Bernal S D_2010.pdf - University of Plymouth

2.2. HIGH-LEVEL FEEDBACK
task, such as visual search.
The different types of attention have been modelled extensively. Walther and Koch (2007) pro­
vide a comprehensive overview of existing models, and propose a unifying framework which
captures most of the attentional effects. By implementing modulation functions at each pro­
cessing level, their model is capable of reproducing spuiial and feature based atiention both in
a top-down and bottom-up fashion.
Additionally, the model is capable of simulating object-based attention, which can encompass
a variety of effects. These range from spatially focusing on an object to enhancing the rele­
vant features of the target object during a search task. This is achieved by making use of the
same complex features employed for feedforward recognition, during the lop-down attention
process. The HMAX model (Seire et al. 2007c) was extended to provide an example of feature-
based attention using this principle, and results showed an increased performance over a pure
botlom-Hp attention implementation (Walther and Koch 2007). The present thesis also pro­
vides a feedback extension of the HMAX model, which has many theoretical similarities to this
approach, including the sharing of features between object recognition and top-down attention.
It has been argued that attention by itself may explain the existence of cortical feedback connec­
tions (Macknik and Martinez-Conde 2007), without requiring further complex interpretations.
In fact, most of the approaches allude to some kind of atlentJonal mechanism when describ­
ing the role of feedback, in most bia.sed competition models, e.g. Deco and Rolls (2004).
attention is taken as a synonym for feedback. For adaptive resonance approaches (Grossberg
et al. 2007) attention is one of the multiple functions of feedback connections, which are also
involved in learning or perceptual grouping. In contrast, Lee and Mumford (2003) argue the
sophisticated machinery of feedback should not be limiled to biased competition models of at­
tention, but insiead should account for more complex perceptual inference processes. But even
in generative-oriented models such as Bayesian inference or predictive coding, the top-down
prit)rs or high-level predictions are sometimes referred to as a form of attentional modulation
(Spratiing 2008a. Chikkeruret al, 2009).
The inconsistency and disparity between the various definitions of attention might reflect the
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