Carnegie Mellon University
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Knowing Less Than We Can Tell: Assessing Metacognitive Knowledge in Subjective, Multi-Attribute Choice

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posted on 2025-08-20, 19:44 authored by Trent CashTrent Cash
<p dir="ltr">Metacognition – thinking about thinking – is fundamental to nearly all facets of human cognition. In recent years, there has been a growing interest in the role that metacognition plays in judgment and decision making, referred to as metareasoning. However, the metareasoning literature has focused almost exclusively on objective reasoning tasks in which metacognitive judgments can be compared to objective standards of performance. In this Dissertation, we seek to extend the metareasoning literature to subjective, multi-attribute choice decisions, such as which house to buy or which partner to date. We specifically investigate decision makers’ metacognitive knowledge of the weight they place on various cues when making multi-attribute choice decisions. In the Preamble, we review the extant literature on metacognitive knowledge and metacognition more broadly. In Chapter 1, we present and validate a novel paradigm for assessing the accuracy of metacognitive knowledge in multi-attribute choice. In Chapter 2, we investigate how the accuracy of metacognitive knowledge is influenced by the complexity of the choice task. In Chapter 3, we explore the relative influence that different sources of metacognitive information have on the generation of accurate metacognitive knowledge. In Chapter 4, we examine the ability of decision makers to apply known attribute weights in a multi-attribute choice task, thus capturing metacognitive control. Finally, in Chapter 5, we test decision makers’ metacognitive knowledge of the influence that framing effects have on their attribute weights. We then summarize our results in Chapter 6 and offer future directions for extending this line of inquiry.</p>

History

Date

2025-08-15

Degree Type

  • Dissertation

Thesis Department

  • Psychology

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Daniel M. Oppenheimer

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