Four experiments were conducted to investigate the relationship between the binding of visual features (as measured by their aftereffects on subsequent binding) and the learning of feature-conjunction probabilities. Both binding and learning effects were obtained, but they did not interact. Interestingly, (shape-color) binding effects disappeared with increasing practice, presumably because of the fact that only 1 of the features involved was relevant to the task. However, this instability was only observed for arbitrary, not highly overlearned combinations of simple geometric features and not for real objects (colored pictures of a banana and strawberry), where binding effects were strong and resistant to practice. These findings suggest that learning has no direct impact on the strength or resistance of bindings or on speed with which features are bound; however, learning does affect the amount of attention particular feature dimensions attract, which again can influence which features are considered in binding.

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Luck and Vogel (1997) showed that the storage capacity of visual working memory is about four objects and that this capacity does not depend on the number of features making up the objects. Thus, visual working memory seems to process integrated objects rather than individual features, just as verbal working memory handles higher-order "chunks" instead of individual features or letters. In this article, we present a model based on synchronization and desynchronization of reverberatory neural assemblies, which can parsimoniously account for both the limited capacity of visual working memory, and for the temporary binding of multiple assemblies into a single pattern. A critical capacity of about three to four independent patterns showed up in our simulations, consistent with the results of Luck and Vogel. The same desynchronizing mechanism optimizing phase segregation between assemblies coding for separate features or multifeature objects poses a limit to the number of oscillatory reverberations. We show how retention of multiple features as visual chunks (feature conjunctions or objects) in terms of synchronized reverberatory assemblies may be achieved with and without long-term memory guidance.

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Many recent behavioral and neuroscientific studies have revealed the importance of investigating meditation states and traits to achieve an increased understanding of cognitive and affective neuroplasticity, attention and self-awareness, as well as for their increasingly recognized clinical relevance. The investigation of states and traits related to meditation has especially pronounced implications for the neuroscience of attention, consciousness, self-awareness, empathy and theory of mind. In this article we present the main features of meditation-based mental training and characterize the current scientific approach to meditation states and traits with special reference to attention and consciousness, in light of the articles contributed to this issue.