continuous evolution of statistical estimators for optimal decision-making连续演化的统计估计最优决策.pdf
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Continuous Evolution of Statistical Estimators for
Optimal Decision-Making
Ian Saunders*, Sethu Vijayakumar
Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
Abstract
In many everyday situations, humans must make precise decisions in the presence of uncertain sensory information. For
example, when asked to combine information from multiple sources we often assign greater weight to the more reliable
information. It has been proposed that statistical-optimality often observed in human perception and decision-making
requires that humans have access to the uncertainty of both their senses and their decisions. However, the mechanisms
underlying the processes of uncertainty estimation remain largely unexplored. In this paper we introduce a novel visual
tracking experiment that requires subjects to continuously report their evolving perception of the mean and uncertainty of
noisy visual cues over time. We show that subjects accumulate sensory information over the course of a trial to form a
continuous estimate of the mean, hindered only by natural kinematic constraints (sensorimotor latency etc.). Furthermore,
subjects have access to a measure of their continuous objective uncertainty, rapidly acquired from sensory information
available within a trial, but limited by natural kinematic constraints and a conservative margin for error. Our results provide
the first direct evidence of the continuous mean and uncertainty estimation mechanisms in humans that may underlie
optimal decision making.
Citation: Saunders I, Vijayakumar S (2012) Continuous Evolution of Statistical Estimators for Optimal Decision-Making. PLoS ONE 7(6): e37547. doi:10.1371/
journal.pone.0037547
Editor: Marc O. Ernst, Bielefeld University, Germany
Received Novem
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