Neural Dynamics of Motion Processing and Speed Discrimination

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-tuned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the Vl→7 MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100, N00014-95-1-0657, N00014-95-1-0409, N00014-94-1-0597, N00014-95-1-0409); Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI-90-00530

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and posterior parietal cortex can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discotinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624

Neural Dynamics of Motion Grouping: From Aperture Ambiguity to Object Speed and Direction

A neural network model of visual motion perception and speed discrimination is developed to simulate data concerning the conditions under which components of moving stimuli cohere or not into a global direction of motion, as in barberpole and plaid patterns (both Type 1 and Type 2). The model also simulates how the perceived speed of lines moving in a prescribed direction depends upon their orientation, length, duration, and contrast. Motion direction and speed both emerge as part of an interactive motion grouping or segmentation process. The model proposes a solution to the global aperture problem by showing how information from feature tracking points, namely locations from which unambiguous motion directions can be computed, can propagate to ambiguous motion direction points, and capture the motion signals there. The model does this without computing intersections of constraints or parallel Fourier and non-Fourier pathways. Instead, the model uses orientationally-unselective cell responses to activate directionally-tuned transient cells. These transient cells, in turn, activate spatially short-range filters and competitive mechanisms over multiple spatial scales to generate speed-tuned and directionally-tuned cells. Spatially long-range filters and top-down feedback from grouping cells are then used to track motion of featural points and to select and propagate correct motion directions to ambiguous motion points. Top-down grouping can also prime the system to attend a particular motion direction. The model hereby links low-level automatic motion processing with attention-based motion processing. Homologs of model mechanisms have been used in models of other brain systems to simulate data about visual grouping, figure-ground separation, and speech perception. Earlier versions of the model have simulated data about short-range and long-range apparent motion, second-order motion, and the effects of parvocellular and magnocellular LGN lesions on motion perception.Office of Naval Research (N00014-920J-4015, N00014-91-J-4100, N00014-95-1-0657, N00014-95-1-0409, N00014-91-J-0597); Air Force Office of Scientific Research (F4620-92-J-0225, F49620-92-J-0499); National Science Foundation (IRI-90-00530

Interactions between motion and form processing in the human visual system

The predominant view of motion and form processing in the human visual system assumes that these two attributes are handled by separate and independent modules. Motion processing involves filtering by direction-selective sensors, followed by integration to solve the aperture problem. Form processing involves filtering by orientation-selective and size-selective receptive fields, followed by integration to encode object shape. It has long been known that motion signals can influence form processing in the well-known Gestalt principle of common fate; texture elements which share a common motion property are grouped into a single contour or texture region. However, recent research in psychophysics and neuroscience indicates that the influence of form signals on motion processing is more extensive than previously thought. First, the salience and apparent direction of moving lines depends on how the local orientation and direction of motion combine to match the receptive field properties of motion-selective neurons. Second, orientation signals generated by “motion-streaks” influence motion processing; motion sensitivity, apparent direction and adaptation are affected by simultaneously present orientation signals. Third, form signals generated by human body shape influence biological motion processing, as revealed by studies using point-light motion stimuli. Thus, form-motion integration seems to occur at several different levels of cortical processing, from V1 to STS

A ratio model of perceived speed in the human visual system

The perceived speed of moving images changes over time. Prolonged viewing of a pattern (adaptation) leads to an exponential decrease in its perceived speed. Similarly, responses of neurones tuned to motion reduce exponentially over time. It is tempting to link these phenomena. However, under certain conditions, perceived speed increases after adaptation and the time course of these perceptual effects varies widely. We propose a model that comprises two temporally tuned mechanisms whose sensitivities reduce exponentially over time. Perceived speed is taken as the ratio of these filters' outputs. The model captures increases and decreases in perceived speed following adaptation and describes our data well with just four free parameters. Whilst the model captures perceptual time courses that vary widely, parameter estimates for the time constants of the underlying filters are in good agreement with estimates of the time course of adaptation of direction selective neurones in the mammalian visual system

The influence of external and internal motor processes on human auditory rhythm perception

