A Model of Spatial & Object-Based Attention for Active Visual Search
Linda Lanyon* & Susan Denham
Centre for Theoretical & Computational Neuroscience, University of Plymouth, U.K.
*email: linda.lanyon@plymouth.ac.uk
Of particular interest in the visual attention literature currently is the biased competition
hypothesis [1]. Systems level models using this hypothesis have begun to emerge [2] but there
is still much work to be done to explore this hypothesis computationally in biologically
plausible systems. In particular, there is a need to explain the presence of both spatial [3,4] and
object-based [5,6] attentional effects and the different latencies associated with these effects.
Here, the biased competition approach is applied, for the first time, in an active vision
paradigm where retinal inputs change during the scan path. Such an approach raises issues,
such as memory for searched locations across saccades, not addressed by models with static
retinas. At the cellular level, the model simulates both spatial and object-based attentional
effects over time courses seen in single cell recordings in ventral “what” stream areas [5,6,7].
Such effects at the cellular level lead to systems level behaviour that replicates that observed
during active visual search for orientation and colour feature conjunction targets, where the
scan path is guided to target coloured locations in preference to locations containing the target
orientation or blank areas [8].
The model focuses on intermediate stages of visual processing and shows attention arising
as a result of the system dynamics, which are influenced by various biases. Attention evolves
from an early spatial attention window (AW) to being object-based later in the response of the
model neurons. Object-based attention develops within the ventral stream of the model as a
result of biased competition between different features and objects. This allows non-target
features to be suppressed over a time course seen in single cell recordings [5,6,7]. Spatial
competition and selection of the attended location takes place within the dorsal “where”
stream module, representing the lateral intraparietal area (LIP) of posterior parietal cortex.
“Cross-stream” interaction between ventral and dorsal areas is an important component of the
model. As object-based attention develops in the model’s ventral stream, featural information
is passed to LIP, biasing the spatial competition therein. This enables LIP to represent
behaviourally relevant locations [9] and attract the scan path to these. Connections to LIP
allow features to have different priorities in attracting attention. Inspired by psychophysical
findings [10], the AW is scaled according to local stimulus density and this enables potentially
interesting dense areas of a scene to be examined with a series of shorter saccades compared to
those in sparse areas. Saccade onset times [5,6] and inhibition of return behaviour also
replicate those found experimentally. The biased competition approach provides an extensible
framework in this model, which could allow competition within LIP to be extended to include
further bottom-up and top-down factors to compete to attract attention.
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[8] Motter, B. & Belky, E.J. (1998) Vision Research, 38(12), 1805-1815
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[10] Motter, B. & Belky, E.J. (1998) Vision Research, 38(7), 1007-1022