Wittgenstein’s Revenge: How Semantic Algorithms Can Help Survey Research Escape Smedslund’s Labyrinth

Empirical research has shown how semantic algorithms can often predict the statistics of survey data a priori, particularly in topics like “leadership” and “motivation.” In those cases, the survey data reflect the language usages of respondents, not the attitudes toward the topics in question. While this fact seems to bewilder researchers, it opens a computational tool for exploring our semantic construction of psychological reality. Using Dennett’s concept “competence without comprehension,” this article discusses how humans are trapped in a semantic network that we ourselves struggle to understand. Since Smedslund’s work and the language algorithms have common roots in formal logics, the computational algorithms may help us explore the cognitively challenging area of a priori assumptions in psychological research. There may be a computational way to test and explore Smedslund’s ideas of “pseudo-empiricality,” helping science explore the complex area among empirical, logical, and psychological phenomena