Isamu Motoyoshi

This study investigates how statistical image features contribute to natural scene and object recognition using psychophysical experiments and EEG decoding, finding that scenes are better recognized via these features than objects.

The paper introduces a joint probabilistic model for human scene and object recognition using residual computation, showing functional specialization in coarse-to-fine processing.

This study uses EEG to decode material categories and surface properties from visual stimuli, showing that global image features are represented in early visual cortex and enable classification of material qualities.

The paper uses EEG decoding to analyze spatiotemporal cortical dynamics in scene recognition, finding distinct brain regions and timings for different global properties.

The paper uses response-locked classification image analysis to study how spatial attention affects perceptual decision making by analyzing reaction time and contrast detection in dynamic visual tasks.

This study uses response-locked classification image analysis to examine perceptual decision making in contrast detection tasks, revealing spatiotemporal weighting patterns that vary with reaction time.

This paper reconstructs photorealistic texture images from EEG signals using an MVAE-based approach, showing that texture perception can be decoded from low-resolution neural data.