Evidence for a distributed hierarchy of action representation in the brain.
Complex human behavior is organized around temporally distal outcomes. Behavioral studies based on tasks such as normal prehension, multi-step object use and imitation establish the existence of relative hierarchies of motor control. The retrieval errors in apraxia also support the notion of a hierarchical model for representing action in the brain. In this review, three functional brain imaging studies of action observation using the method of repetition suppression are used to identify a putative neural architecture that supports action understanding at the level of kinematics, object centered goals and ultimately, motor outcomes. These results, based on observation, may match a similar functional-anatomic hierarchy for action planning and execution. If this is true, then the findings support a functional-anatomic model that is distributed across a set of interconnected brain areas that are differentially recruited for different aspects of goal-oriented behavior, rather than a homogeneous mirror neuron system for organizing and understanding all behavior.
Grafton
Scott T
ST
Department of Psychology, Room 3837, Building 251, University of California, Santa Barbara, CA 93106, United States. grafton@psych.ucsb.edu
Hamilton
Antonia F de C
AF
D001072:Apraxias / Q000503:physiopathology; D001288:Attention / Q000502:physiology*; D001696:Biomechanical Phenomena; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D005625:Frontal Lobe / Q000502:physiology; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D011939:Mental Recall / Q000502:physiology; D009042:Motivation; D009474:Neurons / Q000502:physiology; D009949:Orientation / Q000502:physiology; D010296:Parietal Lobe / Q000503:physiopathology; D011340:Problem Solving / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology*; D012691:Serial Learning / Q000502:physiology
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