title: Modeling object representations in natural vision
Creators: name: Papale, Paolo
Publisher: name: IMT Alti Studi Lucca
Year: 2019

description: Object perception relies on intensive processing in the human
occitotemporal cortex (OTC). While its large-scale pattern of
object selectivity has been widely described, looking at the
computational processes controlling the spatial organization of
OTC has proven challenging, since visual dimensions are
mutually correlated (e.g., object shape and identity). In the
present thesis, we investigated how different object properties
that are relevant to behavior and share common variance are
represented in our visual cortex in natural vision.
In Chapter 1, we described how our exceptionally reliable
visual system transforms continuous retinal signaling into
meaningful objects. In addition, we demonstrated how this
process is challenging and complex, given the unreliability of the
retinal input and the widespread mutual correlations between
behaviorally relevant object properties (e.g., shape and semantic
category).
In Chapter 2, we described an fMRI study on scene
segmentation. In this first study, we analyzed brain responses
during passive natural image viewing. Subjects attended to
hundreds of natural scenes and we derived brain representations
from each occipital region and compared them to parametric
representations, so to reveal the inner filtering operated by each
brain region.
In contrast to strictly hierarchical and compartmentalized
views on brain selectivity, the whole occipital lobe is involved in
the high-level cognitive task of segmenting foreground and
background, as early as V1. At the same time, contrast and
spatial frequencies are represented also in higher visual regions
such as V4 and LOC.
However, due to the low temporal resolution of fMRI, the
first study alone cannot resolve if those shared representations
reflect a common spatiotemporal process. Thus, in a second
MEG study, presented in Chapter 3, we derived brain
representations in time and space of subjects attending to different objects. We compared these representations to modelderived
representations comprising V1-like features, object
shape and semantic category. We also employed a statistical
approach that compute the relative weight (i.e., orthogonal
component) of each model in explaining the MEG
representations.
By doing so, we found that a small cluster of posterior
sensors independently processes all the tested features as early
as 100-150ms after stimulus onset. Thus, the same features can be
retrieved in the activity of multiple regions, and orthogonal
components of those features are processed by the same cortical
structures at the same latencies. Thus, is there a broader
organization determining these observations? What is the link
between coding of mutual and orthogonal object
representations?
In our third study, presented in Chapter 4, we employed
fMRI to explore the spatial organization of sensitivity to mutual
and orthogonal representations in the human visual cortex.
Subjects attended to object pictures while performing an
unrelated attentive task. By employing a variance partitioning
method, we found that the weight of mutual representations
increases along the visual hierarchy, from posterior to anterior
regions.
Overall, these results depict a complex picture of our visual
cortex. First, there is not a clear selectivity hierarchy, but
information spreads between regions: early cortical areas access
to high-level representations and are involved in complex
cognitive tasks (i.e., object segmentation). Second, concurrent
processing of orthogonal object shape, contrast and category
representations is fast (100-150ms) in the right posterior brain.
And, third, the visual cortex encodes mutual relations between
different features in a topographic fashion while object shape is
encoded along different dimensions, each representing
orthogonal features.