Bernal S D_2010.pdf - University of Plymouth

3.4. EXISTING MODELS
ihL- output of the standard HMAX model (Serre el al. 2007b), described in Section 2.1.2, as the
input to a Bayesian network which simulates the effects of spatial and feature-based attention
(modelling the prefrontal cortex and the lateral intraparietal regions). The network consists of
a node L. encoding the location and scale of the target object; a node O. encoding the identity
of the object; and a set of nodes X, that c(xle the different features and their locations. The
feature nodes receive evidence from the HMAX-based preprocessing network, which extracts
a set of high-level features (roughly corresponding to V2/V4 receptive fields) from the image.
At the same time, they receive top-down feedback from the object location (L) and identity
(O), using conditional probability distribution P{X,\0,L). This distribution is constructed based
on whether the object contains a given feature (obtained from the HMAX parameters), and
whether Ihe feature location matches the spatial attention location (Gaussian centred around
that location).
The model is successful at capturing several attenlional effects such as the pop-out effect and
fealure-based and spatial attention, and predicts eye fixaiions during free viewing and visual
search tasks. However, it cannot be considered a generative model of the visual system as it
cannot produce input images, i.e. the model relies on the HMAX framework to analyze and ex­
tract features. This means the effects of attention on lower visual areas cannot be modelled. The
Bayesian network is limited to a relatively abstract implemenlalion of the high-level interactions
between the ventral and dorsal pathways, lixact inference can be performed using a single up
and down pass of the belief propagaiion algorithm due to the simplicity of the network, where
only the feature layer has more than one node.
Another interesting architecture, and one which takes into account temporal as well as spatial
information, is the Hierarchical Temporal Memory (HTM) proposed by George and Hawkins
(2009). The model assumes that images are generated by a hierarchy of causes, and that a
particular cause at one level unfolds into a sequence of causes ai a lower level. An HTM can
be considered a special type of Bayesian network which contains a variable coding the spatial
patterns, and a second variable coding sequences of those spatial patterns (represented using a
Markov chain).
123