Bayesian optimal use of visual feature cues in visual attention
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1
University of Dundee , United Kingdom
How do we utilise visual cues in order to guide attention? While popular accounts (feature integration theory, guided search, and low-level salience) vary in a number of ways, they all assume a max-of-sensory-outputs selection mechanism. Such a max-observer can be expected to be optimal in contrived situations where targets and distracters have equal variability along some sensory dimension such as luminance, contrast or orientation. However it may be overoptimistic to assume that this equal variability is always the case in natural visual environments. So the question arises, is the attention system suboptimal by making the assumption of equal variance about target and distracter features? If so, then current popular models are adequate and notions of optimality in attentional phenomena would be in doubt. If not, and human performance is close to Bayesian optimal, then existing models would be challenged. To test this, a simple psychophysical present/absent detection task was used, with Gabor target and distracter stimuli. By adding additional orientation uncertainty to distracters, the equal variance assumption is violated. Will subjects perform optimally and exceed the performance of a max-observer? Experiments resulted in receiver-operating-characteristic (ROC) curves for individuals and were compared to predictions of the max-observer and a Bayesian optimal observer. Predictions were obtained through Monte Carlo simulations. A side-by-side comparison of models is fair, as both have a single parameter (which is estimated from the data) corresponding to degree of internal orientation uncertainty. The max-observer has a decision criteria based upon a sensory dimension, while the Bayesian optimal observer has a decision criteria based on the posterior probability of target presence. Under conditions which violate the strict equal variance assumptions, it was found that human performance was not in fact suboptimal. Performance at the task greatly exceeded the highest possible performance obtainable by the max-observer, providing strong evidence against the use of a max-of-sensory-outputs selection mechanism. The fact that the decision criteria is based upon a sensory dimension greatly limits the possible performance of a max-observer. In contrast, the Bayesian optimal observer provided very good fits to human performance (and ROC curve) data. Human performance at this task is not explicable by a max-of-sensory-outputs selection mechanism, which is problematic for feature integration theory, guided search, and low-level salience. These results provide a strong proof-of-concept, in a controlled psychophysical setting, that visual features are evaluated for the posterior probability of target presence. Thus this work adds to the small but growing research that suggests attentional phenomena are by-products of near optimal inference processes.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session I
Citation:
Vincent
B
(2010). Bayesian optimal use of visual feature cues in visual attention.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00018
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Received:
17 Feb 2010;
Published Online:
17 Feb 2010.
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Correspondence:
Benjamin Vincent, University of Dundee, Dundee, United Kingdom, b.t.vincent@dundee.ac.uk