Experimentation is at the core of research in the behavioral and neural sciences, yet observations can be expensive and time-consuming to acquire (e.g., MRI scans, responses from infant participants). A major interest of researchers is designing experiments that lead to maximal accumulation of information about the phenomenon under study with the fewest possible number of observations. In addressing this challenge, statisticians have developed adaptive design optimization methods. This letter introduces a hierarchical Bayes extension of adaptive design optimization that provides a judicious way to exploit two complementary schemes of inference (with past and future data) to achieve even greater accuracy and efficiency in information gain. We demonstrate the method in a simulation experiment in the field of visual perception.

Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention.

In cognitive science, empirical data collected from participants are the arbiters in model selection. Model discrimination thus depends on designing maximally informative experiments. It has been shown that adaptive design optimization (ADO) allows one to discriminate models as efficiently as possible in simulation experiments. In this paper we use ADO in a series of experiments with people to discriminate the Power, Exponential, and Hyperbolic models of memory retention, which has been a long-standing problem in cognitive science, providing an ideal setting in which to test the application of ADO for addressing questions about human cognition. Using an optimality criterion based on mutual information, ADO is able to find designs that are maximally likely to increase our certainty about the true model upon observation of the experiment outcomes. Results demonstrate the usefulness of ADO and also reveal some challenges in its implementation.

This paper describes recent progress in the development of the Buckeye Corpus of Speech, a phonetically labeled corpus of conversational American English speech, first described in [1]. With the publication of the second phase of transcription, the corpus has nearly doubled in size from the first release. We briefly give an overview of the corpus, report on additional stud-ies of inter-labeler agreement, and describe a new GUI designed to facilitate searching the annotated speech files. Index Terms: corpora, transcription, phonetics, search tool 1.

Despite the popularity of connectionist models in cognitive science, their performance can often be difficult to evaluate. Inspired by the geometric approach to statistical model selection, we introduce a conceptually similar method to examine the global behavior of a connectionist model, by counting the number and types of response patterns it can simulate. The Markov Chain Monte Carlo-based algorithm that we constructed (cid:222)nds these patterns efficiently. We demonstrate the approach using two localist network models of speech perception.

We present a preliminary analysis of transcriber consistency in labeling and segmentation of words and phones in the Buckeye corpus of spontaneous, informal speech. We find that pairwise inter-transcriber agreement on exact phone label match was 76%, and segmentation agreement within 20 % of phone pair length was 75%, though longer phones are more consistently segmented than shorter phones. Patterns of consistency variation in labeling are observed as a function of phonetic categories that are similar to patterns reported for read speech. More agreement is seen on consonants than on vowels, and on fricatives and labials than on other consonant classes. In general, we find that shorter, more reduced words and phones result in more transcriber disagreement. 1.

