A Self-Organizing Neural Model for Motor Equivalent Phoneme Production

This paper describes a model of speech production called DIVA that highlights issues of self-organization and motor equivalent production of phonological units. The model uses a circular reaction strategy to learn two mappings between three levels of representation. Data on the plasticity of phonemic perceptual boundaries motivates a learned mapping between phoneme representations and vocal tract variables. A second mapping between vocal tract variables and articulator movements is also learned. To achieve the flexible control made possible by the redundancy of this mapping, desired directions in vocal tract configuration space are mapped into articulator velocity commands. Because each vocal tract direction cell learns to activate several articulator velocities during babbling, the model provides a natural account of the formation of coordinative structures. Model simulations show automatic compensation for unexpected constraints despite no previous experience or learning under these constraints.National Science Foundation (IRI-87-16960, IRI-90-24877

Neural Network Modeling of Sensory-Motor Control in Animals

National Science Foundation (IRI 90-24877, IRI 87-16960); Air Force Office of Scientific Research (F49620-92-J-0499); Office of Naval Research (N00014-92-J-1309

Acoustic Space Movement Planning in a Neural Model of Motor Equivalent Vowel Production

Recent evidence suggests that speakers utilize an acoustic-like reference frame for the planning of speech movements. DIVA, a computational model of speech acquisition and motor equivalent speech production, has previously been shown to provide explanations for a wide range of speech production data using a constriction-based reference frame for movement planning. This paper extends the previous work by investigating an acoustic-like planning frame in the DIVA modeling framework. During a babbling phase, the model self-organizes targets in the planning space for each of ten vowels and learns a mapping from desired movement directions in this planning space into appropriate articulator velocities. Simulation results verify that after babbling the model is capable of producing easily recognizable vowel sounds using an acoustic planning space consisting of the formants F1 and F2. The model successfully reaches all vowel targets from any initial vocal tract configuration, even in the presence of constraints such as a blocked jaw.Office of Naval Research (N00014-91-J-4100, N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0499

Articulating: the neural mechanisms of speech production

Speech production is a highly complex sensorimotor task involving tightly coordinated processing across large expanses of the cerebral cortex. Historically, the study of the neural underpinnings of speech suffered from the lack of an animal model. The development of non-invasive structural and functional neuroimaging techniques in the late 20th century has dramatically improved our understanding of the speech network. Techniques for measuring regional cerebral blood flow have illuminated the neural regions involved in various aspects of speech, including feedforward and feedback control mechanisms. In parallel, we have designed, experimentally tested, and refined a neural network model detailing the neural computations performed by specific neuroanatomical regions during speech. Computer simulations of the model account for a wide range of experimental findings, including data on articulatory kinematics and brain activity during normal and perturbed speech. Furthermore, the model is being used to investigate a wide range of communication disorders.R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; R01 DC016270 - NIDCD NIH HHSAccepted manuscrip

The global Cretaceous-Tertiary fire: Biomass or fossil carbon

The global soot layer at the K-T boundary indicates a major fire triggered by meteorite impact. However, it is not clear whether the principal fuel was biomass or fossil carbon. Forests are favored by delta value of C-13, which is close to the average for trees, but the total amount of elemental C is approximately 10 percent of the present living carbon, and thus requires very efficient conversion to soot. The PAH was analyzed at Woodside Creek, in the hope of finding a diagnostic molecular marker. A promising candidate is 1-methyl-7-isopropyl phenanthrene (retene,), which is probably derived by low temperature degradation of abietic acid. Unlike other PAH that form by pyrosynthesis at higher temperatures, retene has retained the characteristic side chains of its parent molecule. A total of 11 PAH compounds were identified in the boundary clay. Retene is present in substantial abundance. The identification was confirmed by analysis of a retene standard. Retene is characteristic of the combustion of resinous higher plants. Its formation depends on both temperature and oxygen access, and is apparently highest in oxygen-poor fires. Such fires would also produce soot more efficiently which may explain the high soot abundance. The relatively high level of coronene is not typical of a wood combustion source, however, though it can be produced during high temperature pyrolysis of methane, and presumably other H, C-containing materials. This would require large, hot, low O2 zones, which may occur only in very large fires. The presence of retene indicates that biomass was a significant fuel source for the soot at the Cretaceous-Tertiary boundary. The total amount of elemental C produced requires a greater than 3 percent soot yield, which is higher than typically observed for wildfires. However, retene and presumably coronene imply limited access of O2 and hence high soot yield

