Production constraints on learning novel onset phonotactics

Three experiments addressed the hypothesis that production factors constrain phonotactic learning in adult English speakers, and that this constraint gives rise to a markedness effect on learning. In Experiment 1, an acoustic measure was used to assess consonant–consonant coarticulation in naturally produced nonwords, which were then used as stimuli in a phonotactic learning experiment. Results indicated that sonority-rising sequences were more coarticulated than -plateauing sequences, and that listeners learned novel-rising onsets more readily than novel-plateauing onsets. Experiments 2 and 3 addressed the specific questions of whether (1) the acoustic correlates of coarticulation or (2) the coarticulatory patterns of self-productions constrained learning. In Experiment 2, stimuli acoustics were altered to control for coarticulatory differences between sequence type, but a clear markedness effect was still observed. In Experiment 3, listeners’ self-productions were gathered and used to predict their treatment of novel-rising and -plateauing sequences. Results were that listeners’ coarticulatory patterns predicted their treatment of novel sequences. Overall, the findings suggest that the powerful effects of statistical learning are moderated by the perception–production loop in language.     

Grammatical pattern learning by human infants and cotton-top tamarin monkeys

There is a surprising degree of overlapping structure evident across the languages of the world. One factor leading to cross-linguistic similarities may be constraints on human learning abilities. Linguistic structures that are easier for infants to learn should predominate in human languages. If correct, then (a) human infants should more readily acquire structures that are consistent with the form of natural language, whereas (b) non-human primates' patterns of learning should be less tightly linked to the structure of human languages. Prior experiments have not directly compared laboratory-based learning of grammatical structures by human infants and non-human primates, especially under comparable testing conditions and with similar materials. Five experiments with 12-month-old human infants and adult cotton-top tamarin monkeys addressed these predictions, employing comparable methods (familiarization-discrimination) and materials. Infants rapidly acquired complex grammatical structures by using statistically predictive patterns, failing to learn structures that lacked such patterns. In contrast, the tamarins only exploited predictive patterns when learning relatively simple grammatical structures. Infant learning abilities may serve both to facilitate natural language acquisition and to impose constraints on the structure of human languages.     

Dynamic Structure in Diagnostic Structured Interviewing: A Comparative Test of Accuracy and Efficiency

The incorporation of Bayesian logic into diagnostic interviewing may assist with empirically based diagnostic assessment strategies in practice settings, balancing cost effectiveness, administration demands, and accuracy, yet few demonstrations of such a system have been undertaken in the context of mental health diagnosis. The present study represented an initial feasibility demonstration of whether a simplified Bayesian approach offered comparative advantages in interview accuracy and efficiency against a standard assessment procedure. Two different diagnostic algorithms were compared targeting three selected diagnoses: generalized anxiety disorder (GAD), major depressive disorder (MDD), and social phobia (SP). The first algorithm was from a standard semi-structured diagnostic interview, and the second was from a dynamic system using diagnostic base rate information to select interview content. The dynamic algorithm reduced administration time and uniformly matched or improved accuracy over standard procedures. Preparation of this article was supported in part by National Institute of Mental Health Grant R03 MH60134, an award from the University of Hawai‘i Research Council, and awards from the Hawaii Departments of Health and Education to the first author.     

Utilisation des intervalles de confiance au lieu des tests de signification : aspects épistémologiques et pratiques

The use of confidence intervals instead of significance tests is strongly recommended by the fifth edition of the manual of the American Psychological Association (2001). This possibility as well as other improvements in statistical practice are discussed in the framework of the major theoretical options subtending statistical inference and the way they have been applied in psychology for about 50 years. First, the suggestion of a complete ban on statistical testing is examined and rejected. Next, a procedure consisting in measuring the fit of two competing models based on the likelihood ratio is judged interesting and commendable. Finally, the superiority of an approach based on confidence intervals instead of significance tests is assessed and illustrated by its application to an experimental study aiming to demonstrate the absence instead of the presence of an effect of the independent variable.     

Paul Meehl and the evolution of statistical methods in psychology

In his landmark 1978 paper, Paul Meehl delineated, with remarkable clarity, some fundamental challenges facing soft psychology as it attempts to test theory with data. In the quarter century that followed, Meehl’s views stimulated much debate and progress, while continually evolving to keep pace with that progress. This paper pays homage to Meehl’s prescience, and traces the impact of his ideas on the recent shift of emphasis away from hypothesis testing and toward confidence interval estimates of effect size.     

