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Vilotijevic, A.

Publications and source records attributed to Vilotijevic, A..

2 recordsLinked to original sources

Covert shifts of attention towards the visual periphery induce pupil dilation

People are best able to detect stimuli in peripheral vision when their pupils are large, and best able to discriminate stimuli in central vision when their pupils are small. However, it is unclear whether our visual system makes use of this by dilating the pupils when attention is directed towards peripheral vision. Therefore, we tested whether pupil size adapts to the breadth of attention. We found that pupils dilate with increasing attentional breadth, both when attention is diffusely spread and when attention is directed at specific locations in peripheral vision. We further found a correlation with performance, suggesting a functional benefit of this effect. Based on our results and others, we propose that cognitively driven pupil dilation is not an epiphenomenal marker of Locus Coeruleus activity, as is often assumed, but rather is an adaptive response that reflects an emphasis on peripheral vision.

neuroscience↗

Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis

Cognitive pupillometry is the measurement of pupil size to investigate cognitive processes such as attention, mental effort, working memory, and many others. Currently, there is no commonly agreed-upon methodology for conducting cognitive-pupillometry experiments, and approaches vary widely between research groups and even between different experiments from the same group. This lack of consensus makes it difficult to know which factors to consider when conducting a cognitive-pupillometry experiment. Here we provide a comprehensive, hands-on guide to methods in cognitive pupillometry, with a focus on trial-based experiments in which the measure of interest is the task-evoked pupil response to a stimulus. We cover all methodological aspects of cognitive pupillometry: experimental design; preprocessing of pupil-size data; and statistical techniques to deal with multiple comparisons when testing pupil-size data. In addition, we provide code and toolboxes (in Python) for preprocessing and statistical analysis, and we illustrate all aspects of the proposed workflow through an example experiment and example scripts.

animal behavior and cognition↗