A Self-Organizing Neural Network Model for Redundant Sensory-Motor Control, Motor Equivalence, and Tool Use

A neural network is introduced which provides a solution of the classical motor equivalence problem, whereby many different joint configurations of a redundant manipulator can all be used to realize a desired trajectory in 3-D space. To do this, the network self-organizes a mapping from motion directions in 3-D space to velocity commands in joint space. Computer simulations demonstrate that, without any additional learning, the network can generate accurate movement commands that compensate for variable tool lengths, clamping of joints, distortions of visual input by a prism, and unexpected limb perturbations. Blind reaches have also been simulated.National Science Foundation (IRI-87-16960, IRI-90-24877

Neural Modeling and Imaging of the Cortical Interactions Underlying Syllable Production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.National Institute on Deafness and Other Communication Disorders (R01 DC02852, RO1 DC01925

Changes in the McGurk Effect across Phonetic Contexts. I. Fusions

The McGurk effect has generally been studied within a limited range of phonetic contexts. With the goal of characterizing the McGurk effect through a wider range of contexts, a parametric investigation across three different vowel contexts, /i/, /α/, and /u/, and two different syllable types, consonant-vowel (CV) and vowel-consonant (VC), was conducted. This paper discusses context-dependent changes found specifically in the McGurk fusion phenomenon (Part II addresses changes found in combination percepts). After normalizing for differences in the magnitude of the McGurk effect in different contexts, a large qualitative change in the effect across vowel contexts became apparent. In particular, the frequency of illusory /g/ percepts increased relative to the frequency of illusory /d/ percepts as vowel context was shifted from /i/ to /α/ to /u/. This trend was seen in both syllable sets, and held regardless of whether the visual stimulus used was a /g/ or /d/ articulation. This qualitative change in the McGurk fusion effect across vowel environments corresponded systematically with changes in the typical second formant frequency patterns of the syllables presented. The findings are therefore consistent with sensory-based theories of speech perception which emphasize the importance of second formant patterns as cues in multimodal speech perception.National Institue on Deafness and other Communication Disorders (R29 02852); Alfred P. Sloan Foundation and National Institute on Deafness and other Communication Disorders (R29 02852

Involvement of the cortico-basal ganglia-thalamocortical loop in developmental stuttering

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.R01 DC007683 - NIDCD NIH HHS; R01 DC011277 - NIDCD NIH HHSPublished versio

A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm

This paper describes a self-organizing neural model for eye-hand coordination. Called the DIRECT model, it embodies a solution of the classical motor equivalence problem. Motor equivalence computations allow humans and other animals to flexibly employ an arm with more degrees of freedom than the space in which it moves to carry out spatially defined tasks under conditions that may require novel joint configurations. During a motor babbling phase, the model endogenously generates movement commands that activate the correlated visual, spatial, and motor information that are used to learn its internal coordinate transformations. After learning occurs, the model is capable of controlling reaching movements of the arm to prescribed spatial targets using many different combinations of joints. When allowed visual feedback, the model can automatically perform, without additional learning, reaches with tools of variable lengths, with clamped joints, with distortions of visual input by a prism, and with unexpected perturbations. These compensatory computations occur within a single accurate reaching movement. No corrective movements are needed. Blind reaches using internal feedback have also been simulated. The model achieves its competence by transforming visual information about target position and end effector position in 3-D space into a body-centered spatial representation of the direction in 3-D space that the end effector must move to contact the target. The spatial direction vector is adaptively transformed into a motor direction vector, which represents the joint rotations that move the end effector in the desired spatial direction from the present arm configuration. Properties of the model are compared with psychophysical data on human reaching movements, neurophysiological data on the tuning curves of neurons in the monkey motor cortex, and alternative models of movement control.National Science Foundation (IRI 90-24877); Office of Naval Research (N00014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI 90-24877