This simulator models hemispatial neglect, a common consequence of right-hemisphere stroke in which attention to the left side of space is systematically reduced, and the visual-scanning training used to rehabilitate it. A patient's detection ability is modelled as a smooth attentional gradient across a 190° arc of simulated visual space; running a trial block scatters 48 targets across that arc, rolls a detection outcome for each from the gradient, and renders the result in 3D as a ring of markers that light up green (detected) or red (missed) around a central fixation point, alongside a coloured probability-gradient floor. Adjustable severity and gradient-sharpness sliders set the patient's starting deficit, and a training-intensity slider controls how strongly repeated trial blocks shift the attentional field back toward the neglected side — reproducing, in simplified form, the rebalancing effect documented for scanning-based neglect rehabilitation.