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

A variety of studies have demonstrated that organizing stimuli into categories can affect the way the stimuli are perceived. We explore the influence of categories on perception through one such phenomenon, the perceptual magnet effect, in which discriminability between vowels is reduced near prototypical vowel sounds. We present a Bayesian model to explain why this reduced discriminability might occur: It arises as a consequence of optimally solving the statistical problem of perception in noise. In the optimal solution to this problem, listeners’ perception is biased toward phonetic category means because they use knowledge of these categories to guide their inferences about speakers ’ target productions. Simulations show that model predictions closely correspond to previously published human data, and novel experimental results provide evidence for the predicted link between perceptual warping and noise. The model unifies several previous accounts of the perceptual magnet effect and provides a framework for exploring categorical effects in other domains.

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 low-level vision. 1

The goal of perception is to infer the hidden states in the hierarchical process by which sensory data are generated. Human behavior is consistent with the optimal statistical solution to this problem in many tasks, including cue combination and orientation detection. Understanding the neural mechanisms underlying this behavior is of particular importance, since probabilistic computations are notoriously challenging. Here we propose a simple mechanism for Bayesian inference which involves averaging over a few feature detection neurons which fire at a rate determined by their similarity to a sensory stimulus. This mechanism is based on a Monte Carlo method known as importance sampling, commonly used in computer science and statistics. Moreover, a simple extension to recursive importance sampling can be used to perform hierarchical Bayesian inference. We identify a scheme for implementing importance sampling with spiking neurons, and show that this scheme can account for human behavior in cue combination and the oblique effect. 1

This paper presents a new architecture for automatic continuous speech Recognizer. HEAR relies on both parametric speech models (HMMs) and episodic memory. We propose an evaluation on the Wall Street Journal corpus, a standard continuous speech recognition task, and compare the results with a stateof-the-art HMM baseline. HEAR is shown to be a viable and a competitive architecture. While the HMMs have been studied and optimized during decades, their performance seems to converge to a limit which is lower than human performance. On the contrary, episodic memory modeling for speech recognition as applied in HEAR offers flexibility to enrich the recognizer with information the HMMs lack. This opportunity as well as future work are exposed in a discussion. Index Terms: Continuous speech recognition, episodic memory, hybrid architecture

Optimal control entails combining probabilities and utilities. However, for most practical problems, probability densities can be represented only approximately. Choosing an approximation requires balancing the benefits of an accurate approximation against the costs of computing it. We propose a variational framework for achieving this balance and apply it to the problem of how a neural population code should optimally represent a distribution under resource constraints. The essence of our analysis is the conjecture that population codes are organized to maximize a lower bound on the log expected utility. This theory can account for a plethora of experimental data, including the reward-modulation of sensory receptive fields, GABAergic effects on saccadic movements, and risk aversion in decisions under uncertainty. 1

Researchers in both educational and developmental psychology have suggested that children are not particularly adept hypothesis testers, and that their behavior can often appear irrational. However, a growing body of research also suggests that people do engage in rational inference on a variety of tasks. Recently researchers have begun testing the idea that reasoners may be sampling hypotheses from an internal probability distribution when making inferences. If children are reasoning in this way, this might help to explain some seemingly irrational behavior seen in previous experiments. Forty 4-year-olds were tested on a probabilistic inference task that required them to make repeated guesses about which of two types of blocks had been randomly sampled from a population. Results suggest that children can sample from a probability distribution as evidenced by the fact that, as a group, they engaged in probability matching and that the dependency between successive guesses decreased over time.

Probabilistic models have recently received much attention as accounts of human cognition. problems behind cognitive tasks and their optimal solutions, rather than considering mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models that use an inventory of stored examples to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling, and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension, making predictions about everyday events, concept learning, and reconstruction from memory show that exemplar models can often account for human performance with only a few exemplars, for both simple and relatively complex prior distributions. These results suggest that exemplar models provide a possible mechanism for implementing at least some forms of Bayesian inference. Exemplar models and Bayesian inference 3 Exemplar models as a mechanism for performing Bayesian inference

Exemplar models as a mechanism for