About this Research Topic
The brain is constantly confronted with a wealth of sensory information that must be processed efficiently to facilitate appropriate reactions. One way of optimizing this processing effort is to predict incoming sensory information based on previous experience so that expected information is processed efficiently and resources can be allocated to novel or surprising information. Theoretical and computational studies led to the formulation of the predictive coding framework (Friston 2005, Hawkins and Blakeslee 2004, Mumford 1992, Rao and Ballard 1999). Predictive coding states that the brain continually generates models of the world based on context and information from memory to predict sensory input. In terms of brain processing, a predictive model is created in higher cortical areas and communicated through feedback connections to lower sensory areas. In contrast, feedforward connections process and project an error signal, i.e. the mismatch between the predicted information and the actual sensory input (Rao & Ballard, 1999). The predictive model is constantly updated according to this error signal.
Although central concepts of this framework reach back to early perception science (Helmholtz 1863), these ideas remain in conflict with mainstream models of cortical processing in which feedforward projections integrate essential information and feedback connections serve only modulatory purposes (i.e. gain control).
In recent years however, the concept of predictive coding has been validated by a number of brain imaging studies investigating predictive feedback and the processing of prediction errors (i.e. Alink et al. 2010, Bar 2007, DenOuden et al. 2010, Egner et al. 2010, Rauss et al. 2011, Smith and Muckli 2010, Summerfield et al. 2006, Todorovic et al. 2011). Predictive coding is considered a significant paradigm shift in neuroscience, affecting every level of cortical processing and warrants inclusion in a unifying theory of the brain (Friston 2010), even though empirical evidence remains relatively scarce.
This research topic will focus on the latest evidence for the core features in the predictive coding framework – the role of (1) predictive feedback and (2) forward projected prediction errors. The term ‘predictive coding framework’ is adopted here to accommodate different models. Theoretical contributions, reviews, and empirical contributions using neurophysiological and brain imaging methods are welcomed for this issue.
Although central concepts of this framework reach back to early perception science (Helmholtz 1863), these ideas remain in conflict with mainstream models of cortical processing in which feedforward projections integrate essential information and feedback connections serve only modulatory purposes (i.e. gain control).
In recent years however, the concept of predictive coding has been validated by a number of brain imaging studies investigating predictive feedback and the processing of prediction errors (i.e. Alink et al. 2010, Bar 2007, DenOuden et al. 2010, Egner et al. 2010, Rauss et al. 2011, Smith and Muckli 2010, Summerfield et al. 2006, Todorovic et al. 2011). Predictive coding is considered a significant paradigm shift in neuroscience, affecting every level of cortical processing and warrants inclusion in a unifying theory of the brain (Friston 2010), even though empirical evidence remains relatively scarce.
This research topic will focus on the latest evidence for the core features in the predictive coding framework – the role of (1) predictive feedback and (2) forward projected prediction errors. The term ‘predictive coding framework’ is adopted here to accommodate different models. Theoretical contributions, reviews, and empirical contributions using neurophysiological and brain imaging methods are welcomed for this issue.
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