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

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

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

Sensory feedback control in speech: Neural circuits and individual differences

Speech production involves a combination of feedforward and sensory feedback-based control mechanisms. The latter have been characterized in experiments involving real-time perturbations during speech. Unexpected perturbations of auditory feedback result in corrective motor responses with a minimum latency of approximately 100 ms after perturbation onset. These responses have been shown for both pitch and formant frequency perturbations, and the responsible neural circuitry includes portions of the superior temporal gyrus and ventral premotor cortex (vPMC). Corrective responses to somatosensory perturbations (such as a downward force applied to the jaw) occur approximately 50 ms after perturbation onset and are mediated by ventral somatosensory cortex and vPMC. The degree to which an individual weights auditory versus somatosensory feedback varies substantially. Such differences rely in part on differences in sensory acuity, e.g., a speaker with relatively poor hearing is likely to rely more heavily on somatosensory feedback control mechanisms than auditory feedback control mechanisms. Additionally, reliance on feedback control may be modulated to compensate for impairments in the feedforward system for speech, e.g., adults who stutter show a reduced reliance on auditory feedback control compared to fluent speakers, perhaps because auditory feedback can have a deleterious effect on speech initiation in stuttering

Exploring auditory-motor interactions in normal and disordered speech

Auditory feedback plays an important role in speech motor learning and in the online correction of speech movements. Speakers can detect and correct auditory feedback errors at the segmental and suprasegmental levels during ongoing speech. The frontal brain regions that contribute to these corrective movements have also been shown to be more active during speech in persons who stutter (PWS) compared to fluent speakers. Further, various types of altered auditory feedback can temporarily improve the fluency of PWS, suggesting that atypical auditory-motor interactions during speech may contribute to stuttering disfluencies. To investigate this possibility, we have developed and improved Audapter, a software that enables configurable dynamic perturbation of the spatial and temporal content of the speech auditory signal in real time. Using Audapter, we have measured the compensatory responses of PWS to static and dynamic perturbations of the formant content of auditory feedback and compared these responses with those from matched fluent controls. Our findings indicate deficient utilization of auditory feedback by PWS for short-latency online control of the spatial and temporal parameters of articulation during vowel production and during running speech. These findings provide further evidence that stuttering is associated with aberrant auditory-motor integration during speech.Published versio

Feedforward and feedback control in apraxia of speech: effects of noise masking on vowel production

PURPOSE: This study was designed to test two hypotheses about apraxia of speech (AOS) derived from the Directions Into Velocities of Articulators (DIVA) model (Guenther et al., 2006): the feedforward system deficit hypothesis and the feedback system deficit hypothesis. METHOD: The authors used noise masking to minimize auditory feedback during speech. Six speakers with AOS and aphasia, 4 with aphasia without AOS, and 2 groups of speakers without impairment (younger and older adults) participated. Acoustic measures of vowel contrast, variability, and duration were analyzed. RESULTS: Younger, but not older, speakers without impairment showed significantly reduced vowel contrast with noise masking. Relative to older controls, the AOS group showed longer vowel durations overall (regardless of masking condition) and a greater reduction in vowel contrast under masking conditions. There were no significant differences in variability. Three of the 6 speakers with AOS demonstrated the group pattern. Speakers with aphasia without AOS did not differ from controls in contrast, duration, or variability. CONCLUSION: The greater reduction in vowel contrast with masking noise for the AOS group is consistent with the feedforward system deficit hypothesis but not with the feedback system deficit hypothesis; however, effects were small and not present in all individual speakers with AOS. Theoretical implications and alternative interpretations of these findings are discussed.R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HH

A Self-Organizing Neural Network for Learning a Body-Centered Invariant Representation of 3-D Target Position

This paper describes a self-organizing neural network that rapidly learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets (Bullock, Grossberg, and Guenther, 1993).National Science Foundation (IRI-87-16960, IRI-90-24877); Air Force Office of Scientific Research (F49620-92-J-0499