Crystallized Intelligence: Exploring the Dark Matter of Intelligence

Do you know the capital of Mozambique? What does the word Надежда mean? Which author voluntarily declined the Nobel Prize in Literature? Although these questions may appear unrelated, they could all serve as indicators of crystallized intelligence (gc). At the same time, they illustrate some of the challenges involved in defining, assessing, and modeling gc. Several questions arise: How can such diverse content be traced back to a single underlying construct? What role do language skills play? And does the knowledge really reflect widely shared cultural content or more specialized expertise?It also becomes evident that the likelihood of answering the above questions correctly depends on factors such as birthplace, native language, and year of birth. Someone born in Maputo, a Russian speaker, or a person who witnessed the media controversy in 1964, when Sartre famously declined the Nobel Prize on the day it was announced, will likely know the correct answers. A fair assessment of gc across national borders, language groups, and historical eras is therefore challenging. If the construct of gc is so multifaceted, difficult to measure fairly, and often vague in its operationalization, why should we study gc at all? In this chapter, we aim to address this question by first outlining prominent theoretical perspectives on gc (Section 1), followed by a discussion about its structure and organization (Section 2). The subsequent sections examine gc from a developmental perspective (Section 3) and review empirical findings on its determinants and consequences (Section 4). We then turn to the main focus of the chapter—the measurement and psychometric modeling of gc (Sections 5). Finally, we discuss how large language models can inspire future work on how to understand, measure, and model gc (Section 6). Taken together, these perspectives demonstrate that gc remains not only a central pillar of intelligence research but also a construct whose mysteries can be re-examined in light of advances in artificial intelligence and large language models.