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Human perception of faces is widely believed to rely on automatic processing by a domain-specifi c, modular component of the visual system. Scalp-recorded event-related potential (ERP) recordings indicate that faces receive special stimulus processing at around 170 ms poststimulus onset, in that faces evoke an enhanced occipital negative wave, known as the N170, relative to the activity elicited by other visual objects. As predicted by modular accounts of face processing, this early face-specifi c N170 enhancement has been reported to be largely immune to the infl uence of endogenous processes such as task strategy or attention. However, most studies examining the infl uence of attention on face processing have focused on non-spatial attention, such as object-based attention, which tend to have longer-latency effects. In contrast, numerous studies have demonstrated that visual spatial attention can modulate the processing of visual stimuli as early as 80 ms poststimulus – substantially earlier than the N170. These temporal characteristics raise the question of whether this initial face-specifi c processing is immune to the infl uence of spatial attention. This question was addressed in a dual-visualstream ERP study in which the infl uence of spatial attention on the face-specifi c N170 could be directly examined. As expected, early visual sensory responses to all stimuli presented in an attended location were larger than responses evoked by those same stimuli when presented in an unattended location. More importantly, a signifi cant face-specifi c N170 effect was elicited by faces that appeared in an attended location, but not in an unattended one. In summary, early face-specifi c processing is not automatic, but rather, like other objects, strongly depends on endogenous factors such as the allocation of spatial attention. Moreover, these fi ndings underscore the extensive infl uence that top-down attention exercises over the processing of visual stimuli, including those of high natural salience.

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain-machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.