Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices
Optimal decision making in complex environments requires dynamic learning from unexpected events. To speed up learning, we should heavily weight information that indicates state-action-outcome contingency changes and ignore uninformative fluctuations in the environment. Often, however, unrelated inf...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-10-01
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| Series: | NeuroImage |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922005547 |
| _version_ | 1811293913187614720 |
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| author | Hans Kirschner Adrian G. Fischer Markus Ullsperger |
| author_facet | Hans Kirschner Adrian G. Fischer Markus Ullsperger |
| author_sort | Hans Kirschner |
| collection | DOAJ |
| description | Optimal decision making in complex environments requires dynamic learning from unexpected events. To speed up learning, we should heavily weight information that indicates state-action-outcome contingency changes and ignore uninformative fluctuations in the environment. Often, however, unrelated information is hard to ignore and can potentially bias our learning. Here we used computational modelling and EEG to investigate learning behaviour in a modified probabilistic choice task that introduced two task-irrelevant factors that were uninformative for optimal task performance, but nevertheless could potentially bias learning: pay-out magnitudes were varied randomly and, occasionally, feedback presentation was enhanced by visual surprise. We found that participants’ overall good learning performance was biased by distinct effects of these non-normative factors. On the neural level, these parameters are represented in a dynamic and spatiotemporally dissociable sequence of EEG activity. Later in feedback processing the different streams converged on a central to centroparietal positivity reflecting a signal that is interpreted by downstream learning processes that adjust future behaviour. |
| first_indexed | 2024-04-13T05:09:32Z |
| format | Article |
| id | doaj.art-d905760957634902a418968370d657f4 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-13T05:09:32Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-d905760957634902a418968370d657f42022-12-22T03:01:05ZengElsevierNeuroImage1095-95722022-10-01259119437Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choicesHans Kirschner0Adrian G. Fischer1Markus Ullsperger2Institute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany; Corresponding author.Department of Education and Psychology, Freie Universität Berlin, D-14195 Berlin, GermanyInstitute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany; Center for Behavioral Brain Sciences, D-39106 Magdeburg, GermanyOptimal decision making in complex environments requires dynamic learning from unexpected events. To speed up learning, we should heavily weight information that indicates state-action-outcome contingency changes and ignore uninformative fluctuations in the environment. Often, however, unrelated information is hard to ignore and can potentially bias our learning. Here we used computational modelling and EEG to investigate learning behaviour in a modified probabilistic choice task that introduced two task-irrelevant factors that were uninformative for optimal task performance, but nevertheless could potentially bias learning: pay-out magnitudes were varied randomly and, occasionally, feedback presentation was enhanced by visual surprise. We found that participants’ overall good learning performance was biased by distinct effects of these non-normative factors. On the neural level, these parameters are represented in a dynamic and spatiotemporally dissociable sequence of EEG activity. Later in feedback processing the different streams converged on a central to centroparietal positivity reflecting a signal that is interpreted by downstream learning processes that adjust future behaviour.http://www.sciencedirect.com/science/article/pii/S1053811922005547 |
| spellingShingle | Hans Kirschner Adrian G. Fischer Markus Ullsperger Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices NeuroImage |
| title | Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices |
| title_full | Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices |
| title_fullStr | Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices |
| title_full_unstemmed | Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices |
| title_short | Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices |
| title_sort | feedback related eeg dynamics separately reflect decision parameters biases and future choices |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922005547 |
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