Bernal S D_2010.pdf - University of Plymouth

6.1. ANALYSIS Ot RESULTS
step. Although feedback originates from empty high-level representations, it becomes non-flat
as it is modulated by the conditional probability Sables (CPTs) weights (see Section 4.5 for de­
tails), which explains why the resulting S3 distribution has more noise than the one with no
feedback. Even the most extreme case which processed only the lowest band and implemented
the complete belief update method (stronger feedback effects), situated the square prototype in
founh place, a surprisingly good result considering the limitations.
The Kanizsa square, which can be considered a strongly occluded square, was correctly catego­
rized with no feedback and obtained significantly high positions for the upward update method
with loopy feedback (first for the 4x4 S2 RF size and third for the averaged response). Even
when reducing the number of bands to one and using the complete update method, the Kanizsa
square still showed consistently good results. Overall, these results suggest that a similar cate­
gorization performance can be achieved by the model even when including the feedback loop
during the initial botlom-up pass. However, further research is required to prove this hypothesis
and to obtain a belter understanding of the factors affecting feedforward categorization in loopy
Bayesian networks.
The categorization of Kanizsa input images as squares is critical in order to simulate illusory
contour completion without the need to clamp any high-level square representation. Instead the
model should recognize the input Kanizsa figure as a square and feed back the corresponding
information. The current categorization results using feedback do not provide an appropriate
square representation, as the square stale does not show the highest value or, if it does, the over­
all distribution is extremely noisy. This can be solved in the future by improving the feedforward
categorization performance so that the Kanizsa figure elicits a clear square representation and
by improving the feedback reconstruction from C2 to S2. This should allow lo obtain an au­
tomatic illusory contour response just by feeding in the Kanizsa input image lo the network.
Current results using an idealized S3 square representation (Figure 5.31) are encouraging and
support this claim as they manages to elicit the illusory contour in lower regions.
An interesting control test to perform would be to systematically rotate the Kanizsa pacmen
by varying degrees. The categorization of the Kanizsa figure should be affected such that for
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