Different Spatial Scales of Shape Similarity Representation in Lateral and Ventral LOC. Drucker, Aguirre. Cerebral Cortex 2009


Uses the same stimulus as earlier study of Fourier basis and shape

  1. ” The results, indicating a coarse spatial coding of shape features in lateral LOC and a more focused coding of the entire shape space within ventral LOC, may be related to hierarchical
    models of object processing.”
  2. Small regions of cortex respond to different classes of images, and furthermore “… small regions of cortex contain populations capable of representing the entire space of images in a category”
  3. “A counterpoint to this apparent specialization has been the demonstration that information regarding object category is also contained in the distributed pattern of voxel responses across
    and between these specialized regions (…).”
  4. The results show there are also coarse-scale representations.  “This type of representation might
    correspond to the ‘‘chorus of fragments’’ model of Edelman and Intrator (1997), where individual properties of objects are represented by separate neural populations.”
  5. “Our focus here is upon the representation of variations in stimulus identity within a simplified object category… In practice, the structure of a parameterized space of shapes can be recovered from human behavioral responses (e.g., reaction times or similarity judgments) …”
    1. This similarity may also be reflected in neural patterns, which is what they check out here
  6. “Does a similar system of neural representation exist within human visual cortex? The human lateral occipital complex (LOC) shows similar functional properties to those previously ascribed to IT structures in the macaque. This region responds more strongly when a viewer is presented with images of parseable objects, as opposed to images that have no 2- or 3- dimensional interpretation, and appears largely indifferent to the method of object perception, for example, objects may be defined by luminance, texture, motion, or stereo difference”
    1. For IT and macaque, see this
    2. Ventral LOC is also called posterior fusiform sulcus (pFS)
  7. “Two recent studies have demonstrated a relationship between the perceptual similarity and the
    distributed pattern of neural activity in LOC (Op de Beeck et al. 2008; Haushofer et al. 2008),”
  8. During fMRI scanning, subjects viewed 16 different shapes defined by radial frequency components (RFCs; a series of sine waves of various frequencies describing perturbations from
    a circle; Zahn and Roskies 1972; Fig. 1)

    1. Idea of RFCs actually being used in shape recognition was eventually “experimentally rejected” but it makes convenient stimuli, also totally abstracted with no categorical boundaries
  9. Shapes were modified by altering amplitude and phase of a particular frequency component
  10. Here neural adaptation (depends on habituation) to shape is studied on neural level
  11. “We asked in this study if the degree of recovery from neural habituation at different cortical sites was proportional to the transition in similarity between 2 stimuli.”
  12. “In this study, we investigated if the distributed pattern of response can inform as to the identity
    of stimulus variation within an object category;”
  13. “Continuous Neural Adaptation in Ventral LOC Is Proportional to Shape Similarity”
    1. No adaptation effects found in lateral LOC
    2. Magnitude in change in shape matched linearly with change in ventral LOC
  14. “An alternative explanation for the proportional recovery from adaptation in ventral LOC is that the extreme stimuli (those from the corners of the stimulus space) may evoke a larger neural response generally (e.g., Kayaert et al. 2005). As the larger distance stimulus transitions tend to include these
    extreme stimuli to a greater extent, perhaps the apparent recovery from adaptation is actually a larger response to these extreme stimuli independent of an adaptation effect.”

    1. This is not the case, however, as the results show that “the proportional recovery from adaptation seen in ventral LOC indicates the presence of a population code for stimulus shape and cannot be attributed to a generally greater neural response to extreme stimuli.”
  15. “Distributed Pattern Responses Distinguish between Shapes”
  16. Use SVMs to analyze data at coarse spatial level, which worked well
  17. “The accuracy of the SVM analysis and the identified patch within lateral LOC indicates that the distributed voxel pattern of activity in that area carries information about shape.However, the pattern difference between shapes need not reflect the similarity of the stimuli or indeed have any particular structure. The SVM requires only that patterns be different in order to distinguish them—no assumptions about similarity structure are made or used.”
  18. “Within lateral LOC, the strongly discriminant responses seen in the SVM analysis were found to also reflect stimulus similarity consistently across subjects (t4 = 10.0, P = 0.001). In contrast,
    the distributed pattern of response in ventral LOC had a weaker correlation with the perceptual similarity of the stimuli (t4 = 1.2, P = 0.3) (Fig. 4A). The difference between these subregions of
    area LOC was significant (t4 = 11.4, P = 0.0003).”

