Chapter 14 Coding of stimulus invariances by inferior temporal neurons

doi:10.1016/S0079-6123(08)63330-0

Progress in Brain Research

Volume 112, 1996, Pages 195-211

https://doi.org/10.1016/S0079-6123(08)63330-0 Get rights and content

Primates are able to recognize a particular object despite major differences in the retinal images of the object. Inferior temporal cortex is suggested to be involved in this invariant object recognition. Neurons of this cortex show shape selectivity and it has been shown that this shape selectivity is relatively invariant for changes in the position and size of the shape. We show that macaque inferior temporal cortical neurons may respond to shapes that are defined by relative motion or by texture differences. The degree of shape selectivity can vary for the different visual cues, but overall shape preference is invariant for shapes defined either by luminance, by relative motion or by texture. Also, we found that shape selective inferior temporal neurons respond to partial occluded shapes and that their shape selectivity is similar with and without the partial occlusion. These results suggest that inferior temporal neurons, as a population, can code for an abstract, stimulus invariant shape or object part.

References (25)

G.H. Henry et al.
Orientation specificity and response variability of cells in the striate cortex
Vis. Res.
(1973)
N.K. Logothetis et al.
Shape representation in the inferior temporal cortex of monkeys
Curr. Biol.
(1995)
SaryGy. et al.
Orientation discrimination of motion-defined gratings
Vis. Res.
(1994)
J.L. Gallant et al.
Selectivity for polar, hyperbolic, and cartesian gratings in macaque visual cortex
Science
(1993)
C.G. Gross et al.
Visual properties of neurons in the inferotemporal cortex of the macaque
J. Neurophysiol.
(1972)
D. Hubel et al.
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
J. Physiol. (London)
(1962)
M. Ito et al.
Size and position invariance of neuronal responses in monkey inferior temporal cortex
J. Neurophysiol.
(1995)
E. Iwai et al.
Two visual foci in the temporal lobe of monkeys
H. Komatsu et al.
Color selectivity of neurons in the inferior temporal cortex of the awake macaque monkey
J. Neurosci.
(1992)
KovacsGy. et al.
Selectivity of macaque inferior temporal neurons for partially occluded shapes
J. Neurosci.
(1995)

N.K. Logothetis et al.

V4 responses to gratings defined by random textured motion

Invest. Opthalmol. Visual Sci.

(1990)

A. Lueshow et al.

Inferior temporal mechanisms for invariant object recognition

Cerebral Cortex

(1994)

Cited by (51)

