Learning to filter out visual distractors
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1
MR Research Center, Szentágothai J. Knowledge Center - Semmelweis University, Hungary
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2
Pázmány Péter Catholic University, Hungary
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3
Department of Psychology, University of California, United States
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4
Pázmány Péter Catholic University - Semmelweis University, Hungary
When learning to master a visual task in a cluttered natural environment, it is important to optimize the processing of task-relevant information and to efficiently filter out distractors. However, the mechanisms that suppress task-irrelevant information are not well understood. Here we show that training leads to a selective increase in motion coherence detection thresholds for task-irrelevant motion directions that interfered with the processing of task-relevant directions during training. Furthermore, using functional magnetic resonance imaging (fMRI) we found that training attenuated neural responses associated with the task-irrelevant direction compared to the task-relevant direction in the visual cortical areas involved in processing of visual motion (including are MT+). Furthermore, using EEG it was also found that learning reduces perceptual sensitivity to the distractors primarily by modulating the integration and evaluation of sensory evidence during decision making as opposed to attenuating the afferent gain of task-irrelevant visual information. These findings reveal that perceptual learning relies on learning to suppress and more efficiently filter out task-irrelevant visual information.
Conference:
12th Meeting of the Hungarian Neuroscience Society, Budapest, Hungary, 22 Jan - 24 Jan, 2009.
Presentation Type:
Poster Presentation
Topic:
Behavioural neuroscience
Citation:
Gal
V,
Kozak
LR,
Kóbor
I,
Bankó
É,
Serences
JT and
Vidnyanszky
Z
(2009). Learning to filter out visual distractors.
Front. Syst. Neurosci.
Conference Abstract:
12th Meeting of the Hungarian Neuroscience Society.
doi: 10.3389/conf.neuro.01.2009.04.089
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Received:
03 Mar 2009;
Published Online:
03 Mar 2009.
*
Correspondence:
Viktor Gal, MR Research Center, Szentágothai J. Knowledge Center - Semmelweis University, Budapest, Hungary, gviktor@digitus.itk.ppke.hu