687 research outputs found
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
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