Learning a face space for experiments on human identity

Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images. Meeting these requirements is an exacting task, and existing models of human identity and appearance are often unworkably abstract, artificial, uncanny, or biased. Here, we use a variational autoencoder with an autoregressive decoder to learn a face space from a uniquely diverse dataset of portraits that control much of the variation irrelevant to human identity and appearance. Our method generates photorealistic portraits of fictive identities with a smooth, navigable latent space. We validate our model's alignment with human sensitivities by introducing a psychophysical Turing test for images, which humans mostly fail. Lastly, we demonstrate an initial application of our model to the problem of fast search in mental space to obtain detailed "police sketches" in a small number of trials.Comment: 10 figures. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018). *JWS and JCP contributed equally to this submissio

Modeling Human Categorization of Natural Images Using Deep Feature Representations

Over the last few decades, psychologists have developed sophisticated formal models of human categorization using simple artificial stimuli. In this paper, we use modern machine learning methods to extend this work into the realm of naturalistic stimuli, enabling human categorization to be studied over the complex visual domain in which it evolved and developed. We show that representations derived from a convolutional neural network can be used to model behavior over a database of >300,000 human natural image classifications, and find that a group of models based on these representations perform well, near the reliability of human judgments. Interestingly, this group includes both exemplar and prototype models, contrasting with the dominance of exemplar models in previous work. We are able to improve the performance of the remaining models by preprocessing neural network representations to more closely capture human similarity judgments.Comment: 13 pages, 7 figures, 6 tables. Preliminary work presented at CogSci 201

Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels

Modern convolutional neural networks (CNNs) are able to achieve human-level object classification accuracy on specific tasks, and currently outperform competing models in explaining complex human visual representations. However, the categorization problem is posed differently for these networks than for humans: the accuracy of these networks is evaluated by their ability to identify single labels assigned to each image. These labels often cut arbitrarily across natural psychological taxonomies (e.g., dogs are separated into breeds, but never jointly categorized as "dogs"), and bias the resulting representations. By contrast, it is common for children to hear both "dog" and "Dalmatian" to describe the same stimulus, helping to group perceptually disparate objects (e.g., breeds) into a common mental class. In this work, we train CNN classifiers with multiple labels for each image that correspond to different levels of abstraction, and use this framework to reproduce classic patterns that appear in human generalization behavior.Comment: 6 pages, 4 figures, 1 table. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018

Cytological Evidence that Alternative Transposition by Ac Elements Causes Reciprocal Translocations and Inversions in Zea mays L.

McClintock discovered the first transposable element system, the Ac/Ds system in maize. She also reported that the Ac/Ds system could cause chromosome breakage and chromosome aberrations; however, the underlying mechanism was not known. Here, we recovered reciprocal translocations and inversions that were produced by alternative transposition in maize lines containing closely linked Ac transposable element termini. The cytological breakpoints of 7 reciprocal translocations and 2 pericentric inversions were determined by cytological analysis in this study

Capturing human category representations by sampling in deep feature spaces

Understanding how people represent categories is a core problem in cognitive science. Decades of research have yielded a variety of formal theories of categories, but validating them with naturalistic stimuli is difficult. The challenge is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires a workable representation of these stimuli. Deep neural networks have recently been successful in solving a range of computer vision tasks and provide a way to compactly represent image features. Here, we introduce a method to estimate the structure of human categories that combines ideas from cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep image generators. We provide qualitative and quantitative results as a proof-of-concept for the method's feasibility. Samples drawn from human distributions rival those from state-of-the-art generative models in quality and outperform alternative methods for estimating the structure of human categories.Comment: 6 pages, 5 figures, 1 table. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018

Observed changes in surface atmospheric energy over land

The temperature of the surface atmosphere over land has been rising during recent decades. But surface temperature, or, more accurately, enthalpy which can be calculated from temperature, is only one component of the energy content of the surface atmosphere. The other parts include kinetic energy and latent heat. It has been advocated in certain quarters that ignoring additional terms somehow calls into question global surface temperature analyses. Examination of all three of these components of atmospheric energetics reveals a significant increase in global surface atmospheric energy since the 1970s. Kinetic energy has decreased but by over two orders of magnitude less than the increases in both enthalpy and latent heat which provide approximately equal contributions to the global increases in heat content. Regionally, the enthalpy or the latent heat component can dominate the change in heat content. Although generally changes in latent heat and enthalpy act in concert, in some regions they can have the opposite signs

Learning deep taxonomic priors for concept learning from few positive examples

Human concept learning is surprisingly robust, allowing forprecise generalizations given only a few positive examples.Bayesian formulations that account for this behavior requireelaborate, pre-specified priors, leaving much of the learningprocess unexplained. More recent models of concept learningbootstrap from deep representations, but the deep neural net-works are themselves trained using millions of positive and neg-ative examples. In machine learning, recent progress in meta-learning has provided large-scale learning algorithms that canlearn new concepts from a few examples, but these approachesstill assume access to implicit negative evidence. In this paper,we formulate a training paradigm that allows a meta-learningalgorithm to solve the problem of concept learning from fewpositive examples. The algorithm discovers a taxonomic prioruseful for learning novel concepts even from held-out supercat-egories and mimics human generalization behavior—the firstto do so without hand-specified domain knowledge or negativeexamples of a novel concept