Numerical estimation is a fundamental cognitive skill supporting numerous everyday decisions. Yet, our understanding remains fragmented. This dissertation addresses this gap by examining numerical real-world estimation through the factors that shape it. With Manuscript I, I broaden the understanding of the foundations of numerical estimation by showing that, beyond domain knowledge, symbolic-number mapping also underlies estimation. In Manuscripts II and III, I deliberate how estimation changes when the knowledge base is updated. In Manuscript II, I examine how providing information can trigger knowledge updating and thus improve estimation. In Manuscript III, I investigate hindsight bias—the tendency to recall prior estimates as more accurate after learning the actual values. I provide evidence that this bias arises from the same updating processes that improve estimation. With this dissertation, I deepen our theoretical understanding of the factors shaping numerical real-world estimation while providing guidance for supporting estimation accuracy.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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