Predictive coding is undoubtedly the currently most widely accepted general description of the principles of information processing in the brain. It suggests that the brain forms predictions for upcoming sensory events. Incoming information is compared with these predictions and their difference, the “prediction error” is used to improve future predictions. A very large part of the neurophysiological evidence supporting predictive coding describes neural signal assumed to represent prediction errors.
Sensory deviance detection refers to processing the sensory input with regards to the regular aspects of the preceding stimulation. Sensory events not conforming to one or more previously detected regularities are termed “deviants”. It is important to note that stimulus change per se does not necessarily constitute deviation nor is change a prerequisite of deviation (i.e., repeating a stimulus may violate some sensory regularities). Sensory deviance detection processes have been commonly studied by neuroimaging methods, such as electric and magnetic event-related brain potentials (e.g., the mismatch negativity and its magnetic counterpart).
Based on the obvious overlap between these two initially separate research areas there has been much interaction between them: Neural signals indexing sensory deviance detection have been interpreted as prediction errors and in turn, empirical findings of sensory deviance detection lended further support for predictive coding theories. Very recently, however, some scientists have also brought up unresolved issues regarding this connection: deviance detection related phenomena unexplained by predictive coding principles and data contrasting inferences drawn on predictive coding principles.
The current research topic aims to 1) bring together new empirical and modeling studies of sensory deviance detection bearing relevance to predictive coding and
2) provide reviews of the state of the art both for general issues (such suggestions to resolve the problems previously brought up or further discrepancies between findings in sensory deviance detection and predictive coding principles) as well as for applications of predictive coding principles and sensory deviance detection to various research fields (e.g., special groups of humans, etc.).
While both predictive coding and deviance detection are very wide topics, this Research Topic aims to gather manuscripts, which are relevant for both fields of research. Further, while we will be happy to include studies on all sensory modalities and on any variant of predictive coding, research on deviations from fully abstract rules and work on theories outside predictive coding may not fit the planned scope of the current research topic. Finally, studies employing any scientifically sound behavioral, brain imaging, and modeling method are welcome as long as they bring theoretical insights into linking or separating predictive coding and sensory deviance detection.
Predictive coding is undoubtedly the currently most widely accepted general description of the principles of information processing in the brain. It suggests that the brain forms predictions for upcoming sensory events. Incoming information is compared with these predictions and their difference, the “prediction error” is used to improve future predictions. A very large part of the neurophysiological evidence supporting predictive coding describes neural signal assumed to represent prediction errors.
Sensory deviance detection refers to processing the sensory input with regards to the regular aspects of the preceding stimulation. Sensory events not conforming to one or more previously detected regularities are termed “deviants”. It is important to note that stimulus change per se does not necessarily constitute deviation nor is change a prerequisite of deviation (i.e., repeating a stimulus may violate some sensory regularities). Sensory deviance detection processes have been commonly studied by neuroimaging methods, such as electric and magnetic event-related brain potentials (e.g., the mismatch negativity and its magnetic counterpart).
Based on the obvious overlap between these two initially separate research areas there has been much interaction between them: Neural signals indexing sensory deviance detection have been interpreted as prediction errors and in turn, empirical findings of sensory deviance detection lended further support for predictive coding theories. Very recently, however, some scientists have also brought up unresolved issues regarding this connection: deviance detection related phenomena unexplained by predictive coding principles and data contrasting inferences drawn on predictive coding principles.
The current research topic aims to 1) bring together new empirical and modeling studies of sensory deviance detection bearing relevance to predictive coding and
2) provide reviews of the state of the art both for general issues (such suggestions to resolve the problems previously brought up or further discrepancies between findings in sensory deviance detection and predictive coding principles) as well as for applications of predictive coding principles and sensory deviance detection to various research fields (e.g., special groups of humans, etc.).
While both predictive coding and deviance detection are very wide topics, this Research Topic aims to gather manuscripts, which are relevant for both fields of research. Further, while we will be happy to include studies on all sensory modalities and on any variant of predictive coding, research on deviations from fully abstract rules and work on theories outside predictive coding may not fit the planned scope of the current research topic. Finally, studies employing any scientifically sound behavioral, brain imaging, and modeling method are welcome as long as they bring theoretical insights into linking or separating predictive coding and sensory deviance detection.
