This is a seminar jointly organized by the three groups working on quantum physics and technology at Department of Physics, Kindai University, namely Condensed-Matter Theory (CMT), Quantum Control (QC), and QMB Laboratories.

Scheduled talks

 


Time and Date: 9:00-, April 21, 2021

Room: Webcast via Zoom

Speaker: Iigaya, Kiyohito (California Institute of Technology)

Title: Neural principles of subjective value construction

Abstract: It is an open question how humans construct the subjective value of complex objects (stimuli), such as artistic paintings or photographs. While great progress has been made toward understanding how the brain adjusts the value of objects through reinforcement-learning, little is known about how the value arises in the brain in the first place. Here, we propose and provide evidence that the brain constructs the value of a novel stimulus by extracting and assembling common features. Notably, because those features are shared across a broad range of stimuli, we show that simple linear regression in the feature space can work as a single neural mechanism to construct the value across stimulus domains. In large-scale behavioral experiments with human participants, we show that a simple model of feature abstraction and linear summation can predict the subjective value of paintings, photographs, as well as shopping items whose values change according to different goals. The model shows a remarkable generalization across stimulus types and participants, e.g. when trained on liking ratings for photographs, the model successfully predicts a completely different set of art painting ratings. Also, we show that these general features emerge through image recognition training in a deep convolutional neural network, without explicit training on the features, suggesting that features relevant for value computation arise through natural experience. Furthermore, using fMRI, we found evidence that the brain actually performs value computation hierarchically by transforming low-level visual features into high-level abstract features which in turn are transformed into valuation. We conclude the feature-based value computation is a general neural principle enabling us to make flexible and reliable value computations for a wide range of complex stimuli.


Time and Date: 10:45-, May 12, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Goto, Shimpei (QMB)

Title: TBA

Abstract: TBA


Time and Date: 10:45-, May 19, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Kukita, Shingo (QC)

Title: TBA

Abstract: TBA


Time and Date: 10:45-, May 26, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Kondo, Yasushi (QC)

Title: TBA

Abstract: TBA


Time and Date: 10:45-, June 2, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Kaneko, Ryui (QMB)

Title: TBA

Abstract: TBA


Time and Date: 10:45-, June 9, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Mikkelsen, Mathias (QMB)

Title: TBA

Abstract: TBA


Time and Date: 10:45-, June 16, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Danshita, Ippei (QMB)

Title: TBA

Abstract: TBA


Time and Date: 10:45-, June 23, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Kagamihara, Daichi (QMB)

Title: TBA

Abstract: TBA


Time and Date: 10:45-, June 30, 2021

Room: Rm. 31-808, 8th Floor, 31st Bldg. + Webcast via Zoom

Speaker: Kasamatsu, Kenichi (CMT)

Title: TBA

Abstract: TBA


 

Talks in the past

Fisical year 2021


Fiscal year 2020

Fiscal year 2019