Spatial properties of objects predict patterns of neural response in the ventral visual pathway
Introduction
Although a large number of studies have investigated the functional properties of high-level regions in the ventral visual pathway, an overarching framework for how information is represented topographically in this region is not fully resolved (Op de Beeck et al., 2008, Grill-Spector and Weiner, 2014). Neuroimaging studies using univariate analyses have shown that regions of the ventral visual pathway are selective for different categories of objects (Kanwisher, 2010). The location of these regions is broadly consistent across individuals, suggesting that common organising principles underpin the topography of this region (Malach et al., 1995, Kanwisher et al., 1997, Epstein and Kanwisher, 1998, Cohen et al., 2000, Downing et al., 2001). The importance of this topography is evident in multivariate studies that have shown that the pattern of response across the entire ventral stream can distinguish a greater range of object categories than shown in previous univariate studies (Haxby et al., 2001, Spiridon and Kanwisher, 2002, Kriegeskorte et al., 2008b). The potential importance of the pattern of response is demonstrated by the fact that the ability to discriminate particular object categories is still evident when the most category-selective regions are removed from the analysis (Haxby et al., 2001).
The distributed nature of the fMRI response to different categories of objects within the ventral visual pathway has been interpreted as showing a topographic map of object forms; a hypothesis known as ‘object form topography’ (Haxby et al., 2001). However, it is not clear what dimensions are important for this object form topography. A variety of evidence has suggested that patterns of response in the ventral visual pathway are linked to the categorical or semantic information that the images convey (Kriegeskorte et al., 2008b, Naselaris et al., 2009, Connolly et al., 2012). Evidence for other organising principles can be found in the large-scale patterns of response to animacy (Chao et al., 1999, Kriegeskorte et al., 2008b, Clarke and Tyler, 2014) or to the real-world size of objects (Konkle and Oliva, 2012, Konkle and Caramazza, 2013). However, it remains unclear how these higher level categorical or semantic properties in the ventral visual pathway might arise from the image-based representations found in early visual regions (Op de Beeck et al., 2008).
In recent studies, we have asked to what extent image-based representations might underpin category-selective patterns of response in the ventral visual pathway (Rice et al., 2014, Watson et al., 2014). We found that similarities in the patterns of fMRI response between different categories of objects could be predicted by corresponding similarities in their low-level image properties. However, more needs to be known about which image properties are important. Previous studies have suggested that ventral temporal cortex may be sensitive to the spatial properties of the visual scene (Levy et al., 2001, Malach et al., 2002, Golomb and Kanwisher, 2012, Cichy et al., 2013, Troiani et al., 2014), but it has not been clear whether this reflects a modification of the underlying categorical organisation based on the way natural object categories are viewed or whether spatial properties themselves represent a fundamental organising principle in the ventral visual pathway (Kanwisher, 2001).
The aim of this study was to systematically investigate the importance of spatial properties to the pattern of response across the ventral visual pathway. To address this question, we determined the effect of changes in the actual size of the image on the patterns of response to different types of object using multi-voxel pattern analysis (MVPA). The image size manipulation is known to have a negligible effect on the perception of objects but will clearly have a profound effect on the spatial energy across the image. If the pattern of response is invariant to changes in image size then we would expect similar patterns of response to the same object category presented at different image sizes. If, on the other hand, the patterns of response are sensitive to changes in image size, we would expect significant differences in the pattern of response to the same object presented at different image sizes. Next, we used a computational approach to more directly measure energy across the image. Our aim was to determine whether more subtle differences in spatial properties beyond those carried by the large changes conveyed by size could explain patterns of response in the ventral visual pathway.
Section snippets
Participants
20 participants (8 males; median age 26; age range 19–36) took part in the experiment. All participants were neurologically healthy, right-handed, and had normal or corrected-to-normal vision. Written consent was obtained for all participants and the study was approved by the York Neuroimaging Centre Ethics Committee.
Stimuli
Visual stimuli were back-projected onto a custom in-bore acrylic screen at a distance of approximately 57 cm from the participant with all images subtending approximately 10.7° of
Results
Patterns of neural response were measured for 4 different object categories (bottle, chair, face, house) presented at 2 different sizes (large, small). Behavioural performance on the one-back task was near the ceiling (mean accuracy = 97.40%, SEM = .41%), indicating that participants maintained attention throughout the scan. Fig. 4a shows the normalised group responses to each condition across the ventro-temporal ROI. Responses above the mean are shown in red, and responses below the mean are shown
Discussion
The aim of this study was to investigate how spatial properties of images influence patterns of neural response in the ventral visual pathway. We found that patterns of neural response to images of different object categories are influenced by the actual size of the images. We then used a computational approach to more directly test whether more subtle variance in the spatial properties of images could explain the different patterns of response to objects in the ventral temporal cortex. Our
Acknowledgments
We would like to thank Danai Beintari for her assistance with data collection and analysis, and Grace Rice for her work in developing the stimulus set.
References (46)
- et al.
Distinct representations for facial identity and changeable aspects of faces in the human temporal lobe
Neuroimage
(2004) - et al.
fMR-adaptation reveals a distributed representation of inanimate objects and places in human visual cortex
Neuroimage
(2005) - et al.
Differential processing of objects under various viewing conditions in the human lateral occipital complex
Neuron
(1999) - et al.
Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?
Neuroimage
(2004) - et al.
Attention reduces spatial uncertainty in human ventral temporal cortex
Curr. Biol.
(2015) - et al.
A real-world size organization of object responses in occipitotemporal cortex
Neuron
(2012) - et al.
Matching categorical object representations in inferior temporal cortex of man and monkey
Neuron
(2008) - et al.
The topography of high-order human object areas
Trends Cogn. Sci.
(2002) - et al.
Bayesian reconstruction of natural images from human brain activity
Neuron
(2009) - et al.
Advances in functional and structural MR image analysis and implementation as FSL
Neuroimage
(2004)
How distributed is visual category information in human occipito–temporal cortex? An fMRI study
Neuron
Visual field maps in human cortex
Neuron
Patterns of response to visual scenes are linked to the low-level properties of the image
Neuroimage
Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns
Nature
Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects
Nat. Neurosci.
Probing principles of large-scale object representation: category preference and location encoding
Hum. Brain Mapp.
Object-specific semantic coding in human perirhinal cortex
J. Neurosci.
The visual word form area
Brain
The representation of biological classes in the human brain
J. Neurosci.
A cortical area selective for visual processing of the human body
Science
Different spatial scales of shape similarity representation in lateral and ventral LOC
Cereb. Cortex
A cortical representation of the local visual environment
Nature
Higher level visual cortex represents retinotopic, not spatiotopic, object location
Cereb. Cortex
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