    1. Mixed evidence for this being attributable strictly to retinotopic similarity
  19. “The RFC-Amplitude and RFC-Phase Axes Are Differentially Represented at Coarse and Fine Neural Scales”
  20. “Although the distributed neural similarity matrix measured from lateral LOC was strongly correlated with the stimulus similarity matrix, there appeared to be aspects of the structure
    of the neural response not evident in the stimulus matrix”
  21. Earlier studies on these shapes showed results that had phase and amplitude being recognized as orthogonal and equally important but that wasn’t completely replicated here.  Results here say the dimensions are “equally perceptually salient”, but that they are not perceived equivalently
  22. “…both aspects of the stimulus space [amplitude, phase] are represented by the within-voxel population code within ventral LOC… A rather different result was observed for the distributed
    pattern of response within lateral LOC. There, the distributed pattern across subjects reflected the shapes primarily in terms of RFC-amplitude but not RFC-phase”
  23. “For example, clusters of neurons might represent the tightness of the ‘‘knobs’’ of the shapes (defined by RFC-amplitude) independent of the direction that those knobs point within the overall shape (defined by RFC-phase). RFC-amplitude and RFC-phase may be taken as similar to ‘‘feature’’ and ‘‘envelope’’ parameters of Op  de Beeck et al. (2008), respectively; we thus contribute a similar finding in that features are represented in the distributed pattern in lateral LOC much more reliably than the overall shape envelope.”
  24. “Based upon the differential sensitivity to shape identity for the adaptation and distributed pattern methods, we argue that although both the lateral and ventral components of area LOC contain neural population codes for shape, the spatial scale of these representations differ. Specifically, the
    absence of a distributed pattern effect within ventral LOC is evidence for a homogeneous representation of the shape space, such that the average response of any one voxel does not differentiate between the shapes, whereas the presence of a distributed code and the absence of an
    adaptation effect in lateral LOC suggests that there is a heterogenous distribution of shape representation,”
  25. “Within ventral LOC, no meaningful tuning for the shape space can be identified: The amplitude of the response is no different for different shapes. This indicates that ventral LOC voxels are broadly tuned for shape identity. In contrast, lateral LOC voxels show relatively narrow tuning: there is a progressive decline in the response of a voxel for shapes more distant from the shape for which the voxel is best tuned (which was frequently a stimulus from the edges of the stimulus space). Moreover, lateral LOC voxels appear more narrowly tuned for the RFC-amplitude, as compared with the RFC-phase dimension of the shape space, consistent with our previous observation”
  26. “The narrow tuning observed in lateral LOC may also explain the absence of a linear adaptation response in this region to transitions in shape space. If a given voxel is narrowly tuned to a particular region of the shape space, then it may only show recovery from adaptation for stimulus transitions within its tuned area.”
  27. <Discussion>
  28. ” By using a continuous carryover design, our study was capable of examining neural similarity both on a coarse, across-voxel scale by distributed pattern analysis, as well as on a fine, within-voxel scale using continuous neural adaptation. We can thus compare the information provided at distributed and focal levels.”
  29. “Unlike ventral LOC, the lateral portion of LOC did not show adaptation responses that were linearly related to shape similarity. We found that the narrow tuning of lateral LOC voxels could explain this finding, indicating that each particular voxel has a population of neurons that are tuned to one specific region of the shape space. Consequently, most of the transitions between stimuli would not induce neural adaptation within the voxel as they would be transitions between stimuli not within the voxel’s receptive field.”
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