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Recent research indicates that non- invasive stimulation of the afferent auricular vagal nerve (tVNS) may modulate various cognitive and affec-tive functions, likely via activation of the locus coeruleus- norepinephrine (LC- NE) system. In a series of ERP studies we found that the attention- related P300 component is enhanced during continuous vagal stimula-tion, compared to sham, which is also related to increased salivary alpha amylase levels (a putative indirect marker for central NE activation). In another study, we investigated the effect of continuous tVNS on the late positive potential (LPP), an electrophysiological index for motivated atten-tion toward emotionally evocative cues, and the effects of tVNS on later recognition memory (1- week delay). Here, vagal stimulation prompted earlier LPP differences (300- 500 ms) between unpleasant and neutral scenes. During retrieval, vagal stimulation significantly improved memory performance for unpleasant, but not neutral pictures, compared to sham stimulation, which was also related to enhanced salivary alpha amylase levels. In line, unpleasant images encoded under tVNS compared to sham stimulation also produced enhanced ERP old/new differences (500- 800 ms) during retrieval indicating better recollection. Taken together, our studies suggest that tVNS facilitates attention, learning and episodic memory, likely via afferent projections to the arousal- modulated LC- NE system. We will, however, also show data that point to critical stimulation parameters (likely duration and frequency) that need to be considered when applying tVNS
Bottom-up saliency is often cited as a factor driving the choice of fixation locations of human observers, based on the (partial) success of saliency models to predict fixation densities in free viewing. However, these observations are only weak evidence for a causal role of bottom-up saliency in natural viewing behaviour. To test bottom-up saliency more directly, we analyse the performance of a number of saliency models---including our own saliency model based on our recently published model of early visual processing (Schütt & Wichmann, 2017, JoV)---as well as the theoretical limits for predictions over time. On free viewing data our model performs better than classical bottom-up saliency models, but worse than the current deep learning based saliency models incorporating higher-level information like knowledge about objects. However, on search data all saliency models perform worse than the optimal image independent prediction. We observe that the fixation density in free viewing is not stationary over time, but changes over the course of a trial. It starts with a pronounced central fixation bias on the first chosen fixation, which is nonetheless influenced by image content. Starting with the 2nd to 3rd fixation, the fixation density is already well predicted by later densities, but more concentrated. From there the fixation distribution broadens until it reaches a stationary distribution around the 10th fixation. Taken together these observations argue against bottom-up saliency as a mechanistic explanation for eye movement control after the initial orienting reaction in the first one to two saccades, although we confirm the predictive value of early visual representations for fixation locations. The fixation distribution is, first, not well described by any stationary density, second, is predicted better when including object information and, third, is badly predicted by any saliency model in a search task.
Editorial
(2019)