Extracting low-dimensional psychological representations from convolutional neural networks

Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images. While the predictive power of these networks is high, it is often not clear whether they also offer useful explanations of the task at hand. Convolutional neural network representations have been shown to be predictive of human similarity judgments for images after appropriate adaptation. However, these high-dimensional representations are difficult to interpret. Here we present a method for reducing these representations to a low-dimensional space which is still predictive of similarity judgments. We show that these low-dimensional representations also provide insightful explanations of factors underlying human similarity judgments.Comment: Accepted to CogSci 202

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 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

Regional carbon dioxide permit trading in the United States: coalition choices for Pennsylvania

An overview is given of the growing number of regional associations in which states have entered into voluntary arrangements to limit greenhouse gas (GHG) emissions. In particular, in the Regional Greenhouse Gas Initiative (RGGI), a number of northeastern states have joined to create a regional GHG cap and trade program, beginning with the utility industry. Analysis is made of the five key issues relating to these current and potential climate action associations: the extent of the total and individual state mitigation cost-savings across all sectors from potential emission permit trading coalitions; the size of permit markets associated with the various coalitions; the relative advantages of joining various coalitions for swing states such as Pennsylvania; the implications of the exercise of market power in the permit market; and the total and individual state/country cost-savings from extending the coalition beyond US borders. It is shown that overall efficiency gains from trading with a system of flexible state caps, with greater overall cost savings increasing with increasing geographic scope.Regional Greenhouse Gas Initiative; Cap and trade program; Market power in the permit market; Mitigation costs; The size of permit market; Coalition choices for Pennsylvania

Examining Loss of Soul in Education

One does not have to walk long in the hallways of schools to see that something is very wrong. We search in our students for the passion and enthusiasm for learning that they possessed before entering school. Instead, we find idle bystanders content to remain distant from formal instruction and from their hearts. Too often they remain dazed, passive consumers window shopping the mall of education. Education becomes something to get through with a grade or a degree rather than a clearing for deep experience. A common malady of education today is the familiar emptiness found in the experiences of both teachers and students