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  1. Is the P300 component a manifestation of context updating?Emanuel Donchin & Michael G. H. Coles - 1988 - Behavioral and Brain Sciences 11 (3):357.
    To understand the endogenous components of the event-related brain potential (ERP), we must use data about the components' antecedent conditions to form hypotheses about the information-processing function of the underlying brain activity. These hypotheses, in turn, generate testable predictions about the consequences of the component. We review the application of this approach to the analysis of the P300 component. The amplitude of the P300 is controlled multiplicatively by the subjective probability and the task relevance of the eliciting events, whereas its (...)
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  • A computational model of inhibitory control in frontal cortex and basal ganglia.Thomas V. Wiecki & Michael J. Frank - 2013 - Psychological Review 120 (2):329-355.
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  • A theory of memory retrieval.Roger Ratcliff - 1978 - Psychological Review 85 (2):59-108.
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  • Individual differences in attention influence perceptual decision making.Michael D. Nunez - 2015 - Frontiers in Psychology 6.
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  • A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.Pascal Fries - 2005 - Trends in Cognitive Sciences 9 (10):474-480.
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  • Computational psychiatry.P. Read Montague, Raymond J. Dolan, Karl J. Friston & Peter Dayan - 2012 - Trends in Cognitive Sciences 16 (1):72-80.
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  • Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony.Michel le Van Quyen & Antoine Lutz - unknown
    The quantification of phase synchrony between neuronal signals is of crucial importance for the study of large-scale interactions in the brain. Two methods have been used to date in neuroscience, based on two distinct approaches which permit a direct estimation of the instantaneous phase of a signal [Phys. Rev. Lett. 81 (1998) 3291; Human Brain Mapping 8 (1999) 194]. The phase is either estimated by using the analytic concept of Hilbert transform or, alternatively, by convolution with a complex wavelet. In (...)
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  • Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning.Anne Gabrielle Eva Collins & Michael Joshua Frank - 2016 - Cognition 152 (C):160-169.
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