Musical rhythm is composed of organized temporal patterns, and the processes underlying rhythm perception are found to engage both auditory and motor systems. Despite behavioral and neuroscience evidence converging to this audio-motor interaction, relatively little is known about the effect of specific motor processes on auditory rhythm perception. This doctoral thesis was devoted to investigating the influence of both external and internal motor processes on the way we perceive an auditory rhythm. The first half of the thesis intended to establish whether overt body movement had a facilitatory effect on our ability to perceive the auditory rhythmic structure, and whether this effect was modulated by musical training. To this end, musicians and non-musicians performed a pulse-finding task either using natural body movement or through listening only, and produced their identified pulse by finger tapping. The results showed that overt movement benefited rhythm (pulse) perception especially for non-musicians, confirming the facilitatory role of external motor activities in hearing the rhythm, as well as its interaction with musical training. The second half of the thesis tested the idea that indirect, covert motor input, such as that transformed from the visual stimuli, could influence our perceived structure of an auditory rhythm. Three experiments examined the subjectively perceived tempo of an auditory sequence under different visual motion stimulations, while the auditory and visual streams were presented independently of each other. The results revealed that the perceived auditory tempo was accordingly influenced by the concurrent visual motion conditions, and the effect was related to the increment or decrement of visual motion speed. This supported the hypothesis that the internal motor information extracted from the visuomotor stimulation could be incorporated into the percept of an auditory rhythm. Taken together, the present thesis concludes that, rather than as a mere reaction to the given auditory input, our motor system plays an important role in contributing to the perceptual process of the auditory rhythm. This can occur via both external and internal motor activities, and may not only influence how we hear a rhythm but also under some circumstances improve our ability to hear the rhythm.Musikalische Rhythmen bestehen aus zeitlich strukturierten Mustern akustischer Stimuli. Es konnte gezeigt werden, dass die Prozesse, welche der Rhythmuswahrnehmung zugrunde liegen, sowohl motorische als auch auditive Systeme nutzen. Obwohl sich für diese auditiv-motorischen Interaktionen sowohl in den Verhaltenswissenschaften als auch Neurowissenschaften übereinstimmende Belege finden, weiß man bislang relativ wenig über die Auswirkungen spezifischer motorischer Prozesse auf die auditive Rhythmuswahrnehmung. Diese Doktorarbeit untersucht den Einfluss externaler und internaler motorischer Prozesse auf die Art und Weise, wie auditive Rhythmen wahrgenommen werden. Der erste Teil der Arbeit diente dem Ziel herauszufinden, ob körperliche Bewegungen es dem Gehirn erleichtern können, die Struktur von auditiven Rhythmen zu erkennen, und, wenn ja, ob dieser Effekt durch ein musikalisches Training beeinflusst wird. Um dies herauszufinden wurde Musikern und Nichtmusikern die Aufgabe gegeben, innerhalb von präsentierten auditiven Stimuli den Puls zu finden, wobei ein Teil der Probanden währenddessen Körperbewegungen ausführen sollte und der andere Teil nur zuhören sollte. Anschließend sollten die Probanden den gefundenen Puls durch Finger-Tapping ausführen, wobei die Reizgaben sowie die Reaktionen mittels eines computerisierten Systems kontrolliert wurden. Die Ergebnisse zeigen, dass offen ausgeführte Bewegungen die Wahrnehmung des Pulses vor allem bei Nichtmusikern verbesserten. Diese Ergebnisse bestätigen, dass Bewegungen beim Hören von Rhythmen unterstützend wirken. Außerdem zeigte sich, dass hier eine Wechselwirkung mit dem musikalischen Training besteht. Der zweite Teil der Doktorarbeit überprüfte die Idee, dass indirekte, verdeckte Bewegungsinformationen, wie sie z.B. in visuellen Stimuli enthalten sind, die wahrgenommene Struktur von auditiven Rhythmen beeinflussen können. Drei Experimente untersuchten, inwiefern das subjektiv wahrgenommene Tempo einer akustischen Sequenz durch die Präsentation unterschiedlicher visueller Bewegungsreize beeinflusst wird, wobei die akustischen und optischen Stimuli unabhängig voneinander präsentiert wurden. Die Ergebnisse zeigten, dass das wahrgenommene auditive Tempo durch die visuellen Bewegungsinformationen beeinflusst wird, und dass der Effekt in Verbindung mit der Zunahme oder Abnahme der visuellen Geschwindigkeit steht. Dies unterstützt die Hypothese, dass internale Bewegungsinformationen, welche aus visuomotorischen Reizen extrahiert werden, in die Wahrnehmung eines auditiven Rhythmus integriert werden können. Zusammen genommen, 5 zeigt die vorgestellte Arbeit, dass unser motorisches System eine wichtige Rolle im Wahrnehmungsprozess von auditiven Rhythmen spielt. Dies kann sowohl durch äußere als auch durch internale motorische Aktivitäten geschehen, und beeinflusst nicht nur die Art, wie wir Rhythmen hören, sondern verbessert unter bestimmten Bedingungen auch unsere Fähigkeit Rhythmen zu identifizieren

