Misperceptions of speed are accounted for by the responses of neurons in macaque cortical area MT

Facilitation and inhibition effects of anodal and cathodal tDCS over areas MT+ on the flash-lag effect

The perceived position of a moving object in vision entails an accumulation of neural signals over space and time. Due to neural signal transmission delays, the visual system can not acquire immediate information about the moving object's position. Although physiological and psychophysical studies on the flash-lag effect (FLE), a moving object is perceived ahead of a flash even they are aligned at the same location, have shown that the visual system develops the mechanisms of predicting the object's location to compensate for the neural delays, the neural mechanisms of motion-induced location prediction are not still understood well. Here, we investigated the role of neural activity changes in areas MT+ (specialized for motion processing) and the potential contralateral processing preference of MT+ in modulating the FLE. Using transcranial direct current stimulations (tDCS) over the left and right MT+ between pre-and post-tests of the FLE in different motion directions, we measured the effects of tDCS on the FLE. The results found that anodal and cathodal tDCS enhanced and reduced the FLE with the moving object heading to but not deviating from the side of the brain stimulated, respectively, compared to sham tDCS. These findings suggest a causal role of area MT+ in motion-induced location prediction, which may involve the integration of position information.

How are response properties in the middle temporal area related to inference on visual motion patterns?

Neurons in the primate middle temporal area (MT) respond to moving stimuli, with strong tuning for motion speed and direction. These responses have been characterized in detail, but the functional significance of these details (e.g. shapes and widths of speed tuning curves) is unclear, because they cannot be selectively manipulated. To estimate their functional significance, we used a detailed model of MT population responses as input to convolutional networks that performed sophisticated motion processing tasks (visual odometry and gesture recognition). We manipulated the distributions of speed and direction tuning widths, and studied the effects on task performance. We also studied performance with random linear mixtures of the responses, and with responses that had the same representational dissimilarity as the model populations, but were otherwise randomized. The width of speed and direction tuning both affected task performance, despite the networks having been optimized individually for each tuning variation, but the specific effects were different in each task. Random linear mixing improved performance of the odometry task, but not the gesture recognition task. Randomizing the responses while maintaining representational dissimilarity resulted in poor odometry performance. In summary, despite full optimization of the deep networks in each case, each manipulation of the representation affected performance of sophisticated visual tasks. Representation properties such as tuning width and representational similarity have been studied extensively from other perspectives, but this work provides new insight into their possible roles in sophisticated visual inference.

A video-driven model of response statistics in the primate middle temporal area

Neurons in the primate middle temporal area (MT) encode information about visual motion and binocular disparity. MT has been studied intensively for decades, so there is a great deal of information in the literature about MT neuron tuning. In this study, our goal is to consolidate some of this information into a statistical model of the MT population response. The model accepts arbitrary stereo video as input. It uses computer-vision methods to calculate known correlates of the responses (such as motion velocity), and then predicts activity using a combination of tuning functions that have previously been used to describe data in various experiments. To construct the population response, we also estimate the distributions of many model parameters from data in the electrophysiology literature. We show that the model accounts well for a separate dataset of MT speed tuning that was not used in developing the model. The model may be useful for studying relationships between MT activity and behavior in ethologically relevant tasks. As an example, we show that the model can provide regression targets for internal activity in a deep convolutional network that performs a visual odometry task, so that its representations become more physiologically realistic.

Auditory modulation of spiking activity and local field potentials in area MT does not appear to underlie an audiovisual temporal illusion

The timing of brief stationary sounds has been shown to alter the perceived speed of visual apparent motion (AM), presumably by altering the perceived timing of the individual frames of the AM stimuli and/or the duration of the interstimulus intervals (ISIs) between those frames. To investigate the neural correlates of this "temporal ventriloquism" illusion, we recorded spiking and local field potential (LFP) activity from the middle temporal area (area MT) in awake, fixating macaques. We found that the spiking activity of most MT neurons (but not the LFP) was tuned for the ISI/speed (these parameters covaried) of our AM stimuli but that auditory timing had no effect on that tuning. We next asked whether the predicted changes in perceived timing were reflected in the timing of neuronal responses to the individual frames of the AM stimuli. Although spiking dynamics were significantly, if weakly, affected by auditory timing in a minority of neurons, the timing of spike responses did not systematically mirror the predicted perception of stimuli. Conversely, the duration of LFP responses in β- and γ-frequency bands was qualitatively consistent with human perceptual reports. We discovered, however, that LFP responses to auditory stimuli presented alone were robust and that responses to audiovisual stimuli were predicted by the linear sum of responses to auditory and visual stimuli presented individually. In conclusion, we find evidence of auditory input into area MT but not of the nonlinear audiovisual interactions we had hypothesized to underlie the illusion. NEW & NOTEWORTHY We utilized a set of audiovisual stimuli that elicit an illusion demonstrating "temporal ventriloquism" in visual motion and that have spatiotemporal intervals for which neurons within the middle temporal area are selective. We found evidence of auditory input into the middle temporal area but not of the nonlinear audiovisual interactions underlying this illusion. Our findings suggest that either the illusion was absent in our nonhuman primate subjects or the neuronal correlates of this illusion lie within other areas.

