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Abstract Attention enables the selective processing of relevant information. Two types of selective attention, spatial and feature attention, have separable neural effects but in real life are often used together. Here, we asked how these types of attention interact to affect information coding in a frontoparietal 'multiple-demand' (MD) network, essential for attentional control.
Sequential and perceptual predictability influence miFC In the previous section, we characterised brain activity in response to task and observed differences in brain activity in response to switch or sequentially predicable trials. This section explores whether there are differences in functional connectivity in response to switch dimensions.
Abstract Background Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation. Objective We demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time. Methods To validate the method, Bayesian optimization was employed using participants’ binary judgements about the intensity of phosphenes elicited through tACS.
Thirty one healthy participants (20 female, 28.39 ± 5.63 years, range: 20–41 years, three left-handed) took part in the real-time optimization study; of these, ten took part in Experiment 1, 11 in Experiment 2, and ten in Experiment 3. For Experiment 2, one participant was excluded due to excessive head movement for both runs.
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