Predictive coding of cognitive processes in natural and artificial systems

With recent developments in artificial intelligence (AI), there is great interest in how mechanisms of human cognitive processing may be instantiated in those models and how those models may help us better understand human cognitive and neural processes. Recent research suggests predictive coding theories and associated generative models may help explain the processes of visual perception and language production, while newer AI models include mechanisms akin to human memory and attention. This special issue of <i>Cognitive Neuroscience: Current Debates, Research & Reports</i> presents 16 new papers that highlight important topics and present exciting new data, models, and controversies. The articles include a new discussion paper by Parr, Pezzulo, and Friston exploring how transformer architectures utilize non-Markovian generative models and how an attention-like process is critical for processing complex sequential data. This is followed by seven insightful commentaries and a reply from the authors. A discussion paper on a new neurocomputational model of syntax is provided by Murphy, in which predictive processes are integrated in a multi-level, hierarchical syntax architecture. This is followed by five commentaries suggesting important evolutionary and developmental perspectives and ways to explore and test the model. Finally, an empirical article by Bastug, Roeber, and Schröger on auditory perception presents new evidence suggesting that distracting information requires less cognitive processing when it is predictable. The topics of this special issue are evolving rapidly and promise to be at the heart of future developments in artificial learning systems and theories of the brain mechanisms that mediate cognitive processes.