Results 1  10
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27
Theorybased causal induction
 In
, 2003
"... Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various s ..."
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Cited by 33 (14 self)
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Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the cooccurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computationallevel analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domaingeneral statistical inference guided by domainspecific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.
Rational approximations to rational models: Alternative algorithms for category learning
"... Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible fo ..."
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Cited by 24 (4 self)
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Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of “rational process models” that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson’s (1990, 1991) Rational Model of Categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose two alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure
Modeling Human Performance in Statistical Word Segmentation
"... What mechanisms support the ability of human infants, adults, and other primates to identify words from fluent speech using distributional regularities? In order to better characterize this ability, we collected data from adults in an artificial language segmentation task similar to Saffran, Newport ..."
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Cited by 20 (5 self)
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What mechanisms support the ability of human infants, adults, and other primates to identify words from fluent speech using distributional regularities? In order to better characterize this ability, we collected data from adults in an artificial language segmentation task similar to Saffran, Newport, and Aslin (1996) in which the length of sentences was systematically varied between groups of participants. We then compared the fit of a variety of computational models— including simple statistical models of transitional probability and mutual information, a clustering model based on mutual information by Swingley (2005), PARSER (Perruchet & Vintner, 1998), and a Bayesian model. We found that while all models were able to successfully complete the task, fit to the human data varied considerably, with the Bayesian model achieving the highest correlation with our results.
One and done? Optimal decisions from very few samples
 Cognitive Science Society
, 2009
"... In many situations human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and that predicted by Bayesian inference: p ..."
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Cited by 17 (5 self)
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In many situations human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and that predicted by Bayesian inference: people often appear to make judgments based on a few samples from a probability distribution, rather than the full distribution. Although samplebased approximations are a common implementation of Bayesian inference, the very limited number of samples used by humans seems to be insufficient to approximate the required probability distributions. Here we consider this discrepancy in the broader framework of statistical decision theory, and ask: if people were making decisions based on samples, but samples were costly, how many samples should people use? We find that under reasonable assumptions about how long it takes to produce a sample, locally suboptimal decisions based on few samples are globally optimal. These results reconcile a large body of work showing sampling, or probabilitymatching, behavior with the hypothesis that human cognition is well described as Bayesian inference, and suggest promising future directions for studies of resourceconstrained cognition.
Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition
 Behavioral and Brain Sciences
, 2011
"... To be published in Behavioral and Brain Sciences (in press) ..."
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Cited by 12 (0 self)
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To be published in Behavioral and Brain Sciences (in press)
Perceptual multistability as Markov Chain Monte Carlo inference
 Advances in Neural Information Processing Systems 22
, 2009
"... While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of realworld tasks, and it remains unclear how the human mind approximates Bayesian computations algorithmically. We explore the proposal that for ..."
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Cited by 6 (3 self)
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While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of realworld tasks, and it remains unclear how the human mind approximates Bayesian computations algorithmically. We explore the proposal that for some tasks, humans use a form of Markov Chain Monte Carlo to approximate the posterior distribution over hidden variables. As a case study, we show how several phenomena of perceptual multistability can be explained as MCMC inference in simple graphical models for lowlevel vision. 1
Modeling Human Performance in Restless Bandits with Particle Filters
"... Bandit problems provide an interesting and widelyused setting for the study of sequential decisionmaking. In their most basic form, bandit problems require people to choose repeatedly between a small number of alternatives, each of which has an unknown rate of providing reward. We investigate rest ..."
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Cited by 4 (0 self)
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Bandit problems provide an interesting and widelyused setting for the study of sequential decisionmaking. In their most basic form, bandit problems require people to choose repeatedly between a small number of alternatives, each of which has an unknown rate of providing reward. We investigate restless bandit problems, where the distributions of reward rates for the alternatives change over time. This dynamic environment encourages the decisionmaker to cycle between states of exploration and exploitation. In one environment we consider, the changes occur at discrete, but hidden, time points. In a second environment, changes occur gradually across time. Decision data were collected from people in each environment. Individuals varied substantially in overall performance and the degree to which they switched between alternatives. We modeled human performance in the restless bandit tasks with two particle filter models, one that can approximate the optimal solution to a discrete restless bandit problem, and another simpler particle filter that is more psychologically plausible. It was found that the simple particle filter was able to account for most of the individual differences.
Context, Learning, and Extinction
"... A. Redish et al. (2007) proposed a reinforcement learning model of contextdependent learning and extinction in conditioning experiments, using the idea of “state classification ” to categorize new observations into states. In the current article, the authors propose an interpretation of this idea i ..."
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Cited by 4 (3 self)
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A. Redish et al. (2007) proposed a reinforcement learning model of contextdependent learning and extinction in conditioning experiments, using the idea of “state classification ” to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They focus on renewal and latent inhibition, 2 conditioning paradigms in which contextual manipulations have been studied extensively, and show that online Bayesian inference within a model that assumes an unbounded number of latent causes can characterize a diverse set of behavioral results from such manipulations, some of which pose problems for the model of Redish et al. Moreover, in both paradigms, context dependence is absent in younger animals, or if hippocampal lesions are made prior to training. The authors suggest an explanation in terms of a restricted capacity to infer new causes.
Seeking Confirmation Is Rational for Deterministic Hypotheses
"... The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the bestknown biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single pre ..."
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Cited by 3 (1 self)
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The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the bestknown biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. This analysis rests on two assumptions: (a) that people predict the next event in a sequence in a way that is consistent with Bayesian inference; and (b) when testing hypotheses, people test the hypothesis to which they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people’s predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses (Experiments 3 and 4).
Demand Forecasting Behavior: System Neglect and Change Detection
 Management Science
, 2011
"... This research analyzes how individuals make forecasts based on time series data, and tests an intervention designed to improve forecasting performance. Using data from a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecaster ..."
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Cited by 2 (0 self)
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This research analyzes how individuals make forecasts based on time series data, and tests an intervention designed to improve forecasting performance. Using data from a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecasters overreact to errors in relatively stable environments, but underreact to errors in relatively unstable environments. Surprisingly, the performance loss due to systematic judgment biases is larger in stable than in unstable environments. In a second study, we test an intervention designed to mitigate these biased reaction patterns. In order to reduce the salience of recent demand signals, and emphasize the environment generating theses signals, we require forecasters to prepare a forecast in other timeseries before returning to their original timeseries. This intervention improves forecasting performance.