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Single trial MEG recordings can predict the subject's ability to recognize a natural scene

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84172

Rieger,  JW
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84144

Plum,  F
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83926

Gegenfurtner,  KR
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Rieger, J., Plum, F., Gegenfurtner, K., Braun C, Preissl, H., & Bülthoff, H. (2000). Single trial MEG recordings can predict the subject's ability to recognize a natural scene. Poster presented at 3. Tübinger Wahrnehmungskonferenz (TWK 2000), Tübingen, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E557-A
Abstract
To test the predictive power of brain activity during encoding of a natural scene for subsequent recognition. Evoked magnetic fields (EMF) were recorded while subjects performed a delayed matchto- sample task with briefly presented and masked natural images. On each trial a digitized image was presented for 37ms on a projection screen and immediately followed by a pattern mask (1 sec). In the subsequent query phase, the subjects first had to judge whether they would be able to recognize the image. Then the target and three distractor images were shown, and the subjects had to indicate the target image (4AFC). During the first 600 ms of presentation EMF’s were recorded with a CTF 151 channels whole cortex system. We then used the MEG activity from individual trials to predict the subject’s behavioural response on that trial. To circumvent the effects of guessing, we used only those trials in which the subjects confidence judgement and recognition performance agreed. We tested two classifiers, the partial correlation (PC) of a trial with the mean vectors of correct and false trials and Support Vector (SV) classification which seeks a separating hyperplane by maximizing the distance to the nearest samples of each class. In 76.7 of the trials the subjects confidence judgement and response agreed. In 78 of these trials the subjects gave a correct response. With PC on average 75.3 of the correct and 76.3 of the false trials were correctly classified. But for only one subject best classification was obtained with PC. Classification by support vector machines were typically about 10-15 better than with the PC classifier. Average performance with the best support vector classifier was about 90.7 for the correct and about 92.8 for the false trials. It is possible, with about 90 accuracy, to predict in single trials subjects’ subsequent recognition performance from the early information in the evoked magnetic fields recorded while subjects were viewing the stimulus.