Abstract
We learn to recognize objects in the world in environments whose attentional demands vary greatly. How does such learning depend upon task-dependent attentional demands? Object recognition needs to be tolerant, or invariant, with respect to position, size, and object view changes. In monkeys and humans, a key area for recognition is the anterior inferotemporal cortex (ITa). Recent neurophysiological data show that ITa cells with high object selectivity often have low position tolerance. We propose a neural model whose cells learn to simulate this tradeoff, as well as ITa responses to image morphs, while explaining how invariant recognition properties may arise gradually due to processes across multiple cortical areas, including the cortical magnification factor, multiple receptive field sizes, and top-down attentive matching and learning properties that may be tuned by task requirements to attend to either concrete or abstract visual features. The model predicts that data from the tradeoff and image morph tasks emerge from different task-dependent levels of attentive vigilance in the animals performing them. Computer simulations predict how receptive field properties would change under different task-sensitive vigilance levels. The model also predicts how vigilance may be controlled by mismatches between top-down learned expectations and bottom-up perceptual inputs, leading to acetylcholine release in neocortical circuits and an increase in vigilance. These results emphasize the importance of top-down attentional mechanisms in object learning and recognition, and of the need to carefully monitor task demands in studies of perceptual and cognitive processing.
Supported in part by CELEST, an NSF Science of Learning Center, and by the SyNAPSE program of the Defense Advanced Research Projects Agency.