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Through the years, dietetics education has decreased its emphasis on food courses. The literature indicates that as directors of dietetics undergraduate programs must decide between essential and nonessential curriculum items, [...]

The P300 component of the human event-related brain potential has often been linked to the processing of rare, surprising events. However, the formal computational processes underlying the generation of the P300 are not well known. Here, we formulate a simple model of trial-by-trial learning of stimulus probabilities based on Information Theory. Specifically, we modeled the surprise associated with the occurrence of a visual stimulus to provide a formal quantification of the "subjective probability" associated with an event. Subjects performed a choice reaction time task, while we recorded their brain responses using electroencephalography (EEG). In each of 12 blocks, the probabilities of stimulus occurrence were changed, thereby creating sequences of trials with low, medium, and high predictability. Trial-by-trial variations in the P300 component were best explained by a model of stimulus-bound surprise. This model accounted for the data better than a categorical model that parametrically encoded the stimulus identity, or an alternative model of surprise based on the Kullback-Leibler divergence. The present data demonstrate that trial-by-trial changes in P300 can be explained by predictions made by an ideal observer keeping track of the probabilities of possible events. This provides evidence for theories proposing a direct link between the P300 component and the processing of surprising events. Furthermore, this study demonstrates how modelbased analyses can be used to explain significant proportions of the trial-by-trial changes in human event-related EEG responses. [ABSTRACT FROM AUTHOR]

The estimates that humans make of statistical dependencies in the environment and therefore their representation of uncertainty crucially depend on the integration of data over time. As such, the extent to which past events are used to represent uncertainty has been postulated to vary over the cortex. For example, primary visual cortex responds to rapid perturbations in the environment, while frontal cortices involved in executive control encode the longer term contexts within which these perturbations occur. Here we tested whether primary and executive regions can be distinguished by the number of past observations they represent. This was based on a decay-dependent model that weights past observations from a Markov process and Bayesian Model Selection to test the prediction that neuronal responses are characterized by different decay half-lives depending on location in the brain. We show distributions of brain responses for short and long term decay functions in primary and secondary visual and frontal cortices, respectively. We found that visual and parietal responses are released from the burden of the past, enabling an agile response to fluctuations in events as they unfold. In contrast, frontal regions are more concerned with average trends over longer time scales within which local variations are embedded. Specifically, we provide evidence for a temporal gradient for representing context within the prefrontal cortex and possibly beyond to include primary sensory and association areas.