Mapping multiple features in the population response of visual cortex

Basole, Amit; White, Leonard; Fitzpatrick, David

doi:10.1038/nature01721

Cited by 162 publications

(161 citation statements)

References 26 publications

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“…Although this approach has been amended by the suggestion of two-stage (21) or three-stage (22) processing schemes, as well as by the addition of ''component cells,'' ''pattern cells,'' and cascade models that could explain further details of motion perception (23,24), this framework remains the most widely accepted conception of how motion percepts are generated. Not all observations fit this general concept of visual cortical processing, however, and there are clearly other ways of thinking about visual organization and function (25)(26)(27)(28).…”

Section: Discussionmentioning

confidence: 99%

“…Of these, algorithmic strategies for feature detection (12,13,(29)(30)(31) and models sensitive to the spatiotemporal energy in image sequences (27,(32)(33)(34)(35) have received the most attention. The inclusion of assumptions about the 3D world-e.g., ''reflective constraints'' (12) or reliance on oriented filters to extract spatiotemporal energy (33, 34)-have been used to increase the explanatory power of these approaches.…”

Section: Discussionmentioning

confidence: 99%

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An empirical explanation of the flash-lag effect

Wojtach

Sung

Truong

et al. 2008

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

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When a flash of light is presented in physical alignment with a moving object, the flash is perceived to lag behind the position of the object. This phenomenon, known as the flash-lag effect, has been of particular interest to vision scientists because of the challenge it presents to understanding how the visual system generates perceptions of objects in motion. Although various explanations have been offered, the significance of this effect remains a matter of debate. Here, we show that: (i) contrary to previous reports based on limited data, the flash-lag effect is an increasing nonlinear function of image speed; and (ii) this function is accurately predicted by the frequency of occurrence of image speeds generated by the perspective transformation of moving objects. These results support the conclusion that perceptions of the relative position of a moving object are determined by accumulated experience with image speeds, in this way allowing for visual behavior in response to real-world sources whose speeds and positions cannot be perceived directly.image speed ͉ motion perception ͉ percentile rank ͉ perspective transformation ͉ motion processing T o produce biologically useful perceptions of motion, humans and other animals must contend with the fact that projected images cannot uniquely specify the real world speeds and positions of objects. This quandary-referred to as the inverse optics problem-is a consequence of the transformations that occur when objects in three-dimensional (3D) space project onto a two-dimensional (2D) surface, thus conflating the physical determinants of speed and position in the retinal image ( Fig. 1). Recent investigations of brightness, color, and form have suggested that to contend with this problem in other perceptual domains the visual system has evolved to operate empirically, generating percepts that represent the world in terms of accumulated experience with images and their possible sources rather than by using stimulus features as such (1-4). Given this evidence, we suspected that the flash-lag effect might be a signature of this visual strategy as it pertains to the perception of motion. We therefore examined the hypothesis that the perception of lag is determined by the frequency of occurrence of image speeds generated by moving objects. As explained in Discussion, perceiving speed in this way would allow observers to produce generally successful visually guided responses toward objects whose actual speeds and positions cannot be derived in any direct way from their projected images. Thus, explaining the flash-lag effect is important for understanding vision and visual behavior.To test this hypothesis, the frequency of occurrence of image speeds in relation to their corresponding 3D sources must be known. Since the precise distances, trajectories, and speeds of objects in the world are not simultaneously measurable with any current technology, we created a computer-simulated environment in which the relationship between moving objects and their projections on an image ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An empirical explanation of the flash-lag effect

Wojtach

Sung

Truong

et al. 2008

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

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“…Using this approach, the map of orientation preference in v1 was found to depend heavily on several other stimulus properties, such as bar length, direction and speed 93 . These results cast doubt on the multiple-feature-map interpretation.…”

