Wednesday, May 11, 2011

Human Visual Searches Are Nearly Optimal in Efficiency

The ultimate idealist would claim that all of nature and the universe exist inside the mind of God. If we change our mind, we change the world in which we exist.

But a realist says that reality exists whether we recognize it or not. A tree falls in the forest and makes noise whether we are there to hear it or not. The stars come out every night whether we understand them or not. Further, we are designed to understand and comprehend this outside reality which we also inhabit. A strong indicator that the realists have the correct approach rests with neurological findings about the way humans think. Our visual searches are nearly optimal, as if we were designed to see through camouflage to what is actually there.  -- the blog author

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Nature Neuroscience

Behavior and neural basis of near-optimal visual search

Wei Ji Ma, Vidhya Navalpakkam, Jeffrey M Beck, Ronald van den Berg & Alexandre Pouget

Abstract:
The ability to search efficiently for a target in a cluttered environment is one of the most remarkable functions of the nervous system. This task is difficult under natural circumstances, as the reliability of sensory information can vary greatly across space and time and is typically a priori unknown to the observer. In contrast, visual-search experiments commonly use stimuli of equal and known reliability. In a target detection task, we randomly assigned high or low reliability to each item on a trial-by-trial basis. An optimal observer would weight the observations by their trial-to-trial reliability and combine them using a specific nonlinear integration rule. We found that humans were near-optimal, regardless of whether distractors were homogeneous or heterogeneous and whether reliability was manipulated through contrast or shape. We present a neural-network implementation of near-optimal visual search based on probabilistic population coding. The network matched human performance.

Full article available on line at:
http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.2814.html
| May 8, 2011

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