Words can be pronounced in multiple ways in casual speech. Corpus analyses of the frequency with which these pronunciation variants occur (e.g., Patterson & Connine, 2001) show that typically, one pronunciation variant tends to predominate; this raises the question of whether variant recognition is aligned with exposure frequency. We explored this issue in words containing one of four phonological contexts, each of which favors one of four surface realizations of word-medial /t/: [t], [ʔ], [ɾ], or a deleted variant. The frequencies of the four realizations in all four contexts were estimated for a set of words in a production experiment. Recognition of all pronunciation variants was then measured in a lexical decision experiment. Overall, the data suggest that listeners are sensitive to variant frequency: Word classification rates closely paralleled production frequency. The exceptions to this were [t] realizations (i.e., canonical pronunciations of the words), a finding which confirms other results in the literature and indicates that factors other than exposure frequency affect word recognition.

An ideal experiment is one in which data collection is efficient and the results are maximally informative. This standard can be difficult to achieve because of uncertainties about the consequences of design decisions. We demonstrate the success of a Bayesian adaptive method (adaptive design optimization, ADO) in optimizing design decisions when comparing models of the time course of forgetting. Across a series of testing stages, ADO intelligently adapts the retention interval in order to maximally discriminate power and exponential models. Compared with two different control (non-adaptive) methods, ADO distinguishes the models decisively, with the results unambiguously favoring the power model. Analyses suggest that ADO’s success is due in part to its flexibility in adjusting to individual differences. This implementation of ADO serves as an important first step in assessing its applicability and usefulness to psychology.

One account of how pronunciation variants of spoken words (center-> “senner” or “sennah”) are recognized is that sublexical processes use information about variation in the same phonological environments to recover the intended segments (Gaskell & Marslen-Wilson, 1998). The present study tests the limits of this phonological inference account by examining how listeners process for the first time a pronunciation variant of a newly learned word. Recognition of such a variant should occur as long as it possesses the phonological structure that legitimizes the variation. Experiments 1 and 2 identify a phonological environment that satisfies the conditions necessary for a phonological inference mechanism to be operational. Using a word-learning paradigm, Experiments 3 through 5 show that inference alone is not sufficient for generalization but could facilitate it, and that one condition that leads to generalization is meaningful exposure to the variant in an overheard conversation, demonstrating that lexical processing is necessary for variant recognition.

Spoken words undergo frequent and often predictable variation in pronunciation. One form of variation is medial /t/ deletion, in which words like center and cantaloupe are pronounced without acoustic cues indicative of syllable-initial /t/. Three experiments examined the consequences of this missing phonetic information on lexical activation. In Experiments 1, the Ganong (1980) paradigm was used to measure the strength of activation of /t/-deleted variants, comparing labeling and response time results with their citation counterparts. Phonemic restoration was used in Experiment 2 to generalize the results. In the final experiment, Experiment 1 was replicated with a large number of trials so that the time course of activation could be mapped. Results show lexical influences on labeling begin sooner and reach a higher level for the citation than the /t/-deleted variant, although the overall shapes of their activation profiles are similar.

Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it possible to determine these values, and thereby identify an optimal experimental design. After describing the method, it is demonstrated in two content areas in cognitive psychology in which models are highly competitive: retention (i.e., forgetting) and categorization. The optimal design is compared with the quality of designs used in the literature. The findings demonstrate that design optimization has the potential to increase the informativeness of the experimental method.

Computational models are powerful tools that can enhance the understanding of scientific phenomena. The enterprise of modeling is most productive when the reasons underlying the adequacy of a model, and possibly its superiority to other models, are understood. This chapter begins with an overview of the main criteria that must be considered in model evaluation and selection, in particular explaining why generalizability is the preferred criterion for model selection. This is followed by a review of measures of generalizability. The final section demonstrates the use of five versatile and easy-to-use selection methods for choosing between two mathematical models of protein folding.

Vitevitch and Luce (1998) showed that the probability with which phonemes co-occur in the language (phonotactic probability) affects the speed with which words and nonwords are named. Words with high phonotactic probabilities between phonemes were named more slowly than words with low probabilities, whereas with nonwords, just the opposite was found. To reproduce this reversal in performance, a model would seem to require not merely sublexical representations, but sublexical representations that are relatively independent of lexical representations. ARTphone (Grossberg, Boardman, & Cohen, 1997) is designed to meet these requirements. In this study, we used a technique called parameter space partitioning to analyze ARTphone’s behavior and to learn if it can mimic human behavior and, if so, to understand how. To perform best, differences in sublexical node probabilities must be amplified relative to lexical node probabilities to offset the additional source of inhibition from top-down masking) that is found at the sublexical level.

