Subjects were presented with data, described as the simulated output of a computerized radar system, consisting of dots that could fall in any one of twelve sectors. They were told that the process generating the data might be in any one of four mutually exclusive states. Displays showed for each state how likely it was that each dot would fall in each sector; an auxiliary display showed the prior probabilities of each of the four states. Subjects were required to estimate posterior probabilities of each state after each datum; comparison of these estimates with the correct values calculated from Bayes' theorem provided the dependent variables
The proportion of simulations where the true value of the parameter fell outside of the 95% credible...
A probabilistic computational level model of conditional inference is proposed that can explain pola...
Bayesian theories of cognition assume that people can integrate probabilities rationally. However, s...
Subjects were presented with data, described as the simulated output of a computerized radar system,...
3 experiments investigated the effects on posterior probability estimates of: (1) prior probabilitie...
Subjects saw samples from each of two populations of numbers and made intuitive inferences about whi...
A Probabilistic Information Processing System (PIP) uses men and machines in a novel way to perform ...
n a simple experimental environment a group of subjects was asked to give estimates of a second grou...
The age-old question of the generalizability of the results of experiments that are conducted in art...
The present study investigated the effects of prior probability, mode of presenting information, and...
When people revise subjective probabilities in light of data, revisions are less than the amount pre...
We depart from Savage’s (1954) common state space assumption and introduce a model that allows for a...
The Bayesian theorem was formulated in the 18th century and has been adopted as the theoretical basi...
We investigate human departures from Bayesian optimality in an inference task in which subjects esti...
An approach to the assessment of probabilistic inference is described which quantifies the performan...
The proportion of simulations where the true value of the parameter fell outside of the 95% credible...
A probabilistic computational level model of conditional inference is proposed that can explain pola...
Bayesian theories of cognition assume that people can integrate probabilities rationally. However, s...
Subjects were presented with data, described as the simulated output of a computerized radar system,...
3 experiments investigated the effects on posterior probability estimates of: (1) prior probabilitie...
Subjects saw samples from each of two populations of numbers and made intuitive inferences about whi...
A Probabilistic Information Processing System (PIP) uses men and machines in a novel way to perform ...
n a simple experimental environment a group of subjects was asked to give estimates of a second grou...
The age-old question of the generalizability of the results of experiments that are conducted in art...
The present study investigated the effects of prior probability, mode of presenting information, and...
When people revise subjective probabilities in light of data, revisions are less than the amount pre...
We depart from Savage’s (1954) common state space assumption and introduce a model that allows for a...
The Bayesian theorem was formulated in the 18th century and has been adopted as the theoretical basi...
We investigate human departures from Bayesian optimality in an inference task in which subjects esti...
An approach to the assessment of probabilistic inference is described which quantifies the performan...
The proportion of simulations where the true value of the parameter fell outside of the 95% credible...
A probabilistic computational level model of conditional inference is proposed that can explain pola...
Bayesian theories of cognition assume that people can integrate probabilities rationally. However, s...