Background beliefs in Bayesian inference

Evans, Jonathan St. B. T.; Handley, Simon J.; Over, David E.; Perham, Nick

doi:10.3758/bf03195279

Cited by 43 publications

(52 citation statements)

References 26 publications

(37 reference statements)

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“…Contrary to previous results illustrating deficits in the ability to combine likelihood ratios with prior odds (e.g., Kahneman & Tversky, 1972), people seem quite accurate in assessing the posterior probabilities of causal relationships. This may be a consequence of using priors that are derived from extended experience, rather than base-rates provided in an experimental scenario (Evans, Handley, Over, & Perham, 2002).…”

Section: Resultsmentioning

confidence: 99%

From mere coincidences to meaningful discoveries

2007

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Section: Resultsmentioning

confidence: 99%

From mere coincidences to meaningful discoveries

2007

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“…Responses within 5% of the estimate produced by using the focal base rate only were labelled as likelihood neglect. We coded a likelihood neglect category because previous work has shown that people sometimes overweight the base rates instead of neglecting them (see Evans, Handley, Over & Perham, 2002). All remaining responses were labelled as other.…”

Section: Resultsmentioning

confidence: 99%

When does Information about Causal Structure Improve Statistical Reasoning?

McNair

Feeney

2014

Quarterly Journal of Experimental Psychology

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Causal structure and statistical reasoning 2 Causal structure and statistical reasoning 3 AbstractBase rate neglect on the mammography problem can be overcome by explicitly presenting a causal basis for the typically vague false positive statistic (Krynski and Tenenbaum, 2007).One account of this causal facilitation effect is that people make probabilistic judgements over intuitive causal models parameterised with the evidence in the problem. Poorly defined or difficult to map evidence interferes with this process, leading to errors in statistical reasoning. To assess whether the construction of parameterised causal representations is an intuitive or deliberative process, in Experiment 1 we combined a secondary load paradigm with manipulations of the presence or absence of an alternative cause in typical statistical reasoning problems. We found limited effects of a secondary load, no evidence that information about an alternative cause improves statistical reasoning, but some evidence that it reduces base rate neglect errors. In Experiments 2 and 3 where we did not impose a load, we observed causal facilitation effects. The amount of Bayesian responding in the causal conditions was impervious to the presence of a load (Experiment 1) and to the precise statistical information that was presented (Experiment 3). However, we found less Bayesian responding in the causal condition than did Krynski and Tenenbaum (2007). We conclude with a discussion of the implications of our findings and the suggestion that there may be population effects in the accuracy of statistical reasoning.Causal structure and statistical reasoning 4

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“…We call these pre-experimental beliefs intuitive estimates. It is plausible that Bayesian reasoning, like syllogistic reasoning, is influenced by how well the inferential conclusion accords with participants' beliefs about the real world (Anderson et al, 2015;Evans, Handley, Over, & Perham, 2002). We hypothesized that the posterior probabilities that participants provide reflect, in part, their intuitive estimates of the conditional probabilities that they are being asked to compute.…”

Section: [Base Rate P(h)]mentioning

confidence: 99%

Beliefs and Bayesian reasoning

2016

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We examine whether judgments of posterior probabilities in Bayesian reasoning problems are affected by reasoners' beliefs about corresponding real-world probabilities. In an internet-based task, participants were asked to determine the probability that a hypothesis is true (posterior probability, e.g., a person has a disease, given a positive medical test) based on relevant probabilities (e.g., that any person has the disease and the true and false positive rates of the test). We varied whether the correct posterior probability was close to, or far from, independent intuitive estimates of the corresponding 'real-world' probability. Responses were substantially closer to the correct posterior when this value was close to the intuitive estimate. A model in which the response is a weighted sum of the intuitive estimate and an additive combination of the probabilities provides an excellent account of the results.

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Background beliefs in Bayesian inference

Cited by 43 publications

References 26 publications

From mere coincidences to meaningful discoveries

From mere coincidences to meaningful discoveries

When does Information about Causal Structure Improve Statistical Reasoning?

Beliefs and Bayesian reasoning

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