Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI -87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-l309

A LabVIEW program to obtain the initiating component of a vergence eye movement in an open-loop experiment

The vergence oculomotor system is used in viewing objects moving in depth, such as when a baseball player tracks a ball moving towards him. It is composed of two components according to the Dual Mode Theory; a fast preprogrammed initiating component and a slow feedback-controlled sustaining component. The initiating component is described with open-loop control and the sustaining component is described as a closed-loop feedback control system. Previously, several languages have been utilized to develop programs to study and isolate the open-loop portion of vergence eye movements. Presenting a subject with a stimulus, which initiates an open-loop response has been speculated to adapt the vergence system more then other stimuli. This study utilizes LabVIEW 6i in developing a program capable of real time experimentation to study the open-loop portion of vergence eye movements. LabVIEW offers many options to the programmer and operator with a user-friendly interface for program development as well as an open architecture, allowing flexibility for future studies. This research validated that LabVIEW can be used for open-loop experimentation through a timing analysis and a comparison proving that responses obtained from this code are similar to data published in the literature

Vector Disparity Sensor with Vergence Control for Active Vision Systems

This paper presents an architecture for computing vector disparity for active vision systems as used on robotics applications. The control of the vergence angle of a binocular system allows us to efficiently explore dynamic environments, but requires a generalization of the disparity computation with respect to a static camera setup, where the disparity is strictly 1-D after the image rectification. The interaction between vision and motor control allows us to develop an active sensor that achieves high accuracy of the disparity computation around the fixation point, and fast reaction time for the vergence control. In this contribution, we address the development of a real-time architecture for vector disparity computation using an FPGA device. We implement the disparity unit and the control module for vergence, version, and tilt to determine the fixation point. In addition, two on-chip different alternatives for the vector disparity engines are discussed based on the luminance (gradient-based) and phase information of the binocular images. The multiscale versions of these engines are able to estimate the vector disparity up to 32 fps on VGA resolution images with very good accuracy as shown using benchmark sequences with known ground-truth. The performances in terms of frame-rate, resource utilization, and accuracy of the presented approaches are discussed. On the basis of these results, our study indicates that the gradient-based approach leads to the best trade-off choice for the integration with the active vision system

Towards binocular active vision in a robot head system

This paper presents the first results of an investigation and pilot study into an active, binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognizing objects in a highly-cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the ‘searchlight metaphor’ of biological systems. We present results of a first pilot investigation that yield a maximum vergence error of 6.4 pixels, while seven of nine known objects were recognized in a high-cluttered environment. Finally a “stepping stone” visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the Field of View resulting from any individual saccade

Neural Representations for Sensory-Motor Control, II: Learning a Head-Centered Visuomotor Representation of 3-D Target Position

A neural network model is described for how an invariant head-centered representation of 3-D target position can be autonomously learned by the brain in real time. Once learned, such a target representation may be used to control both eye and limb movements. The target representation is derived from the positions of both eyes in the head, and the locations which the target activates on the retinas of both eyes. A Vector Associative Map, or YAM, learns the many-to-one transformation from multiple combinations of eye-and-retinal position to invariant 3-D target position. Eye position is derived from outflow movement signals to the eye muscles. Two successive stages of opponent processing convert these corollary discharges into a. head-centered representation that closely approximates the azimuth, elevation, and vergence of the eyes' gaze position with respect to a cyclopean origin located between the eyes. YAM learning combines this cyclopean representation of present gaze position with binocular retinal information about target position into an invariant representation of 3-D target position with respect to the head. YAM learning can use a teaching vector that is externally derived from the positions of the eyes when they foveate the target. A YAM can also autonomously discover and learn the invariant representation, without an explicit teacher, by generating internal error signals from environmental fluctuations in which these invariant properties are implicit. YAM error signals are computed by Difference Vectors, or DVs, that are zeroed by the YAM learning process. YAMs may be organized into YAM Cascades for learning and performing both sensory-to-spatial maps and spatial-to-motor maps. These multiple uses clarify why DV-type properties are computed by cells in the parietal, frontal, and motor cortices of many mammals. YAMs are modulated by gating signals that express different aspects of the will-to-act. These signals transform a single invariant representation into movements of different speed (GO signal) and size (GRO signal), and thereby enable YAM controllers to match a planned action sequence to variable environmental conditions.National Science Foundation (IRI-87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-1309

Binocular Eye Movements Are Adapted to the Natural Environment.

Humans and many animals make frequent saccades requiring coordinated movements of the eyes. When landing on the new fixation point, the eyes must converge accurately or double images will be perceived. We asked whether the visual system uses statistical regularities in the natural environment to aid eye alignment at the end of saccades. We measured the distribution of naturally occurring disparities in different parts of the visual field. The central tendency of the distributions was crossed (nearer than fixation) in the lower field and uncrossed (farther) in the upper field in male and female participants. It was uncrossed in the left and right fields. We also measured horizontal vergence after completion of vertical, horizontal, and oblique saccades. When the eyes first landed near the eccentric target, vergence was quite consistent with the natural-disparity distribution. For example, when making an upward saccade, the eyes diverged to be aligned with the most probable uncrossed disparity in that part of the visual field. Likewise, when making a downward saccade, the eyes converged to enable alignment with crossed disparity in that part of the field. Our results show that rapid binocular eye movements are adapted to the statistics of the 3D environment, minimizing the need for large corrective vergence movements at the end of saccades. The results are relevant to the debate about whether eye movements are derived from separate saccadic and vergence neural commands that control both eyes or from separate monocular commands that control the eyes independently.SIGNIFICANCE STATEMENT We show that the human visual system incorporates statistical regularities in the visual environment to enable efficient binocular eye movements. We define the oculomotor horopter: the surface of 3D positions to which the eyes initially move when stimulated by eccentric targets. The observed movements maximize the probability of accurate fixation as the eyes move from one position to another. This is the first study to show quantitatively that binocular eye movements conform to 3D scene statistics, thereby enabling efficient processing. The results provide greater insight into the neural mechanisms underlying the planning and execution of saccadic eye movements

Is there a relationship between prism fusion range and vergence facility?

Aim: To investigate the relationship between prism fusion range (PFR) and vergence facility (VF) measurements in subjects with normal binocular vision. Methods: Twenty-eight subjects (mean age 19 ± 1 years) with normal binocular single vision (BSV) underwent measurement of the PFR and VF in a varied order, at a test distance of 1/3 m. The PFR measurements recorded were the base out (BO) range to blur and break point and base in (BI) range to break point. The total PFR was calculated. The VF was assessed over a 1 min time period using a 12(Δ)BO/ 3(Δ)BI flip prism and recorded in cycles per minute (cpm). Results: No correlation was demonstrable between any of the single measures of the PFR and the VF results. The BO PFR to break point and the BI PFR results obtained (means 46(Δ) BO and 14(Δ) BI) were not significantly different from quoted ‘normal’ values. The VF results obtained (mean 12 ± 4.2 cpm) were found to be significantly different from the reported mean value. Conclusion: In a group of young adults with normal BSV, no correlation between PFR and VF was found. The two tests may quantify different aspects of vergence or, alternatively, results of one or both tests in this study may be unreliable