Pier-Olivier Caron

Segmenting Texts Into Meaningful Word Groups: Beginning Readers’ Prosody and Comprehension

It was hypothesized that prosodic reading facilitates beginning readers’ comprehension by allowing them to segment the text into meaningful word groups. Two prosodic features of the oral reading of second-grade students were considered: lack of inappropriate pauses and attention to punctuation. To examine the unique contribution of these features to reading comprehension, fluency (speed and accuracy of reading) and vocabulary were controlled. As expected, both prosodic features were significantly related to comprehension, jointly explaining as much variance as fluency. Accordingly, it might be counterproductive to encourage reading speed and accuracy at the expense of prosody.

Matching without learning

Matching behavior is a phenomenon describing response rate ratios of an organism as a function of their associated reinforcer rate ratios. The generalized matching law (GML), its quantitative formulation, has been frequently found to explain over 80% of the variance in concurrent reinforcement schedules. However, a previous paper found by means of Monte Carlo simulations that matching behavior could be due to environmental constraints on behavior rather than a mere decision-making process. The purpose of the current study is to systemically investigate the influence of constraints induced by concurrent schedules of reinforcement. A Monte Carlo simulation was carried out. Results showed that the GML reached much better explained variances with real (and artificial) organisms than the current simulated results. Thus, a learning process seems partly necessary to generate matching behavior. According to the current findings, concurrent reinforcement schedules clearly induced a quantitative dependency between behavior rates and reinforcer rates. The simulation demonstrates that matching behavior is not only a consequence of a behavioral (decision-making) process, but of environmental conditions also.

The allocation of playing behavior of children in mainstream day care according to social reinforcer ratios

ABSTRACT Previous works on the generalized matching law in natural settings studied behavioral patterns of participants having developmental disabilities. From a translational view (applying findings from basic science to applied research and reciprocally), it is of interest to evaluate the sensitivity of typical behavior to reinforcement among typical children. In the current study, we examined the relationship between children’s naturally occurring rate of play behaviors and rate of social reinforcers awarded by their educator and peers. Three typical children’s behaviors were observed via descriptive analyses in their usual day care room during periods of free play. Results show that the generalized matching law explained 94%, 73%, and 90% of the variance of the relation between behavior and reinforcer rate ratios and that children’s behaviors were sensitive to social attention (sensitivity of .88, .67, and .87). Allocation of playing behaviors of typical preschool children followed the matching law predictions. The natural variability of response and reinforcer rates (their unpredictability) within natural settings is discussed. We suggest that there may be no need in natural settings to wait for the operant to reach a steady state as long as participants have been consistently behaving in the contingencies of reinforcement.

On the statistical properties of operant settings and their contribution to the evaluation of sensitivity to reinforcement

When using the matching law in applied settings, a recurring problem is to assess when subjects adjust their responses as a function of their associated reinforcers. Specifically, the main concern is to determine whether subjects’ behavior are sensitive to reinforcement or not. Many researchers have followed (explicitly or implicitly) the criterion that 50% of explained variance is deemed acceptable to consider the subject sensitive. However, it is neither theoretically nor empirically grounded. This article presents a null hypothesis statistical test to assess whether an organism’s behavior is sensitive to reinforcement as quantitatively expressed by the matching law. We first introduce the motivation as to why such a test is warranted, formally described the basis of the model used to compute the null hypothesis and then show some of its advantages. We conclude the article with a hypothetical example.

On the empirical status of the matching law : Comment on McDowell (2013)

The matching law, regardless of the version, is a mathematical model that accounts for an organisms response rate as a function of the reinforcer rate. McDowell (2013) investigated to which extent a combined version of the quantitative law of effect (Herrnstein, 1970) and the generalized matching law (Baum, 1974) accounts for a substantial amount of the variance through several data sets. Even if I agree with most points raised by McDowell, there are 2 important issues within his reanalysis. Two out of 6 studies relied on pooled-subject data that are inappropriate for an investigation of the matching law (Caron, 2013). Moreover, the combined equation was not systemically investigated through all data sets. The current study casts some doubt on the empirical status of modern matching equations and thus shows that they still deserve extensive attention.

Cyberbullying victimization and substance use among Quebec high schools students : the mediating role of psychological distress

Cyberbullying has become a significant public health issue among youth and is associated with numerous mental health problems. While the majority of studies explored its mental consequences using cross-sectional design, this article aims to investigate direct and indirect links between cybervictimization, psychological distress and substance use among youth, using a longitudinal design. From the representative sample of the Quebec Youth Romantic Relationships Survey, 1 540 students aged 14-20 years participated in 3 Waves. A mediated model was used to investigate direct and indirect links between cybervictimization, controlling for exposure to interparental violence, measured at Wave 1, psychological distress at Wave 2, and substance use at Wave 3 (alcohol, marijuana and other drugs). Findings revealed that cyberbullying victims (18.14%, 10.03%, 1.95% respectively for alcohol, cannabis and other drugs) were more likely to consume substances than non-victims (11.37%, 4.95%, 0.8%). They also show that cybervictimization (β = 1.41, p < .001), exposure to interparental violence (β = .08, p < .001) and being a girl (β = -3.78, p < .001) were significantly associated to psychological distress. Psychological distress was found to partially mediate the association between cyberbullying victimization and later substance use. By highlighting the role of psychological distress in the association between cyberbullying and substance use, these results are relevant for prevention and treatment for victims. Indeed, findings from this study underline the need to focus primarily on psychological distress among cyberbullying victims, with an emphasis on gender and possible past victimizations such as exposure to interparental violence.

Minimum average partial correlation and parallel analysis: The influence of oblique structures

ABSTRACT Parallel analysis (Horn 1965) and the minimum average partial correlation (MAP; Velicer 1976) have been widely spread as optimal solutions to identify the correct number of axes in principal component analysis. Previous results showed, however, that they become inefficient when variables belonging to different components strongly correlate. Simulations are used to assess their power to detect the dimensionality of data sets with oblique structures. Overall, MAP had the best performances as it was more powerful and accurate than PA when the component structure was modestly oblique. However, both stopping rules performed poorly in the presence of highly oblique factors.

A Monte Carlo examination of the broken-stick distribution to identify components to retain in principal component analysis

ABSTRACT The broken-stick (BS) is a popular stopping rule in ecology to determine the number of meaningful components of principal component analysis. However, its properties have not been systematically investigated. The purpose of the current study is to evaluate its ability to detect the correct dimensionality in a data set and whether it tends to over- or underestimate it. A Monte Carlo protocol was carried out. Two main correlation matrices deemed usual in practice were used with three levels of correlation (0, 0.10 and 0.30) between components (generating oblique structure) and with different sample sizes. Analyses of the population correlation matrices indicated that, for extremely large sample sizes, the BS method could be correct for only one of the six simulated structure. It actually failed to identify the correct dimensionality half the time with orthogonal structures and did even worse with some oblique ones. In harder conditions, results show that the power of the BS decreases as sample size increases: weakening its usefulness in practice. Since the BS method seems unlikely to identify the underlying dimensionality of the data, and given that better stopping rules exist it appears as a poor choice when carrying principal component analysis.