Tuning for shape dimensions in macaque inferior temporal cortex

Kayaert, Greet; Biederman, Irving; Beeck, Hans Op de; Vogels, Rufin

doi:10.1111/j.1460-9568.2005.04202.x

Cited by 126 publications

(141 citation statements)

References 42 publications

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“…Researchers employing the adaptation paradigm focus on the populations of neurons recruited and often neglect the intensity of the response. It is well known that many neurons in the visual system are tuned to the optimal presentation of stimulus properties (Desimone et al, 1984;Kayaert et al, 2005). Assuming that the neurons are sensitive to all the categories of stimuli presented in the current experiments (rather than being category specific), but have different tuning properties for these categories, it would be expected that all test stimulus categories are affected by the adaptors approximately to the same degree.…”

Section: Discussionmentioning

confidence: 99%

Is the rapid adaptation paradigm too rapid? Implications for face and object processing

Nemrodov

Itier

2012

NeuroImage

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Rapid adaptation is an adaptation procedure in which adaptors and test stimuli are presented in rapid succession. The current study tested the validity of this method for early ERP components by investigating the specificity of the adaptation effect on the face-sensitive N170 ERP component across multiple test stimuli. Experiments 1 and 2 showed identical response patterns for house and upright face test stimuli using the same adaptor stimuli. The results were also identical to those reported in a previous study using inverted face test stimuli (Nemrodov and Itier, 2011). In Experiment 3 all possible adaptor-test combinations between upright face, house, chair and car stimuli were used and no interaction between adaptor and test category, expected in the case of test-specific adaptation, was found. These results demonstrate that the rapid adaptation paradigm does not produce category-specific adaptation effects around 170-200 ms following test stimulus onset, a necessary condition for the interpretation of adaptation results. These results suggest the rapid categorical adaptation paradigm does not work.

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Section: Discussionmentioning

confidence: 99%

Is the rapid adaptation paradigm too rapid? Implications for face and object processing

Nemrodov

Itier

2012

NeuroImage

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“…Two additional studies (56,57) showed that single-unit activity recorded from anterior IT cortex includes information necessary to distinguish curved vs. straight nonfamiliar irregular shapes, at both the individual and population levels. It is possible that their recording sites were located within/near ACP.…”

Section: Discussionmentioning

confidence: 99%

Curvature-processing network in macaque visual cortex

Yue

Pourladian

Tootell

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Our visual environment abounds with curved features. Thus, the goal of understanding visual processing should include the processing of curved features. Using functional magnetic resonance imaging in behaving monkeys, we demonstrated a network of cortical areas selective for the processing of curved features. This network includes three distinct hierarchically organized regions within the ventral visual pathway: a posterior curvature-biased patch (PCP) located in the near-foveal representation of dorsal V4, a middle curvature-biased patch (MCP) located on the ventral lip of the posterior superior temporal sulcus (STS) in area TEO, and an anterior curvature-biased patch (ACP) located just below the STS in anterior area TE. Our results further indicate that the processing of curvature becomes increasingly complex from PCP to ACP. The proximity of the curvature-processing network to the well-known face-processing network suggests a possible functional link between them.curvature patches | face patches | curved Gabor filters D ecades of research have focused on understanding visual feature processing, particularly along the ventral visual pathway. Such studies have shown that neurons in lower-order visual areas (e.g., V1) respond strongly to simple oriented contours (1), whereas neurons in higher-order visual areas (e.g., inferior temporal cortex) respond selectively to more complex image features and/or visual categories (2-4), in ways that are not yet fully understood. To link these extremes in visual information processing, many studies have aimed to clarify the optimal "trigger" features at intermediate levels of the visual cortical hierarchy.Among these features, stimulus curvature has not been well studied. This is surprising because, strictly, all lines are curved to some extent, except for the single exception of a perfectly straight line. This ubiquity of curved shapes also extends to 3D surfaces (5). In nature, where much of our visual system presumably evolved, perfectly flat surfaces are rare. Even the flattest of natural features (e.g., oceans, sandy beaches) are often curved to some extent, due to wind, water motion, and even the curvature of the earth. Thus, it is important to understand curvature processing to fully unravel the steps in cortical visual processing.Among the few studies to test single neuron responses to curvature per se, Gallant et al. (6,7) reported that a significant percentage of neurons in macaque cortical area V4 is selective for curved stimuli. Intriguingly, these authors also noted that neurons preferring curved patterns were often anatomically clustered together. Subsequently, demonstrated that neurons in the parafoveal representation of dorsal V4 respond robustly to the curvature component of complex shapes. To our knowledge, there have been no systematic studies of curvature at levels below V4 in macaques.Intriguingly, some evidence suggests that the processing of curvature may interact selectively with the processing of faces. For instance, perceptual deficits in face reco...

