5,122 research outputs found
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
Producing power-law distributions and damping word frequencies with two-stage language models
Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statisticalmodels that can generically produce power laws, breaking generativemodels into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes-the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process-that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology.48 page(s
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
A role for the developing lexicon in phonetic category acquisition
Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning
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
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