The execution and the comprehension of motor sequences correspond to the propagation of activity within specific chains. Figure 1. Schematic representation of the chain model derived from Chersi et al. Colored ellipses represent pools of neurons that encode specific motor acts in the parietal cortex IPL or intentions in the prefrontal cortex PFC. Lines indicate the connections between different pools. Due to the dual property of mirror neurons i.
Taken together, the reviewed results strongly support the notion that the processing of language stimuli, at least for sentences expressing a motor content, modulates the activity of the motor system and that this modulation specifically concerns those sectors of the motor system where the effector involved in the processed sentence is represented.
Interestingly, depending on the temporal relation between language and motor tasks, processing action words can facilitate or interfere with overt motor behavior.
The model we propose to explain these observations is based on three main points. First, the processing of action-related sentences involves the chained activation of specific pools of mirror neurons that encode the motor acts referred to in the sentences Chersi et al. This is the same mechanism as the one taking place during the recognition of actions done by other individuals.
Second, as shown by recent experiments Fogassi et al. The third point concerns the dynamics of neuronal pools. The detailed analysis of the experiments reported above has revealed that interference occurs between and ms after stimulus presentation, whereas facilitation becomes evident between and ms after sentence appearance Boulenger et al.
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These time scales suggest that short term neural dynamics may be the cause underlying these phenomena. In vitro recordings have shown that neuronal responses result from the combination of several dynamic processes occurring at different time scales. In general it is possible to distinguish two main components that determine the neuronal response: 1 an early but brief buildup of ionic currents typically potassium that causes an adaptation of the firing rates; 2 a slow but long lasting accumulation of neurotransmitters NMDA, GABA, AMPA and other ions e.
More precisely, for high enough spike frequencies a calcium-dependent potassium current see e. Simultaneously, due to incoming spikes the concentration of neurotransmitters increases rapidly and fades away slowly after the input has ceased this is especially true for NMDA. Additionally, the accumulation of calcium Powers et al. Taken together these effects produce a time window up to half a second after stimulation during which neurons decrease their firing rate and thus reach their maximum activity more slowly, and a facilitation time window from half a second to about a second during which pools react more rapidly.
The general mechanism proposed in our study is therefore the following. During the processing of an action-related sentence, pools of mirror neurons that encode the single phases motor acts of the expressed action are activated due to a motor resonance mechanism. Neuronal activity propagates along the chain and sequentially activates the motor neurons connected downstream. Although pools fire only for a short interval of time around — ms synaptic currents decay at a much slower rate due to their slower internal dynamics. The firing rate adaptation current is active shortly after the firing of the pool causing a momentary activity slowdown.
When a response action has to be produced, the prefrontal cortex PFC activates the corresponding neuronal chain. The precise activation profile of each pool in the chain will depend on the degree of overlap it has with any previously activated pools of other chains and on how big the time interval between the activations is.
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More precisely, the larger the overlap, the stronger the influence. Furthermore, pools will respond faster or more slowly depending on whether their activation falls within the adaptation or the facilitation phase of previous pools. In order to test our hypothesis, we simulated an experiment by virtually combining those by Buccino et al.
In our experiment, a hypothetical subject has to watch a screen where one of two short sentences can appear. Each action is encoded by a neuronal chain composed of pools that represent the different motor acts. When the subject reads the displayed sentences, neurons that encode the described motor acts start to fire due to a mirror resonance process.
One important characteristics of our model is that neuronal pools encoding the same motor act involving the same effector but being part of different chains share a small fraction of neurons and axonal projections. In our simulated experiment the elaboration of the sentence is assumed to last around ms, with the peak to peak time interval between two pools being around ms.
Note that this is possible because mirror neurons do not explicitly encode the agent of an action nor the objects involved. The neural network we used in our simulations was composed of six pools of neurons, each one coding a specific motor act. The pools were arranged in three chains of two pools each see Figure 2. Figure 2. Schematic representation of the chained organization of the network. Each large circle represents a pool of neurons small spheres encoding a specific motor act. Lines represent the connections between neurons.
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Lines on the left represent external inputs that start the chains. The behavior of each neuronal pool is described by a firing rate model with time-dependent synaptic currents Dayan and Abbott, This allows us to both compactly represent complex interactions between excitatory and inhibitory neurons within the pools and explicitly take into account the dynamics of ionic currents and neurotransmitters. The set of equations is the following:. This signal has been modeled as a bell shaped activity peak lasting ms. Furthermore, the connectivity i. All other connections including self connections have been set to zero.
The firing rate adaptation has been modeled as a current, that, when activated, will hyperpolarize the neurons of a pool, slowing down any spiking that may be occurring. All the parameters in this model have been chosen in order to reproduce as close as possible biological data. Figure 3 shows the currents and the firing rate of a single pool in response to an external stimulus.
Figure 3. Time course of each variable of a pool after stimulation gray peak. The green curve represents the response of the pool scale on the left , the blue curve is the synaptic current, the yellow curve is the firing rate adaptation current, and the dashed line is the resultant total current current scale on the right.
Figure 4. The results of the simulated experiment are reported in Figure 5. In our implementation this input I ext is simulated as a bell shaped activation of the duration of ms. Note that both in the experiments and in the model each sentence is considered as a whole. The detailed modeling of single words comprehension is beyond the scope of this paper.
