Trial-by-trial fluctuations in the event-related electroencephalogram reflect dynamic changes in the degree of surprise
Trial-by-trial fluctuations in the event-related electroencephalogram reflect dynamic changes in the degree of surprise
The P300 component of the human event-related brain potential has often been linked to the processing of rare, surprising events. However, the formal computational processes underlying the generation of the P300 are not well known. Here, we formulate a simple model of trial-by-trial learning of stimulus probabilities based on Information Theory. Specifically, we modeled the surprise associated with the occurrence of a visual stimulus to provide a formal quantification of the "subjective probability" associated with an event. Subjects performed a choice reaction time task, while we recorded their brain responses using electroencephalography (EEG). In each of 12 blocks, the probabilities of stimulus occurrence were changed, thereby creating sequences of trials with low, medium, and high predictability. Trial-by-trial variations in the P300 component were best explained by a model of stimulus-bound surprise. This model accounted for the data better than a categorical model that parametrically encoded the stimulus identity, or an alternative model of surprise based on the Kullback-Leibler divergence. The present data demonstrate that trial-by-trial changes in P300 can be explained by predictions made by an ideal observer keeping track of the probabilities of possible events. This provides evidence for theories proposing a direct link between the P300 component and the processing of surprising events. Furthermore, this study demonstrates how model-based analyses can be used to explain significant proportions of the trial-by-trial changes in human event-related EEG responses
electroencephalography, learning, reaction time, brain
12539-12545
Mars, Rogier B.
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Debener, Stefan
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Gladwin, Thomas E.
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Harrison, Lee M.
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Haggard, Patrick
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Rothwell, John C.
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Bestmann, Sven
35207f43-dd6d-4ac5-a14f-a1d3256a16d3
2008
Mars, Rogier B.
299e2706-0e30-4e02-a7ed-bb6b0e0ffc22
Debener, Stefan
e6bf9143-09a8-45c0-8536-3564885375d4
Gladwin, Thomas E.
7bc8fe1a-4ec1-4e3e-a832-5cf3bdf6f573
Harrison, Lee M.
563fe644-9f46-4455-a764-518b2f95b5cc
Haggard, Patrick
b68055e6-ac9c-4422-9d2b-32d7d851fb7a
Rothwell, John C.
fd939bef-ef9b-44c9-8553-03b7e8a6b399
Bestmann, Sven
35207f43-dd6d-4ac5-a14f-a1d3256a16d3
Mars, Rogier B., Debener, Stefan, Gladwin, Thomas E., Harrison, Lee M., Haggard, Patrick, Rothwell, John C. and Bestmann, Sven
(2008)
Trial-by-trial fluctuations in the event-related electroencephalogram reflect dynamic changes in the degree of surprise.
Journal of Neuroscience, 28 (47), .
(doi:10.1523/JNEUROSCI.2925-08.2008).
Abstract
The P300 component of the human event-related brain potential has often been linked to the processing of rare, surprising events. However, the formal computational processes underlying the generation of the P300 are not well known. Here, we formulate a simple model of trial-by-trial learning of stimulus probabilities based on Information Theory. Specifically, we modeled the surprise associated with the occurrence of a visual stimulus to provide a formal quantification of the "subjective probability" associated with an event. Subjects performed a choice reaction time task, while we recorded their brain responses using electroencephalography (EEG). In each of 12 blocks, the probabilities of stimulus occurrence were changed, thereby creating sequences of trials with low, medium, and high predictability. Trial-by-trial variations in the P300 component were best explained by a model of stimulus-bound surprise. This model accounted for the data better than a categorical model that parametrically encoded the stimulus identity, or an alternative model of surprise based on the Kullback-Leibler divergence. The present data demonstrate that trial-by-trial changes in P300 can be explained by predictions made by an ideal observer keeping track of the probabilities of possible events. This provides evidence for theories proposing a direct link between the P300 component and the processing of surprising events. Furthermore, this study demonstrates how model-based analyses can be used to explain significant proportions of the trial-by-trial changes in human event-related EEG responses
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Published date: 2008
Keywords:
electroencephalography, learning, reaction time, brain
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Local EPrints ID: 70201
URI: http://eprints.soton.ac.uk/id/eprint/70201
ISSN: 0270-6474
PURE UUID: 622d0fd1-da6c-4ff7-b25d-8777cbfb78a1
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Date deposited: 28 Jan 2010
Last modified: 13 Mar 2024 19:57
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Contributors
Author:
Rogier B. Mars
Author:
Stefan Debener
Author:
Thomas E. Gladwin
Author:
Lee M. Harrison
Author:
Patrick Haggard
Author:
John C. Rothwell
Author:
Sven Bestmann
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