Sources of richness and ineffability for phenomenally conscious states.

Conscious states-state that there is something it is like to be in-seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness.

High-performing neural network models of visual cortex benefit from high latent dimensionality.

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core representational principles of computational models in neuroscience. Here we examined the geometry of DNN models of visual cortex by quantifying the latent dimensionality of their natural image representations. A popular view holds that optimal DNNs compress their representations onto low-dimensional subspaces to achieve invariance and robustness, which suggests that better models of visual cortex should have lower dimensional geometries.

Scene context is predictive of unconstrained object similarity judgments.

What makes objects alike in the human mind? Computational approaches for characterizing object similarity have largely focused on the visual forms of objects or their linguistic associations. However, intuitive notions of object similarity may depend heavily on contextual reasoning-that is, objects may be grouped together in the mind if they occur in the context of similar scenes or events. Using large-scale analyses of natural scene statistics and human behavior, we found that a computational model of the associations between objects and their scene contexts is strongly predictive of how humans spontaneously group objects by similarity.

A new procedure, free from human assessment that automatically grades some facial skin structural signs. Comparison with assessments by experts, using referential atlases of skin ageing.

Objective: To develop an automatic system that grades the severity of facial signs through 'selfies' pictures taken by women of different ages and ethnics.

Methods: 1140 women from three ethnics (African-American, Asian, Caucasian), of different ages (18-80 years old), took 'selfies' by high resolution smartphones cameras under different conditions of lighting or facial expressions. A dedicated software, was developed, based on a Convolutional Neural Network (CNN) that integrates training data from referential Skin Aging Atlases.