The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Consequences of erudite vernacular utilized irrespective of necessity: problems with using long words needlessly
Abstract
Most texts on writing style encourage authors to avoid overly-complex words. However, a majority of undergraduates admit to deliberately increasing the complexity of their vocabulary so as to give the impression of intelligence. This paper explores the extent to which this strategy is effective. Experiments 1–3 manipulate complexity of texts and find a negative relationship between complexity and judged intelligence. This relationship held regardless of the quality of the original essay, and irrespective of the participants' prior expectations of essay quality. The negative impact of complexity was mediated by processing fluency. Experiment 4 directly manipulated fluency and found that texts in hard to read fonts are judged to come from less intelligent authors. Experiment 5 investigated discounting of fluency. When obvious causes for low fluency exist that are not relevant to the judgement at hand, people reduce their reliance on fluency as a cue; in fact, in an effort not to be influenced by the irrelevant source of fluency, they over-compensate and are biased in the opposite direction. Implications and applications are discussed. Copyright © 2005 John Wiley & Sons, Ltd.
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Overcoming intuition: Metacognitive difficulty activates analytic reasoning.
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Self-citationWhen Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications
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What large language models know and what people think they know
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Metadata
- Authors
- Oppenheimer, D. M.
- Venue
- Applied Cognitive Psychology
- Publisher
- Wiley
- Published
- 2006
- Volume
- 20
- Issue
- 2
- Pages
- 139-156
- ISSN
- 0888-4080 (ISSN-L)
- 1099-0720
- OpenAlex
- W2056533510
- Cited by
- 353
- References
- 24
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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