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

Barriers to reconstructive surgical care among surgical humanitarian aid recipients in Cartagena, Colombia

Medical Schoolhttps://deepblue.lib.umich.edu/bitstream/2027.42/148186/1/petersonj.pd

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

Sharing the Lord’s Supper: Finding Fellowship and Love in a Sacramental, Communal Meal

This thesis project identifies the biblical practice of celebrating the Lord’s Supper weekly in conjunction with a communal meal. At this project’s Ministry Context, Garden City Bible Fellowship, there is a lack of fellowship and love. This project investigates whether the implementation of the Lord’s Supper and a communal meal on a weekly basis will build fellowship and love in a biblical way. After being approved by the leadership of this church, the topic will be introduced to the congregation via a sermon on the subject, culminating in a question and answer session for anyone who has questions before participating. Research will begin by participants voluntarily and anonymously completing an introductory survey, which will reveal their current views on the Lord’s Supper and the love feast. These participants will then voluntarily participate in the Lord’s Supper and the communal meal weekly for the next five weeks. Subsequently, those who have participated in three or more communal meals will complete the conclusory survey, which will be compared with the introductory survey to discover themes or slippages

Expansion of perturbation theory applied to shim rotation automation of the Advanced Test Reactor

textIn 2007, the Department of Energy (DOE) declared the Advanced Test Reactor (ATR) a National Scientific User Facility (NSUF). This declaration expanded the focus of the ATR to include diversified classes of academic and industrial experiments. An essential part of the new suite of more accurate and flexible codes being deployed to support the NSUF is their ability to predict reactor behavior at startup, particularly the position of the outer shim control cylinders (OSCC). The current method used for calculating the OSCC positions during a cycle startup utilizes a heuristic trial and error approach that is impractical with the computationally intensive reactor physics tools, such as NEWT. It is therefore desirable that shim rotation prediction for startup be automated. Shim rotation prediction with perturbation theory was chosen to be investigated as one method for use with startup calculation automation. A modified form of first order perturbation theory, called phase space interpolated perturbation theory, was developed to more accurately model shim rotation prediction. Shim rotation prediction is just one application for this new modified form of perturbation theory. Phase space interpolated perturbation theory can be used on any application where the range of change to the system is known a priori, but the magnitude of change is not known. A cubic regression method was also developed to automate shim rotation prediction by using only forward solutions to the transport equation.Mechanical Engineerin

A Complete Characterization of Nash Solutions in Ordinal Games

The traditional field of cardinal game theory requires that the objective functions, which map the control variables of each player into a decision space on the real numbers, be well defined. Often in economics, business, and political science, these objective functions are difficult, if not impossible to formulate mathematically. The theory of ordinal games has been described, in part, to overcome this problem.Ordinal games define the decision space in terms of player preferences, rather than objective function values. This concept allows the techniques of cardinal game theory to be applied to ordinal games. Not surprisingly, an infinite number of cardinal games of a given size exist. However, only a finite number of corresponding ordinal games exist.This thesis seeks to explore and characterize this finite number of ordinal games. We first present a general formula for the number of two-player ordinal games of an arbitrary size. We then completely characterize each 2x2 and 3x3 ordinal game based on its relationship to the Nash solution. This categorization partitions the finite space of ordinal games into three sectors, those games with a single unique Nash solution, those games with multiple non-unique Nash solutions, and those games with no Nash solution. This characterization approach, however, is not scalable to games larger than 3x3 due to the exponentially increasing dimensionality of the search space. The results for both 2x2 and 3x3 ordinal games are then codified in an algorithm capable of characterizing ordinal games of arbitrary size. The output of this algorithm, implemented on a PC, is presented for games as large as 6x6. For larger games, a more powerful computer is needed. Finally, two applications of this characterization are presented to illustrate the usefulness of our approach

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