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Who “Believes” in the Gambler’s Fallacy and Why?
Humans possess a remarkable ability to discriminate structure from randomness in the environment. However, this ability appears to be systematically biased. This is nowhere more evident than in the Gambler’s Fallacy (GF)—the mistaken belief that observing an increasingly long sequence of “heads” fro...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Psychological Association
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5215234/ https://www.ncbi.nlm.nih.gov/pubmed/28054813 http://dx.doi.org/10.1037/xge0000245 |
Sumario: | Humans possess a remarkable ability to discriminate structure from randomness in the environment. However, this ability appears to be systematically biased. This is nowhere more evident than in the Gambler’s Fallacy (GF)—the mistaken belief that observing an increasingly long sequence of “heads” from an unbiased coin makes the occurrence of “tails” on the next trial ever more likely. Although the GF appears to provide evidence of “cognitive bias,” a recent theoretical account (Hahn & Warren, 2009) has suggested the GF might be understandable if constraints on actual experience of random sources (such as attention and short term memory) are taken into account. Here we test this experiential account by exposing participants to 200 outcomes from a genuinely random (p = .5) Bernoulli process. All participants saw the same overall sequence; however, we manipulated experience across groups such that the sequence was divided into chunks of length 100, 10, or 5. Both before and after the exposure, participants (a) generated random sequences and (b) judged the randomness of presented sequences. In contrast to other accounts in the literature, the experiential account suggests that this manipulation will lead to systematic differences in postexposure behavior. Our data were strongly in line with this prediction and provide support for a general account of randomness perception in which biases are actually apt reflections of environmental statistics under experiential constraints. This suggests that deeper insight into human cognition may be gained if, instead of dismissing apparent biases as failings, we assume humans are rational under constraints. |
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