Modeling the development of pronunciation in infant speech acquisition.

Pronunciation is an important part of speech acquisition, but little attention has been given to the mechanism or mechanisms by which it develops. Speech sound qualities, for example, have just been assumed to develop by simple imitation. In most accounts this is then assumed to be by acoustic matching, with the infant comparing his output to that of his caregiver. There are theoretical and empirical problems with both of these assumptions, and we present a computational model- Elija-that does not learn to pronounce speech sounds this way. Elija starts by exploring the sound making capabilities of his vocal apparatus. Then he uses the natural responses he gets from a caregiver to learn equivalence relations between his vocal actions and his caregiver's speech. We show that Elija progresses from a babbling stage to learning the names of objects. This demonstrates the viability of a non-imitative mechanism in learning to pronounce.

Thermally-induced change in the relaxation behavior of skin tissue.

Skin biothermomechanics is highly interdisciplinary, involving bioheat transfer, burn damage, biomechanics, and physiology. Characterization of the thermomechanical behavior of skin tissue is of great importance and can contribute to a variety of medical applications. However, few quantitative studies have been conducted on the thermally-dependent mechanical properties of skin tissue. The aim of the present study is to experimentally examine the thermally-induced change in the relaxation behavior of skin tissue in both hyperthermal and hypothermic ranges. The results show that temperature has great influence on the stress-relaxation behavior of skin tissue under both hyperthermal and hypothermic temperatures; the quantitative relationship that has been found between temperature and the viscoelastic parameter (the elastic fraction or fractional energy dissipation) was temperature dependent, with greatest dissipation at high temperature levels.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty.

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.