Friday Lunch Seminar (English)

January 17, 2025
12:15 〜 13:00 (JST)

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or you can come to the conference room on the 1st floor of the CiNet bldg.

Sign up by noon on January 16.
When we cannot identify your affiliation etc., we may have to turn down your application.
You will be notified of participation details by e-mail on January 16.

Talk Title: Exploration of computational models for explaining lightness/brightness illusions

Yuki Kobayashi
Researcher
Neural Information Engineering Laboratory
Center for Information and Neural Networks (CiNet)
National Institutes of Information and Communications Technology (NICT)

Host PI: Satoshi Nishida

Abstract:
Color is a fundamental aspect of human visual experience. Although the relationship between a single light stimulus and its corresponding color percept is well understood, the same light stimulus does not always produce the same color perception in complex visual contexts, a phenomenon recognized as visual illusions. In lightness perception research, which constitutes an integral part of the study of color perception, numerous illusions have been documented, and researchers have attempted to devise models explaining them all. To date, these models have been primarily assessed by the number of illusions from a set they can accurately predict, a simplistic metric that assumes independence and masks where each model’s strengths and weaknesses lie. Instead, we investigated inter-dependencies (i.e., commonalities) among known lightness illusions by examining correlations in illusion magnitudes. Our findings show that many lightness illusions can be explained by a small number of underlying factors, determined largely by the luminance polarity between targets and their immediate adjacent regions. Employing this more nuanced evaluation method revealed that widely accepted models are strongly biased toward one of these underlying factors, leaving the others unaccounted for. This study thus exposes a critical shortcoming of traditional models and underscores the importance of focusing on underlying factors rather than treating each illusion in isolation.