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HAMLET Proseminar: Greg Zelinsky, Psychology at Stony Brook University
April 19, 2019 @ 3:45 pm - 5:00 pm
3:45 p.m. – 5 p.m. Berkowitz room (338 Psychology Building)
Predicting goal-directed attention control: A tale of two brain-inspired computational models
The ability to control the allocation of attention underlies all visuospatial goal-directed behavior; there would be no sense in having a goal if there was no way to use it to select visual inputs. He will summarize two recent efforts to computationally model this core mechanism of cognitive control. These models are similar in that both are image computable and that the design of each is inspired by mechanisms used by the attention-control system in the primate brain. But the models also differ in their type of brain inspiration and in their neurocomputational approach. One model adopts a computationally narrow approach but is closely parametrized by known neural constraints (MASC, a Model of Attention in the Superior Colliculus). The other model adopts a more powerful deep neural network approach but is less directly brain-inspired (Deep-BCN, a deep network implementation of the widely accepted biased-competition theory of attention control). Together they are intended to span a continuum of brain-inspired model design, ranging from relatively local neural circuits at one end to the broader network of brain areas comprising the primate attention control system at the other.