Attention to distinguishing features in object recognition: An interactive-iterative framework
2018, Cognition
Citation Excerpt :
Despite many other differences, classic theories of object recognition (e.g., Biederman, 1987; Marr & Nishihara, 1978; Poggio & Edelman, 1990; Reisenhuber & Poggio, 1999; Tarr & Bülthoff, 1995; Tarr & Pinker, 1989; Ullman, 1989) are generally united in what might be called the orthodox view—that object recognition is based primarily on a bottom-up analysis of the visual input; recognition is achieved when some temporary representation of the input image matches a stored object representation. The functional architecture of the visual cortex—the increase in the receptive field size and in representational complexity from lower to higher areas in the cortex (Maunsell & Newsome, 1987; Vogels & Orban, 1996)—has been pointed to as consistent with the bottom-up view. Also, findings that the processes involved in object recognition are sometimes remarkably fast, occurring within 100–200 ms of stimulus presentation (e.g., Thorpe, Fize, & Marlot, 1996), have been taken by some researchers as evidence that object recognition can occur largely with feed-forward processing alone (e.g., Wallis & Rolls, 1997; but see Evans & Treisman, 2005).
This article advances a framework that casts object recognition as a process of discrimination between alternative object identities, in which top-down and bottom-up processes interact—iteratively when necessary—with attention to distinguishing features playing a critical role. In two experiments, observers discriminated between different types of artificial fish. In parallel, a secondary, variable-SOA visual-probe detection task was used to examine the dynamics of visual attention. In Experiment 1, the fish varied in three distinguishing features: one indicating the general category (saltwater, freshwater), and one of the two other features indicating the specific type of fish within each category. As predicted, in the course of recognizing each fish, attention was allocated iteratively to the distinguishing features in an optimal manner: first to the general category feature, and then, based on its value, to the second feature that identified the specific fish. In Experiment 2, two types of fish could be discriminated on the basis of either of two distinguishing features, one more visually discriminable than the other. On some of the trials, one of the two alternative distinguishing features was occluded. As predicted, in the course of recognizing each fish, attention was directed initially to the more discriminable distinguishing feature, but when this feature was occluded, it was then redirected to the less discriminable feature. The implications of these findings, and the interactive-iterative framework they support, are discussed with regard to several fundamental issues having a long history in the literatures on object recognition, object categorization, and visual perception in general.
Dynamic gamma frequency feedback coupling between higher and lower order visual cortices underlies perceptual completion in humans
2014, NeuroImage
Citation Excerpt :
Taken together, these findings accord with the suggestion that not only brain regions for cue invariant object/face recognition such as ventral visual cortex (Haxby et al., 1999; Kanwisher and Yovel, 2006; Malach et al., 1995) but also spatial attention relevant parietal brain regions (Corbetta et al., 1998; Fernandez-Duque and Posner, 2001) are crucial for perceptual closure and that this process occurs about 230 ms to 400 ms after stimulus onset. Within the classical view of visual system hierarchy, simple geometric lines and shapes that form complex objects are processed in lower order visual cortex, whereas higher order areas within the ventral visual stream (Mishkin et al., 1983) code invariant object and category information (e.g. Vogels and Orban, 1996) based on feedforward communication from early visual cortex (for a review of these models see Hochstein and Ahissar, 2002). However, recent models of conscious visual perception suggest reverse hierarchical processing (for a review see Hochstein and Ahissar, 2002) whereby higher order visual areas in the ventral and dorsal streams provide top-down feedback to early visual cortex (i.e., predictive coding — Friston, 2003; Rao and Ballard, 1999).
To perceive a coherent environment, incomplete or overlapping visual forms must be integrated into meaningful coherent percepts, a process referred to as “Gestalt” formation or perceptual completion. Increasing evidence suggests that this process engages oscillatory neuronal activity in a distributed neuronal assembly. A separate line of evidence suggests that Gestalt formation requires top-down feedback from higher order brain regions to early visual cortex. Here we combine magnetoencephalography (MEG) and effective connectivity analysis in the frequency domain to specifically address the effective coupling between sources of oscillatory brain activity during Gestalt formation. We demonstrate that perceptual completion of two-tone “Mooney” faces induces increased gamma frequency band power (55–71 Hz) in human early visual, fusiform and parietal cortices. Within this distributed neuronal assembly fusiform and parietal gamma oscillators are coupled by forward and backward connectivity during Mooney face perception, indicating reciprocal influences of gamma activity between these higher order visual brain regions. Critically, gamma band oscillations in early visual cortex are modulated by top-down feedback connectivity from both fusiform and parietal cortices. Thus, we provide a mechanistic account of Gestalt perception in which gamma oscillations in feature sensitive and spatial attention-relevant brain regions reciprocally drive one another and convey global stimulus aspects to local processing units at low levels of the sensory hierarchy by top-down feedback. Our data therefore support the notion of inverse hierarchical processing within the visual system underlying awareness of coherent percepts.
