Neural Correlates of Structure-from-Motion Perception in Macaque V1 and MT

Grunewald, Alexander; Bradley, David; Andersen, Richard A.

doi:10.1523/jneurosci.22-14-06195.2002

Cited by 114 publications

(94 citation statements)

References 40 publications

(53 reference statements)

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“…In other words, in the fear-detection task used, could fMRI amplitude predict whether the subject responded fear or no fear on a trial-by-trial basis? We used a method based on signal detection theory, which is analogous to ROC analysis and which has been used in monkey physiology to link cell responses to behavioral choice (4,11,14). This method gives the probability that a so-called ideal observer, given access only to the fMRI amplitude in a trial, would be able to identify accurately which behavioral response was made in that particular trial (fear present or fear absent).…”

Section: Resultsmentioning

confidence: 99%

Quantitative prediction of perceptual decisions during near-threshold fear detection

Pessoa

Padmala

2005

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

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A fundamental goal of cognitive neuroscience is to explain how mental decisions originate from basic neural mechanisms. The goal of the present study was to investigate the neural correlates of perceptual decisions in the context of emotional perception. To probe this question, we investigated how fluctuations in functional MRI (fMRI) signals were correlated with behavioral choice during a near-threshold fear detection task. fMRI signals predicted behavioral choice independently of stimulus properties and task accuracy in a network of brain regions linked to emotional processing: posterior cingulate cortex, medial prefrontal cortex, right inferior frontal gyrus, and left insula. We quantified the link between fMRI signals and behavioral choice in a whole-brain analysis by determining choice probabilities by means of signaldetection theory methods. Our results demonstrate that voxelwise fMRI signals can reliably predict behavioral choice in a quantitative fashion (choice probabilities ranged from 0.63 to 0.78) at levels comparable to neuronal data. We suggest that the conscious decision that a fearful face has been seen is represented across a network of interconnected brain regions that prepare the organism to appropriately handle emotionally challenging stimuli and that regulate the associated emotional response.decision making ͉ emotion ͉ functional MRI T he brain uses sensory information to make decisions that guide behavior. Currently, there is considerable knowledge, for example, about how contour, depth, and motion information is processed by early visual areas. In contrast, relatively little is known about how perceptual decisions are made. Even simple perceptual decisions, such as determining the presence or absence of a sensory stimulus, are not well understood. A fundamental goal of cognitive neuroscience is to explain how mental decisions originate from basic neural mechanisms.In non-human primates, Newsome, Movshon, and colleagues (1, 2) have pioneered the investigation of the neural correlates of perceptual decisions. Their experiments have probed the mechanisms associated with deciding the direction of motion of an array of moving dots, only a fraction of which move coherently at a time. Newsome et al. (1,2) showed that the activity of directionally selective neurons in middle temporal visual area (MT) could account for the psychophysical performance in a forced-choice direction discrimination task (1, 2). Related studies have quantified the link between performance and cell activity during depth perception (3) and the perception of structure from motion (4). Little is known, however, about the involvement of nonsensory regions in perceptual decisions. Previous neurophysiology studies typically have investigated one, and in some cases two [e.g., visual area 1 (V1) and MT], sensory processing regions (but see ref. 5).Decision-making processes have been investigated with neuroimaging, too. Binder et al. (6) operationally defined decisions in an auditory object identification task in terms of reaction...

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Section: Resultsmentioning

confidence: 99%

Quantitative prediction of perceptual decisions during near-threshold fear detection

Pessoa

Padmala

2005

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

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“…This could explain why CPs for motion discrimination are larger in MST (medial superior temporal area) than MT (Celebrini and Newsome, 1994;, and a similar metric devised for detection tasks is larger in VIP (ventral intraparietal area) than MT (Cook and Maunsell, 2002b). This might also account for why correlations between neuronal firing and choice are modest or absent in V1 (Leopold and Logothetis, 1996;Grunewald et al, 2002). However, in both V1 studies, interpretation is complicated: V1/V2 responses were pooled (Leopold and Logothetis, 1996) or neuronal precision differed between V1 and extrastriate cortex (Grunewald et al 2002).…”