Probabilistic Motion Estimation Based on Temporal Coherence

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence. This temporal grouping can be considered a generalization of the data association techniques used by engineers to study motion sequences. Our temporal-grouping theory is expressed in terms of the Bayesian generalization of standard Kalman filtering. To implement the theory we derive a parallel network which shares some properties of cortical networks. Computer simulations of this network demonstrate that our theory qualitatively accounts for psychophysical experiments on motion occlusion and motion outliers.Comment: 40 pages, 7 figure

A single mechanism can explain the speed tuning properties of MT and V1 complex neurons

A recent study by Priebe et al., (2006) has shown that a small proportion (27%) of primate directionally selective, complex V1 neurons are tuned for the speed of image motion. In this study, I show that the weighted intersection mechanism (WIM) model, which was previously proposed to explain speed tuning in middle temporal neurons, can also explain the tuning found in complex V1 neurons. With the addition of a contrast gain mechanism, this model is able to replicate the effects of contrast on V1 speed tuning, a phenomenon that was recently discovered by Priebe et al., (2006). The WIM model simulations also indicate that V1 neuron spatiotemporal frequency response maps may be asymmetrical in shape and hence poorly characterized by the symmetrical two-dimensional Gaussian fitting function used by Priebe et al., (2006) to classify their cells. Therefore, the actual proportion of speed tuning among directional complex V1 cells may be higher than the 27% estimate suggested by these authors

A neural model of border-ownership from kinetic occlusion

Camouflaged animals that have very similar textures to their surroundings are difficult to detect when stationary. However, when an animal moves, humans readily see a figure at a different depth than the background. How do humans perceive a figure breaking camouflage, even though the texture of the figure and its background may be statistically identical in luminance? We present a model that demonstrates how the primate visual system performs figure–ground segregation in extreme cases of breaking camouflage based on motion alone. Border-ownership signals develop as an emergent property in model V2 units whose receptive fields are nearby kinetically defined borders that separate the figure and background. Model simulations support border-ownership as a general mechanism by which the visual system performs figure–ground segregation, despite whether figure–ground boundaries are defined by luminance or motion contrast. The gradient of motion- and luminance-related border-ownership signals explains the perceived depth ordering of the foreground and background surfaces. Our model predicts that V2 neurons, which are sensitive to kinetic edges, are selective to border-ownership (magnocellular B cells). A distinct population of model V2 neurons is selective to border-ownership in figures defined by luminance contrast (parvocellular B cells). B cells in model V2 receive feedback from neurons in V4 and MT with larger receptive fields to bias border-ownership signals toward the figure. We predict that neurons in V4 and MT sensitive to kinetically defined figures play a crucial role in determining whether the foreground surface accretes, deletes, or produces a shearing motion with respect to the background.This work was supported in part by CELEST (NSF SBE-0354378 and OMA-0835976), the Office of Naval Research (ONR N00014-11-1-0535) and Air Force Office of Scientific Research (AFOSR FA9550-12-1-0436). (NSF SBE-0354378 - CELEST; OMA-0835976 - CELEST; ONR N00014-11-1-0535 - Office of Naval Research; AFOSR FA9550-12-1-0436 - Air Force Office of Scientific Research)Published versio