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Explaining the effects of attention on lexical processes using a single Hebbian neuronal model of the language cortex.
39th Annual General Meeting of the European Brain & Behaviour Society - Trieste, Italy, Sept. 2007. p.13.
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Meaningful familiar stimuli and senseless unknown materials lead to different patterns of brain activation. The major neurophysiological response indexing “sense” is the N400, a late event-related brain response larger for senseless materials (e.g., meaningless pseudowords) than for matched meaningful words. More recently, however, early differences have also been recorded, using Magneto- and Electro-Encephalography (MEG and EEG) – for example, in the Mismatch Negativity (MMN, latency 100-250ms). The MMN is elicited even when subjects are heavily distracted and, in this case, is larger for words than for pseudowords, thus exhibiting the reverse pattern seen for the N400. So far, no single account has been able to explain these seemingly contradictory results. We implemented a neuroanatomically grounded neural-network model of the left-perisylvian language cortex and used it to simulate brain processes of early language acquisition. The network was repeatedly confronted with activation patterns and allowed to adapt by means of Hebbian (long-term potentiation and depression) mechanisms: we observed the formation of input-specific neuronal circuits, i.e., sets of strongly interconnected neurons distributed over a range of areas which responded only to known patterns (“words”). The trained model was then used to simulate the neurophysiological response of the language cortex to words and senseless pseudowords stimuli. We found that variation of the amount of global inhibition of the network (which we interpret as the model correlate of attention) modulated the simulated brain response to words and pseudowords, producing either an N400- or an MMN-like response depending on the amount of global inhibition (or available attentional resources). Our model (1) demonstrates the viability of purely Hebbian, associative learning in a multi-layered network architecture, (2) offers a unifying explanatory account for seemingly inconsistent experimental observations, and (3) makes clear predictions on the effects of attention on latency and magnitude of ERPs to lexical items. Such predictions have been confirmed by recent experimental evidence.