The field-size effect: Short motions look faster than long ones

Reducing the amount of motion information can surprisingly make motion look faster (e.g., motion behind Venetian blinds). We found that a textured pattern moving to the right at speeds ranging from 0.34 to 5.5°/s appeared to move 50% faster when viewed through a short (0.5°) compared with a long (4.5°) horizontal slot. Perceived speed varied inversely with the log of the slot length. We varied the length of rectangular apertures over a tenfold range and manipulated their size, shape, and orientation. We attribute the field-size effect mostly to landmarks provided by the ends of the slots, but we also examined temporal and spatial frequency and lateral inhibition of motion.

Integration of motion energy from overlapping random background noise increases perceived speed of coherently moving stimuli

The perception of visual motion can be profoundly influenced by visual context. To gain insight into how the visual system represents motion speed, we investigated how a background stimulus that did not move in a net direction influenced the perceived speed of a center stimulus. Visual stimuli were two overlapping random-dot patterns. The center stimulus moved coherently in a fixed direction, whereas the background stimulus moved randomly. We found that human subjects perceived the speed of the center stimulus to be significantly faster than its veridical speed when the background contained motion noise. Interestingly, the perceived speed was tuned to the noise level of the background. When the speed of the center stimulus was low, the highest perceived speed was reached when the background had a low level of motion noise. As the center speed increased, the peak perceived speed was reached at a progressively higher background noise level. The effect of speed overestimation required the center stimulus to overlap with the background. Increasing the background size within a certain range enhanced the effect, suggesting spatial integration. The speed overestimation was significantly reduced or abolished when the center stimulus and the background stimulus had different colors, or when they were placed at different depths. When the center- and background-stimuli were perceptually separable, speed overestimation was correlated with perceptual similarity between the center- and background-stimuli. These results suggest that integration of motion energy from random motion noise has a significant impact on speed perception. Our findings put new constraints on models regarding the neural basis of speed perception.

Neural mechanisms of speed perception: transparent motion

Visual motion on the macaque retina is processed by direction- and speed-selective neurons in extrastriate middle temporal cortex (MT). There is strong evidence for a link between the activity of these neurons and direction perception. However, there is conflicting evidence for a link between speed selectivity of MT neurons and speed perception. Here we study this relationship by using a strong perceptual illusion in speed perception: when two transparently superimposed dot patterns move in opposite directions, their apparent speed is much larger than the perceived speed of a single pattern moving at that physical speed. Moreover, the sensitivity for speed discrimination is reduced for such bidirectional patterns. We first confirmed these behavioral findings in human subjects and extended them to a monkey subject. Second, we determined speed tuning curves of MT neurons to bidirectional motion and compared these to speed tuning curves for unidirectional motion. Consistent with previous reports, the response to bidirectional motion was often reduced compared with unidirectional motion at the preferred speed. In addition, we found that tuning curves for bidirectional motion were shifted to lower preferred speeds. As a consequence, bidirectional motion of some speeds typically evoked larger responses than unidirectional motion. Third, we showed that these changes in neural responses could explain changes in speed perception with a simple labeled line decoder. These data provide new insight into the encoding of transparent motion patterns and provide support for the hypothesis that MT activity can be decoded for speed perception with a labeled line model.

Population coding in sparsely connected networks of noisy neurons

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Decorrelation of spiking variability and improved information transfer through feedforward divisive normalization

Response variability is often positively correlated in pairs of similarly tuned neurons in the visual cortex. Many authors have considered correlated variability to prevent postsynaptic neurons from averaging across large groups of inputs to obtain reliable stimulus estimates. However, a simple average of variability ignores nonlinearities in cortical signal integration. This study shows that feedforward divisive normalization of a neuron's inputs effectively decorrelates their variability. Furthermore, we show that optimal linear estimates of a stimulus parameter that are based on normalized inputs are more accurate than those based on nonnormalized inputs, due partly to reduced correlations, and that these estimates improve with increasing population size up to several thousand neurons. This suggests that neurons may possess a simple mechanism for substantially decorrelating noise in their inputs. Further work is needed to reconcile this conclusion with past evidence that correlated noise impairs visual perception.