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Bayesian Models of Dynamic Attentional Selection

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Citation

Yu, A., Dayan, P., & Cohen, J. (2007). Bayesian Models of Dynamic Attentional Selection. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2007), Salt Lake City, UT, USA.


Cite as: https://hdl.handle.net/21.11116/0000-0004-42E3-1
Abstract
Selection amongst potentially conflicting inputs is a critical facet of many decision making tasks. According
to Bayesian optimality principles, the attentional suppression of irrelevant inputs and inappropriate responses
should reflect implicitly encoded prior assumptions about the statistical structure of sensory inputs.
This argument has particularly interesting ramifications for experimental tasks that violate the statistics of
the natural environment. Here, we provide a Bayesian formulation for dynamic attentional selection that elucidates
this problem, and consider the consequences for behavioral performance. We illustrate these issues
using the Eriksen flanker task, a classical paradigm that explores the effects of competing sensory inputs on
response tendencies. In this task, the presence of conflicting flanker stimuli has been shown to interfere with
the processing of a central target stimulus, especially on short reaction-time trials. We show how two distinct
Bayesian inferential principles can explain the detrimental effects of competition in speeded decisions. The
first rests on the notion that the brain may be wired, through either evolutionary adaptation or developmental
learning, to encode a compatibility bias: that is, the prior belief that spatially proximate items in a visual
scene tend to be featurally similar. The second emphasizes the spatial uncertainty induced by overlapping
receptive fields of visual sensory processes, which can give rise to a confusion of stimulus identity early
on during visual presentation. We also elaborate a simpler, approximate, inference model that formalizes
previous work suggesting that different neural structures are involved in the monitoring of conflict and the
detection of unexpected events. Finally, we suggest explicit experimental tests to resolve the remaining conflicts between the models.