Posts tagged sensorimotor learning
Articles and news from the latest research reports.
Doing the math for how songbirds learn to sing
Scientists studying how songbirds stay on key have developed a statistical explanation for why some things are harder for the brain to learn than others.
“We’ve built the first mathematical model that uses a bird’s previous sensorimotor experience to predict its ability to learn,” says Emory biologist Samuel Sober. “We hope it will help us understand the math of learning in other species, including humans.”
Sober conducted the research with physiologist Michael Brainard of the University of California, San Francisco.
Their results, showing that adult birds correct small errors in their songs more rapidly and robustly than large errors, were published in the Proceedings of the National Academy of Sciences (PNAS).
Sober’s lab uses Bengalese finches as a model for researching the mechanisms of how the brain learns to correct vocal mistakes.
The researchers wanted to quantify the relationship between the size of a vocal error, and the probability of the brain making a sensorimotor correction. The experiments were conducted on adult Bengalese finches outfitted with light-weight, miniature headphones.
As a bird sang into a microphone, the researchers used sound-processing equipment to trick the bird into thinking it was making vocal mistakes, by changing the bird’s pitch and altering the way the bird heard itself, in real-time.
“When we made small pitch shifts, the birds learned really well and corrected their errors rapidly,” Sober says. “As we made the pitch shifts bigger, the birds learned less well, until at a certain pitch, they stopped learning.”
The researchers used the data to develop a statistical model for the size of a vocal error and whether a bird learns, including the cut-off point for learning from sensorimotor mistakes. They are now developing additional experiments to test and refine the model.
“We hope that our mathematical framework for how songbirds learn to sing could help in the development of human behavioral therapies for vocal rehabilitation, as well as increase our general understanding of how the brain learns,” Sober says.
Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making
According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject’s learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.