This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.In the psychological sciences, training in the statistical analysis of continuous outcomes (i.e. responses or independent variables) is a fundamental part of our education. The same cannot be said about categorical data analysis (Agresti, 2002; henceforth CDA), the analysis of outcomes that are either inherently categorical (e.g. the response to a yes/no question) or measured in a way that results in categorical grouping (e.g. grouping neurons into different bins based on their firing rates). CDA is common in all behavioral sciences. For example, much research on language production has investigated influences on speakers' choice between two or more possible structures (see e.g. research on syntactic persistence, Bock, 1986;Pickering and Branigan, 1998; among many others; or in research on speech errors). For language comprehension, examples of research on categorical outcomes include eye-tracking experiments (first fixations), picture identification tasks to test semantic understanding, and, of course, comprehension questions. More generally, any kind of forced-choice task, such as multiple-choice questions, and any count data constitute categorical data.Despite this preponderance of categorical data, the use of statistical analyses that have long been known to be questionable for CDA (such as analysis of variance, ANOVA) is still commonplace in our field. While there are powerful modern methods designed for CDA (e.g. ordinary and mixed logit models; see below), they are considered too complicated or simply unnecessary. There is a widely-held belief that categorical outcomes can safely be analyzed using ANOVA, if the arcsine-square-root transformation (Cochran, 1940; Rao, 1960;Winer Contact address: Assistant Professor, Brain and Cognitive Sciences, University of Rochester, Meliora Hall, Box 270268, Rochester, NY 14627-0268, P: (585) 276 3611, E: fjaeger@bcs.rochester.edu, U: http://www.bcs.rochester.ed...

We consider several key aspects of prediction in language comprehension: its computational nature, the representational level(s) at which we predict, whether we use higher level representations to predictively pre-activate lower level representations, and whether we ‘commit’ in any way to our predictions, beyond pre-activation. We argue that the bulk of behavioral and neural evidence suggests that we predict probabilistically and at multiple levels and grains of representation. We also argue that we can, in principle, use higher level inferences to predictively pre-activate information at multiple lower representational levels. We also suggest that the degree and level of predictive pre-activation might be a function of the expected utility of prediction, which, in turn, may depend on comprehenders’ goals and their estimates of the relative reliability of their prior knowledge and the bottom-up input. Finally, we argue that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture whose goal is to infer the message intended by the producer, and in which predictions play a crucial role in explaining the bottom-up input.

Successful speech perception requires that listeners map the acoustic signal to linguistic categories. These mappings are not only probabilistic, but change depending on the situation. For example, one talker’s /p/ might be physically indistinguishable from another talker’s /b/ (cf. lack of invariance). We characterize the computational problem posed by such a subjectively non-stationary world and propose that the speech perception system overcomes this challenge by (1) recognizing previously encountered situations, (2) generalizing to other situations based on previous similar experience, and (3) adapting to novel situations. We formalize this proposal in the ideal adapter framework: (1) to (3) can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance. We focus on two critical aspects of the ideal adapter. First, in situations that clearly deviate from previous experience, listeners need to adapt. We develop a distributional (belief-updating) learning model of incremental adaptation. The model provides a good fit against known and novel phonetic adaptation data, including perceptual recalibration and selective adaptation. Second, robust speech recognition requires listeners learn to represent the structured component of cross-situation variability in the speech signal. We discuss how these two aspects of the ideal adapter provide a unifying explanation for adaptation, talker-specificity, and generalization across talkers and groups of talkers (e.g., accents and dialects). The ideal adapter provides a guiding framework for future investigations into speech perception and adaptation, and more broadly language comprehension.

Speakers show a remarkable tendency to align their productions with their interlocutors'. Focusing on sentence production, we investigate the cognitive systems underlying such alignment (syntactic priming). Our guiding hypothesis is that syntactic priming is a consequence of a language processing system that is organized to achieve efficient communication in an ever-changing (subjectively non-stationary) environment. We build on recent work suggesting that comprehenders adapt to the statistics of the current environment. If such adaptation is rational or near-rational, the extent to which speakers adapt their expectations for a syntactic structure after processing a prime sentence should be sensitive to the prediction error experienced while processing the prime. This prediction is shared by certain error-based implicit learning accounts, but not by most other accounts of syntactic priming. In three studies, we test this prediction against data from conversational speech, speech during picture description, and written production during sentence completion. All three studies find stronger syntactic priming for primes associated with a larger prediction error (primes with higher syntactic surprisal). We find that the relevant prediction error is sensitive to both prior and recent experience within the experiment. Together with other findings, this supports accounts that attribute syntactic priming to expectation adaptation.

When we read or listen to language, we are faced with the challenge of inferring intended messages from noisy input. This challenge is exacerbated by considerable variability between and within speakers. Focusing on syntactic processing (parsing), we test the hypothesis that language comprehenders rapidly adapt to the syntactic statistics of novel linguistic environments (e.g., speakers or genres). Two self-paced reading experiments investigate changes in readers’ syntactic expectations based on repeated exposure to sentences with temporary syntactic ambiguities (so-called “garden path sentences”). These sentences typically lead to a clear expectation violation signature when the temporary ambiguity is resolved to an a priori less expected structure (e.g., based on the statistics of the lexical context). We find that comprehenders rapidly adapt their syntactic expectations to converge towards the local statistics of novel environments. Specifically, repeated exposure to a priori unexpected structures can reduce, and even completely undo, their processing disadvantage (Experiment 1). The opposite is also observed: a priori expected structures become less expected (even eliciting garden paths) in environments where they are hardly ever observed (Experiment 2). Our findings suggest that, when changes in syntactic statistics are to be expected (e.g., when entering a novel environment), comprehenders can rapidly adapt their expectations, thereby overcoming the processing disadvantage that mistaken expectations would otherwise cause. Our findings take a step towards unifying insights from research in expectation-based models of language processing, syntactic priming, and statistical learning.