Section: Basic Properties In the Primary Visual Cortexmentioning

confidence: 99%

“…These results cast doubt on the multiple-feature-map interpretation. Instead, it has been proposed that these multiple maps might arise from the mapping of only one property -spatiotemporal energy 93 . According to this view, the finding of independent maps for multiple features is an artefact of using stimuli that vary in only one feature at a time.…”

Section: Basic Properties In the Primary Visual Cortexmentioning

confidence: 99%

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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“…The ISOI studies also suggested that the rebound was more spatially diffuse and less stimulus-specific than the initial dip (4,7,10). The initial dip thus became the signal of choice in a number of functional imaging studies, not only in ISOI (11)(12)(13)(14)(15) but also in fMRI (16).…”

mentioning

confidence: 98%

Spatiotemporal precision and hemodynamic mechanism of optical point spreads in alert primates

Sirotin

Hillman

Bordier

et al. 2009

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

116

144

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In functional brain imaging there is controversy over which hemodynamic signal best represents neural activity. Intrinsic signal optical imaging (ISOI) suggests that the best signal is the early darkening observed at wavelengths absorbed preferentially by deoxyhemoglobin (HbR). It is assumed that this darkening or "initial dip" reports local conversion of oxyhemoglobin (HbO) to HbR, i.e., oxygen consumption caused by local neural activity, thus giving the most specific measure of such activity. The blood volume signal, by contrast, is believed to be more delayed and less specific. Here, we used multiwavelength ISOI to simultaneously map oxygenation and blood volume [i.e., total hemoglobin (HbT)] in primary visual cortex (V1) of the alert macaque. We found that the hemodynamic ''point spread,'' i.e., impulse response to a minimal visual stimulus, was as rapid and retinotopically specific when imaged by using blood volume as when using the initial dip. Quantitative separation of the imaged signal into HbR, HbO, and HbT showed, moreover, that the initial dip was dominated by a fast local increase in HbT, with no increase in HbR. We found only a delayed HbR decrease that was broader in retinotopic spread than HbO or HbT. Further, we show that the multiphasic time course of typical ISOI signals and the strength of the initial dip may reflect the temporal interplay of monophasic HbO, HbR, and HbT signals. Characterizing the hemodynamic response is important for understanding neurovascular coupling and elucidating the physiological basis of imaging techniques such as fMRI.fMRI ͉ imaging ͉ macaque ͉ visual ͉ neurovascular coupling C erebral hemodynamics respond quickly and specifically to local neural activity (1, 2). Hemodynamic signals are thus used extensively as proxies for such activity in functional neuroimaging techniques like fMRI and intrinsic signal optical imaging (ISOI). There has been considerable debate, however, as to which of the possible hemodynamic signals, e.g., changes in local blood oxygenation, volume, or flow, with their distinct response properties, constitutes the ''best'' signal for inferring neural activity (1,(3)(4)(5).The debate regarding the best signal sharpened with reports of initial dips in both ISOI and fMRI signals. ISOI studies consistently found a brief stimulus-evoked darkening followed by a strong brightening at imaging wavelengths preferentially absorbed in deoxyhemoglobin (HbR) [e.g., at 605 nm (6)]. The darkening, later termed the initial dip*, was interpreted as a local conversion of oxyhemoglobin (HbO) to HbR caused by increased oxygen consumption by local neurons before any active vascular response (1, 7) The subsequent brightening was taken to measure the ''rebound'' in [HbO] † caused by a delayed, stimulus-triggered increase in cerebral blood flow (7).In parallel, some fMRI studies reported finding an initial dip before the rise in the blood oxygen level-dependent (BOLD) signal (8). Because the BOLD signal measures changes in [HbR] alone ‡ , the fMRI initial dip was seen a...

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Mapping multiple features in the population response of visual cortex

Cited by 162 publications

References 26 publications

An empirical explanation of the flash-lag effect

An empirical explanation of the flash-lag effect

Interpreting fMRI data: maps, modules and dimensions

Spatiotemporal precision and hemodynamic mechanism of optical point spreads in alert primates

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