Bernal S D_2010.pdf - University of Plymouth

J.2. MAIN CONTRfBUTlONS
• A review and analysis of the literature reparding object perception, feedback connectiv­
ity, illusory contour completion, generative models, Bayesian networks and belief prop­
agation. This includes a detailed comprehensive explanation of behef propagation in
Bayesian networks with several novel and illuslralive examples.
• A Bayesian network implementing loopy belief propagation that captures the structure
and functionality of HMAX, a feedforward objecl recognition model, and extends it to
include dynamic recurrent feedback.
• Specific approximations and sampling methods thai allow for the inlcgraiion of informa­
tion ill large-scale liaycsian networks with loops and nodes with multiple parents,
• Demonstration that the model can account for invariant objecl categorization, mimicking
the ventral path functionality.
• Demonstralion that the model can account qualitatively for illusory contour formation
and other higher-level feedback effects such as priming, attention and mental imagery.