The pool reaches its maximum activity ms after stimulus onset. The first bump is due to the crosstalk between the first chain and the second chain. The Go signal gray curve is given ms after the stimulus presentation. The activity peak is reached ms after the Go signal.
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Figure 5. As can be seen reading a sentence that contains a motor act present also in the response motor sequence produces an overall decrease in the reaction time of 25 ms. Figure 6. The dashed vertical lines indicate the sentence presentation onset left , the Go signal presentation middle , and moment of maximal activity right. The reaction time data shows that there is a first phase in which interference dominates up to ms and a phase in which facilitation dominates. This effect eventually fades to zero. Figure 7. Reported below are the timings of the experiments of Buccino et al. As reviewed in the first part of the paper, both interference and facilitation are widely observed in TMS and behavioral experiments on language comprehension and motor system activation.
The underlying mechanisms, however, are a topic of ongoing debate. It is interesting to note that one can find similar facilitation and interference effects also in the action observation literature e. In the present work, however, we focused on the controversial results related to language processing. Recently, Sato et al. Boulenger et al. In this work we proposed a simple neural mechanism that is capable of explaining both the facilitatory and the inhibitory interactions between language and action.
Our model is based on a chain structured organization of the parietal and premotor cortex Fogassi et al. Interactions between sensory and motor modalities have been modeled in the present work as a crosstalk between neuronal pools in motor and mirror chains and we have shown that the neural dynamics governing the activation of the pools can qualitatively reproduce the timings observed in behavioral experiments well. Taken together, these results allow us to draw the following conclusions.
Second, this unifying theory suggests that seemingly conflicting behavioral experiments may have observed different time windows of the same mechanism rather than different mechanisms,. This has important theoretical implications because, as previously discussed, it is currently debated in the literature whether the activation of motor and premotor cortices is essential for language understanding or just a by-product of the process. The early activation of the motor system is typically considered a strong point in support for the first thesis.
Showing that interference and facilitation are actually two manifestations of the same process greatly strengthens the embodied view according to which the recruitment of the motor system is fundamental for sentence comprehension. Finally, on the basis of our model we can formulate a variety of predictions that could guide future experimental research. Notwithstanding these interesting results, we are perfectly aware that the mechanisms coming into play during the elaboration of stimuli and decision making are much more complex than depicted here, so our proposal should be considered as a first attempt to model such a complex system.
We believe that this computational modeling work may also prove useful in building a biologically inspired robotic model for use in human—humanoid interaction, which is the longer-term goal of this work. From this perspective it is important that embodiment is taken into account at an appropriate level of abstraction that allows computational models of human biological mechanisms to be transferred to a robotic context. Furthermore, from a scientific perspective, it is clear that additional targeted experimental and modeling work is necessary to better understand the mechanisms underlying the relationship between sentence comprehension and motor system activation.
As a first step, however, we believe it was important to show in this paper that interference and facilitation may well be two sides of the same coin. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. A special thank goes to Gianluca Baldassarre for his help and support. Barsalou, L. Grounded cognition. Simulation, situated conceptualization, and prediction.
Bonini, L. Ventral premotor and inferior parietal cortices make distinct contribution to action organization and intention understanding. CrossRef Full Text. Borghi, A. Language comprehension and hand motion simulation. Borreggine, K. The action-sentence compatibility effect: its all in the timing. Boulenger, V. Cross-talk between language processes and overt motor behavior in the first msec of processing. Brass, M. Movement observation affects movement execution in a simple response task. Acta Psychol. Amst , 3— Buccino, G. Listening to action related sentences modulates the activity of the motor system: a combined TMS and behavioral study.
Brain Res. Chersi, F. Modeling intentional neuronal chains in parietal and premotor cortex.
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A model of intention understanding based on learned chains of motor acts in the parietal lobe. Dayan, P. Theoretical Neuroscience. Computational and Mathematical Modelling of Neural Systems. De Vega, M. On doing two things at once: temporal constraints on actions in language comprehension. Pubmed Abstract Pubmed Full Text. Decety, J. Fischer, M. Embodied language: a review of the role of the motor system in language comprehension. Fogassi, L. Parietal lobe: from action organization to intention understanding. Science , — Gallese, V. Mirror neurons and the social nature of language: the neural exploitation hypothesis.
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Hauk, O. Somatotopic representation of action words in human motor and premotor cortex. Neuron 41, — In The handbook of psycholinguistics. Edited by M. Traxler and M. Gernsbacher, — Amsterdam: Elsevier. This chapter overviews the major issues on syntactic processing in monolingual speakers, overviewing serial or two-stage accounts of parsing and interactive accounts.
It discusses frequency effects, effects of plausibility, prosody, and the research on the immediate integration of the sentence with the non-linguistic context. Traxler, M.
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Hoversten, and T. Sentence processing and interpretation in monolinguals and bilinguals. Edited by E. Smith Cairns, — Hoboken, NJ: John Wiley. This is a chapter overview of the current issues and current models pertaining to syntactic processing in monolingual and bilingual speakers. Users without a subscription are not able to see the full content on this page.
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Subscriber sign in. Forgot password? Don't have an account? Sign in via your Institution. Sign in with your library card. Related Articles about About Related Articles close popup. Dussias , Anne L. Johns , Manuel F. Introduction The main goal of monolingual models of sentence processing is to explain how the syntactic processor or parser assigns structure to an incoming string of words.