Neural mechanisms of speed perception: Transparent motion

Rutgers University.
Journal of Neurophysiology (Impact Factor: 2.89). 08/2013; 110(9). DOI: 10.1152/jn.00333.2013
Source: PubMed


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 to 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.


Available from: Bart Krekelberg, Jan 03, 2014
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    • "Speed tuning and direction selectivity were maximal and stable 90 ms after stimulus onset; although there was a slight overall reduction in firing rate over the remaining 500 ms recorded data. This was likely an effect of adaptation, as has previously been reported in area MT (Kohn and Movshon, 2003; Krekelberg et al., 2006; Schlack et al., 2007). "
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    ABSTRACT: The detection of visual motion requires temporal delays to compare current with earlier visual input. Models of motion detection assume that these delays reside in separate classes of slow and fast thalamic cells, or slow and fast synaptic transmission. We used a data-driven modeling approach to generate a model that instead uses recurrent network dynamics with a single, fixed temporal integration window to implement the velocity computation. This model successfully reproduced the temporal response dynamics of a population of motion sensitive neurons in macaque middle temporal area (MT) and its constituent parts matched many of the properties found in the motion processing pathway (e.g. Gabor like receptive fields, simple and complex cells, spatially asymmetric excitation and inhibition). Reverse correlation analysis revealed that a simplified network based on first and second order space-time correlations of the recurrent model behaved much like a feedforward motion energy (ME) model. The feedforward model, however, failed to capture the full speed tuning and direction selectivity properties based on higher than second order space-time correlations typically found in MT. These findings support the idea that recurrent network connectivity can create temporal delays to compute velocity. Moreover, the model explains why the motion detection system often behaves like a feedforward ME network, even though the anatomical evidence strongly suggests that this network should be dominated by recurrent feedback.
    Frontiers in Systems Neuroscience 12/2014; DOI:10.3389/fnsys.2014.00239
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    • "The dorsal visual pathway is specialized for motion processing. Much research has determined the hierarchical nature of motion processing wherein each stage builds upon the previous stage’s output leading to understanding of the algorithms and connectivity to produce models of the different stages of motion processing (Marr and Ullman, 1981; Adelson and Bergen, 1985; Cavanagh and Mather, 1989; Taub et al., 1997; Krekelberg and Albright, 2005; Pack et al., 2006; Tsui and Pack, 2011; Mineault et al., 2012; Krekelberg and van Wezel, 2013; Patterson et al., 2014; for review see Burr and Thompson, 2011). It is important to note that these models focus on the transformation of motion information and not its integration into object representations. "
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    ABSTRACT: The visual system is split into two processing streams: a ventral stream that receives color and form information and a dorsal stream that receives motion information. Each stream processes that information hierarchically, with each stage building upon the previous. In the ventral stream this leads to the formation of object representations that ultimately allow for object recognition regardless of changes in the surrounding environment. In the dorsal stream, this hierarchical processing has classically been thought to lead to the computation of complex motion in three dimensions. However, there is evidence to suggest that there is integration of both dorsal and ventral stream information into motion computation processes, giving rise to intermediate object representations, which facilitate object selection and decision making mechanisms in the dorsal stream. First we review the hierarchical processing of motion along the dorsal stream and the building up of object representations along the ventral stream. Then we discuss recent work on the integration of ventral and dorsal stream features that lead to intermediate object representations in the dorsal stream. Finally we propose a framework describing how and at what stage different features are integrated into dorsal visual stream object representations. Determining the integration of features along the dorsal stream is necessary to understand not only how the dorsal stream builds up an object representation but also which computations are performed on object representations instead of local features.
    Frontiers in Computational Neuroscience 08/2014; 8:84. DOI:10.3389/fncom.2014.00084 · 2.20 Impact Factor
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    ABSTRACT: Multiple visual stimuli are common in natural scenes, yet it remains unclear how multiple stimuli interact to influence neuronal responses. We investigated this question by manipulating relative signal strengths of two stimuli moving simultaneously within the receptive fields (RFs) of neurons in the extrastriate middle temporal (MT) cortex. Visual stimuli were overlapping random-dot patterns moving in two directions separated by 90°. We first varied the motion coherence of each random-dot pattern and characterized, across the direction tuning curve, the relationship between neuronal responses elicited by bi-directional stimuli and by the constituent motion components. The tuning curve for bi-directional stimuli showed response normalization and can be accounted for by a weighted sum of the responses to the motion components. Allowing nonlinear, multiplicative interaction between the two component responses significantly improved the data fit for some neurons and the interaction mainly had a suppressive effect on the neuronal response. The weighting of the component responses was not fixed, but dependent on relative signal strengths. When two stimulus components moved at different coherence levels, the response weight for the higher-coherence component was significantly greater than that for the lower-coherence component. We also varied relative luminance levels of two coherently moving stimuli and found that MT response weight for the higher-luminance component was also greater. These results suggest that competition between multiple stimuli within a neuron's RF depends on relative signal strengths of the stimuli, and that multiplicative nonlinearity may play an important role in shaping the response tuning for multiple stimuli.
    Journal of Neurophysiology 06/2014; 112(6). DOI:10.1152/jn.00700.2013 · 2.89 Impact Factor
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