How should we decide among competing explanations of a cognitive process given limited observations? The problem of model selection is at the heart of progress in cognitive science. In this paper, Minimum Description Length (MDL) is introduced as a method for selecting among computational models of cognition. We also show that differential geometry provides an intuitive understanding of what drives model selection in MDL. Finally, adequacy of MDL is demonstrated in two areas of cognitive modeling. 1 Model Selection and Model Complexity The development and testing of computational models of cognitive processing are a central focus in cognitive science. A model embodies a solution to a problem whose adequacy is evaluated by its ability to mimic behavior by capturing the regularities underlying observed data. This enterprise of model selection is challenging because of the competing goals that must be satisfied. Traditionally, computational models of cognition have been compared using one of many goodness-of-fit measures. However, use of such a measure can result in the choice of a model that over-fits the data, one that captures idiosyncracies in the particular data set (i.e., noise) over and above the underlying regularities of interest. Such models are considered complex, in that the inherent flexibility in the model enables it to fit diverse patterns of data. As a group, they can be characterized as having many parameters that are combined in a highly nonlinear fashion in the model equation. They do not assume a single structure in the data. Rather, the model contains multiple structures; each obtained by finely tuning the parameter values of the model, and thus can fit a wide range of data patterns. In contrast, simple models, frequently with few parameters, assume a specific structure in the data, which will manifest itself as a narrow range of similar data patterns. Only when one of these patterns occurs will the model fit the data well. The problem of over-fitting data due to model complexity suggests that the goal of model selection should instead be to select the model that generalizes best to all data samples that arise from the same underlying regularity, thus capturing only the regularity, not the noise. To achieve this goal, the selection method must be sensitive to the complexity of a model. There are at least two independent dimensions of model complexity. They are the number of free parameters of a model and its functional form, which refers to the way the parameters are combined in the model equation. For instance, it seems unlikely that two one-parameter models, y = ex and y = x9, are equally complex in their ability to fit data. The two dimensions of model complexity (number of parameters and functional form) and their interplay can improve a model's fit to the data, without necessarily improving generalizability. The trademark of a good model selection procedure, then, is its ability to satisfy two opposing goals. A model must be sufficiently complex to describe the data sample accurately, but without over-fitting the data and thus losing generalizability. To achieve this end, we need a theoretically well-justified measure of model complexity that takes into account the number of parameters and the functional form of a model. In this paper, we introduce Minimum Description Length (MDL) as an appropriate method of selecting among mathematical models of cognition. We also show that MDL has an elegant geometric interpretation that provides a clear, intuitive understanding of the meaning of complexity in MDL. Finally, application examples of MDL are presented in two areas of cognitive modeling. 1.1 Minimum Description Length The central thesis of model selection is the estimation of a model's generalizability. One approach to assessing generalizability is the Minimum Description Length (MDL) principle [1]. It provides a theoretically well-grounded measure of complexity that is sensitive to both dimensions of complexity and also lends itself to intuitive, geometric interpretations. MDL was developed within algorithmic coding theory to choose the model that permits the greatest compression of data. A model family f with parameters e assigns the likelihood f(yle) to a given set of observed data y . The full form of the MDL measure for such a model family is given below. MDL = -In! (yISA) + ~ln( ; ) + In f dS.jdetl(S) where SA is the parameter that maximizes the likelihood, k is the number of parameters in the model, N is the sample size and I(e) is the Fisher information matrix. MDL is the length in bits of the shortest possible code that describes the data with the help of a model. In the context of cognitive modeling, the model that minimizes MDL uncovers the greatest amount of regularity (i.e., knowledge) underlying the data and therefore should be selected. The first, maximized log likelihood term is the lack-of-fit measure, and the second and third terms constitute the intrinsic complexity of the model. In particular, the third term captures the effects of complexity due to functional form, reflected through I(e). We will call the latter two terms together the geometric complexity of the model, for reasons that will become clear in the remainder of this paper. MDL arises as a finite series of terms in an asymptotic expansion of the Bayesian posterior probability of a model given the data for a special form of the parameter prior density [2] . Hence in essence, minimization of MDL is equivalent to maximization of the Bayesian posterior probability. In this paper we present a geometric interpretation of MDL, as well as Bayesian model selection [3], that provides an elegant and intuitive framework for understanding model complexity, a central concept in model selection. 2 Differential Geometric Interpretation of MDL From a geometric perspective, a parametric model family of probability distributions forms a Riemannian manifold embedded in the space of all probability distributions [4]. Every distribution is a point in this space, and the collection of points created by varying the parameters of the model gives rise to a hyper-surface in which "similar" distributions are mapped to "nearby" points. The infinitesimal distance between points separated by the infinitesimal parameter differences de; is given by ds 2 = Y' k. g .. (8 )d8 ; d8 j where g ij(e) is the Riemannian metric tensor. The Fisher information, lij(e), is the natural metric on a manifold of distributions in the context of statistical inference [4]. We argue that the MDL measure of model fitness has an attractive interpretation in such a geometric context.

Two phoneme monitoring experiments examined the influence of Transitional Probability (TP) on phoneme recognition.Target phonemes appeared at the end of Consonant-Vowel-Consonant (CVC) syllables, or as the first element of coda clusters in CVCC syllables.Reliable TP effects were found only for targets in CVCC syllables.The TPs both into and out of the targets influenced listeners' ability to detect them in CVCCs.Furthermore, targets were more difficult to detect in CVCCs than in CVCs.TP may only influence segment recognition when that segment is more difficult to recognise, as when it occurs in a cluster.