Fearless: Josh Griffiths

Continually a strong voice for the underrepresented on campus, working with other students and faculty to take initiative in changing campus policy and culture toward the LGBTQ community, and serving as a leader in multiple groups and organizations on campus, Josh Griffiths ’14 fearlessly advocates for members of our campus community, making Gettysburg a more open and welcoming space. [excerpt

A Diachronic Analysis of Schwa in French

Since the beginning of the formal study of language, linguists have struggled with the phonological problems posed by the mid-central vowel sound schwa. Schwa poses a series of challenges for linguists who study many languages, and this is particularly true for phonologists and phoneticians who specialize in French. Most of the challenges that come from analyzing the articulations of schwa in French arise from the overlap it has with mid- and open-mid-front-rounded vowels in French such as in the second vowel in the word “atelier” (workshop) and the second vowel in the word “appeler” (to call.) In this study a diachronic (historic) analysis of schwa in the French language is performed in order to more easily explain the problems that schwa poses for Franco-linguists today. First of all, the nature of schwa is described and how schwa’s behavior plays into its role in Modern French. Problems proposed by reduced schwa vowels and the phonological processes that cause these reductions in Modern French are described. Vowel reduction is a phonetic process that occurs when changes in the articulation of the vowel such as stress, sonority, and loudness cause the vowel to be “weaker.” Finally, a diachronic analysis of the historical environments of schwa from Old French to Modern French is conducted in order to attempt to explain the challenges posed by schwa in modern French. The methodology for this paper involves finding the phonetic environments in which schwa has traditionally appeared from Old French to Modern French. Changes in the environments between each time period of French are finally examined to see how those changes have influenced modern phonological processes that influence the articulation of schwa. This study has shown that the disappearance and appearance of sounds in the phonemic inventory of French has greatly impacted how schwa is articulated in Modern French. Other linguistic processes such as labialization that were realized on schwa in the past are no longer realized, but they have proven to be essential in shaping the current vowel inventory of French

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

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

Adapting Deep Network Features to Capture Psychological Representations

Deep neural networks have become increasingly successful atsolving classic perception problems such as object recognition,semantic segmentation, and scene understanding, often reach-ing or surpassing human-level accuracy. This success is duein part to the ability of DNNs to learn useful representationsof high-dimensional inputs, a problem that humans must alsosolve. We examine the relationship between the representa-tions learned by these networks and human psychological rep-resentations recovered from similarity judgments. We find thatdeep features learned in service of object classification accountfor a significant amount of the variance in human similarityjudgments for a set of animal images. However, these fea-tures do not capture some qualitative distinctions that are a keypart of human representations. To remedy this, we develop amethod for adapting deep features to align with human sim-ilarity judgments, resulting in image representations that canpotentially be used to extend the scope of psychological exper-iments