Modeling the Modulatory Effect of Attention on Human Spatial Vision

Abstract

We present new simulation results, in which a computational model of interacting visual neurons simultaneously predicts the modula(cid:173) tion of spatial vision thresholds by focal visual attention, for five dual-task human psychophysics experiments. This new study com(cid:173) plements our previous findings that attention activates a winner(cid:173) take-all competition among early visual neurons within one cortical hypercolumn. This "intensified competition" hypothesis assumed that attention equally affects all neurons, and yielded two single(cid:173) unit predictions: an increase in gain and a sharpening of tuning with attention. While both effects have been separately observed in electrophysiology, no single-unit study has yet shown them si(cid:173) multaneously. Hence, we here explore whether our model could still predict our data if attention might only modulate neuronal gain, but do so non-uniformly across neurons and tasks. Specifically, we investigate whether modulating the gain of only the neurons that are loudest, best-tuned, or most informative about the stimulus, or of all neurons equally but in a task-dependent manner, may ac(cid:173) count for the data. We find that none of these hypotheses yields predictions as plausible as the intensified competition hypothesis, hence providing additional support for our original findings.