Scott A. Beardsley

Optic flow and beyond

Optic flow provides all the information necessary to guide a walking human or a mobile robot to its target. Over the past 50 years, a body of research on optic flow spanning the disciplines of neurophysiology, psychophysics, experimental psychology, brain imaging and computational modelling has accumulated. Today, when we survey the field, we find independent lines of research have now converged and many arguments have been resolved; simultaneously the underpinning assumptions of flow theory are being questioned and alternative accounts of the visual guidance of locomotion proposed. At this critical juncture, this volume offers a timely review of what has been learnt and pointers to where the field is going.

The perception and discrimination of speed in complex motion

Random dot kinematograms were used to simulate radial, rotational and spiral optic flow. The stimuli were designed so that, while dot speed increased linearly with distance from the centre of the display, the density of dots remained uniform throughout their presentation. In two experiments, subjects were required to perform a temporal 2AFC speed discrimination task. Experiment 1 measured the perceived speed of a range of optic flow patterns against a rotational comparison stimulus. Radial motions were found to appear faster than rotations by approximately 10%, with a smaller but significant effect for spirals. Experiment 2 measured discrimination thresholds for pairs of similar optic flow stimuli identical in all respects except mean speed. No consistent differences were observed between the speed discrimination thresholds of radial, rotational and spiral motions and a control stimulus with the same speed profile in which motion followed fixed random trajectories. The perceived speed results are interpreted in terms of a model satisfying constraints on motion-in-depth and object rigidity, while speed discrimination appears to be based upon the pooled responses of elementary motion detectors.

Neural Dynamics of Phonological Processing in the Dorsal Auditory Stream

Neuroanatomical models hypothesize a role for the dorsal auditory pathway in phonological processing as a feedforward efferent system (Davis and Johnsrude, 2007; Rauschecker and Scott, 2009; Hickok et al., 2011). But the functional organization of the pathway, in terms of time course of interactions between auditory, somatosensory, and motor regions, and the hemispheric lateralization pattern is largely unknown. Here, ambiguous duplex syllables, with elements presented dichotically at varying interaural asynchronies, were used to parametrically modulate phonological processing and associated neural activity in the human dorsal auditory stream. Subjects performed syllable and chirp identification tasks, while event-related potentials and functional magnetic resonance images were concurrently collected. Joint independent component analysis was applied to fuse the neuroimaging data and study the neural dynamics of brain regions involved in phonological processing with high spatiotemporal resolution. Results revealed a highly interactive neural network associated with phonological processing, composed of functional fields in posterior temporal gyrus (pSTG), inferior parietal lobule (IPL), and ventral central sulcus (vCS) that were engaged early and almost simultaneously (at 80–100 ms), consistent with a direct influence of articulatory somatomotor areas on phonemic perception. Left hemispheric lateralization was observed 250 ms earlier in IPL and vCS than pSTG, suggesting that functional specialization of somatomotor (and not auditory) areas determined lateralization in the dorsal auditory pathway. The temporal dynamics of the dorsal auditory pathway described here offer a new understanding of its functional organization and demonstrate that temporal information is essential to resolve neural circuits underlying complex behaviors.

Within-Subject Joint Independent Component Analysis of Simultaneous fMRI/ERP in an Auditory Oddball Paradigm

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1-8 μV in the time window of the P300 (350-700 ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post-central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI.

Computational modelling of optic flow selectivity in MSTd neurons.

In neurophysiological experiments examining the selectivity of MSTd neurons to visual motion components of optic flow stimuli in monkeys, Duffy and Wurtz (1991) reported cells with double-component (plano-radial and plano-circular) and triple-component (plano-radial-circular) selectivities, while Graziano et al (1994) reported neurons selective to a continuum of optic flow stimuli including spiral motion. Here, we address these reported findings under simulated experimental conditions by examining the development of optic flow selectivity in the hidden units of a two-layer back-propagation network. We also examine network motion sensitivity during simulated psychophysical tests via the addition of a competitive decision layer. Network analysis with neurophysiological stimuli identified a majority of hidden units whose position invariance and motion selectivity were consistent with MSTd responses to the visual motion components of optic flow stimuli reported by Duffy and Wurtz and Graziano et al. Furthermore, the hidden units developed a continuum of optic flow selectivities independent of the biases associated with the specification of the motion selectivity in the output layer. During psychophysical testing, network responses showed motion sensitivities which met or exceeded human performance. Within the limitations imposed by the learning algorithm, the psychophysical results were consistent with a model of global motion perception via local integration along complex motion trajectories.

A laterally interconnected neural architecture in MST accounts for psychophysical discrimination of complex motion patterns.

The complex patterns of visual motion formed across the retina during self-motion, often referred to as optic flow, provide a rich source of information describing our dynamic relationship within the environment. Psychophysical studies indicate the existence of specialized detectors for component motion patterns (radial, circular, planar) that are consistent with the visual motion properties of cells in the medial superior temporal area (MST) of nonhuman primates. Here we use computational modeling and psychophysics to investigate the structural and functional role of these specialized detectors in performing a graded motion pattern (GMP) discrimination task. In the psychophysical task perceptual discrimination varied significantly with the type of motion pattern presented, suggesting perceptual correlates to the preferred motion bias reported in MST. Simulated perceptual discrimination in a population of independent MST-like neural responses showed inconsistent psychophysical performance that varied as a function of the visual motion properties within the population code. Robust psychophysical performance was achieved by fully interconnecting neural populations such that they inhibited nonpreferred units. Taken together, these results suggest that robust processing of the complex motion patterns associated with self-motion and optic flow may be mediated by an inhibitory structure of neural interactions in MST.

