October 2019 UWIN Seminar speakers Anitha Pasupathy and Sam Burden

The first UWIN seminar for the 2019-2020 school year features a pair of short talks by Anitha Pasupathy and Sam Burden. The seminar is on Wednesday, October 9, 2019 at 3:30pm in Husky Union Builiding (HUB) 337. Refreshments will be served prior to the talks.

“Mid-level cortical representation for object recognition”

Anitha Pasupathy, Professor, Department of Biological Structure, University of Washington

“Sensorimotor games: Human/machine collaborative learning and control”

Sam Burden, Assistant Professor, Department of Electrical & Computer Engineering, University of Washington


“Mid-level cortical representation for object recognition” (Anitha Pasupathy)

Recognizing a myriad visual objects rapidly is a hallmark of the primate visual system. Traditional theories of object recognition have focused on how critical form features, e.g. the orientation of edges, may be extracted in early visual cortex and utilized to recognize objects. An alternative view argues that much of early and mid-level visual processing focuses on encoding surface characteristics, e.g. texture. Neurophysiological evidence from primate area V4 supports a third alternative—the joint, but independent, encoding of form and texture—that would be advantageous for segmenting objects from the background in natural scenes and for object recognition that’s independent of surface texture. Future studies that leverage deep convolutional network models can advance our insights into how such a joint representation of form and surface properties might emerge in visual cortex.

“Sensorimotor games: Human/machine collaborative learning and control” (Sam Burden)

While interacting with a machine, humans will naturally formulate beliefs about the machine’s behavior, and these beliefs will affect the interaction. Since humans and machines have imperfect information about each other and their environment, a natural model for their interaction is a game. Such games have been investigated from the perspective of economic game theory, and some results on discrete decision-making have been translated to the neuromechanical setting, but there is little work on continuous sensorimotor games that arise when humans interact in a dynamic closed loop with machines. We study these games both theoretically and experimentally, deriving predictive models for steady-state (i.e. equilibrium) and transient (i.e. learning) behaviors of humans interacting with other agents (humans and machines).