The human language faculty has been claimed to be grounded in the ability to process hierarchically structured sequences. This human ability goes beyond the capacity to process sequences with simple transitional probabilities of adjacent elements observable in non-human primates. Here we show that the processing of these two sequence types is supported by different areas in the human brain. Processing of local transitions is subserved by the left frontal operculum, a region that is phylogenetically older than Broca's area, which specifically holds responsible the computation of hierarchical dependencies. Tractography data revealing differential structural connectivity signatures for these two brain areas provide additional evidence for a segregation of two areas in the left inferior frontal cortex.
The term “predictive brain” depicts one of the most relevant concepts in cognitive neuroscience which emphasizes the importance of “looking into the future”, namely prediction, preparation, anticipation, prospection or expectations in various cognitive domains. Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior. This review discusses research areas which have recognized the importance of prediction and introduces the relevant terminology and leading theories in the field in an attempt to abstract some generative mechanisms of predictive processing. Furthermore, we discuss the process of testing the validity of postulated expectations by matching these to the realized events and compare the subsequent processing of events which confirm to those which violate the initial predictions. We conclude by suggesting that, although a lot is known about this type of processing, there are still many open issues which need to be resolved before a unified theory of predictive processing can be postulated with regard to both cognitive and neural functioning.
Many everyday life predictions rely on the experience and memory of event frequencies, i.e., natural samplings. We used functional magnetic resonance imaging (fMRI) to investigate the neural substrates of prediction under varying uncertainty based on a natural sampling approach. The study focused particularly on a comparison with other types of externally attributed uncertainty, such as guessing, and on the frontomedian cortex, which is known to be engaged in many types of decisions under uncertainty. On the basis of preceding stimulus cues, participants predicted events that occurred with probabilities ranging from p ϭ 0.6 to p ϭ 1.0. In contrast to certain predictions in a control task, predictions under uncertainty elicited activations within a posterior frontomedian area (mesial BA 8) and within a set of subcortical areas which are known to subserve dopaminergic modulations. The parametric analysis revealed that activation within the mesial BA 8 significantly increased with increasing uncertainty. A comparison with other types of uncertainty indicates that frontomedian correlates of frequency-based prediction appear to be comparable with those induced in long-term stimulus-response adaptation processes such as hypothesis testing, in contrast to those engaged in short-term error processing such as guessing.
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