Action-space coding in social contexts

In two behavioural experiments we tested whether performing a spatial task along with another agent changes space representation by rendering some reference frames more/less salient than others. To this end, we used a Simon task in which stimuli were presented in four horizontal locations thus allowing for spatial coding according to multiple frames of reference. In Experiment 1 participants performed a go/no-go Simon task along another agent, each being in charge of one response. In Experiment 2 they performed a two-choice Simon task along another agent, each being in charge of two responses. Results showed that when participants were in charge of only one response, stimulus position was coded only with reference to the centre of the screen hence suggesting that the co-actor's response, or the position of the co-actor, was represented and used as a reference for spatial coding. Differently, when participants were in charge of two responses, no effect of the social context emerged and spatial coding relied on multiple frames of reference, similarly to when the Simon task is performed individually. These findings provide insights on the influence played by the interaction between the social context (i.e. the presence of others) and task features on individual performance

Long-Term Clinical Consequences of Intense, Uninterrupted Endurance Training in Olympic Athletes

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Salvage stereotactic body radiotherapy for isolated lymph node recurrent prostate cancer : single institution series of 94 consecutive patients and 124 lymph nodes

A

Selected papers from QEST 2010

Introduction to special issu

Relazione tra effetto Simon e campo dipendenza-indipendenza

Non present

The Role of Regularization in Deformable Image Registration for Head and Neck Adaptive Radiotherapy

Deformable image registration provides a robust mathematical framework to quantify morphological changes that occur along the course of external beam radiotherapy treatments. As clinical reliability of deformable image registration is not always guaranteed, algorithm regularization is commonly introduced to prevent sharp discontinuities in the quantified deformation and achieve anatomically consistent results. In this work we analyzed the influence of regularization on two different registration methods, i.e. B-Splines and Log Domain Diffeomorphic Demons, implemented in an open-source platform. We retrospectively analyzed the simulation computed tomography (CTsim) and the corresponding re-planning computed tomography (CTrepl) scans in 30 head and neck cancer patients. First, we investigated the influence of regularization levels on hounsfield units (HU) information in 10 test patients for each considered method. Then, we compared the registration results of the open-source implementation at selected best performing regularization levels with a clinical commercial software on the remaining 20 patients in terms of mean volume overlap, surface and center of mass distances between manual outlines and propagated structures. The regularized B-Splines method was not statistically different from the commercial software. The tuning of the regularization parameters allowed open-source algorithms to achieve better results in deformable image registration for head and neck patients, with the additional benefit of a framework where regularization can be tuned on a patient specific basis