We review three areas of research and theory relating to the involvement of motor processing in action observation: behavioural studies on imitation learning, behavioural work on short-term visuomotor interactions, and related neurophysiological and neuroimaging work. A large number of behavioural studies now indicate bi-directional links between perception and action: visual processing can automatically induce related motor processes, and motor actions can direct future visual processing. The related concept of direct matching (Rizzolatti et al., 2001 Rizzolatti, G. and Luppino, G. 2001. The cortical motor system. Neuron, 31: 889–901. [Crossref], [PubMed], [Web of Science ®] [Google Scholar] ) does not, however, imply that observed actions are transduced into a corresponding motor representation that would guarantee an instant and accurate imitation. Rather, studies on the mirror neuron system indicate that action observation engages the observer's own motor prototype of the observed action. This allows for enhanced action recognition, imitation recognition, and, predominantly in humans, imitation and observational learning. Despite the clear impact of action observation on motor representations, recent neuroimaging work also indicates the overlap of imitation learning with processes of non-imitative skill acquisition.

Expectancy for upcoming action requirements is a fundamental prerequisite for human control of action. In the research reported here, we investigated which part of cognitive processing benefits from temporal predictability. In a binary forced-choice paradigm, visual targets were preceded by different intervals. In one condition, targets could be predicted by the length of the intervals. In other conditions, response goals or response effectors could be predicted by the length of the intervals. Behavioral advantages were observed when response effectors were temporally predictable, whereas temporal predictability of response goals and target stimuli was not sufficient. The findings thus show that temporal expectancy in speeded choice-reaction tasks facilitates late, effector-specific motor processing. These findings are of importance not only for our basic understanding of action control but also for any human-machine interaction that involves system delays.

Previous research on dual-tasks has shown that, under some circumstances, actions impair the perception of action-consistent stimuli, whereas, under other conditions, actions facilitate the perception of action-consistent stimuli. We propose a new model to reconcile these contrasting findings. The planning and control model (PCM) of motorvisual priming proposes that action planning binds categorical representations of action features so that their availability for perceptual processing is inhibited. Thus, the perception of categorically action-consistent stimuli is impaired during action planning. Movement control processes, on the other hand, integrate multi-sensory spatial information about the movement and, therefore, facilitate perceptual processing of spatially movement-consistent stimuli. We show that the PCM is consistent with a wider range of empirical data than previous models on motorvisual priming. Furthermore, the model yields previously untested empirical predictions. We also discuss how the PCM relates to motorvisual research paradigms other than dual-tasks.

Humans are sensitive to temporal redundancies in their environment. When the identity of a target stimulus is correlated with the duration of the preceding interval, performance is better for frequent than for infrequent combinations of target and interval. This effect has been demonstrated several times in current timing research. However, it can be accounted for by 2 starkly contrasting explanations. The standard account has explained it in terms of learning associations between intervals and stimulus-response events. But, alternatively the effect might be due to partial trial transition costs, because infrequent time-event combinations are proportionally more often partial transitions (i.e., transitions of either interval, or target). We conducted 3 choice response time experiments to distinguish between both explanations. The results clearly show that the time-event correlation effect is due to learning, not to partial transition costs.