Do children interpret informants' confidence as person-specific or situational?

Children prefer to learn from confident rather than hesitant informants. However, it is unclear how children interpret confidence cues: these could be construed as strictly situational indicators of an informant's current certainty about the information they are conveying, or alternatively as person-specific indicators of how "knowledgeable" someone is across situations. In three studies, 4- and 5-year-olds (Experiment 1: N = 51, Experiment 3: N = 41) and 2- and 3-year-olds (Experiment 2: N = 80) saw informants differing in confidence. Each informant's confidence cues either remained constant throughout the experiment, changed between the history and test phases, or were present during the history but not test phase. Results suggest that 4- and 5-year-olds primarily treat confidence cues as situational, whereas there is uncertainty around younger preschoolers' interpretation due to low performance.

A 'curse of knowledge' in the absence of knowledge? People misattribute fluency when judging how common knowledge is among their peers.

Knowledge can be a curse: Once we have acquired a particular item of knowledge it tends to bias, or contaminate, our ability to reason about a less informed perspective (referred to as the 'curse of knowledge' or 'hindsight bias'). The mechanisms underlying the curse of knowledge bias are a matter of great import and debate. We highlight two mechanisms that have been proposed to underlie this bias-inhibition and fluency misattribution. Explanations that involve inhibition argue that people have difficulty fully inhibiting or suppressing the content of their knowledge when trying to reason about a less informed perspective. Explanations that involve fluency misattribution focus on the feelings of fluency with which the information comes to mind and the tendency to misattribute the subjective feelings of fluency associated with familiar items to the objective ease or foreseeability of that information. Three experiments with a total of 359 undergraduate students provide the first evidence that fluency misattribution processes are sufficient to induce the curse of knowledge bias. These results add to the literature on the many manifestations of the curse of knowledge bias and the many types of source misattributions, by revealing their role in people's judgements of how common, or widespread, one's knowledge is. The implications of these results for cognitive science and social cognition are discussed.

Adjusting Incremental Fit Indices for Nonnormality.

A variety of indices are commonly used to assess model fit in structural equation modeling. However, fit indices obtained from the normal theory maximum likelihood fit function are affected by the presence of nonnormality in the data. We present a nonnormality correction for 2 commonly used incremental fit indices, the comparative fit index and the Tucker-Lewis index. This correction uses the Satorra-Bentler scaling constant to modify the sample estimate of these fit indices but does not affect the population value. We argue that this type of nonnormality correction is superior to the correction that changes the population value of the fit index implemented in some software programs. In a simulation study, we demonstrate that our correction performs well across a variety of sample sizes, model types, and misspecification types.

When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions.

A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category thresholds. Results revealed that factor loadings and robust standard errors were generally most accurately estimated using cat-LS, especially with fewer than 5 categories; however, factor correlations and model fit were assessed equally well with ML. Cat-LS was found to be more sensitive to sample size and to violations of the assumption of normality of the underlying continuous variables. Normal theory ML was found to be more sensitive to asymmetric category thresholds and was especially biased when estimating large factor loadings. Accordingly, we recommend cat-LS for data sets containing variables with fewer than 5 categories and ML when there are 5 or more categories, sample size is small, and category thresholds are approximately symmetric. With 6-7 categories, results were similar across methods for many conditions; in these cases, either method is acceptable.

Are the products of statistical learning abstract or stimulus-specific?

Learners can segment potential lexical units from syllable streams when statistically variable transitional probabilities between adjacent syllables are the only cues to word boundaries. Here we examine the nature of the representations that result from statistical learning by assessing learners' ability to generalize across acoustically different stimuli. In three experiments, we compare two possibilities: that the products of statistical segmentation processes are abstract and generalizable representations, or, alternatively, that products of statistical learning are stimulus-bound and restricted to perceptually similar instances. In Experiment 1, learners segmented units from statistically predictable streams, and recognized these units when they were acoustically transformed by temporal reversals. In Experiment 2, learners were able to segment units from temporally reversed syllable streams, but were only able to generalize in conditions of mild acoustic transformation. In Experiment 3, learners were able to recognize statistically segmented units after a voice change but were unable to do so when the novel voice was mildly distorted. Together these results suggest that representations that result from statistical learning can be abstracted to some degree, but not in all listening conditions.

An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA.

The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have been proposed, but the theoretical and empirical differences between the 2 have not been explored. In this article, we investigate the behavior of these 2 corrections. We show that the virtually unknown correction due to Li and Bentler (2006) , which we label the sample-corrected robust RMSEA, is a consistent estimate of the population ML RMSEA yet drastically reduces bias due to nonnormality in small samples. On the other hand, the popular correction implemented in several SEM programs, which we label the population-corrected robust RMSEA, has poor properties because it estimates a quantity that decreases with increasing nonnormality. We recommend the use of the sample-corrected RMSEA with nonnormal data and its wide implementation.

Epistemic states and traits: preschoolers appreciate the differential informativeness of situation-specific and person-specific cues to knowledge.

Previous research has demonstrated that preschoolers can use situation-specific (e.g., visual access) and person-specific (e.g., prior accuracy) cues to infer what others know. The present studies investigated whether 4- and 5-year-olds appreciate the differential informativeness of these types of cues. In Experiment 1 (N = 50), children used others' prior labeling accuracy as a cue when learning labels for, but not the visual identity of, hidden objects. In Experiment 2 (N = 64), with both cues present, children attended more to visual access than prior accuracy when learning the visual identity of, but not labels for, hidden objects. These findings demonstrate that children appreciate the difference between situation- and person-specific cues and flexibly evaluate these cues depending on what information they are seeking.

'I bet you know more and are nicer too!': what children infer from others' accuracy.

Research has shown that preschoolers monitor others' prior accuracy and prefer to learn from individuals who have the best track record. We investigated the scope of preschoolers' attributions based on an individual's prior accuracy. Experiment 1 revealed that 5-year-olds (but not 4-year-olds) used an individual's prior accuracy at labelling to predict her knowledge of words and broader facts; they also showed a 'halo effect' predicting she would be more prosocial. Experiment 2 confirmed that, overall, 4-year-olds did not make explicit generalizations of knowledge. These findings suggest that an individual's prior accuracy influences older preschoolers' expectations of that individual's broader knowledge as well as their impressions of how she will behave in social interactions.