Applying cognitive electrophysiology to neural modelling of the attentional blink.
Doctor of Philosophy (PhD) thesis, Computing.
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This thesis proposes a connection between computational modelling of cognition and cognitive electrophysiology. We extend a previously published neural network model of working memory and temporal attention (Simultaneous Type Serial Token (ST2 ) model ; Bowman & Wyble, 2007) that was designed to simulate human behaviour during the attentional blink, an experimental nding that seems to illustrate the temporal limits of conscious perception in humans. Due to its neural architecture, we can utilise the ST2 model's functionality to produce so-called virtual event-related potentials (virtual ERPs) by averaging over activation proles of nodes in the network. Unlike predictions from textual models, the virtual ERPs from the ST2 model allow us to construe formal predictions concerning the EEG signal and associated cognitive processes in the human brain. The virtual ERPs are used to make predictions and propose explanations for the results of two experimental studies during which we recorded the EEG signal from the scalp of human participants. Using various analysis techniques, we investigate how target items are processed by the brain depending on whether they are presented individually or during the attentional blink. Particular emphasis is on the P3 component, which is commonly regarded as an EEG correlate of encoding items into working memory and thus seems to re ect conscious perception. Our ndings are interpreted to validate the ST2 model and competing theories of the attentional blink. Virtual ERPs also allow us to make predictions for future experiments. Hence, we show how virtual ERPs from the ST2 model provide a powerful tool for both experimental design and the validation of cognitive models.
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