Bernal S D_2010.pdf - University of Plymouth

6.4. WTUREWURK
by Chikkerur et al. (2009) also implements exact inference on a Bayesian network but models
exclusively high-level atteniion, such that the lower half of the network is non-Bayesian and
strictly feedforward.
The type of input image used by the mtxlel is more complex and detailed than that of previous
ones that were purely theoretical (Lee and Mumford 2003) or employed simplistic toy exam­
ples ((•riston and Kiebel 2(H)9. Lewicki and Scjnowski 1997. Hinton et al. 2006), Those with
comparable input images fail to account for other properties that have been implemented by
the proposed model, such as position and scale invariance (Rao and Ballard 1997, Murray and
Kreutz-Delgado 2007, Chikkerur et al. 2009) or illusory contour completion (Hinlon et al. 2006,
Epshtein et al. 2008).
6.4 Future work
A number of potential improvements and extensions to the proposed model are listed below:
• Run simulations with Kanizsa tij;ure controls that fully lest the hypothesis that the model
performs illusory contour completion. Althe moment, the control data give an ambiguous
answer to the model's performance.
• Perform a systematic analysis of the model parameters for both feedforward and feedback
processing. Some of the key parameters to study arc the number of features per group.
Ihe sparseness of the connectivity matrices and the sampling parameters.
• lj;am heterogeneous feedback weights for features within a group and allow features to
belong to different groups. This should improve the feedback disambiguation capacity
and could lead to improved contextual modulation through lateral interactions.
• Improve the categorization of Kanizsa figures and the feedback C2-S2 reconstruction
to allow automatic illusory contour completion without clamping feedback. This could
also lead to an improvement of the categorization performance over time as a result of
feedback modulation.
• Include adaptation mechanisms that could lead naturally to phenomena such as sensitivity
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