Section: Introductionmentioning

confidence: 98%

“…This might also account for why correlations between neuronal firing and choice are modest or absent in V1 (Leopold and Logothetis, 1996;Grunewald et al, 2002). However, in both V1 studies, interpretation is complicated: V1/V2 responses were pooled (Leopold and Logothetis, 1996) or neuronal precision differed between V1 and extrastriate cortex (Grunewald et al 2002).…”

Section: Introductionmentioning

confidence: 99%

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Macaque V2 Neurons, But Not V1 Neurons, Show Choice-Related Activity

Nienborg¹,

Cumming²

2006

J. Neurosci.

136

144

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In the macaque extrastriate cortex, robust correlations between perceptual choice and neuronal response have been demonstrated, frequently quantified as choice probabilities (CPs). Such correlations are modest in early visual cortex, suggesting that CPs may depend on the position of a neuron in the hierarchy of visual processing. However, previous studies have not compared neurons with similar precision in equivalent tasks.We investigated the role of cortical hierarchy on CP using a task for which significant CPs have been described previously for middle temporal area (MT). We measured CPs in disparity-selective neurons from both V1 and V2. The stimulus was a dynamic random dot stereogram, presented with a near or a far disparity, masked by varying numbers of binocularly uncorrelated dots. Two macaque monkeys reported whether they perceived a circular patch in front or behind a surrounding annulus in a forced choice task.For V2 (n ϭ 69), CP was on average 0.56, the first demonstration of systematic CPs in a visual area as early as V2. In V1 (n ϭ 74), average CP was at chance level (0.51). The pattern was similar in a subgroup of neurons selected such that the statistical precision in the task was on average identical to that reported for MT (mean CP, 0.51 for V1, n ϭ 33; 0.58 for V2, n ϭ 54). This difference between V1 and V2 could not be explained by eye movements, stimulus size relative to the receptive field, or differences in disparity tuning. Rather, it seems to reflect a functional difference (at least in disparity processing) between striate and extrastriate cortex.

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“…Models that assume that this principle is used by the visual system to extract depth from relative velocities will be called motion perspective models. Psychophysical (16)(17)(18) and physiological (19)(20)(21)(22) evidence for relative velocity detectors suggests that they could play an intermediate role in computing 3D shape, and electrophysiological studies have implicated the middle temporal (MT) cortical area, which contains such neurons, as having a significant role in computing 3D structure from motion (23)(24)(25)(26)(27). This approach has been shown to be in general agreement with human perception of rigid objects (3,28,29) but has not been tested on nonrigid motion.…”

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confidence: 99%

Discerning nonrigid 3D shapes from motion cues

Jain

Zaidi²

2011

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

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Many organisms and objects deform nonrigidly when moving, requiring perceivers to separate shape changes from object motions. Surprisingly, the abilities of observers to correctly infer nonrigid volumetric shapes from motion cues have not been measured, and structure from motion models predominantly use variants of rigidity assumptions. We show that observers are equally sensitive at discriminating cross-sections of flexing and rigid cylinders based on motion cues, when the cylinders are rotated simultaneously around the vertical and depth axes. A computational model based on motion perspective (i.e., assuming perceived depth is inversely proportional to local velocity) predicted the psychometric curves better than shape from motion factorization models using shape or trajectory basis functions. Asymmetric percepts of symmetric cylinders, arising because of asymmetric velocity profiles, provided additional evidence for the dominant role of relative velocity in shape perception. Finally, we show that inexperienced observers are generally incapable of using motion cues to detect inflation/deflation of rigid and flexing cylinders, but this handicap can be overcome with practice for both nonrigid and rigid shapes. The empirical and computational results of this study argue against the use of rigidity assumptions in extracting 3D shape from motion and for the primacy of motion deformations computed from motion shears.optic-flow | structure-from-motion

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Neural Correlates of Structure-from-Motion Perception in Macaque V1 and MT

Cited by 114 publications

References 40 publications

Quantitative prediction of perceptual decisions during near-threshold fear detection

Quantitative prediction of perceptual decisions during near-threshold fear detection

Macaque V2 Neurons, But Not V1 Neurons, Show Choice-Related Activity

Discerning nonrigid 3D shapes from motion cues

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