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Losses and External Outcomes Interact to Produce the Gambler’s Fallacy
When making serial predictions in a binary decision task, there is a clear tendency to assume that after a series of the same external outcome (e.g., heads in a coin flip), the next outcome will be the opposing one (e.g., tails), even when the outcomes are independent of one another. This so-called...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5268496/ https://www.ncbi.nlm.nih.gov/pubmed/28125684 http://dx.doi.org/10.1371/journal.pone.0170057 |
Sumario: | When making serial predictions in a binary decision task, there is a clear tendency to assume that after a series of the same external outcome (e.g., heads in a coin flip), the next outcome will be the opposing one (e.g., tails), even when the outcomes are independent of one another. This so-called “gambler’s fallacy” has been replicated robustly. However, what drives gambler’s fallacy behavior is unclear. Here we demonstrate that a run of the same external outcome by itself does not lead to gambler’s fallacy behavior. However, when a run of external outcomes is accompanied by a concurrent run of failed guesses, gambler’s fallacy behavior is predominant. These results do not depend on how participants’ attention is directed. Thus, it appears that gambler’s fallacy behavior is driven by a combination of an external series of events and a concurrent series of failure experiences. |
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