Infrared (IR) absorbance of cellular biomolecules generates a vibrational spectrum, which can be exploited as a “biochemical fingerprint” of a particular cell type. Biomolecules absorb in the mid-IR (2–20 μm) and Fourier-transform infrared (FTIR) microspectroscopy applied to discriminate different cell types (exfoliative cervical cytology collected into buffered fixative solution) was evaluated. This consisted of cervical cytology free of atypia (i.e. normal; n = 60), specimens categorised as containing low-grade changes (i.e. CIN1 or LSIL; n = 60) and a further cohort designated as high-grade (CIN2/3 or HSIL; n = 60). IR spectral analysis was coupled with principal component analysis (PCA), with or without subsequent linear discriminant analysis (LDA), to determine if normal versus low-grade versus high-grade exfoliative cytology could be segregated. With increasing severity of atypia, decreases in absorbance intensity were observable throughout the 1,500 cm−1 to 1,100 cm−1 spectral region; this included proteins (1,460 cm−1), glycoproteins (1,380 cm−1), amide III (1,260 cm−1), asymmetric (νas) PO2− (1,225 cm−1) and carbohydrates (1,155 cm−1). In contrast, symmetric (νs) PO2− (1,080 cm−1) appeared to have an elevated intensity in high-grade cytology. Inter-category variance was associated with protein and DNA conformational changes whereas glycogen status strongly influenced intra-category. Multivariate data reduction of IR spectra using PCA with LDA maximises inter-category variance whilst reducing the influence of intra-class variation towards an objective approach to class cervical cytology based on a biochemical profile.

Smith and Minda (1998, 2002) argued that the response scaling parameter γ in the exemplar-based generalized context model (GCM) makes the model unnecessarily complex and allows it to mimic the behavior of a prototype model. We evaluated this criticism in two ways. First, we estimated the complexity of the GCM with and without the γ parameter and also compared its complexity to that of a prototype model. Next, we assessed the extent to which the models mimic each other, using two experimental designs (Nosofsky & Zaki, 2002, Experiment 3; Smith & Minda, 1998, Experiment 2), chosen because these designs are thought to differ in the degree to which they can discriminate the models. The results show that γ can increase the complexity of the GCM, but this complexity does not necessarily allow mimicry. Furthermore, if statistical model selection methods such as minimum description length are adopted as the measure of model performance, the models will be highly discriminable, irrespective of design.

We introduce a Fourier Transformation technique that enables one to derive closed-form expressions of performance measures (e.g., hit and false alarm rates) of simulation-based models of recognition memory. Application of the technique is demonstrated using the bind cue decide model of episodic memory (BCDMEM; Dennis & Humphreys, 2001). In addition to reducing the time required to test the model, which for models like BCDMEM can be excessive, asymptotic expressions of the measures reveal heretofore unknown properties of the model, such as model predictions being dependent on vector length.

Infrared (IR) absorbance of cellular biomolecules generates a vibrational spectrum, which can be exploited as a “biochemical fingerprint” of a particular cell type. Biomolecules absorb in the mid-IR (2–20 μm) and Fourier-transform infrared (FTIR) microspectroscopy applied to discriminate different cell types (exfoliative cervical cytology collected into buffered fixative solution) was evaluated. This consisted of cervical cytology free of atypia (i.e. normal; n = 60), specimens categorised as containing low-grade changes (i.e. CIN1 or LSIL; n = 60) and a further cohort designated as high-grade (CIN2/3 or HSIL; n = 60). IR spectral analysis was coupled with principal component analysis (PCA), with or without subsequent linear discriminant analysis (LDA), to determine if normal versus low-grade versus high-grade exfoliative cytology could be segregated. With increasing severity of atypia, decreases in absorbance intensity were observable throughout the 1,500 cm−1 to 1,100 cm−1 spectral region; this included proteins (1,460 cm−1), glycoproteins (1,380 cm−1), amide III (1,260 cm−1), asymmetric (νas) PO2− (1,225 cm−1) and carbohydrates (1,155 cm−1). In contrast, symmetric (νs) PO2− (1,080 cm−1) appeared to have an elevated intensity in high-grade cytology. Inter-category variance was associated with protein and DNA conformational changes whereas glycogen status strongly influenced intra-category. Multivariate data reduction of IR spectra using PCA with LDA maximises inter-category variance whilst reducing the influence of intra-class variation towards an objective approach to class cervical cytology based on a biochemical profile.