Bernal S D_2010.pdf - University of Plymouth

2.1. OBJEOTRBCXiGNrnON
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Difl. in spatial arrangement (deg.
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Figure 2.3: a) Rcprcsenlalion of complex objects in IT area, through the activation of cortical
columns. In this example, simplified stimuli (cat's face silhouette) elicit only u
subset of the regions evoked by the complex stimuli (complete cm) Tsunoda et al.
(20()l). b) Selectivity of cells in IT to different spatial arrannenientN of the parts
of an object image. This suggests the spatial arrangement of object parts is also
represented in IT (Yamane et al. 2006).
neurons. This could be related to llie hierarchical categorical representation of IT populalions,
demonstrated recently by Kiani et al. (2007). In this study, cluster analysis was employed to
show that IT populations' responses reconstruct part ol" our intuitive category structure, such as
the global division into animate and inanimate objects, or faces which clustered into primate
and non-primale faces.
Interestingly. Yamane el al. (2006) found neurons were sensitive to a particular spatial arrange­
ment of ihe parts, which suggests an encoding of the spatial relationship between object parts
(Figure 2.3b). The counterpart of the primate's IT in humans is thought to be the lateral occipi­
tal complex (LOC), which also exhibits a feature-based representation of Ihe stimulus (Tanaka
1997). It has been associated with the representation of object parts (Hayworth and Biederman
2006), as well as wilh higher-level functions such as the perception of 3D volume from 2D
shapes (Moore and tingel 2001).
Invariance to certain transformations of the input image also appears to be a prominent prop-
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