If we describe the processing as:
inputs => (statements, algorithms) => outputs
and consider that statements and algorithms can be certain (C) and uncertain (U) for both, there are exist only 2x2 types of processing.
| Type | Statements (Data/Inputs) | Algorithms (Rules/Logic) | Description |
| Calculations | Certain | Certain | Deterministic and Formal. The outcome is precisely known given the input and rules. Focus on precision and proof. |
| Reactions/Emotions | Certain | Uncertain | Adaptive and Heuristic. Known input triggers a rule set that is still being learned, optimized, or is inherently probabilistic. Focus on rapid, learned responses. |
| Thinking | Uncertain | Certain | Inference and Logic. Firm, formal logic is applied to fuzzy, incomplete, or probabilistic inputs to deduce a likely conclusion. Focus on logical deduction under uncertainty. |
| Feeling/Innovation | Uncertain | Uncertain | Intuitive and Interpretive. Both the input data and the rules/model used to process it are ambiguous, subjective, or constantly evolving. Focus on holistic interpretation and intuition. |
Calculations/Reflexes (Certain, Certain)
Core Concept: Execution, Exactness, Determinism.
Technical Analogy: Compilers, basic arithmetic, cryptographic hashing.
Example: A cashier machine adding up prices. The prices (statements) are certain; the addition algorithm is certain. The result is absolute.
Reactions/Emotions (Certain, Uncertain)
Core Concept: Optimization, Immediate Response, Training. This implies a system that is quickly adapting its internal rules based on certain stimuli.
Technical Analogy: Supervised Machine Learning Training (fixed data, optimizing the algorithm), reflexes, stimulus-response mechanisms.
Example: A self-driving car seeing a certain red light (the statement/input). Its response algorithm is not a fixed rule ("stop"), but an uncertain, optimized set of rules (e.g., brake pressure curve, timing) learned from millions of miles of data. The rules are probabilistic, but the input is a known fact.
Thinking (Uncertain, Certain)
Core Concept: Deduction, Analysis, Structured Inference. The system holds firm to its logical framework while dealing with ambiguity.
Technical Analogy: Bayesian Inference, troubleshooting/diagnosis, formal logical reasoning.
Example: A doctor diagnosing a patient. The medical rules (the algorithms/knowledge base) are generally certain (e.g., "This pattern of symptoms means X"). However, the patient's symptoms (statements) are uncertain (e.g., "I have a cough... maybe a fever..."). The doctor uses certain logic on uncertain data to arrive at a probable answer.
Feeling/Innovation (Uncertain, Uncertain)
Core Concept: Intuition, Interpretation, Subjectivity. This captures the essence of open-ended, non-formal decision-making where even the rules are fluid.
Technical Analogy: Unsupervised Learning, emotional intelligence, artistic judgment.
Example: Judging the quality of a piece of art or music. The input (the sensory data) is uncertain (highly subjective, ambiguous, context-dependent), and the rules for judging it (the algorithm of taste, emotional response, aesthetic theory) are also uncertain (personal, culture-bound, and constantly shifting).
The structure of human language has, unconsciously, evolved to mirror the four fundamental ways we process information. Calculations, Reactions, Thinking, and Feeling aren't just technical classifications, but deep human definitions for the four corners of the (C,U)×(C,U) processing matrix.
Learning and Adaptation
The four processing types (calculation, reaction, thinking, feeling) can be directed either:
- Outward - producing outputs that affect the external world
- Inward - affecting the system itself, which manifests as:
- Learning - when the processing modifies statements
- Adaptation - when the processing modifies algorithms