Studies of coordination in rhythmic limb movement have established that certain phase relationships among cycling limbs are preferred, i.e. patterns such as synchrony and anti-synchrony are produced more often and more reliably than arbitrary relations. A speech experiment in which subjects attempt to place a phrase-medial stress at a range of phases within an overall phrase repetition cycle is presented, and analogous results are found. Certain phase relations occur more frequently and exhibit greater stability than others. To a first approximation, these phases are predicted by a simple harmonic model. The observed commonalities between limb movements and spoken rhythm support Leishley's conjecture that a common control strategy underlies the coordination of all rhythmic activity.

We provide a conceptual framework for understanding similarities and differences among various schemes of compositional representation, emphasizing problems that arise in modelling aspects of human language. W e propose six abstract dimensions that suggest a space of possible compositional schemes. Temporality turns out to play a key role in defining several of these dimensions. From studying how schemes fall into this space, it is apparent that there is no single crucial difference between AI and connectionist approaches to representation. Large regions of the space of composition^ schemes remain unexplored, such as the entire class of active, dynamic models that do composition in time. These models offer the possibility of parsing real-time input into useful segments, and thus potentially into linguistic units like words and phrases.

We propose a fully recurrent neural network to model low-level auditory memory in a task to discriminate intensities of sequentially presented tones across a range of varying interstimulus intervals. In this model, memory represents a sensory-trace of the stimulus and takes the form of slow relaxation of a number of units to a globally attractive equilibrium value near zero. The same-different judgment is based on a derivative of the output of the dynamic memory. Gaussian noise added to unit activations was found to improve the resilience of stored information although at the cost of decreased sensitivity. The model exhibits many qualitative properties of human performance on a roving-standard intensity discrimination task.