Grounding predictive coding models in empirical neuroscience research

2013 ◽  
Vol 36 (3) ◽  
pp. 210-211 ◽  
Author(s):  
Tobias Egner ◽  
Christopher Summerfield

AbstractClark makes a convincing case for the merits of conceptualizing brains as hierarchical prediction machines. This perspective has the potential to provide an elegant and powerful general theory of brain function, but it will ultimately stand or fall with evidence from basic neuroscience research. Here, we characterize the status quo of that evidence and highlight important avenues for future investigations.

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.


2005 ◽  
Author(s):  
Amber L. Garcia ◽  
Michael T. Schmitt ◽  
Naomi Ellemers ◽  
Nyla R. Branscombe
Keyword(s):  

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