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Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks.

08:00 EDT 10th April 2019 | BioPortfolio

Summary of "Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks."

Surviving in a continuously changing environment has made the human brain to be able to actively predict the future state of its surroundings. Oddball tasks are specific types of situations during which this nature of the human brain is studied. There are detailed mathematical models which were constructed to explain the brain's perception in these tasks. They considered a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - making the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the aim of the earlier works was to suggest an encoding model, we investigated the reverse problem: if the stimuli's surprise is as the cause of the observer's surprise, it must be possible to decode the surprise of each stimulus, for every single subject, given only their neural responses, i.e. tell how unexpected a specific stimulus has been for them. Employing machine learning tools, we developed a surprise decoding model for binary oddball tasks. We constructed our model using the ideal observer proposed by Meyniel et al., in 2016, and applied it to two datasets, one with visual, and the other with both auditory and visual stimuli. We demonstrated that our decoding model performs very well decently for both of the sensory modalities.

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This article was published in the following journal.

Name: NeuroImage
ISSN: 1095-9572
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