Proposta di nuovi strumenti per comprendere come funziona la cognizione (Novel tools to understand how cognition works)

I think that the main reason why we do not understand the general principles of how knowledge works (and probably also the reason why we have not yet designed and built efficient machines capable of artificial intelligence), is not the excessive complexity of cognitive phenomena, but the lack of the conceptual and methodological tools to properly address the problem. It is like trying to build up Physics without the concept of number, or to understand the origin of species without including the mechanism of natural selection. In this paper I propose some new conceptual and methodological tools, which seem to offer a real opportunity to understand the logic of cognitive processes. I propose a new method to properly treat the concepts of structure and schema, and to perform on them operations of structural analysis. These operations allow to move straightforwardly from concrete to more abstract representations. With these tools I will suggest a definition for the concept of rule, of regularity and of emergent phenomena. From the analysis of some important aspects of the rules, I suggest to distinguish them in operational and associative rules. I propose that associative rules assume a dominant role in cognition. I also propose a definition for the concept of problem. At the end I will briefly illustrate a possible general model for cognitive systems.

💡 Research Summary

The paper opens with a provocative claim: the failure to articulate general principles of cognition—and consequently to build truly intelligent machines—is not due to the intrinsic complexity of mental phenomena, but to the absence of adequate conceptual and methodological tools. The author likens this situation to attempting physics without the notion of number or evolutionary biology without natural selection. To remedy this, the work introduces a suite of new concepts and analytical procedures aimed at providing a clearer logical framework for cognitive processes.
First, the author redefines “structure” and “schema.” A structure is presented as the minimal formal representation of a concrete perceptual or behavioral instance, while a schema is an abstracted collection of such structures that operates at higher levels of generality. The paper proposes a “structural analysis operation” that proceeds in three stages: (1) mapping raw data onto structural representations, (2) grouping these structures into relational patterns, and (3) abstracting schemas from the relational patterns. This pipeline is claimed to enable a systematic transition from concrete experience to increasingly abstract cognitive symbols.
Second, the notion of a “rule” is formalized. Rules are defined as relations linking one structure to another, and they are divided into two families. “Operational rules” are explicit, condition‑action statements that resemble classical symbolic AI procedures. “Associative rules,” by contrast, are probabilistic links derived from experience; they function like weighted connections in neural networks, dynamically adjusting their strength through exposure. The author argues that associative rules dominate cognition, governing language acquisition, concept formation, inference, and problem solving. Their hallmark properties—non‑linearity, context‑dependence, and multi‑path activation—allow them to capture phenomena that purely symbolic models cannot.
Third, the paper offers a fresh definition of a “problem.” A problem is characterized as the detection of a mismatch between a goal state and the current cognitive state, followed by a search for rule applications that can reduce the mismatch. In this view, operational rules provide initial, explicit strategies, while associative rules prune the search space and supply intuitive shortcuts. Thus problem solving emerges as a collaborative process between the two rule systems.
Finally, the author sketches a general cognitive architecture that integrates all the preceding elements. The architecture follows a loop: input → structure/schema mapping → rule selection (operational vs. associative) → output/action → feedback. Feedback updates the weights of associative rules (learning) and may also trigger revisions of operational rules. This model is presented as a unifying framework that bridges the symbolic tradition (rule‑based AI) and the connectionist tradition (neural networks), suggesting that both are necessary components of a complete theory of mind.
The paper acknowledges several limitations. The structural analysis operation is described conceptually but lacks a concrete algorithmic specification, making empirical implementation challenging. The claim that associative rules are dominant is not supported by experimental data, and the proposed general model is not validated through simulations or quantitative predictions. Consequently, future work must focus on formalizing the analysis procedures, designing experiments to test the rule taxonomy, and building computational prototypes that demonstrate the model’s explanatory power.
In sum, the article contributes a bold conceptual overhaul: by formalizing structures, schemas, operational and associative rules, and a feedback‑driven problem‑solving loop, it offers a promising pathway toward a more systematic science of cognition. If the proposed tools can be operationalized and empirically vetted, they could substantially advance both theoretical understanding and the engineering of artificial intelligence systems.