Learning to synchronize : midfrontal theta dynamics during rule switching. Verbeke, Pieter, Ergo, K., De Loof, E., & Verguts, T. JOURNAL OF NEUROSCIENCE, 41(7):1516–1528, 2021. ![Learning to synchronize : midfrontal theta dynamics during rule switching [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model uses bursts at theta frequency to flexibly bind appropriate task modules by synchrony. The 64-channel EEG signal was recorded while 27 human subjects (female: 21, male: 6) performed a probabilistic reversal learning task. In line with the Sync model, postfeedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via Akaike information criterion) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporoparietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful, for example, when multiple stimulus action mappings must be retained and used.
@article{8687585,
abstract = {{In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model uses bursts at theta frequency to flexibly bind appropriate task modules by synchrony. The 64-channel EEG signal was recorded while 27 human subjects (female: 21, male: 6) performed a probabilistic reversal learning task. In line with the Sync model, postfeedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via Akaike information criterion) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporoparietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful, for example, when multiple stimulus action mappings must be retained and used.}},
author = {{Verbeke, Pieter and Ergo, Kate and De Loof, Esther and Verguts, Tom}},
issn = {{0270-6474}},
journal = {{JOURNAL OF NEUROSCIENCE}},
keywords = {{General Neuroscience,cognitive control,midfrontal theta,neural synchrony,rule switching,ANTERIOR CINGULATE CORTEX,COGNITIVE CONTROL,COMPUTATIONAL MODEL,PHASE SYNCHRONY,FRONTAL THETA,EEG-DATA,COMMUNICATION,OSCILLATIONS,MECHANISM,ERROR}},
language = {{eng}},
number = {{7}},
pages = {{1516--1528}},
title = {{Learning to synchronize : midfrontal theta dynamics during rule switching}},
url = {{http://doi.org/10.1523/jneurosci.1874-20.2020}},
volume = {{41}},
year = {{2021}},
}
Downloads: 0
{"_id":"Aouyz5D9yABW6Jh3c","bibbaseid":"verbeke-ergo-deloof-verguts-learningtosynchronizemidfrontalthetadynamicsduringruleswitching-2021","author_short":["Verbeke, Pieter","Ergo, K.","De Loof, E.","Verguts, T."],"bibdata":{"bibtype":"article","type":"article","abstract":"In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model uses bursts at theta frequency to flexibly bind appropriate task modules by synchrony. The 64-channel EEG signal was recorded while 27 human subjects (female: 21, male: 6) performed a probabilistic reversal learning task. In line with the Sync model, postfeedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via Akaike information criterion) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporoparietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful, for example, when multiple stimulus action mappings must be retained and used.","author":[{"firstnames":[],"propositions":[],"lastnames":["Verbeke, Pieter"],"suffixes":[]},{"propositions":[],"lastnames":["Ergo"],"firstnames":["Kate"],"suffixes":[]},{"propositions":[],"lastnames":["De","Loof"],"firstnames":["Esther"],"suffixes":[]},{"propositions":[],"lastnames":["Verguts"],"firstnames":["Tom"],"suffixes":[]}],"issn":"0270-6474","journal":"JOURNAL OF NEUROSCIENCE","keywords":"General Neuroscience,cognitive control,midfrontal theta,neural synchrony,rule switching,ANTERIOR CINGULATE CORTEX,COGNITIVE CONTROL,COMPUTATIONAL MODEL,PHASE SYNCHRONY,FRONTAL THETA,EEG-DATA,COMMUNICATION,OSCILLATIONS,MECHANISM,ERROR","language":"eng","number":"7","pages":"1516–1528","title":"Learning to synchronize : midfrontal theta dynamics during rule switching","url":"http://doi.org/10.1523/jneurosci.1874-20.2020","volume":"41","year":"2021","bibtex":"@article{8687585,\n abstract = {{In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model uses bursts at theta frequency to flexibly bind appropriate task modules by synchrony. The 64-channel EEG signal was recorded while 27 human subjects (female: 21, male: 6) performed a probabilistic reversal learning task. In line with the Sync model, postfeedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via Akaike information criterion) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporoparietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful, for example, when multiple stimulus action mappings must be retained and used.}},\n author = {{Verbeke, Pieter and Ergo, Kate and De Loof, Esther and Verguts, Tom}},\n issn = {{0270-6474}},\n journal = {{JOURNAL OF NEUROSCIENCE}},\n keywords = {{General Neuroscience,cognitive control,midfrontal theta,neural synchrony,rule switching,ANTERIOR CINGULATE CORTEX,COGNITIVE CONTROL,COMPUTATIONAL MODEL,PHASE SYNCHRONY,FRONTAL THETA,EEG-DATA,COMMUNICATION,OSCILLATIONS,MECHANISM,ERROR}},\n language = {{eng}},\n number = {{7}},\n pages = {{1516--1528}},\n title = {{Learning to synchronize : midfrontal theta dynamics during rule switching}},\n url = {{http://doi.org/10.1523/jneurosci.1874-20.2020}},\n volume = {{41}},\n year = {{2021}},\n}\n\n","author_short":["Verbeke, Pieter","Ergo, K.","De Loof, E.","Verguts, T."],"key":"8687585","id":"8687585","bibbaseid":"verbeke-ergo-deloof-verguts-learningtosynchronizemidfrontalthetadynamicsduringruleswitching-2021","role":"author","urls":{"Paper":"http://doi.org/10.1523/jneurosci.1874-20.2020"},"keyword":["General Neuroscience","cognitive control","midfrontal theta","neural synchrony","rule switching","ANTERIOR CINGULATE CORTEX","COGNITIVE CONTROL","COMPUTATIONAL MODEL","PHASE SYNCHRONY","FRONTAL THETA","EEG-DATA","COMMUNICATION","OSCILLATIONS","MECHANISM","ERROR"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://biblio.ugent.be/person/801001536697/publication/export?format=bibtex","dataSources":["TC7BryQp3aBdfCoH3"],"keywords":["general neuroscience","cognitive control","midfrontal theta","neural synchrony","rule switching","anterior cingulate cortex","cognitive control","computational model","phase synchrony","frontal theta","eeg-data","communication","oscillations","mechanism","error"],"search_terms":["learning","synchronize","midfrontal","theta","dynamics","during","rule","switching","verbeke","ergo","de loof","verguts"],"title":"Learning to synchronize : midfrontal theta dynamics during rule switching","year":2021}