Bernal S D_2010.pdf - University of Plymouth

6.1. ANALYSIS OF RESULTS
strong rotations the winner S3 prototype should no longer be a square object. Additionally,
non-aligned inducers .should prevent or reduce the strength of the illusory contours.
A dilVerent question to ihe one addressed in this section, is whether the feedback loop can
improve categorization over time. Again, this can only be tested once the feedback reconstruc­
tion provided by higher layers is improved. As previously argued, (he results shown in Figure
5.31 suggest that feedback may indeed improve calegori/alion, based on how the CI and S2
responses are gradually modulated towards a sharper square representation,
6.1.2.7 Feedback to S3
The example shown in Figure 5.33, despite depicting a very trivial problem, serves to illustrate
the capacity of the model to simulate feedback effects, such as priming or expectation, which
arise from areas outside the ventral pathway such as Ihe prefrontal cortex, fusiform gyrus, pos­
terior parietal cortex or the amygdala (Sutnmerlield and Egner 2009, Bar et al. 2006, Grossberg
et al. 2007, vSabatinelli et al. 200'), Gilbert and Sigman 2007). Furthermore, die model allows to
simulate the activation of high-level object-selective regions due to mental imagery which has
been suggested to be mediated by feedback connections from prefrontal cortex (Ishai 2010).
Importantly, these effects are accommodated as part of the Bayesian network parameters (S3
prior distribution), without the need to include any external artifacts. The example can also be
interpreted as implementing feature attention (enhancing only slates corresponding to animals
in the S3 prior distribudon) and could similarly implement spatial attention by defining a prior
distribution that favours certain locations, specially when processing larger images with several
objects. The Bayesian implementation of attention resembles that proposed by Chikkerur et al.
(2010).
6.1.3 Benefits and limitations of Bayesian networks
Bayesian networks and belief propagation provide a rigorous mathematical framework, grounded
in probability theory, that allows the feedforward and feedback interactions of a system to be
modelled. One of its most attractive and arguably elegant features is its distributed implemen­
tation, wherein all the nodes have an homogeneous internal structure and carry out the same
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