Bayesian transfer in a complex spatial localization task

Learning and inference using complex generative models in a spatial localization task

A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here w...

Efficient Bayesian Task-Level Transfer Learning

In this paper, we show how using the Dirichlet Process mixture model as a generative model of data sets provides a simple and effective method for transfer learning. In particular, we present a hierarchical extension of the classic Naive Bayes classifier that couples multiple Naive Bayes classifiers by placing a Dirichlet Process prior over their parameters and show how recent advances in appro...

Bayesian Prediction in Spatial Data Analysis

Bayesian integration of visual and auditory signals for spatial localization.

Human observers localize events in the world by using sensory signals from multiple modalities. We evaluated two theories of spatial localization that predict how visual and auditory information are weighted when these signals specify different locations in space. According to one theory (visual capture), the signal that is typically most reliable dominates in a winner-take-all competition, whe...

Investigating the Effect of Music on Spatial Learning in a Virtual Reality Task

Background: Spatial learning and navigation is a fundamental cognitive ability consisting of multiple cognitive components. Despite intensive efforts conducted with the assistance of virtual reality technology and functional Magnetic Resonance Imaging (fMRI) modality, the music effect on this cognition and the involved neuronal mechanisms remain elusive. Objectives: We aimed to investigate the...