Inferotemporal neurons represent low-dimensional configurations of parameterized shapes

Beeck, Hans Op de; Wagemans, Johan; Vogels, Rufin

doi:10.1038/nn767

Cited by 251 publications

(268 citation statements)

References 42 publications

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“…Some of the most remarkable findings about anterior IT concern the highly selective neuronal tuning for complex visual patterns reported by some authors (Desimone et al, 1984;Logothetis et al, 1995;Op de Beeck et al, 2001;Brincat and Connor, 2004;Freedman et al, 2005;Brincat and Connor, 2006) and the very high tolerance to stimulus transformations reported by others (Gross et al, 1972;Desimone and Gross, 1979;Perrett et al, 1982;Rolls and Baylis, 1986;Tovee et al, 1994;Rolls, 2000). Remarkably however, to our knowledge, no previous study has investigated the possible connection between these properties in IT.…”

Section: Discussionmentioning

confidence: 79%

Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex

Zoccolan

Kouh²,

Poggio³

et al. 2007

J. Neurosci.

150

160

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Object recognition requires both selectivity among different objects and tolerance to vastly different retinal images of the same object, resulting from natural variation in (e.g.) position, size, illumination, and clutter. Thus, discovering neuronal responses that have object selectivity and tolerance to identity-preserving transformations is fundamental to understanding object recognition. Although selectivity and tolerance are found at the highest level of the primate ventral visual stream [the inferotemporal cortex (IT)], both properties are highly varied and poorly understood. If an IT neuron has very sharp selectivity for a unique combination of object features ("diagnostic features"), this might automatically endow it with high tolerance. However, this relationship cannot be taken as given; although some IT neurons are highly object selective and some are highly tolerant, the empirical connection of these key properties is unknown. In this study, we systematically measured both object selectivity and tolerance to different identity-preserving image transformations in the spiking responses of a population of monkey IT neurons. We found that IT neurons with high object selectivity typically have low tolerance (and vice versa), regardless of how object selectivity was quantified and the type of tolerance examined. The discovery of this trade-off illuminates object selectivity and tolerance in IT and unifies a range of previous, seemingly disparate results. This finding also argues against the idea that diagnostic conjunctions of features guarantee tolerance. Instead, it is naturally explained by object recognition models in which object selectivity is built through AND-like tuning mechanisms.

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Section: Discussionmentioning

confidence: 79%

Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex

Zoccolan

Kouh²,

Poggio³

et al. 2007

J. Neurosci.

150

160

View full text Add to dashboard Cite

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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%

“…This problem might be unsolvable, because the mental object space seems to be only locally continuous. Within each object category, individual objects' shapes can be changed to move parametrically from one exemplar to another 50,[80][81][82][83][84][85] : morphing the faces of two members of a species or of members of two different species is relatively straightforward, as the corresponding features in the two faces are immediately obvious (FIG. 3).…”

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confidence: 99%

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Interpreting fMRI data: maps, modules and dimensions

2008

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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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“…Despite recent successful attempts to apply psychometric classification techniques directly to physical parameter space rather than similarity space (Op de Beeck, Wagemans & Vogels, 2001;Peters, Gabbiani & Koch, 2003) the difficulties in relating perceived similarity to physical stimulus properties have been abundant, and historically were part of the motivation for the development of MDS (Shepard, 1987). Specifically, for compound gratings as used in the present study MDS dimensions only partly correlate with the dimensions of the generating Fourier feature space (Kahana & Bennett, 1994), and confusion errors are predicted neither by pixel-wise pattern correlation (Jüttner et al, 1997) nor by image representations based on Laplacian pyramids or 2D…”

Section: Discussionmentioning

confidence: 99%