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English title | Generalizing the weighted average hypothesis: The cognitive processes underlying probability updating |
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Applicant | Rieskamp Jörg |

Number | 138174 |

Funding scheme | Project funding (Div. I-III) |

Research institution | Fakultät für Psychologie Universität Basel |

Institution of higher education | University of Basel - BS |

Main discipline | Psychology |

Start/End | 01.01.2012 - 31.12.2012 |

Approved amount | 74'322.00 |

probability judgments; uncertainty; information processing; cognitive models

Lead |
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Lay summary |

Dealing with uncertainties is a crucial aspect of people’s everyday lives. We study how people form and update subjective probability estimates of uncertain events. People often do not judge the probability of uncertain events according to the rules of logic and probability theory. Instead, their judgments can often be better described by cognitive judgment strategies. Although these strategies often violate the rules of probability theory, they nevertheless might often lead to good decisions. We compare several models in their ability to capture the processes behind people’s behavior when having to assess and update probabilistic information. For instance, when people have to access the probability of how often it snows during winter, they could rely on a sample of snowy days in the past winter. Of course this sample will contain a sampling error, so that the subjective probability will only to some extent predict the objective probability. In probability updating, people form an initial belief about the probability of an event and then receive additional information that can be used to update the initial belief sequentially. Sometimes the additional information might be irrelevant and should be ignored. In comparing different cognitive models, such as the weighted average model, a similarity model, and the standard Bayesian model, we want aim at identifying the process that describes people’s probability judgments. In general, this project aims for a better understanding of how people use different pieces of information when they have to assess the probability of uncertain events. |

Direct link to Lay Summary | Last update: 21.02.2013 |

Name | Institute |
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Publication |
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Inferring conjunctive probabilities from noisy samples: Evidence for the configural weighted average model. |

Exploring the overestimation of conjunctive probabilities |

Psychologists are open to change, yet wary of rules |

Group / person | Country |
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Types of collaboration |
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Dr. Hakan Nilsson, Uppsala University | Sweden (Europe) |

- in-depth/constructive exchanges on approaches, methods or results - Publication |

Communication | Title | Media | Place | Year |
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Media relations: radio, television | Psychologie in der Krise | Neue Zürcher Zeitung | German-speaking Switzerland | 2012 |

Number | Title | Start | Funding scheme |
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126721 | Computing Conjunctive Probabilities by Combining Constituent Probabilities: An Evaluation of the Weighted Average Model | 01.01.2010 | Project funding (Div. I-III) |

This project studies how people judge and update the subjective probability of uncertain events. Dealing with uncertainties is a crucial aspect of people’s everyday lives. However, people often do not judge the probability of uncertain events according to the rules of logic and probability theory. Instead, their judgments can often be better described by cognitive judgment strategies. Although these strategies often violate rules of probability theory, they nevertheless might often lead to good decisions. A cognitive strategy can be ecologically rational when it leads to good predictions in a particular environment (Gigerenzer et al., 2011; Gigerenzer & Selten, 2001). We focus on a particular cognitive process model, the weighted average model (Juslin et al., 2009; Nilsson et al., 2009). This model suggests that people integrate multiple pieces of probabilistic information by first weighting them according to their importance and then adding them up. This model has been shown to be ecologically rational in situations in which the probability of single events can only be assessed with some error. For instance, when people have to access the probability of how often it rains during summer, they could rely on a sample of days in the past summer. Of course this sample will contain a sampling error, so that the subjective probability will only to some extent predict the objective probability. In our project we explore how good the model can predict people’s probability estimates in comparison to alternative models. In the first phase of the project (starting 1.2010) we were already able to illustrate that the model is quite successful in predicting people’s probability assessment of conjunctive events. The current grant application suggests the extension of this project to test of the generalizability of the weighted average model for situations of probability updating. In probability updating situations, people form an initial belief about the probability of an event and then receive additional information to update the initial belief. Thus, unlike in our previous work, people are not provided with different pieces of information simultaneously, but receive the information sequentially. We will explore whether this updating process also follows the prediction of the weighted average model or whether it can better be described by probability theory, in particular Bayesian updating. We propose three experimental studies in which we want to examine how people update subjective probability estimates for conjunctive and dis-junctive probability assessment tasks. In particular, we will explore whether the cognitive process that leads to the conjunction effect could also lead to the dilution effect. The dilution effect occurs when people revise their initial probability judgment by additional non-diagnostic information that they should better ignore. In general, this project aims for a better under-standing of how people use different pieces of information when they have to access the proba-bility of uncertain events.

Swiss National Science Foundation (SNSF)

Wildhainweg 3P.O. Box

CH-3001 Bern

Phone +41 31 308 22 22

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