Keywords
Bayesian decision theory; parametric estimation; probability density function; visual cognition
Abstract
Bayesian decision theory (BDT) is used to model human performance in tasks where the decision maker must compensate for uncertainty in order to gain rewards and avoid losses. BDT prescribes how the decision maker can combine available data, prior knowledge, and value to reach a decision maximizing expected winnings. Do human decision makers actually use BDT in making decisions? Researchers typically compare overall human performance (total winnings or overall percent correct) to the predictions of BDT but we cannot conclude that BDT is an adequate model for human performance based on just overall performance. We break BDT down into elementary operations and test human ability to execute such operations. In two of the tests human performance deviated only slightly (but systematically) from the predictions of BDT. In the third test we use a novel method to measure the influence of each sample point provided to the human decision maker and compare it to the influence predicted by BDT. When we look at what human decision makers do – in detail – we find that they use sensory information very differently from what the normative BDT observer does. We advance an alternative non-Bayesian model that better predicts human performance. We propose that influence measures are a more sensitive way to discover discrepancies between human and optimal performance than comparing overall performance.
Start Date
15-5-2024 11:00 AM
End Date
15-5-2024 12:00 PM
Location
New York University
Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample
New York University
Bayesian decision theory (BDT) is used to model human performance in tasks where the decision maker must compensate for uncertainty in order to gain rewards and avoid losses. BDT prescribes how the decision maker can combine available data, prior knowledge, and value to reach a decision maximizing expected winnings. Do human decision makers actually use BDT in making decisions? Researchers typically compare overall human performance (total winnings or overall percent correct) to the predictions of BDT but we cannot conclude that BDT is an adequate model for human performance based on just overall performance. We break BDT down into elementary operations and test human ability to execute such operations. In two of the tests human performance deviated only slightly (but systematically) from the predictions of BDT. In the third test we use a novel method to measure the influence of each sample point provided to the human decision maker and compare it to the influence predicted by BDT. When we look at what human decision makers do – in detail – we find that they use sensory information very differently from what the normative BDT observer does. We advance an alternative non-Bayesian model that better predicts human performance. We propose that influence measures are a more sensitive way to discover discrepancies between human and optimal performance than comparing overall performance.