Structural equations modeling: Fit Indices,sample size,and advanced topics

This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth.     

Inferring action structure and causal relationships in continuous sequences of human action

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.     

The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age‐appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition.     

The effect of emotion on statistical reasoning: Findings from a base rates task

In this paper we explore the effect of emotion on statistical reasoning. We employed a base rates task to examine reliance on statistical information in the presence of congruent or incongruent anecdotal information. The impact of emotional content on statistical reasoning was investigated in controls and victims of sexual abuse. Two types of emotional contents were used, one generally emotional, and one related to sexual abuse. The latter category was included to investigate the effect of semantically identical stimuli that could potentially produce different levels of emotion in the victim and control groups. For control participants, emotional contents were associated with decreased use of statistical information and increased use of anecdotal information compared to neutral contents. Results from the victims group showed this effect to be increased for contents related to sexual abuse, suggesting that the effect is linked to the emotional connotation rather than semantic content. Victims perceived contents relating to their experience as more emotional and this was associated with specific reasoning impairments for these topics.     

Cross‐Situational Learning of Phonologically Overlapping Words Across Degrees of Ambiguity

Cross‐situational word learning (XSWL) tasks present multiple words and candidate referents within a learning trial such that word–referent pairings can be inferred only across trials. Adults encode fine phonological detail when two words and candidate referents are presented in each learning trial (2 × 2 scenario; Escudero, Mulak, & Vlach, 2016a ). To test the relationship between XSWL task difficulty and phonological encoding, we examined XSWL of words differing by one vowel or consonant across degrees of within‐learning trial ambiguity (1 × 1 to 4 × 4). Word identification was assessed alongside three distractors. Adults finely encoded words via XSWL: Learning occurred in all conditions, though accuracy decreased across the 1 × 1 to 3 × 3 conditions. Accuracy was highest for the 1 × 1 condition, suggesting fast‐mapping is a stronger learning strategy here. Accuracy was higher for consonant than vowel set targets, and having more distractors from the same set mitigated identification of vowel set targets only, suggesting possible stronger encoding of consonants than vowels.     

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.