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February 10, 2021 /--- -- Russian researchers from HSE University have studied a hypothesis about the ability of visual systems to automatically classify objects and confirmed it with experimental results.
the paper was published in the journal Scientific Reports.
human beings get a lot of information from the environment through their vision.
, we face all kinds of visual stimuli.
, information processing requires cognitive resources.
like a computer processor, the human brain has limited ability to process and store data in its memory.
hypothesis that the visual system somehow "reduces the resolution of the file" to avoid overloading.
result of this "compression", the visual system does not analyze the objects in detail, but categorizes them by simple general attributes, such as size.
, such "raw data" could be used for more comprehensive analysis.
(Photo Source: www.pixabay.com) researchers try to answer the following question: Can visual systems automatically classify objects? In their study, the researchers tried to determine the working conditions for automatic classification.
the visual dispronected negative (vMMN) weight measured using electro-encephalograms (EEGs) as a marker of automatic sensory recognition.
vMMN showed differences between the brain's responses to standard stimuli (frequent) or abnormal stimuli (rare).
vMMN proves that the visual system notices the difference between stimuli and, importantly, it notices the difference without notice.
"We are very interested in the ability of human vision systems to classify a large number of objects.
, for example, when people look at an apple tree, they immediately distinguish the apple from the leaves.
information is automatically generated based on differences between objects," said Vladislav Khvostov, a junior researcher at the HSE Cognitive Research Laboratory at the School of Psychology.
" (Bioon.com) Source: Test show how our visual system avoids overloading Original source: Vladislav A. Khvostov et al, Spatially intermixed objects of different categories are parsed automatic, Scientific Reports (2021). DOI: 10.1038/s41598-020-79828-4
