Why Intelligence Models Must Include Motivation: A Recursive Framework

Intelligence research has produced a paradox: the field widely acknowledges that motivation influences cognitive development, yet virtually every major model of intelligence formally excludes it. The Cattell-Horn-Carroll (CHC) taxonomy, the dominant psychometric framework, contains no motivational component. Cattell's own investment theory treats motivation as an external condition rather than a constitutive element. Sternberg's triarchic theory incorporates practical intelligence but not the drive to acquire it. This paper argues that this exclusion is not merely an oversight but a systematic blind spot that distorts our understanding of intelligence in three specific ways. First, it mischaracterizes intelligence as a static trait rather than a recursive, self-reinforcing system in which knowledge, cognitive performance, and motivation form a closed amplification loop. Second, it renders invisible the role of operational knowledge—learning strategies, logical tools, and strategic thinking—which functions as the primary multiplier within this loop. Third, it leaves the field unable to explain why current artificial intelligence systems, which possess vast knowledge and computational performance but no intrinsic motivation, fail to exhibit the self-directed development that characterizes human intelligence. This paper proposes a three-component recursive model (Knowledge × Performance × Motivation) and argues that intelligence is best understood not as a capacity but as a learning ability—one whose trajectory is determined by the dynamics of this recursive loop rather than by any single component measured in isolation.