How can a patient blind to radial motion discriminate shifts in the center-of-motion?

Within biologically constrained models of heading and complex motion processing, localization of the center-of-motion (COM) is typically an implicit property arising from the precise computation of radial motion direction associated with an observer’s forward self-motion. In the work presented here we report psychophysical data from a motion-impaired stroke patient, GZ, whose pattern of visual motion deficits is inconsistent with this view. We show that while GZ is able to discriminate direction in circular motions she is unable to discriminate direction in radial motion patterns. GZ’s inability to discriminate radial motion is in stark contrast with her ability to localize the COM in such stimuli and suggests that recovery of the COM does not necessarily require an explicit representation of radial motion direction. We propose that this dichotomy can be explained by a circular template mechanism that minimizes a global motion error relative to the visual motion input, and we demonstrate that a sparse population of such templates is computationally sufficient to account for human psychophysical performance in general and in particular, explains GZ’s performance. Recent re-analysis of the predicted receptive field structures in several existing heading models provides additional support for this type of circular template mechanism and suggests the human visual system may have available circular motion mechanisms for heading estimation.

Intention tremor and deficits of sensory feedback control in multiple sclerosis: a pilot study

BackgroundIntention tremor and dysmetria are leading causes of upper extremity disability in Multiple Sclerosis (MS). The development of effective therapies to reduce tremor and dysmetria is hampered by insufficient understanding of how the distributed, multi-focal lesions associated with MS impact sensorimotor control in the brain. Here we describe a systems-level approach to characterizing sensorimotor control and use this approach to examine how sensory and motor processes are differentially impacted by MS.MethodsEight subjects with MS and eight age- and gender-matched healthy control subjects performed visually-guided flexion/extension tasks about the elbow to characterize a sensory feedback control model that includes three sensory feedback pathways (one for vision, another for proprioception and a third providing an internal prediction of the sensory consequences of action). The model allows us to characterize impairments in sensory feedback control that contributed to each MS subject’s tremor.ResultsModels derived from MS subject performance differed from those obtained for control subjects in two ways. First, subjects with MS exhibited markedly increased visual feedback delays, which were uncompensated by internal adaptive mechanisms; stabilization performance in individuals with the longest delays differed most from control subject performance. Second, subjects with MS exhibited misestimates of arm dynamics in a way that was correlated with tremor power. Subject-specific models accurately predicted kinematic performance in a reach and hold task for neurologically-intact control subjects while simulated performance of MS patients had shorter movement intervals and larger endpoint errors than actual subject responses. This difference between simulated and actual performance is consistent with a strategic compensatory trade-off of movement speed for endpoint accuracy.ConclusionsOur results suggest that tremor and dysmetria may be caused by limitations in the brain’s ability to adapt sensory feedback mechanisms to compensate for increases in visual information processing time, as well as by errors in compensatory adaptations of internal estimates of arm dynamics.

Improved multi-unit decoding at the brain?machine interface using population temporal linear filtering

Current efforts to decode control signals from multi-unit (MU) recordings rely on the use of spike sorting to differentiate neurons and the use of firing rates estimated over tens of milliseconds to reconstruct sensorimotor signals. The computational bottleneck associated with the need to identify and sort individual neuron responses poses challenges for the development of portable, real-time, neural decoding systems that can be incorporated into assistive and prosthetic devices for the disabled. Here, we investigate the ability of spike-based linear filtering to reduce computational overhead and improve the accuracy of decoding neuronal signals for populations of spiking neurons. Using a population temporal (PT) decoding framework, the speed and accuracy of spike-based MU decoding were compared with firing rate-based approaches using simulated populations of motor neurons tuned for the velocity of intended movement. For the two linear filtering approaches, the accuracy of decoded movements was examined as a function of the number of recorded neurons, amount of noise, with and without spike sorting, and for training and test motions whose statistics were either similar or dissimilar. Our results suggest that the use of a PT decoding framework can offset the loss in accuracy associated with decoding unsorted MU neural signals. Coupled with up to a 20-fold reduction in the number of decoding weights and the ability to implement the filtering in hardware, this approach could reduce the computational requirements and thus increase the portability of next generation brain-machine interfaces.

Global motion mechanisms compensate local motion deficits in a patient with a bilateral occipital lobe lesion

Successive stages of cortical processing encode increasingly more complex types of information. In the visual motion system this increasing complexity, complemented by an increase in spatial summation, has proven effective in characterizing the mechanisms mediating visual perception. Here we report psychophysical results from a motion-impaired stroke patient, WB, whose pattern of deficits over time reveals a systematic shift in spatial scale for processing speed. We show that following loss in sensitivity to low-level motion direction WB’s representation of speed shifts to larger spatial scales, consistent with recruitment of intact high-level mechanisms. With the recovery of low-level motion processing WB’s representation of speed shifts back to small spatial scales. These results support the recruitment of high-level visual mechanisms in cases where lower-level function is impaired and suggest that, as an experimental paradigm, spatial summation may provide an important avenue for investigating functional recovery in patients following damage to visually responsive cortex.