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The ability to switch attention from one aspect of an object to another or in other words to switch the "attentional set" as investigated in tasks like the "Wisconsin Card Sorting Test" is commonly referred to as cognitive flexibility. In this work we present a biophysically detailed neurodynamical model which illustrates the neuronal base of the processes related to this cognitive flexibility. For this purpose we conducted behavioral experiments which allow the combined evaluation of different aspects of set shifting tasks: uninstructed set shifts as investigated in Wisconsin-like tasks, effects of stimulus congruency as investigated in Stroop-like tasks and the contribution of working memory as investigated in "Delayed-Match-to-Sample" tasks. The work describes how general experimental findings are usable to design the architecture of a biophysical detailed though minimalistic model with a high orientation on neurobiological findings and how, in turn, the simulations support experimental investigations. The resulting model is able to account for experimental and individual response times and error rates and enables the switch of attention as a system inherent model feature: The switching process suggested by the model is based on the memorization of the visual stimuli and does not require any synaptic learning. The operation of the model thus demonstrates with at least a high probability the neuronal dynamics underlying a key component of human behavior: the ability to adapt behavior according to context requirements--cognitive flexibility.

In this work we address key phenomena observed with classical set shifting tasks as the ''Wisconsin Card Sorting Test'' or the ''Stroop'' task: Different types of errors and increased response times reflecting decreased attention. A component of major importance in these tasks is referred to as the ''attentional control'' thought to be implemented by the prefrontal cortex which acts primarily by an amplification of task relevant information. This mode of operation is illustrated by a neurodynamical model developed for a new kind of set shifting experiment: The Wisconsin-Delayed-Match-to-Sample task combines uninstructed shifts as investigated in Wisconsin-like tasks with a Delayed-Match-to-Sample paradigm. These newly developed WDMS experiments in conjunction with the neurodynamical simulations are able to explain the reason for decreased attention in set shifting experiments as well the different consequences of decreased attention in tasks requiring bivalent yes/no responses compared to tasks requiring multivalent responses.

In this work we present an approach to understand neuronal mechanisms underlying perceptual learning. Experimental results achieved with stimulus patterns of coherently moving dots are considered to build a simple neuronal model. The design of the model is made transparent and underlying behavioral assumptions made explicit. The key aspect of the suggested neuronal model is the learning algorithm used: We evaluated an implementation of Hebbian learning and are thus able to provide a straight-forward model capable to explain the neuronal dynamics underlying perceptual learning. Moreover, the simulation results suggest a very simple explanation for the aspect of ''sub-threshold'' learning (Watanabe et al. in Nature 413:844-884, 2001) as well as the relearning of motion discrimination after damage to primary visual cortex as recently reported (Huxlin et al. in J Neurosci 29:3981-3991, 2009) and at least indicate that perceptual learning might only occur when accompanied by conscious percepts.

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