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“…Finally, IT neurons also show selectivity for other features, including object shape 50,51 , viewpoint 52 , position 53,54 and size 53 . These results are in accordance with the findings from fMRI adaptation studies in humans.…”

Section: Monkey Studiesmentioning

confidence: 99%

“…4). Furthermore, it has been shown that single cells and columns in the monkey IT cortex are selective for moderately complex features 96,97 ; that neurons in v4 and in the posterior IT cortex are tuned for simple shape characteristics 98,99 ; and that IT neurons exhibit gradual tuning in simple shape spaces 50,51 . Finally, single-cell recordings and fMRI studies have provided evidence of selectivity for object parts and non-accidental properties (for example, symmetry, parallelism and collinearity) in the ventral temporal cortex 49,100 , as predicted by structural description theories of object recognition 101,102 .…”

Section: Basic Properties In the Ventral Visual Cortexmentioning

confidence: 99%

Interpreting fMRI data: maps, modules and dimensions

2008

Self Cite

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Neuroimaging research over the past decade has revealed a detailed picture of the functional organization of the human brain. Here we focus on two fundamental questions that are raised by the detailed mapping of sensory and cognitive functions and illustrate these questions with findings from the object-vision pathway. First, are functionally specific regions that are located close together best understood as distinct cortical modules or as parts of a larger-scale cortical map? Second, what functional properties define each cortical map or module? We propose a model in which overlapping continuous maps of simple features give rise to discrete modules that are selective for complex stimuli.Advances in brain imaging technology (especially functional MRI (fMRI)) have radically improved our understanding of the functional organization of the human brain (BOX 1). In this Review we describe the organization of the ventral visual pathway, which is characterized by strong selectivity for particular object categories (for example, faces and bodies) at the level of both individual neurons and larger cortical regions. We then consider two central questions: whether this organization reflects maps or modules, and what properties are mapped. In each case we derive clues from the literature on the primary sensory cortex, in which cortical maps have been studied extensively using electrophysiology in animals. We find that apparently modular cortical regions, such as orientation columns and face-selective regions, might be parts of larger maps, and show that it is a substantial challenge to determine the basic properties and dimensions that describe functional organization most parsimoniously. We then propose a new framework that reconciles the existence of graded cortical maps and distinct functional modules. In this framework, the strong category selectivity that exists for faces and other objects might arise from the nonlinear combination of multiple correlated maps for simpler stimulus properties. The ventral visual pathwayThe ventral visual pathway comprises a large cortical region that occupies the ventral and lateral surfaces of the occipital and temporal lobes (FIG. 1). A substantial proportion of fMRI voxels in this pathway are 'object-selective' -that is, they respond more strongly when people view images of objects than when people view scrambled versions of these objects or texture patterns. This object-selective region is often referred to as the lateral occipital complex (LOC) 1 . The LOC has little selectivity for particular stimulus categories 2-4 , but several regions of NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript cortex near the LOC are selective for particular object categories: they respond at least twice as strongly to their 'preferred' stimuli than to other stimuli. For example, in essentially all humans cortical regions can be found that respond selectively to faces (the fusiform face area (FFA) 5,6 and, in many individuals, the occipital face area (OFA)) 7,8 , to pl...

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Tuning for shape dimensions in macaque inferior temporal cortex

Cited by 126 publications

References 42 publications

Is the rapid adaptation paradigm too rapid? Implications for face and object processing

Is the rapid adaptation paradigm too rapid? Implications for face and object processing

Curvature-processing network in macaque visual cortex

Interpreting fMRI data: maps, modules and dimensions

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