Intelligence occupies a paradoxical position in modern knowledge systems. It is simultaneously one of the most frequently invoked concepts across scientific, technological, political, and cultural discourses, and one of the least conceptually stabilized. For more than a century, intelligence has been measured, modeled, simulated, optimized, and debated, yet it has never been consolidated into a unified scientific object with clear ontological, epistemological, and methodological boundaries. The absence of such consolidation has produced a fragmented intellectual landscape in which intelligence is alternately treated as a psychometric variable, a neurological function, a computational capacity, an evolutionary adaptation, or a philosophical abstraction, depending on disciplinary context. This fragmentation is not merely terminological; it reflects a deeper structural limitation in contemporary science, namely the lack of a dedicated discipline capable of studying intelligence as a phenomenon in its own right rather than as a secondary attribute of other processes.
The earliest systematic attempts to study intelligence emerged within psychometrics, particularly through the work of Alfred Binet and Théodore Simon in the early twentieth century, who sought to quantify intellectual abilities for educational purposes (Binet & Simon, 1905). While historically significant, this approach implicitly reduced intelligence to testable cognitive performance within culturally specific frameworks, a limitation later exposed by Stephen Jay Gould’s critique of reification and measurement bias in intelligence testing (Gould, 1981). Subsequent theoretical expansions, such as Charles Spearman’s concept of general intelligence and Howard Gardner’s theory of multiple intelligences, broadened the scope of what might count as intelligence but did not resolve the deeper issue of definition (Gardner, 1983).
Parallel to psychometric traditions, cognitive science emerged in the mid-twentieth century as an interdisciplinary project grounded in the computational metaphor of mind. Influenced by developments in computer science and information theory, pioneers such as Allen Newell and Herbert Simon conceptualized intelligence as symbolic information processing, framing cognition as a form of problem-solving analogous to algorithmic computation (Newell & Simon, 1972).
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- Pitshou Moleka (Autor:in), 2026, Noesology. A Foundational Science of Intelligence Beyond the Human, München, GRIN Verlag, https://www.grin.com/document/1687072
