This paper concerns how motor actions are neurally represented and coded. Action planning and motor preparation can be studied using a specific type of representational activity, motor imagery. A close functional equivalence between motor imagery and motor preparation is suggested by the positive effects of imagining movements on motor learning, the similarity between the neural structures involved, and the similar physiological correlates observed in both imaging and preparing. The content of motor representations can be inferred from motor images at a (...) macroscopic level, based on global aspects of the action and the motor rules and constraints which predict the spatial path and kinematics of movements. A more microscopic neural account calls for a representation of object-oriented action. Object attributes are processed in different neural pathways depending on the kind of task the subject is performing. During object-oriented action, a pragmatic representation is activated in which object affordances are transformed into specific motor schemas. Animal as well as human clinical data implicate the posterior parietal and premotor cortical areas in schema instantiation. A mechanism is proposed that is able to encode the desired goal of the action and is applicable to different levels of representational organization. (shrink)

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)

Despite ample evidence that speech production is associated with extensive trial-to-trial variability, it remains unclear whether this variability represents merely unwanted system noise or an actively regulated mechanism that is fundamental for maintaining and adapting accurate speech movements. Recent work on upper limb movements suggest that inter-trial variability may be not only actively regulated based on sensory feedback, but also provide a type of workspace exploration that facilitates sensorimotor learning. We therefore investigated whether experimentally reducing or magnifying inter-trial formant variability (...) in the real-time auditory feedback during speech production leads to adjustments in formant production variability that compensate for the manipulation, changes the temporal structure of formant adjustments across productions, and enhances learning in a subsequent adaptation task in which a predictable formant-shift perturbation is applied to the feedback signal. Results show that subjects gradually increased formant variability in their productions when hearing auditory feedback with reduced variability, but subsequent formant-shift adaptation was not affected by either reducing or magnifying the perceived variability. Thus, findings provide evidence for speakers’ active control of inter-trial formant variability based on auditory feedback from previous trials, but–at least for the current short-term experimental manipulation of feedback variability–not for a role of this variability regulation mechanism in subsequent auditory-motor learning. (shrink)

To controlforceaccurately under a wide range of behavioral conditions, the central nervous system would either require a detailed, continuously updated representation of the state of each muscle (and the load against which each is acting) or else force feedback with sufficient gain to cope with variations in the properties of the muscles and loads. The evidence for force feedback with adequate gain or for an appropriate central representation is not sufficient to conclude that force is the major controlled variable in (...) normal limb movements.Morton's hypothesis, thatlengthis controlled by a follow-up servo, has a number of difficulties related to the delays, gains, variability, and specificity in feedback pathways comprising potential servo loops. However, experimental evidence is consistent with these pathways providing servo assistance for some movements produced by coactivation of α- and static γ-motoneurons. Dynamic γ-motoneurons may provide an additional input for adaptive control of different types of movements.The idea that feedback is used to compensate for changes in musclestiffnesshas received experimental support under static postural conditions. However, reflexes tend to increase rather than decrease the range of variation in muscle stiffness during some cyclic movements. Theoretical problems associated with the regulation of stiffness are also discussed. The possibilities of separate control systems forvelocityorviscosityare considered, but the evidence is either negative or lacking. I conclude that different physical variables can be controlled depending on the type of limb movement required. The concept of stiffness regulation is also useful under some conditions, but should probably be extended to the regulation of the visco-elastic properties (i.e., the mechanical impedance) of a muscle or joint. (shrink)