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The main aim of this research was to study whether memory dynamics influence older people's choices to the same extent as younger's ones. To do so, we adapted the retrieval-practice paradigm to produce variations in memory accessibility of information on which decisions were made later. Based on previous results, we expected to observe retrieval-induced forgetting (RIF) and choice bias in younger and older participants after they engaged in retrieval practice of some studied attributes. In addition, we aimed to compare both age groups' performance in an experimental condition in which retrieval practice was replaced by reading aloud practice of the same studied items. The results indicated that whereas both age groups showed RIF after performing retrieval practice, biased decisions were observed only in the younger participants. Interestingly, neither older nor younger people showed memory impairment or choice bias in the condition of reading practice. These results extend previous findings and support the idea that, under specific circumstances, elderly people may make better choices than younger people. (Contains 2 tables.)

Schizophrenia has been associated with dysfunction in information integration/segregation dynamics. One of the neural networks whose role has been most investigated in schizophrenia is the default mode network (DMN). In this study, we have explored the possible alteration of integration and segregation dynamics in individuals diagnosed with schizophrenia with respect to healthy controls, based on the study of the topological properties of the graphs derived from the functional connectivity between the nodes of the DMN in the resting state. Our results indicate that the patients show a diminution of the modularity of the DMN and a higher global efficiency, in sparse graphs. Our data emphasise the interest in studying temporal changes in network measures and are compatible with the hypothesis of randomization of functional networks in schizophrenia. [ABSTRACT FROM AUTHOR]

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Cognitive functions result from the interplay of distributed brain areas operating in large-scale networks. These networks can be modelled with a number of parameters that represent their underlying dynamics. One particularly fruitful model to simulate key aspects of the large-scale brain networks is the Kuramoto model, which simulates the phase evolution of several weakly coupled oscillators that represent the mean oscillatory behavior of different cortical regions. Here, we inspected the dependency of two widespread nonlinear complexity markers, Sample Entropy (SampEn) and Lempel-Ziv Complexity (LZC), on EEG activity generated with a Kuramoto phase model where the time delay and connectivity strength among oscillators varied. We also added different levels of noise to the electroencephalogram (EEG) signals. Our results indicated that both complexity metrics reflected the changes in the delays and global synchrony levels, but we found that SampEn was slightly more sensitive to the state transition and its results were less affected by the presence of noise. These results help in the effort to understand the dynamics of EEG recordings and their relationship to large-scale networks.