An empirical investigation of the statistical properties of generalized single-case randomization tests

Adapting Edgington's [J. Psychol. 90 (1975) 57] randomly determined intervention start-point model, Levin and Wampold [Sch. Psychol. Quart. 14 (1999) 59] proposed a set of nonparametric randomization tests for analyzing the data from single-case designs. In the present study, the performance of Levin and Wampold's four basic tests (independent start-point general and comparative effectiveness, simultaneous start-point general and comparative effectiveness) was examined with respect to their Type I error rates and statistical power. Of Levin and Wampold's four tests, all except the independent start-point comparative effectiveness test maintained their empirical Type I error rates and had acceptable power at larger sample-size and effect-size combinations. The one-tailed comparative intervention effectiveness test for the independent start-point model was found to be too liberal, in that it did not maintain its Type I error rate. Although a two-tailed application of that test was found to be conservative at longer series lengths, it had acceptable power at larger sample-size and effect-size combinations. The results support the utility of a versatile new class of single-case designs that permit both within- and between-unit statistical assessments of intervention effectiveness.     

Can Physical Parts of Substances Be Substances? The Dual Models of Analysis in Aristotle’s Notion of Substance

This essay discusses the question of whether the physical parts of a substance per se can still be substances after being separated from whole substances. This paper finds that within his corpus Aristotle gives two contrary answers to that question. To avoid this inconsistency, this essay claims that Aristotle puts forward dual models of analysis when it comes to substances, namely the Artifact Model and the Living Being Model. According to the Artifact Model, the physical parts of a substance per se are still substances after being separated from the whole substance; but according to the Living Being Model, the physical parts of a substance per se are not substances after separation. In addition, this paper also holds that there is a kind of evolutionary relationship of research methodology between the dual models, i.e. the dual models correspond to what is “better known to us” to what is “better known by nature.”     

Two-dimensional Langevin approach to the human stabilogram

Two-dimensional Langevin equation is considered as a model of the center of pressure (COP) random walk at quiet standing condition. The matrix of the mean square displacement describes quantitatively the COP random walk. Twenty-six young subjects were included in the study. Elements of the matrix of the mean square displacement derived from experimental data are well approximated by theoretical expressions derived from the Langevin equation, in the short-term regime. We have studied statistical properties of the COP displacements. Non-Gaussian behaviour of the displacements is indicated by the characteristic functions. New coordinate system constituted and utilised by the postural control system (PCS) was found for every subject. This new coordinate system is turned with respect to the system defined by the anatomy of the body. In this new coordinate system the matrix of the mean square displacement takes the form close to diagonal. The status of PCS in this new coordinate system can be quantified by the elements of the diffusion matrix, which are the measure of the stochastic activity of that system, rotation angle of new coordinate system and the friction coefficient. We have applied this analysis to examine how the visual inputs affect the PCS. We have found that the stochastic activity of the PCS increases after exclusion of the visual inputs. We have also shown that the visual system does not affect the friction coefficient. Furthermore, we have found that orientation of the new coordinate system chosen by PCS at included visual inputs differs from the orientation at excluded visual inputs.     

Pitches that Wire Together Fire Together: Scale Degree Associations Across Time Predict Melodic Expectations

The ongoing generation of expectations is fundamental to listeners’ experience of music, but research into types of statistical information that listeners extract from musical melodies has tended to emphasize transition probabilities and n-grams, with limited consideration given to other types of statistical learning that may be relevant. Temporal associations between scale degrees represent a different type of information present in musical melodies that can be learned from musical corpora using expectation networks, a computationally simple method based on activation and decay. Expectation networks infer the expectation of encountering one scale degree followed in the near (but not necessarily immediate) future by another given scale degree, with previous work suggesting that scale degree associations learned by expectation networks better predict listener ratings of pitch similarity than transition probabilities. The current work outlines how these learned scale degree associations can be combined to predict melodic continuations and tests the resulting predictions on a dataset of listener responses to a musical cloze task previously used to compare two other models of melodic expectation, a variable-order Markov model (IDyOM) and Temperley's music-theoretically motivated model. Under multinomial logistic regression, all three models explain significant unique variance in human melodic expectations, with coefficient estimates highest for expectation networks. These results suggest that generalized scale degree associations informed by both adjacent and nonadjacent relationships between melodic notes influence listeners’ melodic predictions above and beyond n-gram context, highlighting the need to consider a broader range of statistical learning processes that may underlie listeners’ expectations for upcoming musical events.