## Abstract

The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. This analysis rests on two assumptions: (a) that people predict the next event in a sequence in a way that is consistent with Bayesian inference; and (b) when testing hypotheses, people test the hypothesis to which they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people's predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses (Experiments 3 and 4).

Original language | English (US) |
---|---|

Pages (from-to) | 499-526 |

Number of pages | 28 |

Journal | Cognitive science |

Volume | 35 |

Issue number | 3 |

DOIs | |

State | Published - Apr 2011 |

Externally published | Yes |

## All Science Journal Classification (ASJC) codes

- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Artificial Intelligence

## Keywords

- Bayesian inference
- Confirmation bias
- Decision making
- Determinism
- Hypothesis testing
- Rational analysis