Model based analysis of fMRI-data: Applying the sSoTS framework to the neural basic of preview search.

The current work aims to unveil the neural circuits under- lying visual search over time and space by using a model-based analysis of behavioural and fMRI data. It has been suggested by Watson and Humphreys [31] that the prioritization of new stimuli presented in our visual field can be helped by the active ignoring of old items, a process they termed visual marking. Studies using fMRI link the marking pro- cess with activation in superior parietal areas and the precuneus [4, 18, 27, 26]. Marking has been simulated previously using a neural-level ac- count of search, the spiking Search over Time and Space (sSoTS) model, which incorporates inhibitory as well as excitatory mechanisms to guide visual selection. Here we used sSoTS to help decompose the fMRI signals found in a preview search procedure, when participants search for a new target whilst ignoring old distractors. The time course of activity linked to inhibitory and excitatory processes in the model was used as a regres- sor for the fMRI data. The results showed that different neural networks were correlated with top-down excitation and top-down inhibition in the model, enabling us to fractionate brain regions previously linked to vi- sual marking. We discuss the contribution of model-based analysis for decomposing fMRI data

Effects of sleep deprivation on neural functioning: an integrative review

Sleep deprivation has a broad variety of effects on human performance and neural functioning that manifest themselves at different levels of description. On a macroscopic level, sleep deprivation mainly affects executive functions, especially in novel tasks. Macroscopic and mesoscopic effects of sleep deprivation on brain activity include reduced cortical responsiveness to incoming stimuli, reflecting reduced attention. On a microscopic level, sleep deprivation is associated with increased levels of adenosine, a neuromodulator that has a general inhibitory effect on neural activity. The inhibition of cholinergic nuclei appears particularly relevant, as the associated decrease in cortical acetylcholine seems to cause effects of sleep deprivation on macroscopic brain activity. In general, however, the relationships between the neural effects of sleep deprivation across observation scales are poorly understood and uncovering these relationships should be a primary target in future research

Monoaminergic modulation of human attentional and executive function

SIGLEAvailable from British Library Document Supply Centre-DSC:D195684 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The Noradrenergic (alpha)2 Agonist Clonidine Modulates Behavioral And Neuroanatomical Correlates Of Human Attentional Orienting And Alerting

s. Introduction Arousal and selective attention are multidimensional psychological processes that interact closely with one another, both behaviourally and neuroanatomically. There is accumulating evidence that the noradrenergic (NA) system may contribute to both arousal and selective attention, as well as play an important role in mediating the interaction between these functions (Arnsten and Constant, 1992; Smith and Nutt, 1996; Coull et al., 1997). Visual spatial orienting tasks, originally developed by Posner (Posner et al., 1980), have provided an experimental paradigm with which to investigate both selective directed focal spatial expectations as well as general, unfocused alerting. Fernandez-Duque and Posner have recently demonstrated that the spatial orienting and alerting effects in the Posner task provide independent indices of attention and arousal, respectively (Fernandez-Duque and Posner, 1997). In spatial orienting tasks subjects de

Creating User Profiles from a Command-Line Interface: A Statistical Approach

Proceeding of: 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP), Trento, Italy, June 22-26 2009.Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, an approach for creating and recognizing automatically the behavior profile of a user from the commands (s)he types in a commandline interface, is presented. Specifically, in this research, a computer user behavior is represented as a sequence of UNIX commands. This sequence is transformed into a distribution of relevant subsequences in order to find out a profile that defines its behavior. Then, statistical methods are used for recognizing a user from the commands (s)he types. The experiment results, using 2 different sources of UNIX command data, show that a system based on our approach can efficiently recognize a UNIX user. In addition, a comparison with a HMM-base method is done. Because a user profile usually changes constantly, we also propose a method to keep up to date the created profiles using an age-based mechanism.Publicad