Right fusiform response patterns reflect visual object identity rather than semantic similarity
2013, NeuroImage
Citation Excerpt :
Invariance is a key characteristic of shape processing in LO (Kanwisher, 1997; Liu et al., 2008; Malach et al., 1995; Schacter et al., 1995). In the monkey brain, relative shape preference in IT is invariant for cue (luminance, relative motion or texture), size or location (Vogels and Orban, 1996). Position-tolerant identity information can be derived from inferior temporal (IT) neuronal activity (Hung et al., 2005; Tanaka, 2003).
We previously reported the neuropsychological consequences of a lesion confined to the middle and posterior part of the right fusiform gyrus (case JA) causing a partial loss of knowledge of visual attributes of concrete entities in the absence of category-selectivity (animate versus inanimate). We interpreted this in the context of a two-step model that distinguishes structural description knowledge from associative-semantic processing and implicated the lesioned area in the former process. To test this hypothesis in the intact brain, multi-voxel pattern analysis was used in a series of event-related fMRI studies in a total of 46 healthy subjects. We predicted that activity patterns in this region would be determined by the identity of rather than the conceptual similarity between concrete entities. In a prior behavioral experiment features were generated for each entity by more than 1000 subjects. Based on a hierarchical clustering analysis the entities were organised into 3 semantic clusters (musical instruments, vehicles, tools). Entities were presented as words or pictures. With foveal presentation of pictures, cosine similarity between fMRI response patterns in right fusiform cortex appeared to reflect both the identity of and the semantic similarity between the entities. No such effects were found for words in this region. The effect of object identity was invariant for location, scaling, orientation axis and color (grayscale versus color). It also persisted for different exemplars referring to a same concrete entity. The apparent semantic similarity effect however was not invariant. This study provides further support for a neurobiological distinction between structural description knowledge and processing of semantic relationships and confirms the role of right mid-posterior fusiform cortex in the former process, in accordance with previous lesion evidence.
Visual System
2012, The Human Nervous System, Third Edition
Unsupervised natural visual experience rapidly reshapes size-invariant object representation in inferior temporal cortex
2010, Neuron
Citation Excerpt :
At the highest stage of this stream, the inferior temporal cortex (IT), a tolerant object representation is obtained in which individual IT neurons have a preference for some objects (“selectivity”) over others, and this rank-order preference is largely maintained across identity-preserving image transformations (Ito et al., 1995; Logothetis and Sheinberg, 1996; Tanaka, 1996; Vogels and Orban, 1996). Though most IT neurons are not strictly “invariant” (DiCarlo and Maunsell, 2003; Ito et al., 1995; Logothetis and Sheinberg, 1996; Vogels and Orban, 1996), reasonably sized populations of these so-called “tolerant” neurons can support object recognition tasks (Afraz et al., 2006; Hung et al., 2005; Li et al., 2009). However, we do not yet understand how IT neurons construct this tolerant response phenomenology.
We easily recognize objects and faces across a myriad of retinal images produced by each object. One hypothesis is that this tolerance (a.k.a. “invariance”) is learned by relying on the fact that object identities are temporally stable. While we previously found neuronal evidence supporting this idea at the top of the nonhuman primate ventral visual stream (inferior temporal cortex, or IT), we here test if this is a general tolerance learning mechanism. First, we found that the same type of unsupervised experience that reshaped IT position tolerance also predictably reshaped IT size tolerance, and the magnitude of reshaping was quantitatively similar. Second, this tolerance reshaping can be induced under naturally occurring dynamic visual experience, even without eye movements. Third, unsupervised temporal contiguous experience can build new neuronal tolerance. These results suggest that the ventral visual stream uses a general unsupervised tolerance learning algorithm to build its invariant object representation.
Location-specific deviant responses to object sequences in macaque inferior temporal cortex
2024, Scientific Reports

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Chapter 14 Coding of stimulus invariances by inferior temporal neurons

Vis. Res.

Curr. Biol.

Vis. Res.

Selectivity for polar, hyperbolic, and cartesian gratings in macaque visual cortex

Science

Visual properties of neurons in the inferotemporal cortex of the macaque

J. Neurophysiol.

Receptive fields, binocular interaction and functional architecture in the cat's visual cortex

J. Physiol. (London)

Size and position invariance of neuronal responses in monkey inferior temporal cortex

J. Neurophysiol.

Two visual foci in the temporal lobe of monkeys

Color selectivity of neurons in the inferior temporal cortex of the awake macaque monkey

J. Neurosci.

Selectivity of macaque inferior temporal neurons for partially occluded shapes

J. Neurosci.

V4 responses to gratings defined by random textured motion

Invest. Opthalmol. Visual Sci.

Inferior temporal mechanisms for invariant object recognition

Cerebral Cortex