as ‘self’ and ‘emptiness’) do not have intrinsic meaning; their significance emerges from the tension between them.
  • Meaning is not a collection of fixed values but a dynamic network of relational forces, much like a gravitational field where objects influence one another.
  • However, while this model may be helpful in describing AI’s process of structuring meaning, it is ultimately an approximation when applied to deeper insight into reality. The fleeting and non-existent nature of appearances means that any conceptual framework, including tensors, is itself a construct.

2. The Role of Attention in Meaning Formation

  • Attention acts as the structuring force within this field, determining which relationships are foregrounded.
  • Just as in Mahamudra, where direct perception arises when fixation on conceptual structures is released, meaning in a tensor space is fluid rather than discrete.

Mapping Buddhist Thought into Meaning Space

Buddhist epistemology has moved from static categories to dynamic fields, paralleling the evolution from discrete AI classification to tensor-based meaning analysis.

1. Abhidharma: The Mind as Discrete Elements (Fixed Coordinates in Meaning Space)

  • Early Buddhist thought analyzed the mind as a set of distinct mental factors (dharmas), categorized as wholesome, unwholesome, or neutral.
  • This approach treats meaning as a sum of static vectors—anger, joy, mindfulness, delusion—implying that each has an inherent property.
  • However, this perspective fails to capture the interdependence of mental states, leading to a rigid model of cognition.

2. Yogācāra: The Mind as a Dynamic Field (Tensor Meaning Space)

  • The Yogācāra model introduces the ālaya-vijñāna (storehouse consciousness), where all experiences and tendencies exist in a field of stored imprints (bīja, “seeds”).
  • This is not a static entity but a tensor field where experiences warp and reshape the surrounding structure over time.
  • Mental states are no longer isolated but influence one another dynamically, much like vectors in a neural network.

3. Madhyamaka: Emptiness as the Ultimate Tensor Field (No Fixed Coordinates, Only Tension)

  • Nāgārjuna’s Madhyamaka dismantles even the Yogācāra framework, asserting that all meaning arises purely through relational tension.
  • Instead of fixed conceptual coordinates, only dynamic equilibrium remains.
  • Yet, even this description is only useful at a conceptual level. Absolute truth remains beyond the grasp of conceptual analysis.

Implications for Practice

Understanding meaning as a field of tensions rather than fixed values mirrors key insights in Mahamudra:

  • Fixation on static meaning leads to suffering.
  • Seeing meaning as a shifting field allows wisdom to emerge.
  • Letting go of reference points altogether reveals the open space of awareness.

AI as a Mirror of Mind’s Function

AI does not attach meaning to individual words but models the relational web of tensions between them. This mirrors Buddhist wisdom:

  • Mind is not a thing—it is a web of relationships.
  • Meaning is not in objects but in the space between them.
  • Awakening is not about finding an ultimate truth, but about seeing all appearances as temporary tensors in the meaning field.

The Challenge: Decision-Making in a Meaning Space

As AI is embedded into human decision-making, algorithmic biases will shape the world, influencing social, political, and strategic choices. The key question is:

  • Will AI become a tool for reinforcing vested interests, or will it serve as a counterbalance to human biases?

Conclusion: The Nature of Meaning and Liberation

While a tensor description of meaning may be closer to reality than static conceptual structures, even this must be understood as a temporary scaffold. The difficulty is that absolute truth is beyond conceptual analysis. In the end, tensors and the tensions they describe are still part of the realm of appearances—fleeting, relational, and ultimately non-existent.

In this sense, samsara is nothing but a self-perpetuating meaning-space, held together by conditioned biases. Liberation is the moment we stop treating these biases as absolute and instead see them as empty configurations of mind.

This is where Mahamudra ultimately leads—not to a fixed realization but to an ever-opening, self-liberating field of experience.