Motion noise changes directional interaction between transparently moving stimuli from repulsion to attraction

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.

Distributed and Dynamic Neural Encoding of Multiple Motion Directions of Transparently Moving Stimuli in Cortical Area MT

Segmenting visual scenes into distinct objects and surfaces is a fundamental visual function. To better understand the underlying neural mechanism, we investigated how neurons in the middle temporal cortex (MT) of macaque monkeys represent overlapping random-dot stimuli moving transparently in slightly different directions. It has been shown that the neuronal response elicited by two stimuli approximately follows the average of the responses elicited by the constituent stimulus components presented alone. In this scheme of response pooling, the ability to segment two simultaneously presented motion directions is limited by the width of the tuning curve to motion in a single direction. We found that, although the population-averaged neuronal tuning showed response averaging, subgroups of neurons showed distinct patterns of response tuning and were capable of representing component directions that were separated by a small angle--less than the tuning width to unidirectional stimuli. One group of neurons preferentially represented the component direction at a specific side of the bidirectional stimuli, weighting one stimulus component more strongly than the other. Another group of neurons pooled the component responses nonlinearly and showed two separate peaks in their tuning curves even when the average of the component responses was unimodal. We also show for the first time that the direction tuning of MT neurons evolved from initially representing the vector-averaged direction of slightly different stimuli to gradually representing the component directions. Our results reveal important neural processes underlying image segmentation and suggest that information about slightly different stimulus components is computed dynamically and distributed across neurons.

SIGNIFICANCE STATEMENT

Natural scenes often contain multiple entities. The ability to segment visual scenes into distinct objects and surfaces is fundamental to sensory processing and is crucial for generating the perception of our environment. Because cortical neurons are broadly tuned to a given visual feature, segmenting two stimuli that differ only slightly is a challenge for the visual system. In this study, we discovered that many neurons in the visual cortex are capable of representing individual components of slightly different stimuli by selectively and nonlinearly pooling the responses elicited by the stimulus components. We also show for the first time that the neural representation of individual stimulus components developed over a period of ∼70-100 ms, revealing a dynamic process of image segmentation.

Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT

We investigated how neural activity in the middle temporal area of the macaque monkey changes after 3 sec of exposure to a visual stimulus and used this to gain insight into the assumptions underlying the fMRI adaptation method (fMRIa). We studied both changes in tuning curves following weak and strong motion stimuli (adaptation) and the differences between a first and second exposure to the same stimulus (repetition suppression). Typically, tuning curves had smaller amplitudes and narrower tuning widths after strong adaptation; this was true for single neurons, multi-unit activity (MUA), the evoked local field potential (LFP), as well as gamma band activity. Repetition typically led to reduced responses. This reduction was correlated with direction selectivity and not explained by neural fatigue. Our data, however, warn against a simplistic view of the consequences of adaptation. First, a considerable fraction of neurons and sites showed response enhancements after adaptation, especially when probed with a stimulus that moved opposite to the direction of the adapting stimulus. Second, adaptation was stimulus selective only on a time scale of ∼100 msec. Third, aggregate measures of neural activity (MUA, LFPs) had substantially different adaptation effects. Fourth, there were qualitative differences between our findings in MT and earlier findings in IT cortex. We conclude that selective adaptation effects in fMRIa are relatively easy to miss even when they exist (for instance by presenting stimuli for too long, or because neurons that enhance after adaptation cancel out the effect of neurons that suppress). Moreover, we argue that adaptation should be understood in the context of the computations that a neural circuit perform. Using fMRIa as a tool to uncover neural selectivity requires a better understanding of this circuitry and its consequences for adaptation.