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Brain-based modeling and neurophysiological investigations of attention-language interactions
British Association for Cognitive Neuroscience (BACN) Annual Meeting, London, UCL, September 2009, Abstract #59
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We applied neuroanatomically and neurobiologically grounded computational modeling in conjunction with magneto-encephalography (MEG) experimental methods to address the long-standing debate on how linguistic knowledge is represented in the human brain. Recent simulations obtained with a realistic neural-network model of the left perisylvian cortex demonstrated that Hebbian synaptic plasticity mechanisms can lead to the emergence of cortical representations for words consisting of strongly connected distributed circuits exhibiting non-linear activation dynamics. Such a model replicates and explains existing patterns of neurophysiological data and makes testable predictions about the brain responses to words and pseudowords under different degrees of attention. In particular, it predicts that if ample attentional resources are made available to linguistic processes, pseudoword responses should be larger than word responses (as in the N400 pattern – see Figure 1, panels (A),(C)), whereas if attentional resources are scarce, the opposite pattern should emerge (words > pseudowords, as in mismatch negativity experiments – see Fig. 1, panels (B),(D)). In addition, simulation results show that neurophysiological responses to familiar words should not be significantly modulated by the availability of attentional resources, whereas responses to unfamiliar, “unrepresented” linguistic items (pseudowords) should. To test these predictions, we carried out a novel MEG experiment in which subjects were presented with the same auditory stimuli (spoken familiar words and matched unfamiliar pseudowords) in two different sessions and attention was manipulated across them. We found that: (I) when subjects attended the auditory stimuli, brain responses to pseudowords were larger than those to words, whereas when attention was directed away from the linguistic input, the opposite pattern emerged (words > pseudowords). In addition: (II) while the magnetic mismatch negativity (MMN) response to words did not significantly change across sessions (indicating that the cortical circuits underlying word representations are relatively immune to changes in attention levels), the MMN to pseudowords exhibited profound variability. These results confirm the model’s predictions and provide evidence in support of the hypothesis that words are represented in the brain as distributed action-perception circuits exhibiting all-or-none non-linear dynamics.