Reconstructing visual illusory experiences from human brain activity.

Visual illusions provide valuable insights into the brain's interpretation of the world given sensory inputs. However, the precise manner in which brain activity translates into illusory experiences remains largely unknown. Here, we leverage a brain decoding technique combined with deep neural network (DNN) representations to reconstruct illusory percepts as images from brain activity.

Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

Achievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on neural decoding and encoding analyses where DNN unit activations and human brain activity are predicted from each other.

Characterization of deep neural network features by decodability from human brain activity.

Achievements of near human-level performance in object recognition by deep neural networks (DNNs) have triggered a flood of comparative studies between the brain and DNNs. Using a DNN as a proxy for hierarchical visual representations, our recent study found that human brain activity patterns measured by functional magnetic resonance imaging (fMRI) can be decoded (translated) into DNN feature values given the same inputs. However, not all DNN features are equally decoded, indicating a gap between the DNN and human vision.

Microstructural properties of the vertical occipital fasciculus explain the variability in human stereoacuity.

Stereopsis is a fundamental visual function that has been studied extensively. However, it is not clear why depth discrimination (stereoacuity) varies more significantly among people than other modalities. Previous studies have reported the involvement of both dorsal and ventral visual areas in stereopsis, implying that not only neural computations in cortical areas but also the anatomical properties of white matter tracts connecting those areas can impact stereopsis.

Sparse Ordinal Logistic Regression and Its Application to Brain Decoding.

Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity. Classification and regression models are respectively used to predict discrete and continuous variables of interest. However, cognitive and behavioral parameters that we wish to decode are often ordinal variables whose values are discrete but ordered, such as subjective ratings.

A relative frame of reference underlies reversed depth perception in anticorrelated random-dot stereograms.

Binocular disparity is represented by interocular cross-correlation of visual images in the striate and some extrastriate cortices. This correlation-based representation produces reversed depth perception in a binocularly anticorrelated random-dot stereogram (aRDS) when it is accompanied by an adjacent correlated RDS (cRDS). Removal of the cRDS or spatial separation between the aRDS and cRDS abolishes reversed depth perception.