Influence of colour vision on attention to, and impression of, complex aesthetic images.

Humans exhibit colour vision variations due to genetic polymorphisms, with trichromacy being the most common, while some people are classified as dichromats. Whether genetic differences in colour vision affect the way of viewing complex images remains unknown. Here, we investigated how people with different colour vision focused their gaze on aesthetic paintings by eye-tracking while freely viewing digital rendering of paintings and assessed individual impressions through a decomposition analysis of adjective ratings for the images.

Optimal policy for multi-alternative decisions.

Everyday decisions frequently require choosing among multiple alternatives. Yet the optimal policy for such decisions is unknown. Here we derive the normative policy for general multi-alternative decisions.

Locally embedded presages of global network bursts.

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially nonbursting network state is not fully understood. In this study, we develop a state-space reconstruction method combined with high-resolution recordings of cultured neurons.

Task-dependent recurrent dynamics in visual cortex.

The capacity for flexible sensory-action association in animals has been related to context-dependent attractor dynamics outside the sensory cortices. Here, we report a line of evidence that flexibly modulated attractor dynamics during task switching are already present in the higher visual cortex in macaque monkeys. With a nonlinear decoding approach, we can extract the particular aspect of the neural population response that reflects the task-induced emergence of bistable attractor dynamics in a neural population, which could be obscured by standard unsupervised dimensionality reductions such as PCA.

Integrated information and dimensionality in continuous attractor dynamics.

There has been increasing interest in the integrated information theory (IIT) of consciousness, which hypothesizes that consciousness is integrated information within neuronal dynamics. However, the current formulation of IIT poses both practical and theoretical problems when empirically testing the theory by computing integrated information from neuronal signals. For example, measuring integrated information requires observing all the elements in a considered system at the same time, but this is practically very difficult.

Gradual Development of Visual Texture-Selective Properties Between Macaque Areas V2 and V4.

Complex shape and texture representations are known to be constructed from V1 along the ventral visual pathway through areas V2 and V4, but the underlying mechanism remains elusive. Recent study suggests that, for processing of textures, a collection of higher-order image statistics computed by combining V1-like filter responses serves as possible representations of textures both in V2 and V4. Here, to gain a clue for how these image statistics are processed in the extrastriate visual areas, we compared neuronal responses to textures in V2 and V4 of macaque monkeys.

Optimal policy for value-based decision-making.

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions.

Population Code Dynamics in Categorical Perception.

Categorical perception is a ubiquitous function in sensory information processing, and is reported to have important influences on the recognition of presented and/or memorized stimuli. However, such complex interactions among categorical perception and other aspects of sensory processing have not been explained well in a unified manner. Here, we propose a recurrent neural network model to process categorical information of stimuli, which approximately realizes a hierarchical Bayesian estimation on stimuli.

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions.

Saliency-based color accessibility.

Perception of color varies markedly between individuals because of differential expression of photopigments in retinal cones. However, it has been difficult to quantify the individual cognitive variation in colored scene and to predict its complex impacts on the behaviors. We developed a method for quantifying and visualizing information loss and gain resulting from individual differences in spectral sensitivity based on visual salience.

Image statistics underlying natural texture selectivity of neurons in macaque V4.

Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures.

Defining statistical perceptions with an empirical Bayesian approach.

Extracting statistical structures (including textures or contrasts) from a natural stimulus is a central challenge in both biological and engineering contexts. This study interprets the process of statistical recognition in terms of hyperparameter estimations and free-energy minimization procedures with an empirical Bayesian approach. This mathematical interpretation resulted in a framework for relating physiological insights in animal sensory systems to the functional properties of recognizing stimulus statistics.

Whether dots moving in two directions appear coherent or transparent depends on directional biases induced by surrounding motion.

When two random-dot patterns moving in different directions are superimposed, motion appears coherent or transparent depending on the directional difference. In addition, when a pattern is surrounded by another pattern that is moving, the perceived motion of the central stimulus is biased away from the direction of the surrounding motion. That phenomenon is known as induced motion.

Acquisition of nonlinear forward optics in generative models: two-stage "downside-up" learning for occluded vision.

We propose a two-stage learning method which implements occluded visual scene analysis into a generative model, a type of hierarchical neural network with bi-directional synaptic connections. Here, top-down connections simulate forward optics to generate predictions for sensory driven low-level representation, whereas bottom-up connections function to send the prediction error, the difference between the sensory based and the predicted low-level representation, to higher areas. The prediction error is then used to update the high-level representation to obtain better agreement with the visual scene.

Neuronal population decoding explains the change in signal detection sensitivity caused by task-irrelevant perceptual bias.

Spatiotemporal context in sensory stimulus has profound effects on neural responses and perception, and it sometimes affects task difficulty. Recently reported experimental data suggest that human detection sensitivity to motion in a target stimulus can be enhanced by adding a slow surrounding motion in an orthogonal direction, even though the illusory motion component caused by the surround is not relevant to the task. It is not computationally clear how the task-irrelevant component of motion modulates the subject's sensitivity to motion detection.

Discriminating natural image statistics from neuronal population codes.

The power law provides an efficient description of amplitude spectra of natural scenes. Psychophysical studies have shown that the forms of the amplitude spectra are clearly related to human visual performance, indicating that the statistical parameters in natural scenes are represented in the nervous system. However, the underlying neuronal computation that accounts for the perception of the natural image statistics has not been thoroughly studied.

Opposing effects of contextual surround in human early visual cortex revealed by functional magnetic resonance imaging with continuously modulated visual stimuli.

Spatial context in vision has profound effects on neural responses and perception. Recent animal studies suggest that the effect of surround on a central stimulus can dramatically change its character depending on the contrast of the center stimulus, but such a drastic change has not been demonstrated in the human visual cortex. To examine the dependency of the surround effect on the contrast of the center stimulus, we conducted an functional magnetic resonance imaging experiment by using a low or a high contrast in the center region while the surround contrast was sinusoidally modulated between the two contrasts.