Model Based Analysis of fMRI-Data: Applying the sSoTS Framework to the Neural Basic of Preview Search
The current work aims to unveil the neural circuits underlying visual search over time and space by using a model-based analysis of behavioural and fMRI data. It has been suggested by Watson and Humphreys  that the prioritization of new stimuli presented in our visual field can be helped by the active ignoring of old items, a process they termed visual marking. Studies using fMRI link the marking process with activation in superior parietal areas and the precuneus [4,18,27,26]. Marking has been simulated previously using a neural-level account of search, the spiking Search over Time and Space (sSoTS) model, which incorporates inhibitory as well as excitatory mechanisms to guide visual selection. Here we used sSoTS to help decompose the fMRI signals found in a preview search procedure, when participants search for a new target whilst ignoring old distractors. The time course of activity linked to inhibitory and excitatory processes in the model was used as a regressor for the fMRI data. The results showed that different neural networks were correlated with top-down excitation and top-down inhibition in the model, enabling us to fractionate brain regions previously linked to visual marking. We discuss the contribution of model-based analysis for decomposing fMRI data.
KeywordsNMDA Convolution Dition Deconvolution
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