Characterisation of a computationally defined treatment target for anxiety and depression
Preferential learning from negative at the expense of positive events, has been causally linked to anxiety and depression. This suggests that interventions which target such negative learning bias may reduce symptoms of the illness, although the best way to achieve this is not clear. Recent computat...
| 主要な著者: | , |
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| フォーマット: | Conference item |
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Elsevier
2017
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| 要約: | Preferential learning from negative at the expense of positive events, has been causally linked to anxiety and depression. This suggests that interventions which target such negative learning bias may reduce symptoms of the illness, although the best way to achieve this is not clear. Recent computational work suggests that people preferentially learn from outcomes with high information content (i.e. which improve prediction of the future), and that central norepinephrine acts to report the information content of the outcomes. We tested whether it was possible to manipulate learning bias and associated central norepinephric activity by controlling the information content of positive and negative events in a computer based task. |
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