With the explosion of "big data," digital repositories of texts and images are growing rapidly. These datasets present new opportunities for psychological research, but they require new methodologies before researchers can use these datasets to yield insights into human cognition. We present a new method that allows psychological researchers to take advantage of text and image databases: a procedure for measuring human categorical representations over large datasets of items, such as arbitrary words or pictures.
View Article and Find Full Text PDFMost cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications.
View Article and Find Full Text PDFIdentifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event never occurs.
View Article and Find Full Text PDFThe appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme-based treatment of the argument form is formalized within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert's expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to how people evaluate this argument, suggesting that such an approach might be beneficial to argumentation research generally.
View Article and Find Full Text PDFWe measured utility curves for the hypothetical monetary costs as a function of time engaged in three everyday physical activities: walking, standing, and sitting. We found that activities requiring more physical exertion resulted in steeper discount curves, i.e.
View Article and Find Full Text PDFChildren learn their native language by exposure to their linguistic and communicative environment, but apparently without requiring that their mistakes be corrected. Such learning from "positive evidence" has been viewed as raising "logical" problems for language acquisition. In particular, without correction, how is the child to recover from conjecturing an over-general grammar, which will be consistent with any sentence that the child hears? There have been many proposals concerning how this "logical problem" can be dissolved.
View Article and Find Full Text PDFNatural language is full of patterns that appear to fit with general linguistic rules but are ungrammatical. There has been much debate over how children acquire these "linguistic restrictions," and whether innate language knowledge is needed. Recently, it has been shown that restrictions in language can be learned asymptotically via probabilistic inference using the minimum description length (MDL) principle.
View Article and Find Full Text PDFThere is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time.
View Article and Find Full Text PDFAccounts of subjective randomness suggest that people consider a stimulus random when they cannot detect any regularities characterizing the structure of that stimulus. We explored the possibility that the regularities people detect are shaped by the statistics of their natural environment. We did this by testing the hypothesis that people's perception of randomness in two-dimensional binary arrays (images with two levels of intensity) is inversely related to the probability with which the array's pattern would be encountered in nature.
View Article and Find Full Text PDFThe selectivities of neurons in primary visual cortex are often considered to be adapted to the statistics of natural images. Accordingly, simple cell-like tuning emerges when unsupervised learning models that seek sparse representations of input probabilities are trained on natural scenes. However, orientation tuning develops before structured vision starts, rendering these previous results moot as models of activity-dependent development.
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