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Topic: contrast

Seminar
3 seminars

In Neuroscience and Vision Science

Seminar · Vision Science

Binocular combination of light

Daniel H. Baker · University of York (USA)

Thu, Jul 14, 2022 · 15:00 UTC

The brain combines signals across the eyes. This process is well-characterized for the perceptual anatomical pathway through V1 that primarily codes contrast, where interocular normalization ensures that responses are approximately equal for monocular and binocular stimulation. But we have much less understanding of how luminance is combined binocularly, both in the cortex and in subcortical structures that govern pupil diameter. Here I will describe the results of experiments using a novel combined EEG and pupillometry paradigm to simultaneously index binocular combination of luminance flicke

Seminar · Vision Science

Stereo vision in humans and insects

Jenny Read · Newcastle University

Wed, May 12, 2021 · 13:30 UTC

Stereopsis – deriving information about distance by comparing views from two eyes – is widespread in vertebrates but so far known in only class of invertebrates, the praying mantids. Understanding stereopsis which has evolved independently in such a different nervous system promises to shed light on the constraints governing any stereo system. Behavioral experiments indicate that insect stereopsis is functionally very different from that studied in vertebrates. Vertebrate stereopsis depends on matching up the pattern of contrast in the two eyes; it works in static scenes, and may have evolved

Seminar · Cognition

Predicting Patterns of Similarity Among Abstract Semantic Relations

Nick Ichien · UCLA

Thu, Jul 9, 2020 · 16:00 UTC

In this talk, I will present some data showing that people’s similarity judgments among word pairs reflect distinctions between abstract semantic relations like contrast, cause-effect, or part-whole. Further, the extent that individual participants’ similarity judgments discriminate between abstract semantic relations was linearly associated with both fluid and crystallized verbal intelligence, albeit more strongly with fluid intelligence. Finally, I will compare three models according to their ability to predict these similarity judgments. All models take as input vector representations of in