page_title: Bi-directional hypothesis of language and action
section_title: Circuit organization
hierarchical_section_title: Bi-directional hypothesis of language and action / Organization of Neural Substrates / Circuit organization
caption_attribution_description: English: Adapted from Shebani et al. (2013). Gray represents the language comprehension network, which is connected to the motor representation of the arm (yellow) when arm words are being comprehended.
context_page_description: The bi-directional hypothesis of language and action proposes that the sensorimotor and language comprehension areas of the brain exert reciprocal influence over one another. This hypothesis argues that areas of the brain involved in movement and sensation, as well as movement itself, influence cognitive processes such as language comprehension. In addition, the reverse effect is argued, where it is proposed that language comprehension influences movement and sensation. Proponents of the bi-directional hypothesis of language and action conduct and interpret linguistic, cognitive, and movement studies within the framework of embodied cognition and embodied language processing. Embodied language developed from embodied cognition, and proposes that sensorimotor systems are not only involved in the comprehension of language, but that they are necessary for understanding the semantic meaning of words.
context_section_description: It has been proposed that the control of movement is organized hierarchically, where movement is not controlled by individually controlling single neurons, but that movements are represented at a gross, more functional level. A similar concept has been applied to the control of cognition, resulting in the theory of cognitive circuits. This theory proposes that there are functional units of neurons in the brain that are strongly connected, and act coherently as a functional unit during cognitive tasks. These functional units of neurons, or "thought circuits," have been referred to as the "building blocks of cognition". Thought circuits are believed to have been originally formed from basic anatomical connections, that were strengthened with correlated activity through Hebbian learning and plasticity. Formation of these neural networks has been demonstrated with computational models using known anatomical connections and Hebbian learning principles. For example, sensory stimulation through interaction with an object activates a distributed network of neurons in the cortex. Repeated activation of these neurons, through Hebbian plasticity, may strengthen their connections and form a circuit. This sensory circuit may then be activated during the perception of known objects.
  This same concept has been applied to action and language, as understanding of the meaning of action words requires an understanding of the action itself. During language and motor skill development, one likely learns to associate an action word with an action or a sensation. This action or sensation, and the correlated sensorimotor areas involved, are then incorporated into the neural representation of that concept. This leads to semantic topography, or the activation of motor areas related to the meaning and bodily association of action language. These networks may be organized into "kernels," areas highly activated by language comprehension tasks, and "halos," brain areas in the periphery of networks that experience slightly increased activation. It has been hypothesized that language comprehension is housed in the left-perisylvian neuronal circuit, forming the "kernel," and sensorimotor regions are peripherally activated during semantic processing of action language, forming the "halo".
caption: Proposed organization of motor-semantic neural circuits for action language comprehension. Gray dots represent areas of language comprehension, creating a network for comprehending all language. The semantic circuit of the motor system, particularly the motor representation of the legs (yellow dots), is incorporated when leg-related words are comprehended. Adapted from Shebani et al. (2013).
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