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Raymond J. Dolan

Researcher at University College London

Publications -  940
Citations -  150202

Raymond J. Dolan is an academic researcher from University College London. The author has contributed to research in topics: Prefrontal cortex & Functional magnetic resonance imaging. The author has an hindex of 196, co-authored 919 publications receiving 138540 citations. Previous affiliations of Raymond J. Dolan include VU University Amsterdam & McGovern Institute for Brain Research.

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Synchronization of Medial Temporal Lobe and Prefrontal Rhythms in Human Decision Making

TL;DR: A mnemonic guidance of human decision making, beyond anticipation of expected reward, is supported by hippocampal–prefrontal theta synchronization, according to magnetoencephalography results.
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Evidence for surprise minimization over value maximization in choice behavior

TL;DR: It is shown that human decision-making is better explained by surprise minimization compared to utility maximization, and a limitation of purely economic motivations in explaining choice behavior is highlighted and the importance of belief-based motivations is emphasized.
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The time course of binding to striatal dopamine D2 receptors by the neuroleptic ziprasidone (CP-88,059-01) determined by positron emission tomography

TL;DR: PET and11C-raclopride were used to assess the time course of binding to central dopamine D2 receptors by the novel neuroleptic ziprasidone and found that with increasing time between dosing and PET scanning there was a curvilinear increase in BP, so that all studies performed at or after 18 h post-dose gave BPs in the normal range
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Noradrenaline blockade specifically enhances metacognitive performance

TL;DR: This study is the first to show a pharmacological enhancement of metacognitive performance, in the absence of any effect on perceptual decision making, and points to a regulatory role for noradrenergic neurotransmission in perceptual metacognition.
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Consistent spectral predictors for dynamic causal models of steady-state responses.

TL;DR: This paper examines the dynamic repertoires of nonlinear conductance-based neural population models and proposes a generative model of their power spectra and introduces a predictor of spectral activity based on sampling the system's Jacobian over the orbits of hidden neuronal states.