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Cited by 253

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

What's next? Judging sequences of binary events.

Oskarsson, An T., Van Boven, Leaf, McClelland, Gary H., Hastie, Reid·2009·Psychological Bulletin

Abstract

The authors review research on judgments of random and nonrandom sequences involving binary events with a focus on studies documenting gambler's fallacy and hot hand beliefs. The domains of judgment include random devices, births, lotteries, sports performances, stock prices, and others. After discussing existing theories of sequence judgments, the authors conclude that in many everyday settings people have naive complex models of the mechanisms they believe generate observed events, and they rely on these models for explanations, predictions, and other inferences about event sequences. The authors next introduce an explanation-based, mental models framework for describing people's beliefs about binary sequences, based on 4 perceived characteristics of the sequence generator: randomness, intentionality, control, and goal complexity. Furthermore, they propose a Markov process framework as a useful theoretical notation for the description of mental models and for the analysis of actual event sequences.

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Metadata

Authors
Oskarsson, A. T., Van Boven, L., McClelland, G. H., & Hastie, R.
Venue
Psychological Bulletin
Published
2009
Volume
135
Issue
2
Pages
262-285
ISSN
  • 0033-2909 (ISSN-L)
  • 1939-1455
OpenAlex
W3123045587
Cited by
253
References
110
FWCI
8.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: What's next? Judging sequences of binary events. https://app.cochat.ai/papers/10.1037/a0014821
The paper
Oskarsson, A. T., Van Boven, L., McClelland, G. H., & Hastie, R. (2009). What's next? Judging sequences of binary events. Psychological Bulletin. https://doi.org/10.1037/a0014821

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.