Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people.

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. We call this method discrete Markov chain Monte Carlo with people (d-MCMCP). We illustrate our method by evaluating the following categories over datasets: emotions as represented by facial images, moral concepts as represented by relevant words, and seasons as represented by images drawn from large online databases. Three experiments demonstrate that d-MCMCP is powerful and flexible enough to work with complex, naturalistic stimuli drawn from large online databases.

When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data.

Identifying 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. A rational analysis using Bayesian inference predicts that inferences from absent data should depend on how much the absence is expected to occur, with less probable absences being more salient. We tested this prediction in two experiments in which we elicited people's judgments about patterns in the data as a function of absence salience. We found that people were able to decide that absences either were mere coincidences or were indicative of a significant pattern in the data in a manner that was consistent with predictions of a simple Bayesian model.

Exploring Human Cognition Using Large Image Databases.

Most 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. We discuss how some of the challenges of using natural images as stimuli in experiments can be addressed through increased sample sizes, using representations from computer vision, and developing new experimental methods. Finally, we illustrate these points by summarizing recent work using large image databases to explore questions about human cognition in four different domains: modeling subjective randomness, defining a quantitative measure of representativeness, identifying prior knowledge used in word learning, and determining the structure of natural categories.

The Appeal to Expert Opinion: Quantitative Support for a Bayesian Network Approach.

The 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. We subsequently present two experiments as an example of the potential for future research in this vein, demonstrating that participants' quantitative ratings of the convincingness of a proposition that has been supported with an appeal to expert opinion were broadly consistent with the predictions of the Bayesian model.

Monetary cost for time spent in everyday physical activities.

We 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., perceived cost as a function of time. We also examined the effects of gain vs. loss framing (whether the activity brought additional rewards or prevented losses) as well as the effects of the individual factors of gender, income, and BMI. Steeper discount curves were associated with higher income (annual household ≥ median of $45,000) and gain framing (which indicates loss aversion). There were interactions between gender and frame, and also income and frame: Females and higher income participants showed loss aversion whereas males and lower income participants were not affected by framing. Males showed less discounting in gain frames relative to females, whereas females showed less discounting in loss frames relative to males. In gain frames, higher income participants discounted more but in loss frames there was no effect of income. We also found individual tendencies for discounting across activities: if an individual exhibited steeper discounting for one activity, they were also more likely to exhibit steeper discounting for the other activities. These results have implications for designers of interventions to encourage non-exercise physical activities, suggesting that loss-framed incentives are more effective for women and those with middle class (or greater) incomes. Furthermore loss framed incentives have more uniform impact across income brackets because people discount loss frames similarly regardless of income whereas those with middle-class incomes are not as motivated by gain frames. Our results also demonstrate a general method for examining the costs of effort associated with everyday activities.

Language learning from positive evidence, reconsidered: a simplicity-based approach.

Children 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. In this study, we review recent formal results showing that the learner has sufficient data to learn successfully from positive evidence, if it favors the simplest encoding of the linguistic input. Results include the learnability of linguistic prediction, grammaticality judgments, language production, and form-meaning mappings. The simplicity approach can also be "scaled down" to analyze the learnability of specific linguistic constructions, and it is amenable to empirical testing as a framework for describing human language acquisition.

The probabilistic analysis of language acquisition: theoretical, computational, and experimental analysis.

There 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. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.

Subjective randomness and natural scene statistics.

Accounts 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. We estimated natural scene probabilities for small binary arrays by tabulating the frequencies with which each pattern of cell values appears. We then conducted an experiment in which we collected human randomness judgments. The results show an inverse relationship between people's perceived randomness of an array pattern and the probability of the pattern appearing in nature.

The logical problem of language acquisition: a probabilistic perspective.

Natural 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. Here, we extend the MDL approach to give a simple and practical methodology for estimating how much linguistic data are required to learn a particular linguistic restriction. Our method provides a new research tool, allowing arguments about natural language learnability to be made explicit and quantified for the first time. We apply this method to a range of classic puzzles in language acquisition. We find some linguistic rules appear easily statistically learnable from language experience only, whereas others appear to require additional learning mechanisms (e.g., additional cues or innate constraints).

An unsupervised learning model of neural plasticity: Orientation selectivity in goggle-reared kittens.

The 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. A more stringent examination of such models comes from experiments demonstrating altered neural response properties in goggle-reared kittens. We show that an unsupervised learning model of cortical responsivity accounts well for the dramatic effects of stimulus driven development during goggle-rearing.