Invariant recognition of goal-directed hand actions: a physiologically plausible neural model The recognition of transitive, goal-directed actions requires highly selective processing of shape details of effector and goal object, and high robustness with respect to image transformations at the same time. The neural mechanisms required for solving this challenging recognition task remain largely unknown. We propose a neurophysiologically-inspired model for the recognition of transitive grasping actions, which combines high selectivity for different grips with strong position invariance. The model is based on well-established physiologically plausible simple neural mechanisms. Invariance is accomplished by combining nonlinear pooling (by maximum operations) and a specific neural representation of the relative position of object and effector based on a gain-field like mechanism. The proposed architecture accomplishes accurate recognition of different grip types on real video data and reproduces correctly several properties of action-selective neurons in occipital, parietal and premotor areas. In addition, the model shows that the accurate recognition of goal-directed actions can be accomplished without an explicit reconstruction of the 3-D structure of effectors and objects, as assumed in many technical systems for the recognitions of hand actions.